thermochemical conversion of press seed cake produced from...

Thermochemical Conversion of Press Seed Cake Produced

from Non–Edible Biomass

A thesis submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

Kotaiah Naik Dhanavath

M. Tech in Chemical Engineering

Andhra University, Visakhapatnam, India

Chemical and Environmental Engineering, School of Engineering

College of Science Engineering and Health

RMIT University

Melbourne, Australia

July 2018

http://www.sciencedirect.com/science/article/pii/S1364032115012010

http://www.sciencedirect.com/science/article/pii/S1364032115012010

i

Declaration

I certify that except where due acknowledgement has been made, the work is that of the

author alone; the work has not been submitted previously, in whole or in part, to qualify for

any other academic award; the content of the thesis is the result of work which has been

carried out since the official commencement date of the approved research program; any

editorial work, paid or unpaid, carried out by a third party is acknowledged; and, ethics

procedures and guidelines have been followed.

Kotaiah Naik Dhanavath

July 2018

ii

Dedication

To my father, Varya Naik Dhanavath (1960–2005)

“Sometimes I still just can’t believe you’re gone, you’re with me

every single day, believe me.”

iii

Acknowledgements

First and foremost, I wish to acknowledge and thank my supervisors, Professor Rajarathinam

Parthasarathy, Chemical and Environmental Engineering, School of Engineering, College of

Science Engineering and Health, RMIT University, Melbourne, Australia, and Dr Satyavathi

Bankupalli, Principal Scientist, Indian Institute of Chemical Technology (CSIR–IICT)

Hyderabad, India, for their guidance, unfailing attention and constant encouragement

throughout the course of my research work.

I acknowledge RMIT–IICT Joint Research Program for supporting me through an

International Scholarship throughout my study at CSIR–IICT and RMIT University. I would

like to thank RMIT–IICT Joint Research Program coordinators Dr M. Lakshmi Kantham, Dr

S. Chandrasekhar, Professor Suresh K. Bhargava and Dr Jie Wu.

I am deeply grateful to Council of Scientific and Industrial Research (CSIR) –Indian Institute

of Chemical Technology (IICT), Hyderabad, and the Indian government department of

science and technology, New Delhi, India. Bio–Energy Scheme (GAP–0363) funding makes

this project and thesis possible.

My sincere thanks go to Dr. Kalpit Shah for sharing the knowledge during my research work

at RMIT University. I would also like to thank the IICT Sr. Principal Scientist, Dr. Pratap

Kumar, thank you for the continuous encouragement and constructive discussion in various

research projects.

Also thanks to IICT technical staff: Mr. Balaji, Mr. Mallikarjun, Mr. Goverdhan, Mr.

Narshima, Mr. Kumar, Mr. Kiran, Mr, Babu and Mr. Madhu. Throughout my research

program, the helpful laboratory staffs have provided me with great help and assistance with

various technical instruments and chemicals. I am grateful to all my friends at CSIR–IICT,

and RMIT University for contributing to a pleasant atmosphere especially my thanks go to

M. Shuaib Ahmed, K. Monika, Saikrishna, A. Anil, Sayanasree Varala, Sumalatha Eda,

Vineet, Alka Kumari and Md. Sakinul Islam.

Finally, I would like to thank my family for their endless love, support, encouragement, and

prayers. I would especially like to convey my gratitude to my mother Dhasly and my wife

Parvathi for their strong support.

iv

Publications

Publications Arose from this Project

[1] D. Kotaiah Naik, M. Shuaib Ahmed, Vineet Aniya, R. Parthasarathy, B.Satyavathi,

Torrefaction Assessment and Kinetics of De–oiled Karanja Seed Cake, Environmental

progress & sustainable energy 36 (2017) 758–765.

[2] Kotaiah Naik Dhanavath, Suresh K. Bhargava, Satyavathi Bankupalli, Rajarathinam

Parthasarathy, An experimental study to investigate the effect of torrefaction

temperature on the kinetics of gas generation. Journal of Environmental Chemical

Engineering 6 (2018) 3332–3341.

[3] D. Kotaiah Naik, K. Monika, S. Prabhakar, R. Parthasarathy, B.Satyavathi, Pyrolysis of

sorghum bagasse biomass into bio–char and bio–oil products: a thorough

physicochemical characterization. Journal of Thermal Analysis Calorimetry 127 (2017)

1277–1289.

[4] Kotaiah Naik Dhanavath, Kalpit Shah, Satyavathi Bankupalli, Suresh K. Bhargava,

Rajarathinam Parthasarathy, Derivation of optimum operating conditions for the slow

pyrolysis of Mahua press seed cake in a fixed bed batch reactor for bio–oil production.

Journal of Environmental Chemical Engineering 5 (2017) 4051–4063.

[5] Kotaiah Naik Dhanavath, Md. Sakinul Islam, Satyavathi Bankupalli, Suresh K.

Bhargava, Kalpit Shah, Rajarathinam Parthasarathy, Experimental Investigations into

Liquefaction of Karanja Press Seed Cake in the presence of pyrolyctic bio–oil derived

from the same feedstock. Journal of Environmental Chemical Engineering 5 (2017)

4986–4993.

[6] Kotaiah Naik Dhanavath, Kalpit Shah, Md. Sakinul Islam, Anil Ronte, Rajarathinam

Parthasarathy, Suresh K. Bhargava, Satyavathi Bankupalli, Experimental Investigations

on Entrained Flow Gasification of Torrefied Karanja Press Seed Cake. Journal of

Environmental Chemical Engineering 6 (2018) 1242–1249.

v

[7] Kotaiah Naik Dhanavath, Kalpit Shah, Satyavathi Bankupalli, Suresh K. Bhargava,

Rajarathinam Parthasarathy. Oxygen–Steam Gasification of Karanja Press Seed Cake:

Fixed Bed Experiments, ASPEN Plus Process Model Development and Benchmarking

with Saw Dust, Rice Husk and Sunflower Husk. Journal of Environmental Chemical

Engineering 6 (2018) 3061–3069.

Journal Papers under Review

[1] Kotaiah Naik Dhanavath, Kalpit Shah, Rajarathinam Parthasarathy, Suresh K.

Bhargava, Bankupalli Satyavathi, Production and characterization of bio–oil and bio–

char from the pyrolysis of Karanja press seed cake. Fuel Process Technology (2018).

[2] Kotaiah Naik Dhanavath, Kalpit Shah, Rajarathinam Parthasarathy, Suresh K.

Bhargava, Bankupalli Satyavathi, A review of thermochemical conversion of agricultural

residual biomass. Renewable and Sustainable Energy Reviews (2018).

Conference Papers (Peer–Reviewed)

[1] D. Kotaiah Naik, B. Satyavathi, R. Parthasarathy. Pre–treatment of Karanja Biomass

via Torrefaction: Effect on Syngas Yield and Char Composition. APCChE Congress.

Melbourne, Victoria (2015).

vi

Table of Contents

Declaration……………………………………………………………………….…………….i

Dedication………………………….………………………………………………………….ii

Acknowledgements……………………………………………………………...……………iii

Publications ……………………………………………………………………………..……iv

Table of Contents……………………………………………………………………………..vi

List of Figures……………………………………………………………………….…..……xi

List of Tables…………………………………………………………………………....……xv

List of Abbreviations…………………………………………………………………...….xviii

Nomenclature………………………....……………………………………………………...xx

Executive Summary…………………………………………………………….……………..1

Chapter 1 Introduction

1.1 Project Rationale……………………………………………………………………..……5

1.2 Objectives and Research Questions………………………………………………...……10

1.3 Structure of the Thesis……………………………………………………………………11

1.4 Final remarks..............……………………………………………………………………13

Chapter 2 Literature Overview

2.1 Thermochemical Conversion Technologies ………………………………………..……15

2.2 Torrefaction……………………………………………………………………….……...16

2.3 Pyrolysis…………………………………………………………………………….……23

2.3.1 Principle of Pyrolysis ………………………...…………………………..……23

2.3.2 Classification of Pyrolysis …………………………………………….……….23

2.3.2.1 Slow Pyrolysis……………………………………………………......24

2.3.2.2 Fast/Flash Pyrolysis……………………………………………..........25

2.3.2.3 Catalytic Pyrolysis………………………………………………........25

2.3.2.4 Microwave Pyrolysis……………………………………………........26

vii

2.4 Liquefaction…………………………………………………………………………...…32

2.4.1 Principle of Liquefaction ………………………...…………………………….32

2.4.2 Products of Liquefaction ………………………...…………………………….34

2.4.3 Influence of Operating Conditions……………...…………………….………..35

2.5 Gasification………………………………………………………………………………39

2.5.1 Principle of Gasification ………………………...……………………..………39

2.5.2 Gasification Technologies………………...……………………………....…....40

2.5.2.1 Fixed Bed Gasifier …………………...……………………..…..…...42

2.5.2.2 Fluidized Bed Gasifier………………...……………………...……...44

2.5.2.3 Entrained Flow Gasifier ……………...……………………...………44

2.5.3 Effects of Operating Parameters……………...……………………….....……..45

2.5.3.1 Temperature……………………...…………………………..…….…46

2.5.3.2 Equivalence Ratio………………...………………..………..….…....47

2.5.3.3 Steam/biomass Ratio ………………...…………………...…….…....47

2.5.3.4 Particle Size ……………………...………………………….…….…48

2.6 Summary…………………….......………....………...………………………….…….…51

Chapter 3 Torrefaction Assessments and Kinetics of Karanja Press Seed Cake

3.1 Introduction……………………………………………………………………..………..53

3.2 Experimental……………………………………………………………………..………55

3.2.1 Materials…………………………………………………………….………….55

3.2.2 Equipment: Fixed–Bed Reactor …………………………………….…………56

3.2.3 Experimental Procedure …………………………………………….…………56

3.2.4 Experimental Analysis ……………………..……………………………….…57

3.3 Results and Discussion………………………………………………………...…………58

3.3.1 Thermal Degradation of Solid Product……………………………...…………58

3.3.2 Characteristics of Torrefied Karanja Cake……………………………..………60

3.3.2.1 Elemental Analysis………………………………………………...…60

3.3.2.2 Heating Values of Torrefied Karanja and Energy Yield………..……61

3.3.3 Kinetic Model for Karanja PSC Torrefaction………………....................…….63

3.3.4 Fitting Weight Loss Curves………………………………………….....………64

3.3.5 Kinetic Parameters Evaluation……………………………………..…..………65

3.4 Conclusion…………………………………………………………………………..……67

viii

Chapter 4 Pyrolysis of Mahua PSC and Sorghum Bagasse into Bio–char and Bio–oil

4.1 Derivation of Optimum Operating Conditions for the Slow Pyrolysis of Mahua Press

Seed Cake in a Fixed Bed Batch Reactor for Bio–oil Production…………………......….….72

4.1.1 Introduction……………………………………………………...…………….…….…72

4.1.2 Materials and Methods………………………………………………………...….……75

4.1.2.1 Materials……………………………………………………….…………..…75

4.1.2.2 Experimental Design and Statistical Analysis………………….…….………75

4.1.2.3 Apparatus and Procedure….…………….……………….………...…………78

4.1.2.4 Feedstock and Product Characterisation……………………………..………80

4.1.3 Results and Discussion…………………………………………..………….…….……82

4.1.3.1 Elemental Analysis for the Mahua PSC, Bio–char and Bio–oil Produced from

the Pyrolysis Process……………………………………………………...…….……82

4.1.3.2 RSM Modelling and Experimental Observations……………..…….……….83

4.1.3.3 Effect of Operating Variables on the Bio–oil and Bio–char Yield…….…….86

4.1.3.4 GC–MS Analysis of Bio–oil……………………………………..………..…90

4.1.3.5 FT–IR Analysis of Bio–char…………………………………………..…..…91

4.1.3.6 Gas Composition………………………………………..…………....………94

4.1.3.7 Mass Balance……………………………………………………...….………95

4.1.3.8 Energy Balance……………………………………………………….………96

4.1.3.9 Economic Analysis………………………………….……………..…………98

4.1.4 Conclusions……………………………………………………….……………..……103

4.2 Pyrolysis of Sorghum Bagasse Biomass into Bio–oil and Bio–char Products: A Thorough

Physicochemical Characterisation…………………...…………………......................…….105

4.2.1 Materials and methods…………………………………………………….…….……105

4.2.1.1 Characterisation of Raw Biomass and Bio–char Obtained from Pyrolysis

Process………………………………………………………….…………………..…….…106

4.2.2 Results and Discussion………………………………………….……………….……106

4.2.2.1 Mass Loss during Pyrolysis of High Sorghum Biomass………..……..……106

4.2.2.2 Characterisation of Bio–Char……………………………………..……...…107

4.2.2.3 Characterisation of Bio–oil……………………………………....…………113

4.2.3 Conclusions………………………………………………….…………………..……117

ix

Chapter 5 Experimental Investigations on the Effect of Pyrolyctic Bio–oil during the

Liquefaction of Karanja Press Seed Cake

5.1 Introduction ………………………………………………………………………….…119

5.2 Materials and Methods……………………………………………………………….…122

5.2.1 Wet Biomass Feedstock Source and its Properties…………...………………122

5.2.2 Apparatus and Experimental Procedure for Pyrolysis………………..………123

5.2.3 Apparatus and Experimental Procedure for Liquefaction………...……..……123

5.2.4 Sample Workup and Analysis…………………………………………..….…125

5.3 Results and Discussion of Liquefaction …………………………………….……….…126

5.3.1 Pyrolysis Results and Characterisation………………..…………...…………126

5.3.2 Liquefaction Results and Characterisation ………………………..….………131

5.3.3 Chemical Properties of the Liquefied Karanja PSC (Bio–crude) Samples…...135

5.4 Conclusions………………………………………………………………………..……137

Chapter 6 Experimental Investigation on Entrained Flow Gasification of Torrefied

Karanja Press Seed Cake as Feedstock

6.1 Introduction………………………………………………..……………………………140

6.2 Experimental………………………………………………………………..………..…143

6.2.1 Raw Material and Its Characteristics………………………………….....……143

6.2.2 Experimental Setup and Procedure……………………………………...……144

6.3 Results and Discussion of the Gasification Experiments…………………………….…150

6.3.1 Effect of Temperature on Syngas Composition and Cold Gas Efficiency……150

6.3.2 Effect of ER on the Syngas Composition…………………………….….……152

6.3.3 Effect of Steam Addition on Syngas Composition..………………………….153

6.3.4 Effect of Biomass Particle Size (Dp) on Syngas Composition and Carbon

Conversion………………..…………………………………………………………...……154

6.4 Conclusions…………………..…………………..…………………..……..…………..155

Chapter 7 Oxygen–Steam Gasification of Karanja Press Seed Cake: Fixed Bed

Experiments, ASPEN Plus Process Model Development and Benchmarking with Saw

Dust, Rice Husk and Sunflower Husk

7.1. Introduction……………….…………………..…………………..……………...…….158

7.2. Experimental…………………..……………..…………………..……….…...…….…161

x

7.2.1 Raw Materials……………..…………………..…………………...…………161

7.2.2 Experimental Investigations.……………..…………………..……….………162

7.2.3 Gas Analysis.……………………………………………………….…………163

7.3 Modelling of Biomass Gasifier.……………..……………………...………..…………163

7.4 Results and Discussions.………………………………………………..……....………166

7.4.1 Model Validation.……………….…………………………..………...………166

7.4.2 Sensitivity Analysis.…………………………………………………..………168

7.4.2.1 Effect of Temperature on Syngas Composition….……....…………168

7.4.2.2 Effect of ER on Syngas Gas Composition ……..……………..……171

7.4.2.3 Effect of Steam/Biomass Ratio on Syngas Composition…..……….174

7.5 Conclusions …………………….……..…………………..………..………….….……176

Chapter 8 Conclusions and Recommendations………………………………….………178

References……………………………………………………………………………...…..183

xi

List of Figures

Chapter 1

Fig.1.1. Biomass constituents in plant cell wall……………………………………...………..9

Chapter 2

Fig.2.1. Schematic of thermochemical conversion of agricultural residue…………..………15

Fig.2.2. Composition of the torrefaction reaction products………………………….………19

Fig.2.3. A schematic of agricultural residue pyrolysis process………………………………24

Fig.2.4. A schematic of agricultural biomass liquefaction…………………………………33

Fig.2.5. A schematic of biomass gasification………………………………………………42

Fig.2.6. Schematic diagram of (a) updraft and (b) downdraft fixed bed gasifiers…...………43

Fig.2.7. (a) Fluidized bed reactor (b) Entrained flow reactor………………………………45

Chapter 3

Fig.3.1. Schematic experimental setup for torrefaction unit…………………………………57

Fig.3.2. Weight loss map as a function of temperature and residence time (weight loss of

30%: solid (•) symbol)………………………………………………………..………………59

Fig.3.3. Van Krevelen diagram of torrefied deoiled Karanja cake…………..………………61

Fig.3.4. (a) HHV and (b) Energy yield as a function of reactor temperature and residence

time………………………………………………………………………………………...…62

Fig.3.5. (a) Energy yield and (b) HHV as a function of weight loss at different

temperatures………………………………………………………………...………………..63

Fig.3.6. Logarithm of non–dimensional solid residue versus time (first–order fit)…….……65

Fig.3.7. Weight loss of Karanja PSC at various temperatures (experimental and model

prediction for different temperatures)……………………………...........…………...………66

xii

Chapter 4

Fig.4.1.1. Pyrolysis experimental setup including the bench–scale fixed bed reactor…….…80

Fig.4.1.2. The relationship between the predicted and actual yield of (a) bio–oil, (b) bio–

char…………………………………………………………………………………...………86

Fig.4.1.3. Mahua bio–oil yield in 3D response surface (left) and contour plots (right),

respectively: effect of pyrolysis temperature (X1) and retention time (X2) at 0.3 L/min N2

flow rate………………………………………………………………………………………89

Fig.4.1.4. Gas chromatography mass spectrometry chromatograms of Mahua bio–oil obtained

by pyrolysis process at 475 °C…………………………………………………….…………91

Fig.4.1.5. Fourier transforms infrared spectra of (a) raw PSC (b) bio–char @ 400 °C (c) bio–

char @ 475 °C (d) bio–char @ 550 °C………………………………………………………92

Fig.4.1.6. The effect of reaction temperature on bio–gas composition of pyrolysis of Mahua

PSC……………………………………………………………………………………...……95

Fig.4.1.7. Mass balance for pyrolysis of mahua biomass.……………………………………95

Fig.4.1.8. Energy balance for the process……………………………………………………97

Fig.4.2.1. Effect of temperature on products yields in the pyrolysis of sorghum biomass…107

Fig.4.2.2. SEM images of (a) raw biomass (b) bio–char at 350 °C (C) bio–char at 400 °C (d)

bio–char at 450 °C (e) bio–char at 500 °C (f) bio–char at 550 °C……………………….…110

Fig.4.2.3. Powder XRD patterns of sorghum biomass and bio–char obtained at temperatures

from 350 to 550 °C…………………………………………………………………..………111

Fig.4.2.4. FT–IR spectra of (a) raw biomass (b) bio–char at 350 °C (c) bio–char at 400 °C (d)

bio–char at 450 °C (e) bio–char at 500 °C (f) bio–char at 550 °C…………………….……112

Fig.4.2.5. FT–IR transmittance spectra of (a) bio–oil at 350 °C (b) bio–oil at 400 °C (c) bio–

oil at 450 °C (d) bio–oil at 500 °C (e) bio–oil at 550 °C………………………………...…113

Fig.4.2.6. GC–MS total ion current chromatogram of the liquid fractions/ bio–oils obtained

by flash pyrolysis of bio–mass at 450 °C…………………………………………...………115

xiii

Chapter 5

Fig.5.1. Block diagram of the combined pyrolysis–liquefaction process……………..……122

Fig.5.2. Schematic diagram of the reaction tube used in the liquefaction of Karanja PSC...125

Fig.5.3. Effect of temperature on liquid yield from pyrolysis process………………….…..126

Fig.5.4. Fourier transforms infrared spectra of PBO at (a) 350 °C, (b) 450 °C………….…128

Fig.5.5. Effects of temperature and reaction time on the liquefaction pressure……….……133

Chapter 6

Fig.6.1. Schematic process flow for torrefaction process…………………………..………145

Fig.6.2. Process flow diagram of 1.0 kg/h. Entrained–flow gasification process: A. Hopper,

B. Rotary valve attached with Screw Feeder, C. O2 Cylinder, D. Steam generator, E. Pre–

mixer (electrically heated), F. Pre–heater (electrically heated), G. Gasifier (covered with split

furnace), H. Cyclone separator, I. Heat exchanger , J. Scrubber, K. Cooling water, L. Gas–

liquid separator, M. Gas chromatograph……………………………………………………148

Fig.6.3. Entrained–flow biomass gasification facilities (1.0 kg/h capacity).....….…………149

Fig.6.4. Effect of temperature on (a) syngas composition and (b) cold gas efficiency

(CGE)……………………………………………………………………………………….152

Fig.6.5. Effect of equivalence ratio (ER) on (a) syngas composition (b) lower heating value

(LHV)……………………………………………………………………………….………153

Fig.6.6. Effect of steam to biomass ratio (SBR) on (a) syngas composition and (b) lower

heating value (LHV)………………………………………………………………...………154

Fig.6.7. Effect of particle size on (a) syngas composition and (b) carbon conversion ….…155

Chapter 7

Fig.7.1. Schematic diagram of fixed–bed gasification unit………………………...….……162

Fig.7.2. Aspen Plus flow sheet of the fixed bed gasification process………………………165

Fig.7.3. Effect of temperature on syngas composition for (a) Karanja PSC, (b) rice husk, (c)

sawdust, and (d) sunflower husk feedstock (ER=0.23 and SBR=0.3) (results obtained from

simulation)………………..............................................................................................……170

xiv

Fig.7.4. (a) Effect of temperature on carbon conversion (b) Effect of temperature on cold gas

efficiency for Karanja, rice husk, saw dust and sunflower husk feedstock (ER=0.23 and

SBR=0.3) (results obtained from simulation)………....……………………………………170

Fig.7.5. Effect of equivalence ratio on product syngas gas composition for (a) Karanja, (b)

Rice husk, (c) Sawdust, and (d) Sunflower husk feedstock (Gasifier temperature = 1000°C,

and SBR=0.3) (results obtained from simulation)....………………………………….……172

Fig.7.6. (a) Effect of equivalence ratio (ER) on lower heating value (LHV) of syngas at

temperature of 800, 900 and 1000°C for Karanja PSC and (b) Effect of ER on LHV of

Karanja, rice husk, saw dust and sunflower husk feedstock at 1000°C (SBR=0.3) (results

obtained from simulation).......................................................................................……...…174

Fig.7.7. Effect of steam to biomass ratio (SBR) on syngas gas composition for (a) Karanja

and (b) sunflower husk feedstock (at 1000°C and ER of 0.23) (results obtained from

simulation)................................................................................………………………..……175

Fig.7.8. Effect of steam to biomass ratio on the H2/CO ratio (results obtained from

simulation).............................................................................………………………….……176

xv

List of Tables

Chapter 2

Table 2.1 A list of elemental and composition analysis and higher heating value of

biomasses……………………………………………………………………….…………20

Table 2.2 Operating modes and conditions of agricultural residue pyrolysis and bio–oil

yield..........................................................................................................................................28

Table 2.3 Operating conditions used in the liquefaction of agricultural residual biomass, and

the yield and HHV of bio–crude produced ………………………………………….………37

Table 2.4 A list of operating conditions and performance of agricultural biomass

gasification……………………………………………………………………………...……49

Chapter 3

Table 3.1 Properties of Karanja Press Seed Cake………………………….......…….………55

Table 3.2 Effect of temperature on thermal degradation of Karanja PSC at 200–300°C……59

Table 3.3 Percentage elemental composition analysis for torrefied Karanja PSC...............…60

Table 3.4 Estimated modeled parameters for thermal degradation of Karanja PSC................66

Table 3.5 Carbonisation and volatilization rate constant at different temperature……….…67

Table 3.6 Comparison of present work with the literature reported…………………………67

Chapter 4

Table 4.1.1 Proximate, ultimate and calorific value of Mahua PSC…………………………81

Table 4.1.2 Ultimate analysis and calorific value of bio–char and bio–oil co–produced from

pyrolysis of Mahua PSC………………………………………………………………...……83

Table 4.1.3 Box–Behnken design matrix and response of bio–oil yield for the pyrolysis of

PSC……………………………………………………………………………………...……84

Table 4.1.4 ANOVA for the response surface quadratic model and respective model term for

bio–oil yield……………………………………………………………………………..……89

xvi

Table 4.1.5 ANOVA for response surface linear model and respective model term for bio–

char yield………………………………………………………………………………..……90

Table 4.1.6 Chemical constituents of bio–oil identified by GC–MS…………………...……93

Table 4.1.7 FT–IR functional groups and frequency of raw PSC and bio–char………..……94

Table 4.1.8 Mass distribution in the pyrolysis of mahua ……………………...……….……96

Table 4.1.9 Energy management for the process……………………………………….……97

Table 4.1.10 Assumptions for 1500 kg/h bio–oil production from slow pyrolysis of Mahua

PSC…………………………………………………………………………………………101

Table 4.1.11 Economic analysis of the pyrolysis……………………………………………102

Table 4.2.1 Elemental analyses of bio–char co–produced from pyrolysis of high sorghum

biomass…..…………………………………………………………………………….……108

Table 4.2.2 Composition of high sorghum biomass……………………………….......……109

Table 4.2.3 FT–IR functional groups and frequency of bio–char……………………......…112

Table 4.2.4 FT–IR functional groups and frequency of bio–oil…………………….…....…114

Table 4.2.5 Chemical constituents of bio–oil (at 450 °C) identified by GC–MS…..........…116

Chapter 5

Table 5.1 Elemental analysis of Karanja PSC feedstock………………………….......……123

Table 5.2 Compositional assay of Karanja cake……………………………………………123

Table 5.3 FT–IR functional groups and the corresponding wavenumbers (frequency) for

PBO…………………………………………………………………………………………128

Table 5.4 Chemical constituents of PBO (obtained at 450°C) determined by GC–MS……129

Table 5.5 Liquefaction experiment results (Operating condition for these reactions are

reported in Table 5.3)……………………………………………………………………….134

Table 5.6 FT–IR functional groups and frequency of bio–crude……………………...……136

Table 5.7 GC–MS analysis results for the bio–crude obtained in the liquefaction of Karanja

PSC at 240°C using a retention time of 150 min………………………………………...…136

xvii

Chapter 6

Table 6.1 Proximate and Ultimate analysis of raw Karanja PSC……………………...……144

Table 6.2 Elemental analysis of torrefied biomass co–produced from torrefaction of Karanja

PSC…………………………………………………………………………………….……145

Table 6.3 Experimental Matrix………………………………………………………..……149

Table 6.4 Main gasification reactions………………………………………………………151

Chapter 7

Table 7.1 Results of proximate and ultimate analyses of biomass feedstocks used in this

work…………………………………………………………………………………………161

Table 7.2 Important biomass gasification reactions ……………………………………..…166

Table 7.3 Feed input and operating conditions of the Aspen Plus simulation model………167

Table 7.4 Comparison of experimental and simulation results by the gasification of Karanja

PSC in a fixed bed reactor at 800 °C and 900 °C……………………………………...……168

Table 7.5 The molar composition of syngas, carbon conversion and LHV values for Karanja

PSC obtained using ASPEN Plus model (ER=0.23 and SBR=0.3)…………….….….……171

Table 7.6 The molar composition of syngas produced by gasification, carbon conversion and

LHV have been investigated using the simulation model for Karanja PSC…………......…173

Table 7.7 The molar composition of syngas produced by gasification also carbon conversion

and LHV have been investigated using the simulation model for Karanja PSC……………175

xviii

List of Abbreviations

AICSIP All–India Coordinated Sorghum Improvement Project

ANOVA Analysis of variance

BBD Box–Behnken Design

BHT Butylated Hydroxytoluene

CAPEX Capital Expenditure ($)

CGES Syngas Cold Gas Efficiency (%)

CHNSO Carbon, Hydrogen, Sulfur, Oxygen

CSIR Council of Scientific and Industrial Research

CV Calorific Value (MJ/Nm3)

DCM Dichloromethane

Dp Particle Size (mm)

DTA Differential thermal analysis

ER Equivalence Ratio

FAME Fatty Acid Methyl Esters

FFA Free Fatty Acids

FT Fischer–Trope

FTIR Fourier Transform Infrared

GC Gas Chromatography

GC–MS Gas Chromatography Mass Spectrometry

GCV Gross Calorific Value

GLS Gas–Liquid Separator

H/C Hydrogen to Carbon Ratio

H/D Height to Diameter

HHV Higher Heating Value (MJ/Nm3)

ICRISAT International Crops Research Institute for the Semi–Arid Tropics

ID Inner Diameter (mm)

IICT Indian Institute of Chemical Technology

LHV Lower Heating Value (MJ/Nm3)

MFC Mass Flow Controller

MW Molecular Weight

ND Not detectable

xix

NIST National Institute of Standards and Technology

NPV Net Present Value

O/C Oxygen to Carbon Ratio

OD Outer Diameter (mm)

OPEX Operational Expenditure ($)

PBO Pyrolytic Bio–oil

PID Proportional–Integral–Derivative

PSC Press Seed Cake

RGIBBS Gibbs Reactor

RMIT Royal Melbourne Institute of Technology

RSM Response Surface Methodology

RT Retention Time (min)

RYIELD Yield Reactor

SBB Sorghum Bagasse Biomass

SBR Steam to Biomass Ratio

SEM Scanning Electronic Microscope

SS Stainless Steel

TCD Thermal Conductivity detector

TGA Thermo Gravimetric Analysis

TICC Total Ion Current Chromatogram

TKPSC Torrefied Karanja Press Seed Cake

VFD Variable–Frequency Drive

XRD X–Ray Diffraction

https://www.nist.gov/

xx

Nomenclature

Bc Torrefied char

Bk Un–torrefied matter

BSR Solid residue

Denergy Energy density

Ea Activation energy

k Rate constant

kc Carbonisation rate constant

kv Volatilisation rate constant

QB, QM, QN2, Qr Energy of biomass, Moisture, Nitrogen

QCBO, QBC, QBG Condensing bio–oil, bio–char, bio–gas

R Gas constant (kJ/mol K)

R2 Correlation coefficients

t Time (min)

T Temperature (°C)

tr Space residence time (min)

WK Oven dry weight (g)

Wlp Weight of the total liquid products (g)

Wr Amount of un–liquefied Karanja PSC residue (g)

Wrec Total amount of reactants (g)

X1 Temperature (°C)

X2 Retention time (min)

X3 Nitrogen gas flow rate (L/min)

Xij, Xij' Coded independent factors

Yenergy Energy yield

yi Predicted response

Ysoild Solid product yield

β0 Constant regression coefficient

βj, βjj, βjj' Factors for the linear, quadratic, Interaction effects

εi Error

1

Executive Summary

Demand for energy and its resources are increasing every day due to the rapid growth of

population and urbanization. As the major conventional energy resources like coal, petroleum

and natural gas are on the verge of getting depleted in this century, biomass, which is an

important renewable energy resource, can be used to produce renewable bioenergy (i.e., heat

and electricity), biochemical and biofuels. However, the use of biomass for energy, fuels and

chemicals production has generated significant concerns across the globe, especially in

developing nations, due to the shortage of food and cultivable land and extremely high

population density. This has led to the use of non–edible biomass resources such as Karanja,

Jatropha, Neem, Mahua, and Sorghum. These biomass resources are widely used for

extracting bio–oil in countries like India, Pakistan, Bangladesh, China, and Sri Lanka.

However, after the extraction of bio–oil, a significant portion of biomass (i.e., ~60 wt.% of

the total biomass) is left as a residual waste and generally referred as Press Seed Cake (PSC).

These PSCs, despite having a very high organic content, are currently being landfilled. The

current work focuses on the thermochemical conversion of PSCs with an aim to produce

bioenergy, biochemical and biofuels. The present study has utilized PSC generated from

Karanja, Mahua, and Sorghum. Thermochemical conversion processes that include

torrefaction, pyrolysis, gasification, and liquefaction have been investigate in detail. The

focus of the thesis is therefore to study the thermochemical conversion of PSC produced from

lignocellulosic biomass regardless of the type of original plant source.

The first part of this study demonstrated the efficiency of torrefaction process in upgrading

the transport, storage and grinding characteristics of Karanja PSC, which is a lignocellulosic

biomass. Torrefaction was carried out at different temperatures using residence time ranging

from 10 to 90 min. The torrefaction experiments were performed using temperature in the

range of 20–300 °C in a bench–scale vertical fixed bed reactor. The results showed that a

significant change in elemental composition occurs with the reduction in O/C and H/C

thereby increasing the calorific value and hydrophobicity of the torrefied biomass. The

weight loss and the total energy remained in the fuel after torrefaction was found to be 30–

35% and 80–85%, respectively. The HHV of the torrefied biomass was determined to be in

the range of 19.5–21.5 MJ/kg. The kinetic parameters for thermal degradation namely,

activation energy and pre–exponential factor, were determined from the experimental data

2

as10.55 kJ/mol 0.341 min-1, respectively using a simple kinetic model involving single–step

reaction mechanism for bio–char.

The second and third parts of this study systematically investigated the pyrolysis of Mahua

PSC and Sorghum, respectively in a bench–scale vertical fixed bed reactor. Both Mahua and

PSC and Sorghum are also valuable lignocellulosic biomasses. Effect of pyrolysis

temperature on the production of bio–char, bio–oil and bio–gas was studied in detail. The

advanced characterisation of bio–char, bio–oil and bio–gas was performed using scanning

electron microscope (SEM), x–ray diffraction (XRD), elemental analyser (CHNS), calorific

value (CV), Fourier transform infrared (FTIR) spectroscopy and gas chromatography–mass

spectrometry (GC–MS). The results obtained indicate that an increase in the pyrolysis

temperature from 350 to 550 °C leads to a decrease in the bio–char yield from 42.55 to

30.38%. On the other hand, the maximum bio–oil yield of 15.94% was obtained at 450 °C.

The GC–MS analyses of bio–oil samples revealed the presence of various important

chemicals such as octadecenoic acid, p–cresol, 2,6–dimethoxy phenol, 4–ethyl 2–methoxy

phenol, phenol, o–guaiacol, octadecanoic acid and free fatty acids.

In the fourth part of the study, experimental investigations on the liquefaction of Karanja PSC

were carried out in the presence of pyrolytic bio–oil (PBO) produced from the slow pyrolysis

of the same feedstock. The effects of PBO to PSC ratio and liquefaction temperature were

investigated with an aim to achieve the highest liquefaction conversion. Also, a study was

carried out to compare the influence of PBO on liquefaction with that of a mixture of a

conventional solvent such as phenol and an acid catalyst such as sulphuric acid. A detailed

chemical analysis of PBO and liquefied product (bio–crude) was carried out using FT–IR,

and GC–MS techniques. The results showed that the Karanja PSC could be directly liquefied

in the presence of PBO at moderate reaction conditions. A maximum liquefaction conversion

of 99% was obtained at a reaction temperature of 240 °C, a residence time of 160 min and a

Karanja PSC to PBO ratio of 1:6. In contrast, ~ 94% conversion was obtained for the same

residence time but at a significantly lower temperature of 160 °C when Karanja PSC, phenol

and sulphuric acid were used in the mass ratio of 1:2:0.6.

In the fifth part of the study, oxygen–steam based entrained flow gasification of torrefied

Karanja PSC was carried out in a bench–scale entrained flow reactor with a capacity of 1

kg/hr. The temperature was varied from 600 to 1100 °C. The equivalence ratio (ER), and

steam to biomass ratio (SBR) values was ranged from 0.1 to 1.0 while the particle size, Dp

3

was ranged from 0.5 to 3.0 mm. The aim was to obtain the optimum operating conditions for

the entrained flow gasification of the torrefied Karanja PSC. The results obtained show that

the optimum operating parameters include the temperature of 1100 °C, ER of 0.3, SBR of 0.4

and the particle size of 0.5 mm. The highest values of LHV, CGE, and the carbon conversion

were found to be ~12 MJ/Nm3, ~90% and of 98%, respectively for the torrefied Karanja PSC.

In the sixth part of the study, an ASPEN Plus process simulation was carried out. A

thermochemical equilibrium model (RGIBBS) in ASPEN Plus was used to predict the

gasification behaviour of Karanja PSC. The modelling results were validated with

experimental results obtained in an updraft fixed bed gasifier. Further to this, the model

simulation was extended for different biomass wastes such as sawdust, rice husk, and

sunflower husk. The effects of operating parameters like temperature, ER, and SBR on

syngas composition, LHV and CGE were investigated.

The results obtained from the current study have made a significant contribution in

demonstrating the value addition to PSC from lignocellulosic biomass. The knowledge

gained from the present study can be applied to develop large–scale thermochemical

conversion processes for PSC from any lignocellulosic biomasses with suitable

modifications.

4

Chapter 1 Introduction

5

1 Introduction

1.1 Project Rationale

Sustainable, affordable and reliable means of providing energy to cater the ever increasing

energy demands require identification of renewable fuels and ways of producing them

efficiently. By 2030, it is estimated that the number of people without electricity in the world

will still be over 733–885 million while the number of people without access to clean

cooking fuels will still be around 36 to 41 million (Panos et al., 2016). Fuel has always

played an essential part in the modern human life. Environmental pollution, global warming,

and the greenhouse effects due to the use of fossil fuels has led to the development of

sustainable energy sources like marine, solar, geothermal, wind, hydro, and biomass for

energy production (Elum and Momodu, 2017, Tofigh and Abedian, 2016). The current global

annual bioenergy production of 550 EJ makes up 10% of world’s primary energy supply

(Mathioudakis et al., 2017) thereby making bioenergy as the largest renewable energy source

in the world.

Biomass refers to any organic matter derived from forestry products, marine products, energy

crops, agricultural crops, aquatic plants, pulp derived black liquor, wood and wood waste,

municipal solid waste, sewage waste, and animal waste. All these materials are considered as

potential resources for the production of biofuels and biochemicals (Shuping et al., 2010).

The main applications of biomass include the generation of direct heat, chemicals (through

Fischer–Tropsch and dimethyl ether synthesis), char, fuels, and electricity. Generating and

using biomass energy leads to significant environmental benefits due to its renewable nature

and the associated reduction in greenhouse gas emissions (Serrano et al., 2017).

Use of biomass for bioenergy, biofuels, biochar and biochemicals production, however, is

very limited till date. This is mainly due to the fact that a majority of produced biomass is

used to cater human food requirements. Also, with ever increasing population and food

production demand, a shortage of cultivable and fertile land exists for the production of

biomass for energy purposes. Many developed and developing nations have promoted the use

of the wind and solar over biomass to ensure security and quality of food. This situation has

recently shifted the focus on the use of biomass waste for energy, fuels and chemicals

production.

https://en.wikipedia.org/wiki/Organic_matter

6

Biomass waste materials from agriculture, food and other industries such as saw dust, rice

husk, woody waste and oil palm residue have been extensively studied for bioenergy

production. For example, Cheng et al. (2017) studied the catalytic liquefaction of pine

sawdust to produce bio–crude (Cheng et al., 2017b). Olupot et al. (2016) studied the

gasification of rice husk to generate fuel with an energy content of 30–500 kW in a fixed bed

downdraft gasifier system (Olupot et al., 2016). Bhatia et al., (2017) investigated the

production of biodiesel from oil palm biomass hydrolysate using marine Rhodococcus sp.

YHY01.

Due to the seasonal generation of bio–waste in small quantities at different sites, lack of

transport infrastructure and transport related costs and emissions, lack of policy and price

regulatory framework for biomass waste, the availability of biowaste at an affordable price

and suitable locations is however very limited.

With this in mind, the current work has focused on industrial biomass wastes such as Press

Seed Cake (PSC) derived from various energy crops used in Asian countries for the

extraction of bio–oil. PSCs produced after the bio–oil extraction still constitute around 60

wt% of the total initial raw biomass, and they are currently treated as waste and mostly being

landfilled. Due to PSC’s organic richness, it is expected to generate high–quality bioenergy,

biochar, biofuels, and biochemicals.

The major components of lignocellulosic biomass waste/PSC materials are cellulose,

hemicellulose, and lignin as shown in Fig. 1.1 (Bach and Skreiberg, 2016). These

components are found in many kinds of biomass wastes, which make them the most plentiful

natural carbon sources on earth. Cellulose is a polysaccharide in which D–glucose is linked

uniformly by β–glucosidic bonds. Its molecular formula is (C6H12O6)n. The degree of

polymerisation, indicated by n, is broad–ranging from several thousand to several tens of

thousands. Total hydrolysis of cellulose yields D–glucose (a monosaccharide) but partial

hydrolysis yields a disaccharide (cellobiose) and polysaccharides in which n is in the order of

3 to 10. Cellulose has a crystalline structure and great resistance to acids and alkalis.

Cellulose is a not water–soluble due to the structure of its β–glucosidic bond.

Hemicellulose is polysaccharide whose units are 5–carbon monosaccharides including D–

xylose and D–arabinose, and 6–carbon monosaccharides including D–mannose, D–galactose,

and D–glucose. The 5–carbon monosaccharides outnumber the 6–carbon monosaccharide,

and the average molecular formula becomes (C5H8O4)n. Because the degree of polymerisation

7

(n) of hemicellulose is 50 to 200, which is smaller than that of cellulose, it breaks down more

easily than cellulose, and many hemicelluloses are soluble in alkaline solutions. Common

hemicelluloses include xylan and glucomannan. A number of hemicelluloses in biomass

waste varies depending on tree species and the part of the plant. Lignin is a compound whose

constituent units, phenyl propane and its derivatives are bonded 3–dimensionally. Its

structure is complex and not yet fully understood. It's complex 3–dimensional structure

makes them difficult to decompose by microorganisms and chemicals. Therefore its function

is considered to be conferring mechanical strength and protection to biomass waste.

In addition to the above components, some proteins are also present in biomass waste.

Proteins are macromolecular compounds in which amino acids are polymerized to a high

degree. Proteins are not a primary component of biomass waste, and they account for a lower

proportion compared to the other three components mentioned above.

The methods available for the conversion of biomass waste into energy sources can be

divided broadly into two categories called thermochemical and biochemical conversion

processes. Both processes have their respective advantages and disadvantages, and they have

been well documented in the literature (Jain et al., 2016, Wiedner et al., 2013). The current

thesis focuses on the thermochemical conversion of PSC based on industrial bio–waste.

Studies on PSC based biomass waste are very limited in the literature.

The thermochemical conversion includes four processes namely torrefaction, pyrolysis,

liquefaction and gasification (Oreggioni et al., 2017, Di Blasi et al., 2017, Bach et al., 2017a,

Li et al., 2017b). Torrefaction is a mild pyrolysis process that occurs below 300 °C under

atmospheric pressure in the presence of N2. Therefore, it is well suited for wet biomass

because, under these conditions, the raw biomass is converted into a solid product (bio–char)

and volatiles (condensable liquids and non–condensable gases) (Anukam et al., 2017).

The second thermochemical conversion process is Pyrolysis, which is one of the most

promising methods to produce bio–oil, bio–gas, and bio–char from the thermal degradation of

biomass under an inert atmosphere. Based on the heating rate, pyrolysis process can be

defined as a slow or a fast/flash pyrolysis. The slow pyrolysis, carried out at a heating rate of

0.01 to 10°C/s, is usually adopted for producing bio–char, bio–oil, and bio–gas with bio–char

being the predominant product. The fast/flash pyrolysis carried out at a heating rate of 10 to

1000°C/s, is used for mainly bio–gas and bio–oil production. Depending on the type of

pyrolysis, various types of pyrolysis reactors such as fixed–bed, bubbling fluidized–bed,

8

vacuum, vortex, rotating cone, and free–fall reactors have been employed in previous studies

(Tomás-Pejó et al., 2017). Among them, fixed–bed reactors have been widely used. The

operating temperature used in fixed–bed reactors varies from 350°C to 550°C for bio–oil and

bio–char production, and it can rise to a range of 600–1000°C for bio–gas production. The

pyrolysis stoichiometry, in general, can be expressed as follows (Biswas et al., 2017c).

CxHyOzNpSq + Q (enegy) + N2 → Bio − char + Condensables liquids (Bio − oil) +

Non − condensable gases (CH4 + CO + CO2 + H2) + H2O + N2

The third thermochemical conversion process is Liquefaction. In liquefaction, relatively low

temperatures but high pressures are used for the valorisation of waste biomass. Liquefaction

yields high–quality liquid products (bio–crude) with higher calorific value and lower

water/oxygen content. The bio–crude obtained from the direct liquefaction process is a dark–

colored, semi–liquid with the smoke–like smell and a viscosity 10–10,000 times greater than

that of diesel or biodiesel. Bio–crude contains a significant amount of carboxylic acids such

as acetic acid and formic acid as well as phenolic fractions and carbohydrates (Younas et al.,

2017).

The fourth thermochemical conversion process is Gasification, which is a promising

technology. It involves the conversion of biomass feedstock (low–grade hydrocarbon) into

combustible gases by partial oxidation at elevated temperatures through thermochemical

parallel reactions. Air/oxygen and steam are the gasifying agents used in the process

(Beheshti et al., 2016). The syngas produced from the gasifier is made up mainly of carbon

monoxide (CO) and hydrogen (H2), but it also may contain carbon dioxide (CO2), methane

(CH4), and nitrogen (N2) as well as light hydrocarbons. Syngas is fuel for the generation of

steam, electricity, hydrogen, bio methane and chemicals (using methods such as Fisher–

Tropsch synthesis and dimethyl ether synthesis) (Shehzad et al., 2016). Biomass gasification

using air–steam leads to the production of syngas with a lower heating value (LHV) ranging

from 4–7 MJ/N m3, whereas it leads to syngas with LHV of 10–18 MJ/ Nm3 when produced

with oxygen–steam (Zhou et al., 2016). The LHV depends on the gasifier type, feedstock,

and the operational conditions. The yield of product gas depends on the feedstock material,

gasifying agent, operational conditions, and the reactor design.

As mentioned above, the biomass gasification is carried out in various types of gasifiers

including fixed bed reactors, fluidized bed reactors, and entrained flow reactors to generate

gas from biomass. Different types of gasification technologies for coal and woody biomass

https://www.google.com.au/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwiZj_v4kbLRAhWFnZQKHblkAocQFggcMAA&url=http%3A%2F%2Fchem.libretexts.org%2FCore%2FInorganic_Chemistry%2FChemical_Reactions%2FStoichiometry_and_Balancing_Reactions&usg=AFQjCNG4UYehNxKkGdO5ziHt_WZK2NFM1w&bvm=bv.142059868,d.dGo

9

have been developed and commercialised in recent years. Fixed bed and fluidized bed

gasification processes using oxygen–steam, air–steam, and steam have been developed (Lenis

et al., 2016). These gasification processes are operated at a low temperature in the range of

400–900 °C and atmospheric pressure leading to low carbon conversion but the low yield of

syngas. Main drawbacks of these gasifiers are high tar and dust productions (Pauls et al.,

2016). In contrast, to increase the syngas yield and control ash and tar generation, entrained

flow gasifier has been employed. Entrained flow gasification is a promising technology that

can be used for improving the efficiency of biomass gasification. To ensure high carbon

conversion, this process is conducted at high temperatures, typically in the range of 900–1500

°C and high pressure with small particles and low residence time. This, in turn, gives

entrained flow gasifier fuel flexibility, large industrial–scale applicability, and high–quality

tar–free syngas (Hernández et al., 2010). Entrained flow gasifiers have been commercialised

for coal and liquid fuels; however, there is lack of information and research efforts on the use

of biomass as a potential feedstock for syngas production in entrained flow gasifiers.

Fig.1.1. Biomass constituents in plant cell wall (Bach and Skreiberg, 2016)

10

1.2. Objectives and Research Questions

The aim of this thesis is to investigate the thermochemical conversion of industrially

produced biomass wastes such as PSCs of Karanja, Mahua, and Sorghum bagasse biomass

using torrefaction, pyrolysis, liquefaction, and gasification. The unifying aspect of using

these biomasses in this study is that they are all lignocellulosic biomasses. The knowledge

generated using any of these materials as the feedstock can be extended to other biomasses

because their constituents are the nearly the same, only their concentrations are different.

A significant gap of knowledge exists on the thermochemical conversion of PSC based

lignocellulosic materials. The main knowledge gaps are highlighted as research questions

below:

1. What is the effect of temperature on the energy density of bio–char produced from the

torrefaction of Press Seed Cake?

2. What are the effects of operating conditions on the bio–oil yield from the pyrolysis of

PSC?

3. What are the effects of pyrolytic bio–oil (PBO) amount and temperature on the

conversion of PSC in liquefaction?

4. What are the effects of temperature, equivalence ratio, steam to biomass ratio and particle

size on syngas yield in gasification?

5. How to validate a simulation model for the gasification process using experimental

results?

Based on the above research questions, the following objectives have been developed for the

present study:

1. To investigate the thermochemical transformation via torrefaction of raw Karanja Press

Seed Cake (PSC) to produce high energy density bio–char and study its potential to

generate value–added syngas.

2. To investigate the properties of bio–oil and bio–char produced from Sorghum and Mahua

PSCs by slow pyrolysis using temperatures in the range of 350–550 °C under an inert

atmosphere in a bench scale fixed bed reactor.

3. To investigate the effects of process parameters such as temperature and Karanja

PSC/PBO/catalyst ratio on the Karanja PSC conversion yield in liquefaction.

11

4. To design and develop a pilot–scale entrained flow gasification reactor to investigate the

effects of operating conditions on syngas composition and yield.

5. To develop an Aspen Plus simulation model for gasification process and validate it with

experimental results.

1.3 Structure of the Thesis

Chapter 2: Literature Review

This chapter discusses the thermochemical conversion technologies of torrefaction, pyrolysis,

liquefaction, and gasification focusing on the conversion of lignocellulosic Press Seed Cake

to biofuels. It describes various conventional thermochemical reactors including fixed bed

reactors (updraft and downdraft), fluidized bed reactors and entrained flow reactors.

Chapter 3: Torrefaction Assessments and Kinetics of Karanja Press Seed Cake

This chapter discusses the thermochemical pre–treatment technique of torrefaction in a fixed

bed reactor. It discusses how torrefaction improves the fuel characteristics including energy

density, HHV, and grindability of Karanja Press Seed Cake. A single step reaction

mechanism is presented to describe the overall kinetics of the torrefaction process.

Chapter 4: Pyrolysis of Mahua PSC and Sorghum Bagasse biomass into Bio–oil, and

Bio–char Products

This chapter describes the investigation of pyrolytic reactions of another two lignocellulosic

biomass materials including Mahua PSC (Madhuca indica) and Sorghum bagasse (Sorghum

bicolor) in a fixed bed batch reactor. Both biomasses produce bio–oil, bio–char, and gases.

The optimum conditions to produce bio–oil, bio–char and gases were determined using the

results of a series of analytical techniques involving GC–MS, FT–IR, and Micro–GC.

Chapter 5: Experimental Investigations on the Effect of Pyrolytic Bio–oil during the

Liquefaction of Karanja Press Seed Cake

This chapter describes the thermochemical liquefaction of Karanja PSC in the presence of

pyrolytic bio–oil (PBO). The investigation was conducted in a batch reactor (i.e. autoclave)

12

with the aim of achieving the highest conversion of Karanja PSC to bio–crude. A detailed

chemical analysis of PBO and liquefied product (bio–crude) was carried out employing FT–

IR, and GC–MS techniques and their results are compared.

Chapter 6: Experimental Investigation on Entrained Flow Gasification of Torrefied

Karanja Press Seed Cake as Feedstock

The design and development of oxygen–steam based entrained flow gasification of torrefied

Karanja PSC is described in this chapter. The effects of operating parameters including

temperature, equivalence ratio, particle size and steam to biomass ratio on the syngas

composition were investigated.

Chapter 7: Oxygen–Steam Gasification of Karanja Press Seed Cake: Fixed Bed

Experiments, ASPEN Plus Process Model Development and Benchmarking with Saw

Dust, Rice Husk and Sunflower Husk

This chapter describes simulation model developed using ASPEN Plus software for the

gasification of Karanja PSC. The ASPEN Plus equilibrium model was validated with

experimental results obtained from the fixed bed reactor. The model was applied to a number

of biomasses including rice husk, saw dust, and sunflower husk and the results for these

materials are compared with those for Karanja PSC.

Chapter 8: Conclusions and Recommendations

This chapter summarises the major findings of the thesis and makes recommendations for

further studies based on the main thermochemical conversion techniques.

13

1.4 Final remarks

The thesis attempts to present detailed information on four different types of thermochemical

conversion processes namely, torrefaction, pyrolysis, liquefaction and gasification of

lignocellulosic biomass. Although the study uses different feed materials for different

processes (Karanja PSC for torrefaction, liquefaction and gasification, and Mahua PSC

Sorghum bagasse and Karanja PSC for pyrolysis), the main aim of the work is to study the

influence of thermochemical conversion processes on agricultural waste materials like seed

cakes and bagasse, which can be classified as lignocellulosic biomass. The optimum

operating conditions and the product compositions obtained from this study for the four

conversion processes are due to the reaction of the main contents of lignocellulosic biomass,

namely cellulose, hemicellulose, lignin and protein at various temperatures and pressures

with the other reactants. The results obtained from this study, therefore, should be applicable

to any lignocellulosic agricultural waste materials like seed cakes and bagasse.

14

Chapter 2 Literature Review

15

2 Literature Review

2.1 Thermochemical Conversion Technologies

Press seed cake (PSC), solid residue obtained from agricultural oil seed, can be used to

produce bio–oil, bio–char, syngas, and other chemicals via three different conversion

technologies: thermochemical, chemical and biochemical conversion processes. Selection of

the right conversion technology is a critical step in ensuring that the conversion process is

economically viable and environmentally sustainable. Currently, there are no clearly

established advantages or disadvantages between the thermochemical, chemical and

biochemical pathways for the conversion of solid wastes. However, a review by Chen et al.

(2017a) reported that the thermochemical method is a simpler route than others.

Thermochemical conversion involves the thermal degradation of organic compounds present

in the biomass or related waste. Thermochemical conversion consists of four major

categories; torrefaction, pyrolysis, liquefaction, and gasification. Fig. 2.1 highlights the

distinctions between thermochemical conversion processes.

Fig.2.1. Schematic of thermochemical conversion of agricultural residue

Agricultural

residue

Torrefaction

Thermochemical

conversion

300 800°C

0.1 Mpa

10 60 min

Pyrolysis

Liquefaction

Gasification

150 350°C

0.1 10 Mpa

10 180 min

400 1500°C

0.1 5 Mpa

1 30 min

200 300°C

0.1 Mpa

10 90 min

Feed stockOperating

conditionsMain products

Solid (bio-char)

Liquid (bio-oil)

Gases

16

A more detailed literature review on each of these methods is highlighted in following

sections. Only a limited number of papers have been published on the thermochemical

conversion of PSC based materials, which is the basis of the research work presented in this

thesis. Therefore, this literature review is focused on the thermochemical conversion of

materials that have properties similar to those of PSC based materials.

2.2 Torrefaction

Raw biomass or related waste materials have relatively low volumetric energy density and

high moisture content that lead to high costs in transportation, storage, and processing. Other

disadvantages are the hygroscopic nature and poor grindability of the material. High oxygen

and volatile matter content also make the biomass less favorable to be directly used in

thermochemical conversion processes that were originally designed for fossil fuels.

The high moisture content of the material increases the operating cost for drying prior to

thermochemical conversion processes. The high moisture content in biomass decreases the

overall conversion efficiency due to energy lost in the form of latent heat to vaporise water

during these processes (Yang et al., 2017c, Tran et al., 2016).

Torrefaction has been recognised as an effective method for improving the properties of solid

biomass fuels and upgrading them into more energy–dense and hydrophobic solid fuels,

which can effectively overcome the aforementioned drawbacks of biomass (Bach et al.,

2017c).

Torrefaction is a thermochemical treatment that occurs in the absence of oxygen. Biomass or

related waste materials in torrefaction are treated at a temperature typically higher than

simple drying but less than that of pyrolysis in an inert atmosphere for a medium to long

residence time. Wigley et al. (2017) defined temperature between 230 and 280 °C and

residence times between 15 and 120 min for Pinus radiata biomass.

Similarly, Delgado et al. (2017) mentioned that torrefaction typically occurs between 250 and

300 °C with residence times between 60 and 120 min for cardboard. Chen et al. (2017a) used

a torrefaction temperature of 260 °C for a period of 30 min for cotton stalks. Despite their

different origin, type or physicochemical properties, the torrefaction temperature range

remains similar in the above studies with slight variations in the residence time. This is due to

17

the fact that torrefaction mainly eliminates moisture and low volatiles (i.e., OH– group) which

can be released up to a maximum temperature of 300 oC.

The typical definition of torrefaction is that it is a thermochemical pretreatment process

involving heating of biomass typically around 200–300°C under inert conditions using

relatively short reactor residence time with a slow heating rate of less than 50 °C/min

(Doddapaneni et al., 2017). Typical composition of torrefied biomass or related waste is

shown in Fig. 2.2. It can be seen that torrefaction produces 80 wt.% solids, 15 wt.% gases and

5 wt.% liquid products. The solidified product, called torrefied biomass, exhibits properties

similar to those of coal. The properties of torrefied biomass are discussed later in this section.

Gas products from torrefaction mainly consist of light volatiles including but not limited to

CO, CO2, CH4, H2 and CnHn or CnHn+2 (up to n=6). The liquid fraction mainly consists of

water, alcohol, and acids.

Cai et al. (2017) studied torrefaction of sawdust at different temperature ranges of

temperature including 200−220 °C, 240−260 °C, and 280−300 °C for 60 min in the presence

of nitrogen. Results showed that the reduction in mass was more significant at higher

temperatures. This reduction was mainly due to the removal of water and light volatiles from

solids, and therefore the HHV of the material increased significantly from 20.84 MJ/kg to

22.38 MJ/kg. Along with the increase in energy density and HHV, the grindability of the

torrefied biomass also increased significantly with increase in temperature (Deng et al.,

2009).

As highlighted above, the most important torrefaction parameters are temperature and

residence time whilst the main indicators for torrefaction include solid yield and HHV, which

reveal the ratio of the mass of the remaining solid and that of the parent biomass and the

heating value of the biomass, respectively. Based on these two indicators, the energy yield of

torrefaction can be obtained. Table 2.1 summarises the torrefaction studies on different

biomasses and waste materials. The increases in fixed carbon, torrefied biomass yield and

HHV as a function of temperature and residence times are shown. All published studies

summarise the following characteristics of torrefied biomass:

1. Hydrophobic behavior: Torrefied biomass has hydrophobic characteristics owing to the

removal of OH groups from the hemicellulose. The OH group in the original biomass

gives it the ability to form hydrogen bonds with free water/moisture. This gives biomass a

hygroscopic nature (water repellent property), i.e., it has a reduced hydrophobicity. The

18

presence of high moisture content in biomass is not desirable because it can lead to a high

energy loss, low calorific value, low energy efficiency, and more emissions when the fuel

is burnt. Moreover, the transportation cost of hygroscopic fuel is very high.

Decomposition of cellulose, hemicellulose, and lignin leads to an increase in the

brittleness and therefore to the hydrophobic nature of the biomass. Furthermore, the non–

polar molecules that result from the breakdown of hemicellulose tend to be hydrophobic–

this incidentally aids in the resistance to biodegradation (Uemura et al., 2017). Yan et al.

(2009) reported that torrefied biomass absorbs less water than raw biomass and the

equilibrium moisture content (EMC) of torrefied biomass decreases with increasing

torrefaction severity. For example, the EMC of loblolly pine can decrease from 15.6 to

only 5.3%, after wet torrefaction (WT) at 260 °C for 5 min. Similarly, Bach et al. (2013)

conducted the Torrefaction of birch at 225 °C for 30 min; the EMC was found to be 8.5%.

2. Inhibiting biological decomposition: preventing biological decomposition by torrefaction

process.

3. Improved grindability: This leads to greater efficiency in direct heat applications,

electricity generation, and chemical production (through Fischer–Tropsch and dimethyl

ether synthesis), as well as gaseous fuel generation through entrained flow gasification.

The torrefied biomass is more brittle with much better milling properties owing to its

higher C/H and C/O ratios, requiring far less energy for grinding compared to that of raw

biomass (Deng et al., 2009).

4. Increased HHV of torrefied biomass: increases in torrefaction temperature leads to a

decrease in hydrogen content and an increase in carbon content resulting in a higher

HHV. The increase in HHV and changes in the porosity and density of biomass are also

due to the formation of CO by decarbonylation reactions and CO2 formation due to the

cleavage in hemicelluloses (Kotaiah Naik et al., 2017). The combined gas formation at

higher temperatures and residence times removes more oxygen from torrefied biomass

and thus increases the HHV of the fuel.

5. Size distribution of particles of torrefied biomass: Extra moisture is a factor impacting

further grinding of the biomass. Pulverised torrefied biomass is more uniform, and the

percentage of fine particles of biomass torrefied at higher temperature is greater than that

of raw biomass (Pachón-Morales et al., 2017).

6. Combustion characteristics of the torrefied biomass: The physicochemical properties of

torrefied biomass (TB) are closer to those of coal. Thus it is expected that torrefied

biomass possesses better combustion characteristics than raw biomass (Bach et al., 2015).

19

Torrefied biomass has been found to have lower volatile matter, lower oxygen content,

higher carbon content, and higher combustion intensity compared to raw biomass. Also,

the combustion characteristics of TB have been shown to be better than those of lignite,

with lower emissions and increased combustion efficiency.

Fig.2.2. Composition of the torrefaction reaction products

20

Table 2.1. Elemental and composition analyses and higher heating values for various biomasses

Feed stock

Operating condition Ultimate analysis of torrefied

biomass (wt.%) Torrefied biomass

References Temp.

(°C)

Duration

(min)

Sweeping gas

flow rate

(mL/min)

C H N O Yield (wt.%) HHV

(MJ/kg)

Rice husk 310 30 200 46.9 4.6 0.4 28.6 78.6 18.6 (Zhang et al.,

2016)

Cotton stalk 250 30 _ 51.85 5.79 0.61 34.39 80.25 18.96 (Chen et al.,

2017b)

Spruce 225 30 _ 59.41 5.55 0.17 34.87 _ 23.67 (Bach et al.,

2017b)

Sewage sludge 275 24 _ 29.9 3.2 4.6 9.9 51 13.5

(Atienza-

Martínez et al.,

2017)

Arecanut husk 275 30 _ 70.69 4.54 1.12 23.64 58 25.09 (Gogoi et al.,

2017)

Coconut shell 350 30 _ _ _ _ _ 31 31.9 (Irawan, 2017)

Patula pine

(Pinus patula) 300 30 _ 56.24 5.56 0.15 38.05 67.75 22.46

(Ramos-

Carmona et al.,

2017)

21

Chips of yellow

Cylindrical Poplar 320 60 _ 53.5 5.5 0.44 40.6 80.2 23.2

(Nhuchhen et

al., 2016)

Groundnut Shell 300 60 _ 55 5.29 0.51 37.7 45 22.66 (Garba et al.,

2017)

Sugarcane

Bagasse 300 5 500 56.16 3.94 1.8 37.27 80 20.29

(Anukam et al.,

2017)

Sunflower Seed

Shell 300 60 100 69.5 5.3 0.5 24.6 69.94 27.6

(Bilgic et al.,

2016)

Leucaena 250 30 25 80 .29 2 .78 1 .08 15 .86 24 29 .72 (Huang et al.,

2017)

Coffee Chaff 300 60 _ 60.2 5.7 4 30 _ 24.91 (Buratti et al.,

2017)

Oil Palm Fibre 300 30 1000 49.1 1.91 1.32 38.1 65 16.63 (Chen et al.,

2016a)

Jatropha Curcas

Seed Cake 275 60 _ 68 6.35 3.2 15.5 57 30.25

(Madanayake et

al., 2016)

Olive Tree

Pruning 300 60 4000 53.29 7.11 0.74 38.86 29.62 19.44

(Martín-Lara et

al., 2017)

Sweet Sorghum

Bagasse 300 30 2000 59.3 4.56 0.92 35.23 41.3 26.88

(Yue et al.,

2017)

Tomato Peels 285 30 _ 66.4 7.78 1.66 20.87 75 30.03 (Brachi et al.,

22

2016)

Wheat Straw 300 30 _ 59.65 4.25 1.11 34.43 47.80 22.08 (Bai et al.,

2017)

Rice Straw 300 60 500 59.33 3.01 1.13 17.45 _ _ (Chen et al.,

2017c)

Sugar Cane

Bagasse 300 120 350 58.8 2 0.7 38.3 45 18.8

(Valix et al.,

2017)

Cocoa pod Husk 370 120 _ 53.52 2.74 1.98 41.21 35.5 23.2 (Tsai et al.,

2017)

23

2.3 Pyrolysis

2.3.1 Principle of Pyrolysis

Pyrolysis involves the thermal decomposition of biomass in the absence of oxygen or air at

about 400–600 °C and evaporation of other volatile components. The reaction temperature

can be as high as 800 °C in the case of microwave pyrolysis and as low as 300 °C when

catalytic pyrolysis is carried out. The major products of pyrolysis are similar to those of

torrefaction; solid (char and charcoal), liquid (tar, oil, and pyrolytic oil) and non–

condensable gaseous products (biogas and pyrolytic gas), but their yield and compositions

vary significantly from those of torrefaction. The yield and compositions of solids, liquids,

and gases in pyrolysis depend on a number of factors such as process operating conditions,

reaction type and physicochemical properties of biomass. Moderate temperatures of 400–

550 °C, high heating rate (10 to 100 oC/s) and short residence times (2–3s) favor liquid

production (Soongprasit et al., 2017). In comparison, slower heating rate (0.01 to 10 oC/s)

tends to produce solid products.

Pyrolytic bio–oils produced from lignocellulosic biomass are complex, unstable, and viscous

they contain dissolved solids and chemically dissolved water and have extremely high

oxygen contents > 35% (Soongprasit et al., 2017). The bio–oils produced from pyrolysis

contain hundreds of chemical compounds, including aldehydes, phenols, carboxylic acids,

carbonyls, aromatics, alcohol, and acids. Therefore, upgrading bio–oils through

hydrogenation and catalytic cracking to reduce oxygen content and remove alkalis is

required to facilitate their utilisation (Biradar et al., 2014).

The solid product produced from pyrolysis is called bio–char. Bio–char has various

applications ranging from its direct use in agriculture, soil remediation, and wastewater

treatment. High pyrolysis temperatures produce more stable bio–char with improved

properties such as higher surface areas and lower volatile content.

2.3.2 Classification of Pyrolysis

Pyrolysis is mainly classified into four categories: (1) slow pyrolysis, (2) fast/flash pyrolysis,

(3) catalytic pyrolysis, and (4) microwave pyrolysis. Pyrolysis can be conducted in a fixed

bed, moving bed or fluidized bed reactors. The combination of pyrolysis modes, reactors and

24

products are shown in Fig. 2.3, while a number of studies concerning pyrolysis operations,

optimum conditions and bio–oil and bio–char characteristics are tabulated in Table 2.2.

Fig.2.3. A schematic of agricultural residue pyrolysis process

2.3.2.1 Slow Pyrolysis

As explained earlier, slow pyrolysis is characterised by a heating rate of 0.01 to 10 oC/s and

a long residence time 5–60 min. Slow pyrolysis requires a relatively slow reaction rate at

low operating temperatures to maximise mainly solid product (bio–char) yield (Russell et al.,

2017). Moreira et al. (2017) investigated the slow pyrolysis of cashew nut shell waste and

reported that bio–char, bio–oil and gases yields under N2 flow are 30, 40 and 30 wt.%,

respectively. The maximum value of HHV and pH of bio–char obtained from the slow

pyrolysis of palm kernel shell at 750 °C were 31.55 MJ kg−1 and 10.03, respectively (Ma et

al., 2017). Bhattacharjee and Biswas (2017) studied the properties of bio–oil derived from

the slow pyrolysis of Alternanthera philoxeroides and found that the bio–oil had 20.59% of

phenols, 11.07% amines, 14.59% aldehydes, 15.07% ketones, 16.44% alkenes, and 15.22%

alkanes. They also reported that the liquid, char and gas yields vary in the range of 32.13–

40.1 wt.%, 50.56–23.7 wt.%, 12.71–23.14 wt.%, respectively. In the non–condensable

biogas produced, the main gaseous species include CO2, CO, H2, CH4, C2H4, C2H6, C3H6,

and C3H8. The HHV of the biogas produced was between 1.2 and 10.8 MJ/kg (Serapiglia et

al., 2017, Ghouma et al., 2017).

Mode

Char, ash

Phenols, carboxylic acids,

aliphatics, aromatics,

alcohol, fatty oxygenates,

nitro oxygenates, others

CO, H2, CO2, CH4, N2,

C2H4, C2H6,

Cn (otherhydrocarbons)

Thermochemical

conversionReactor Products

Catalytic

pyrolysis

Microwave

pyrolysis

Fast/Flash

pyrolysis

Slow

pyrolysis

Fixed bed

Fluidized bedPyrolysis

Solid (bio-char)

Non-condensable gases

Liquid (bio-oil)

Moving bed

25

2.3.2.2 Fast/Flash Pyrolysis

Fast pyrolysis is carried out at a high heating rate of 10 to 100 oC/s with a temperature of

400–850 °C for a short span of time varying between 5–30 min to achieve a higher reaction

rate and a higher bio–oil yield. Hence, bio–oil production dominates in fast pyrolysis over

the bio–char and non–condensable gas production. Therefore, fast pyrolysis has received

more consideration for bio–oil production. A typical fast pyrolysis produces 41.1–50.2 wt.%

of liquid product, 24.4–34.8 wt.% of bio–char and 25–30 wt.% of non–condensable gaseous

products (Bahadir et al., 2017). The HHV of bio–oil produced from the fast pyrolysis was in

the range of 21–37.6 MJ/kg (Uçar and Karagöz, 2017). Fast heating rates favor quick

fragmentation of biomass leading to the production of more non–condensable gases and

lower amounts of bio–char. Bio–oil production is also enhanced at fast heating rates due to

the degradation of hemicellulose (which occurs from 200 to 350 °C), cellulose (takes place

from 300 to 400 °C), and lignin (starts slowly from 200 to 550 °C, reaches a constant value

and then increases above 550 °C). Another reason for the increase in bio–oil yield was the

occurrence of a secondary reaction at the third stage of the pyrolysis process (Kan et al.,

2016).

A fast pyrolysis investigation of soap stock showed that the bio–oil yields were between

31.5–43 wt.% (Dai et al., 2017b). Garg et al. (2016) studied the pyrolysis of babool seeds

(Acacia nilotica) and achieved a yield of 35–39 wt.% for bio–oil with an HHV of 36.45

MJ/kg in a fixed bed reactor under a nitrogen atmosphere. Cai and Liu (2016) found that the

weight ratios of bio–oil to bio–char and bio–oil to gases produced from the pyrolysis of rice

husks were 1.85 and 1.86 respectively. The pyrolysis, in this case, was a fast pyrolysis in a

downdraft circulating fluidized bed reactor operated at 550 °C with a residence time of about

2 s. Cai and Liu reported the maximum HHV of bio–oil was 18.07 MJ/kg. Madhu et al.

(2016) examined the fast pyrolysis of cotton shell feedstock at 450 °C using 1 mm particles

and a sweep gas flow rate of 1.75 m3 /h and found that the bio–oil yield was about 51.25%.

The bio–oil was found to have a higher heating value of 19.32 MJ/kg. Its density, viscosity,

and pH were found as 1005 kg/m3, 7.85 cSt and 3.3, respectively which are lower than those

for bio–oils produced from autotrophic cells and wood.

2.3.2.3 Catalytic Pyrolysis

Bio–oils produced from fast/flash or slow pyrolysis processes have very high oxygen

26

content. Higher oxygen content, as mentioned earlier, has several drawbacks. Bio–oils with

higher oxygen contents tend to be unstable and corrosive. They also have lower heating

values. The studies reported in the literature on the catalytic pyrolysis of biomass focus

mainly on the deoxygenation reaction (i.e., removal of oxygen from bio–oil). Catalytic

pyrolysis generally occurs at 300–600 °C with a catalyst–to–biomass ratio of 0.2–5 (Wang et

al., 2017b). However, this process usually involves multi–step reactions: Firstly, the biomass

is decomposed and converted into liquids, bio–char and non–condensable gases. The

pyrolysis vapors are then contacted with the catalyst where oxygen containing compounds

are converted into aliphatics and aromatics by deoxygenation (Duman and Yanik, 2017).

The HHV value of bio–oil produced by catalytic pyrolysis was found to be 42 MJ/kg, which

is close to that of petroleum oil (44 MJ/kg) (Choi et al., 2017).

Recently, a few studies on the catalytic pyrolysis of biomass waste were carried out to

produce high–quality bio–oils. Aqsha et al. (2017) carried out pyrolysis of straw biomass

samples (wheat, flax, oat, and barley) over zeolite ZY–SS catalyst in a bench–scale

horizontal fixed–bed reactor. The use of this catalyst led to 46.44 and 38.77 wt.% yields for

liquid (bio–oil) and solid (bio–char), respectively. However, the yield of the non–

condensable gases (bio–gas) decreased to 13.65%. Oh et al. (2017b) investigated the

catalytic pyrolysis of yellow poplar (Liriodendron tulipifera) using various Ni–based

catalysts (Ni/C, Ni/SBA–15 and Ni/Al–SBA–15). The bio–oil yield achieved was 45.8–48.1

wt%. Chen et al. (2017d) conducted the catalytic fast pyrolysis of cotton stalk with a calcium

oxide (CaO) catalyst in a fixed–bed reactor. In their study, CaO acted as an absorbent

depending on the pyrolysis conditions employed. Messina et al. (2017) indicated that the

bio–oil produced from the in–situ catalytic pyrolysis of peanut shells (Arachis hypogaea)

using modified clinoptilite as a catalyst. Lower O content (32.8 wt.%) and a greater HHV

(25.5 MJ/kg) than that from the direct pyrolysis of the peanut shells which had an O

content of 39.7 wt.% and a heating value of 22.5 MJ/kg.

2.3.2.4 Microwave Pyrolysis

Tech–in Ltd in Hainault, UK introduced the microwave–assisted pyrolysis. The advantages

of microwave pyrolysis over the conventional heating method include the consistent and

uniform internal heating of biomass particles and the reduction in the heat loss to the

surroundings, the simplicity of control and an instantaneous response for rapid start–up and

shut down. Yield and HHV of bio–oil obtained from microwave pyrolysis can be in the

27

range of 18–59 wt. % and 30–42 MJ/kg, respectively. Moreover, microwave pyrolysis is

usually operated at temperatures of 500–800 °C in the presence/absence of a catalyst

(Beneroso et al., 2017). Bio–oils produced from conventional heating methods (direct

pyrolysis) are more viscous and acidic and require further processing including

hydrogenation for the removal of excess oxygen and alkali content (Zainal et al., 2017).

Zhang et al. (2017) examined the microwave pyrolysis of spent edible mushroom substrate

for bio–oil production, over different surface modified ZSM–5 catalysts (PC–ZSM–5, SiO2–

ZSM–5 and EDTA–ZSM–5). It was identified that due to isomerization, dehydration,

decarboxylation, and decarbonylation reactions, bio–oil yield was found to decrease

significantly with a change in catalysts. The study reported that the maximum yields of bio–

oil and coke obtained at 500 °C were 16.0 and 2.2%, respectively with SiO2–ZSM–5

catalysts, and 14.8 and 3%, respectively with PC–ZSM–5 catalyst.

Hossain et al. (2017b) studied the microwave pyrolysis of oil palm fiber for bio–char and H2

production. They reported that a maximum bio–char yield of 48.26 wt.% was obtained at a

temperature of 450 °C, microwave power of 400 W and N2 flow rate of 200 cm3/min. In

addition to this, the maximum H2 yield of 9.79 g/kg was obtained at a temperature of 700 °C,

microwave power of ~510 W and N2 flow rate of 1200 cm3/min. They also optimised the

yields of both H2 and bio–char at the temperature, microwave power, and the N2 flow rate of

450 °C, 400W, and ~ 955 cm3/min, respectively. Liu et al. (2016a) studied the microwave

pyrolysis of tobacco stems under various microwave powers in the range of 400–700 W, the

maximum yield of bio–oil, bio–char and gas products were 29.53–26.2 wt.%, 24.8–72.80

wt.%, and 16.93–49 wt.%, respectively. Mohamed et al. (2016) studied the microwave

pyrolysis of switchgrass with catalysts (i.e., 10 wt.% of Clinoptilolite, 30 wt.% of

Clinoptilolite, 10 wt.% of K3PO4, 30 wt.% of K3PO4, 10 wt.% K3PO4 + 10 wt.% Bentonite,

and 10 wt.% K3PO4 + 10 wt.% Clinoptilolite) to produce bio–oil. They reported a maximum

bio–oil yield of 36.2 wt.% of the sample mixed with 30 wt.% clinoptilolite at 650 °C after 18

min of residence time under microwave irradiation.

28

Table 2.2. Operating modes and conditions of agricultural residue pyrolysis and bio–oil yield

Feed stock Particle

size (mm)

Pyrolysis

mode Reactor

Operating condition

Bio–oil yield

(wt.%) References Temp.

(°C)

Duration

(min)

Heating rate

(°C/min)

Sweeping gas

flow rate

(mL/min)

Tomato Stem <1 Slow Fluidized bed 200–565 30 10 – 18.5 (Hossain et al.,

2017c)

Spent Coffee

Ground <1 Slow Fluidized bed 200–565 30 10 – 16.2

(Hossain et al.,

2017c)

Palm oil Wastes 0.5–2 Slow Fixed–bed 500 60 10 50 – (Lee et al., 2017)

Mixed oil Shale

Semi–Coke <0.9 Slow Retorting 750 – 10 50 8.59 (Jiang et al., 2017)

Soursop (Annona

Muricata L.)

Seed Cake

– Slow Fixed–bed 400 25 15 – 18.6 (Schroeder et al.,

2017)

Sugarcane

Bagasse <1 Slow Fluidized bed 250–650 20 20 – –

(Montoya et al.,

2017)

Apricot (Prunus

Armeniaca L.)

Seed Kernel

0.25 Slow Fixed–bed 450 60 20 20 43.66 (Fadhil, 2017)

Humulus Lupulus 1.25–0.60 Slow Fixed–bed 550 – 10 – 26 (Varol et al., 2017)

29

Mahua oilseed – Slow – 525 – 20 – 50 (Pradhan et al.,

2017)

Pistachio waste – Slow Fixed–bed 550 10–50 20 50–450 21.5

(Taghizadeh-

Alisaraei et al.,

2017)

Flax seed residue – Slow Fixed–bed 500 50 20 5 50.66 (Gouda et al., 2017)

Cashew Nut Shell – Slow Fixed–bed 500 30 22.5 250 11.4 (Moreira et al.,

2017)

Rice straw – Slow Fixed–bed 400 – 20 – 34.5 (Biswas et al.,

2017c)

Wheat straw 0.5–2 Slow Fixed–bed 400 60 20 50 36.7 (Biswas et al.,

2017a)

Azolla – Slow Fixed–bed 400 60 20 50 38.5 (Biswas et al.,

2017b)

Oil Palm

Mesocarp Fiber – Slow Fixed–bed 550 15 10 200 48 (Kabir et al., 2017)

Rice Husk 0.83–1.65 Fast Fixed–bed 500 – 30 35 38.91 (Betemps et al.,

2017)

Durian (Durio

Zibethinus L)

Shell

1–2 Fast Fixed–bed 650 – – 20 57.45 (Tan et al., 2017)

30

Lemongrass

(Cymbopogon

flexuosus)

0.71–1.0 Fast Fixed–bed 450 40 45 – 40.6 (Madhu et al.,

2017)

Wheat Straw – Fast – 500 – – – 30 (Li et al., 2017a)

Corncob <1 Fast Fluidized bed 434 65 – – 64.69 (Oh et al., 2017a)

Wheat Straw – Fast Fixed–bed 500 – 20 100 31.9 (Tomás-Pejó et al.,

2017)

Sugarcane

Bagasse 0.5–0.6 Fast Fixed–bed 500 – 50 100 45.23

(Varma and

Mondal, 2017)

Olive Mill

Solid Wastes 0.425–0.6

Catalytic

(HZSM–5) Fixed–bed 450 – 10 150 52.66

(Christoforou et al.,

2017)

Olive–oil

Cake 0.425–0.6

Catalytic

(SiO2) – 500 30 8 150 39.3

(Hani and Hailat,

2016)

Sunflower

Stalk 1–2.8

Catalytic

(Al–MCM–41) Fixed–bed 600 60 10 50 37.9

(Karnjanakom et

al., 2017)

Pine

Sawdust 0.06

Catalytic

(Ni–Zn/Al2O3) – 400 30 10 – 44.64

(Cheng et al.,

2017c)

Corn Stover 0.1–0.15

Catalytic

(alumina based

solid acid)

Fluidized bed 500 0.02 5 1 16.6 (Wang et al.,

2017a)

Tribonema 0.2–0.45 Catalytic Fixed–bed 450 60 10 50 35.52 (Ji et al., 2017)

31

Minus Residue (Ni–Mg/ZSM–5)

Laminaria

Japonica –

Catalytic

(HZSM–5) Fixed–bed 500 60 10 50 24 (Kim et al., 2017)

Straw Stalk and

Soapstock – Microwave Fixed–bed 550 – – – 43 (Zhou et al., 2017)

Corn Cob – Microwave Fixed–bed 400–500 – – – 42.1 (Ravikumar et al.,

2017)

Soapstock – Microwave Fixed–bed 600 30 – 1 50.41 (Dai et al., 2017a)

Corn Stalk – Microwave Fixed–bed 150–250 – – – 19.6 (Salema et al.,

2017)

Olive Kernels <1 Microwave Fixed–bed 500 – – – 59.53 (Ganesapillai et al.,

2016)

Arundo Donax

Stems

– Microwave Fixed–bed 493 22

– – 40.9

(Bartoli et al.,

2016)

Oil Palm Shell –

Microwave Fixed–bed 700 30 – –

30 (Pianroj et al.,

2016)

32

2.4 Liquefaction

2.4.1 Principle of Liquefaction

Liquefaction, also termed as thermochemical liquefaction or hydrothermal liquefaction

(HTL), is a promising option to convert low–value wet PSC to liquid fuel termed as bio–oil

or bio–crude. Compared with other thermochemical processes including pyrolysis,

combustion, and gasification, liquefaction is conducted at relatively low temperatures of

250–400 °C with 10–120 min duration but at higher pressures of 5–25 MPa, with water or

organic solvent as a medium in the presence or absence of catalyst (Gollakota et al., 2017).

HTL has advantages over other thermochemical processes in that it can directly transform

wet agricultural feedstock with high moisture content without the need for pre–drying

(Karaca and Bektas, 2017). The efficiency of liquefaction process mainly depends on the

reaction temperature, duration (retention time), and composition of biomass feedstock. The

schematic diagram and operating conditions used in the liquefaction of agricultural residue

in recent studies are summarised in Fig.2.4 and Table 2.3.

Liquefaction of solid biomass involves a complex mechanism controlled by several

parameters. To carry out an efficient liquefaction process, measured in terms of product

quality, conversion, cost–effectiveness, and energy efficiency, the selection of appropriate

solvents and catalysts are critical. Solvent choice has a considerable effect on the

liquefaction reaction. Water and various organic solvents such as phenols, alcohols, glycols,

ketones, etc., are established solvents that help to produce low viscosity heavy oils by

effectively breaking down the heterogeneous macromolecular structure of the biomass into

light to moderate hydrocarbons. In addition, various co–solvents and acid catalysts such as

sulphuric acid, hydrochloric acid, phosphoric and oxalic acid are used for increasing bio–

crude yields (Nazari et al., 2017). Thus, HTL is an excellent choice for converting the pre–

treated and washed residues with high moisture content in the range of 10–80 wt.%.

In HTL, H+ and OH– dissociated from water may catalyse hydrolysis in acid– catalysed and

base–catalysed reactions (Chen et al., 2017e). The reaction mechanism during liquefaction

involves the decomposition of unstable and reactive light fragments of biomass components

into smaller compounds by dehydration, dehydrogenation, deoxygenation, and

decarboxylation. Thus, the liquefaction of biomass involves the production of bio–oil

containing monoaromatics and single ring heterocyclic compounds (such as benzene,

33

phenol), aliphatic compounds (such as alkanes, alkenes), oxygenated compounds (such as

long–chain carboxylic acids, esters, aldehydes and ketones), nitrogenated compounds (such

as amides, amines), and polyaromatics (such as naphthalene, indene) (Yang et al., 2016).

Above the critical point of water, supercritical conditions favor decarboxylation, cleavage,

steam reforming and gasification reactions of intermediates and lead to a higher gas yield

(Chen et al., 2017f). However, this technology faces several challenges, including low yield

(generally in the range of 20–60%) and quality issues (corrosive, viscous/high density,

acidic, unstable and low HHV, etc.) of the resulting bio–crude oil, and large capital

investment in reaction/separation systems.

Fig. 2.4. A schematic of agricultural biomass liquefaction

Liquefaction

Reaction mixture

Soluble Insoluble

Agricultural residue

Operating conditions

250-350 oC

5-20 Mpa

Gas

H2, CO2, CH4, CO,

N2, C2H4, etc..

Filtration

Evaporation

Bio-crude

Phenols,

carbohydrates,

fatty acids,...

Drying

Solid residue

Char and ash

34

2.4.2 Products of Liquefaction

Liquefaction yields high–quality liquid products (bio–crude) relative to other

thermochemical methods. The resultant black liquid mixture consists of bio–crude and

unconverted biomass residue, which is dissolved in a solvent for easy filtration. Bio–crude is

a dark brown–colored, energy dense, semi–liquid with smoke–like smell and a viscosity 10–

10,000 times greater than that of diesel or biodiesel (Jena and Das, 2011). The bio–crude

yield depends on the lipid, protein, and carbohydrate content of the original substrate. Bio–

crude contains a significant amount of carboxylic acids such as acetic acid and formic acid as

well as phenolic fractions and carbohydrates and their alkylated derivatives. Bio–crude also

contains phytol and cholesterol derivatives, alkanes, alkenes and long chain heavy aromatic

hydrocarbon compounds (Alhassan et al., 2017). The reaction mechanism during liquefaction

involves the decomposition of unstable and reactive low molecular weight components of

biomass into smaller compounds by dehydration, dehydrogenation, deoxygenation, and

decarboxylation. It also includes the rearrangement of the intermediate elements through

condensation, cyclization, and polymerization reactions to yield new compounds (Palardy et

al., 2017). The physicochemical properties of bio–crude appear to depend strongly on the

type of biomass feedstock and operating conditions of the liquefaction process. The bio–

crude yield and HHV are typically in the ranges of 20–65 wt.% and 25–45 MJ/kg,

respectively. Due to high carbon and oxygen contents and low nitrogen and sulphur contents

of bio–crude produced from raw agricultural feedstock (Table 2.3), its HHV was

significantly greater (10–18 MJ/kg) and comparable to that of petroleum fuel oil (around 43

MJ/kg) (Hossain et al., 2017a). Bio–crude produced from liquefaction usually has

significantly lower elemental O content (6–30 wt.%) compared to that of raw agricultural

biomass and petroleum crude oil. But the carbon content of the bio–crude is usually very

high, in the range of 55–80 wt.%. The H, N and S contents in bio–crude varies from 5–10,

0.3–8 and 0–1 wt.%, respectively (Table 2.3). The non–condensable permanent gases arising

from the liquefaction process include CO2, O2, H2, CH4, CO, N2 and light hydrocarbons

(C2H4, C2H6, C2H2, and C3H8) which are typically analysed using micro chromatography.

The dominant component of the non–condensable permanent gases arising from liquefaction

is typically CO2 (50–60 vol.%) and to a lesser extent H2 (3–6 vol.%). Other gases (CO, CH4)

are very low in volume. CO2, CO, and CH4 are detected in the product gas because they are

produced in reactions such as decarboxylation, deoxygenation, and methanation during the

liquefaction process (Barnés et al., 2017).

35

2.4.3 Influence of Operating Conditions

The basic parameters in liquefaction include operating temperature, retention time, feedstock

load and type of catalysts used. The impact of these parameters has been studied extensively

in the context of converting agricultural biomass into bio–crude.

The two most important parameters in liquefaction are the reaction temperature and the type

of feedstock. It has been observed that the yield of the bio–oil product increases with

increasing reaction temperature (Alma et al., 2016). The main constituents of bio–oil are

predominately formed when the temperature is increased from 250 to 350 °C. A decrease in

total organic carbon recovery under these conditions suggests that the water–soluble organic

compounds undergo inter–and intra–polymerization to form long–chain hydrocarbons

(aromatics, aliphatics, cyclic etc.) which form the bio–crude fraction in the reaction mixture

(Nazem and Tavakoli, 2017). As shown in Table 2.3, with an increase in temperature, the C,

H contents, HHVs and yield of bio–crude increase. The downtrend of N and S contents

indicates that denitrification and desulfuration of bio–crude were favorable at a higher

temperature. The largest HHV of bio–crude was shown as 40.04 MJ/kg for waste

Cyanophyta biomass at 350 °C for a reaction time of 30 min (Xu and Savage, 2017). This

correlated with the highest C and H contents of 78.21 and 10.3%, respectively (Xu and

Savage, 2017). Moreover, the H/C, O/C, N/C and S/C ratios of bio–crude decrease

significantly with increasing temperature (He et al., 2017).

Many researchers have studied the effect of holding time, which is the duration of

liquefaction at a fixed temperature, on the production of bio–crude. Holding time is found to

be between 0 to 60 min, but a few studies have used holding times up to 120 min. Bio–crude

yield increases with increasing holding time, however beyond a certain threshold, further

increases in the holding time lead to a decrease in bio–crude yield. The optimum holding

time depends mainly on the type of biomass, catalyst and operating temperature used (He et

al., 2017). For example, Hadhoum et al. (2016) examined the hydrothermal liquefaction of

oil mill wastewater and found that the bio–crude yield increased from 55.76 wt.% after 15

min to 58.09 wt.% after 30 min. However, when the holding time increased to 45 min, the

yield of bio–crude decreased to 45 wt.% due to the conversion of lighter hydrocarbon

compounds in the bio–crude to gaseous products. Similarly, Chan et al. (2017) found that a

holding time of 60 min at 390 °C for the liquefaction of palm kernel oil led to a maximum

bio–crude yield of 15.85 % but a longer holding time of 120 min resulted in a lower bio–oil

36

yield of 8.48 wt.%.

Another factor affecting the liquefaction performance is biomass–to–solvent ratio. To carry

out an efficient liquefaction process, measured in terms of product quality, conversion, cost–

effectiveness, and energy efficiency, the selection of appropriate solvents is critical. Solvents

have been shown to have a considerable effect on the liquefaction reaction. The most

common hydrothermal liquefaction solvent used is water, as shown in Table 2.3. Organic

solvents used in the liquefaction include phenols, alcohols, glycols, ketones, etc., are useful

for producing low viscosity heavy oil by effectively breaking down the heterogeneous

macromolecular structure of the biomass into the light to moderate hydrocarbons. In addition,

various co–solvents and acid catalysts such as sulphuric acid, hydrochloric acid, phosphoric

and oxalic acid were used for increasing the bio–crude yield (Koriakin et al., 2017). Alma et

al. (2016) studied the thermochemical liquefaction of Scots pine wood (Pinus sylvestris L.)

powder in the presence of pyrolytic bio–oil (PBO), a decrease in the biomass–to–PBO ratio

from 1:7 to 1:14 resulted in a decrease in heavier biomass conversion. The biomass–to–PBO

mass ratio 1:7 was found to lead to a conversion of 97.4% of biomass. However, the

extensive use of PBO was found to lead to the decreased conversion of 54.4%.

Among the numerous studies on agricultural biomass liquefaction, the presence of a catalyst

has been shown to increase bio–crude yields significantly. Two classes of catalysts are used;

homogeneous and heterogeneous. Sodium carbonate (Na2CO3) is the most commonly used

homogeneous catalyst, and other homogeneous catalysts include FeSO4, H2SO4, HCl,

K2CO3, FeS, Fe, etc. Heterogeneous catalysts used in liquefaction include Co/Mo/Al2O3,

Pt/Al2O3, CoMo/c–Al2O3 and Ni/Al2O3, etc. Malins (2017) studied a wide range of catalysts

including FeSO4, ZnSO4, NiSO4, Raney–nickel, Ni65%/SiO2–Al2O3, Na2CO3 and NaOH in

the liquefaction of birch sawdust using temperatures and holding times of 200–340 °C and

5–90 min, respectively. They found that the maximum bio–crude yield of 54.1 % was

obtained at an optimal experimental condition (300 °C, 40 min) in the presence of NaOH (5

wt.%).

37

Table 2.3. Operating conditions used in the liquefaction of agricultural residual biomass, and the yield and HHV of bio–crude produced

Feed stock Catalyst

(wt.%)

Operating condition Elemental analysis (wt.%) Maximum

bio–crude

yield (wt.%)

Maximum

HHV

(MJ/kg)

References Temperature

(°C)

Duration

(min) C H N O S

Waste

Cyanophyta – 350 30 78.21 10.3 4.24 6.57 0.68 31.79 40.04

(Song et al.,

2017)

Cornstalk – 290 30 69.07 6.01 0.49 24.43 – 23.32 32.5 (Zhu et al., 2017)

Cypress – 300 60 67.4 5.3 0.3 26.7 0.23 40.1 – (Liu et al.,

2017b)

Pine Sawdust – 260 120 – – – – – 14.1 29.4 (Hardi et al.,

2017)

Sweet Sorghum

Stalk – 300 30 63.62 8.03 0.84 27.43 – 40.5 25.1

(Yan et al.,

2017)

Blackcurrant

Pomace – 310 10 72.9 9.8 3.4 13.6 0.2 31 35.2

(Deniel et al.,

2016)

Swine

Manure – 340 15 72.58 9.76 4.47 13.19 – 21.96 38 (Xiu et al., 2016)

Sweet Sorghum

Bagasse K2CO3 300 60 73.2 7.7 0.5 15 0.2 61.8 33.1 (Bi et al., 2017)

Spent Mushroom

Compost

10 wt. %

K2CO3 400 15 73.7 8.49 3.53 13.06 1.23 35.05 34.45

(Jasiūnas et al.,

2017)

Pine Sawdust Ni/HZSM–5 300 60 63.29 7.81 0.72 28.18 – 63.5 27.51 (Cheng et al.,

2017a)

38

Spent Coffee

Grounds 5% NaOH 250 10 66.6 9.2 2.3 21.9 – 20.9 31.9

(Yang et al.,

2017a)

Rice Straw SubCO2–

SubH2O 295 30 62.94 7.7 0.54 28.82 – 23.585 28.18

(Yang et al.,

2017b)

Rice Straw NiO 300 120 72.1 7.5 0.9 19.5 – 30.4 31.6 (Younas et al.,

2017)

Birch Sawdust Na2CO3 340 90 64.5 6.2 0.3 29.3 – 54.1 24.9 (Malins, 2017)

Waste Furniture

Sawdust K2CO3 280 15 73.86 7.14 19 – 34.9 31.77

(Jindal and Jha,

2016)

Coconut Shell ZnCl2 300 30 – – – – – 13.9 31.1 (Lee et al., 2016)

Sugarcane

Bagasse MgMnO2 250 15 65.93 10.16 0.38 23.26 0.27 59.5 32.61

(Long et al.,

2016)

Rice Straw 5 wt.% of

Na2CO3 260 60 58.47 7.99 2.15 31.39 – 50.31 25.56 (Cao et al., 2016)

Dried Distillers

Grains K2CO3 340 20 74.5 8.4 8.3 8.8 – 55 35.6

(Biller et al.,

2016)

Blackcurrant

Pomace NaOH 300 60 73.3 9.6 3.4 13.6 0.1 45 35.9

(Déniel et al.,

2016)

39

2.5 Gasification

2.5.1 Principle of Gasification

Gasification is a commercialised thermochemical conversion process most commonly

employed to produce syngas from coal (Ding et al., 2015). This technology has been applied

to a variety of agricultural biomass/PSC feedstocks over the last two decades. Biomass

gasification has been attracting significant attention recently as a means of producing

renewable energy from biomass and for reducing overall greenhouse gas emissions

(Bellouard et al., 2017). Biomass gasification is a thermochemical partial oxidation process

that essentially converts carbonaceous materials into combustible gas or synthesis gas in the

presence of gasifying agents such as air, carbon dioxide, oxygen, and steam or a mixture of

these. The syngas produced from the gasifier mainly contains carbon monoxide (CO) and

hydrogen (H2) with small quantities of carbon dioxide (CO2), methane (CH4), and nitrogen

(N2) as well as light hydrocarbons (ethane and propane) and heavier hydrocarbons such as

tars (Sepe et al., 2016). The quality of syngas produced from the gasification process is

affected by the biomass feedstock, design of the reactor, gasifying agents, the presence of a

catalyst, and process operating conditions (Kuo et al., 2016). The gasification process is

highly flexible and the feedstock can be utilised as a substitute for fossil fuels in the

production of syngas. Hence, which can be further used to produce electricity, hydrogen and

liquid fuels (such as biodiesel and bio–oil) or chemicals (using methods such as Fisher–

Tropsch synthesis and dimethyl ether synthesis) (Serrano et al., 2017).

The lower heating value (LHV) of the syngas ranges from 4 to 18 MJ/Nm3 and depends on

the type of feedstock, gasification technology, and the operational conditions employed. The

air gasification technology generates syngas with lower quality and LHV in the range of 3–6

MJ/Nm3 (Zheng et al., 2016). The oxygen–rich air gasification leads to a relatively better

yield of syngas. However, this technology generates a gas with average LHV in the range of

10 to 16 MJ/Nm3. Compared to the above two methods, the oxygen–steam gasification

technology is a more efficient way of producing high–quality syngas with LHV in the range

of 12 to 18 MJ/Nm3 (Sepe et al., 2016). The byproduct of the gasification process is mainly

bio–char containing a mixture of unconverted organic compounds and ash. The LHV of the

bio–char is in the range of 20 to 30 MJ/kg (Park et al., 2016b).

40

2.5.2 Gasification Technologies

The biomass gasification process involves a combination of different reaction zones

including drying, pyrolysis, oxidation, and reduction. The first stage, drying can be

considered complete when a feedstock temperature is above 150 °C to ensure the evaporation

of all moisture contained in the biomass feedstock. In this stage, the amount of heat required

is directly proportional to the feedstock moisture content.

𝐹𝑢𝑒𝑙 (𝐶𝑥𝐻𝑦𝑂𝑧𝑁𝑝𝑆𝑞) + 𝑄 (ℎ𝑒𝑎𝑡) → 𝑑𝑟𝑦 𝑓𝑢𝑒𝑙 + 𝐻2𝑂 − − − − − (2.3)

Pyrolysis involves the thermal decomposition of agricultural biomass in an O2–deficient

environment at about 230 °C to evaporate other volatile components. Pyrolysis yields solid

(char and charcoal), liquid (tar, oil, and pyrolytic oil) and non–condensable gases products

gases like H2, CO, CH4, etc. The liquid fraction, known as "tar," represents a challenge to

downstream processes, due to its stickiness and high viscosity which leads to a reduction in

efficiency. Ideally, the liquid fractions are cracked to form light hydrocarbons when a

sufficiently high pyrolysis temperature is applied, thus reducing the damage to the

downstream gasifier. The pyrolysis stoichiometry, in general, can be expressed as follows

(Zaafouri et al., 2016).

𝐶𝑥𝐻𝑦𝑂𝑧𝑁𝑝𝑆𝑞 + 𝑄 (ℎ𝑒𝑎𝑡) + 𝑁2 → 𝐵𝑖𝑜 − 𝑐ℎ𝑎𝑟 + 𝐶𝑜𝑛𝑑𝑒𝑛𝑠𝑎𝑏𝑙𝑒𝑠 𝑙𝑖𝑞𝑢𝑖𝑑𝑠 (𝐵𝑖𝑜 − 𝑜𝑖𝑙) +

𝑁𝑜𝑛 − 𝑐𝑜𝑛𝑑𝑒𝑛𝑠𝑎𝑏𝑙𝑒 𝑔𝑎𝑠𝑒𝑠 (𝐶𝐻4 + 𝐶𝑂 + 𝐶𝑂2 + 𝐻2) + 𝐻2𝑂 + 𝑁2 − − − −(2.4)

The oxidation process provides thermal energy needed for drying, pyrolysis, and other

endothermic reactions. Oxygen supplied to the gasifier reacts with the char (C) present in the

feed at a temperature of about 800–1400 °C resulting in the formation of CO (partial

oxidation) and CO2 (total oxidation). The amount of heat released during from complete

oxidation (reaction 2.5) is three times more than that produced during partial oxidation

(reaction 2.6). Finally, the products of combustion react with remaining char in the

gasification zone. The main oxidation reactions during the process are the following (Molino

et al., 2016):

𝐶 + 𝑂2 → 𝐶𝑂2 ∆𝐻 = −394𝑘𝐽

𝑚𝑜𝑙 𝑇𝑜𝑡𝑎𝑙 𝑜𝑥𝑖𝑑𝑎𝑡𝑖𝑜𝑛 (2.5)

𝐶 +1

2𝑂2 → 𝐶𝑂 ∆𝐻 = −111

𝑘𝐽

𝑚𝑜𝑙 𝑃𝑎𝑟𝑡𝑖𝑎𝑙 𝑜𝑥𝑖𝑑𝑎𝑡𝑖𝑜𝑛 (2.6)

𝐻2 +1

2𝑂2 → 𝐻2𝑂 ∆𝐻 = −242

𝑘𝐽

𝑚𝑜𝑙 𝐻𝑦𝑑𝑟𝑜𝑔𝑒𝑛 𝑐𝑜𝑚𝑏𝑢𝑠𝑡𝑖𝑜𝑛 (2.7)


41

Reduction (800–1500 °C) reactions involve a series of endothermic and exothermic reactions.

The products of preceding stages of pyrolysis and oxidation including the gas mixture and

bio–char react with each other resulting in the formation of the final combustible gases such

as H2, CO, and CH4. The major reduction reactions that take place during the process are

Boudouard, water gas, shift conversion and methanation shown below (Mastuli et al., 2017,

Tank et al., 2017):

𝐶 + 𝐶𝑂2 → 2𝐶𝑂 ∆𝐻 = 172𝑘𝐽

𝑚𝑜𝑙 𝐵𝑜𝑢𝑑𝑜𝑢𝑎𝑟𝑑 𝑟𝑒𝑎𝑐𝑡𝑖𝑜𝑛 (2.8)

𝐶 + 𝐻2𝑂 → 𝐶𝑂 + 𝐻2 ∆𝐻 = 131 𝑘𝐽

𝑚𝑜𝑙 𝑊𝑎𝑡𝑒𝑟 𝑔𝑎𝑠 𝑟𝑒𝑎𝑐𝑡𝑖𝑜𝑛 (2.9)

𝐶𝑂 + 𝐻2𝑂 → 𝐶𝑂2 + 𝐻2 ∆𝐻 = −41 𝑘𝐽

𝑚𝑜𝑙 𝑊𝑎𝑡𝑒𝑟 𝑔𝑎𝑠 𝑠ℎ𝑖𝑓𝑡 𝑟𝑒𝑎𝑐𝑡𝑖𝑜𝑛 (2.10)

𝐶 + 2𝐻2 → 𝐶𝐻4 ∆𝐻 = −75 𝑘𝐽

𝑚𝑜𝑙 𝑀𝑒𝑡ℎ𝑎𝑛𝑎𝑡𝑖𝑜𝑛 𝑟𝑒𝑎𝑐𝑡𝑖𝑜𝑛 (2.11)

The syngas products obtained from the gasification process can be explained as follows: CO2

is generated via the decomposition of carboxyl groups at low temperature (< 800 °C) during

oxidation and water gas shift reactions (2.5 and 2.10). CO is generated at elevated

temperatures due to the Boudouard reaction (2.8). H2 is generated in the presence of higher

moisture content (steam) at high temperatures via endothermic reactions (2.9 and 2.10), and

CH4 is generated due to methanation reactions (2.11).

Researchers have recently developed a wide variety of new gasification technologies

including plasma gasification and supercritical water gasification for efficient syngas

generation using different feedstocks (Heidenreich and Foscolo, 2015, Sikarwar et al., 2016).

As mentioned above, the yield and composition of syngas in gasification depends on the

feedstock material, gasifying agent, operational conditions, and design of the reactor (Chen et

al., 2016b). Biomass gasification is generally carried using various conventional gasification

technologies including fixed bed reactors (updraft and downdraft) (de Sales et al., 2017,

Sansaniwal et al., 2017a), fluidized bed reactors (Liu et al., 2016b), and entrained flow

reactors (Göktepe et al., 2017) as shown in Fig.2.5. Fixed bed reactors require high residence

time. However, the advantage of fixed bed reactors is that they can handle relatively large

sized particles (>2 cm), and due to higher residence time, there is usually lower tar

generation. In contrast, fluidized bed reactors require a lower residence time. However, they

need smaller particle sizes (2–5 mm), and the tar generation is higher than fixed bed reactors.

Catalysts have also been used in both fixed and fluidized bed reactors for 1) improving the

42

gasification kinetics and 2) in–situ cracking of tars. The operating temperature for both fixed

bed and fluidized bed is generally between 700–900 °C. In terms of scale up, fixed bed

gasifiers are usually available in sizes between 100 kW to 2 MW while fluidized bed reactors

can range from 2 MW to 200 MW. Entrained flow reactors in contrast to fixed and fluidized

bed reactors can operate at high temperatures (up to 900–1500 °C), need smaller sized

particles (1–5 mm) and require lower residence times. Also, tar generation in entrained flow

systems can be significantly lower due to higher temperatures. The only drawback with

entrained flow system is the scale. Normally, they are only economically viable for large

scale operations (>100 MW). A summary of recent technologies applied in agricultural

biomass gasification can be seen represented in Table 2.4.

Fig.2.5. A schematic of biomass gasification

2.5.2.1 Fixed Bed Gasifier

Fixed bed gasifiers are the most commonly used reactors. Fixed bed gasifiers can be divided

into two categories depending on the direction of the gas flow through the reactor; updraft

and downdraft as seen in Figs 2.6(a) and (b). The updraft fixed bed gasifier is the oldest and

simplest gasification technology. It can offer a high degree of feedstock flexibility including

coal, biomass, petroleum coke, municipal waste, etc., with high moisture and ash contents up

to 50 and 15 %, respectively (Lenis et al., 2016, Kihedu et al., 2016). In the updraft (counter

ModeFeed stock Reactor Products

GasificationAgricultural

biomass

Fixed bed

Fluidized

bed

Entrained

flow

CO, H2, CO2, CH4, N2

Char, ash

Gases

Solid

43

current) fixed bed gasifier, biomass fuel is fed from the top of the reaction chamber while the

gasifying agent (air, O2 or mixture) is fed from the bottom of the unit. The biomass fuel flows

down slowly through the different reaction zones including drying, pyrolysis, combustion,

and reduction, and the ash along with unconverted carbon is removed from the bottom of the

gasifier. While the produced gas passes through the fuel bed, it picks–up volatile matter (tars)

and moisture from the fuel. Therefore, the gas from the updraft gasifier contains condensable

volatiles. The advantages of an updraft gasifier are its fuel flexibility, and it can tolerate

higher ash content, higher moisture content and greater size variation in fuel as compared to

downdraft gasifiers (Tian et al., 2017).

In downdraft gasifiers, both the solid biomass fuel and the gas are moved downwards (co–

current). Downdraft gasifiers are fuel specific and can operate on wood–like biomass

materials and biomass briquettes with a minimum bulk density of 250 kg/m3 and ash content

of less than 5% (Biagini et al., 2016). All the decomposition products from the drying and

pyrolysis zones are forced to pass through the oxidation zone for thermal cracking of volatile

materials. The gas obtained in the absence of oxygen is a mixture of CO2, H2O, CO and H2

with less tar (Aydin et al., 2017, Sansaniwal et al., 2017b). However, the high temperature of

the produced gas, as it comes out of the reactor, contains small amounts of ash and soot. This

gas can also be used either in hot condition (after preliminary cleaning) or cold–clean

condition (after suitable gas clean–up arrangement). The gas from the downdraft gasifiers can

be cleaned to very high purity such that it can be well suited for IC engines and gas turbines

for power generation applications or for direct heating applications where purity of gas is a

critical requirement (Sulaiman et al., 2016, Tańczuk et al., 2017).

Fig.2.6. Schematic diagram of (a) updraft and (b) downdraft fixed bed gasifiers (Sansaniwal et

al., 2017a).

44

2.5.2.2 Fluidized Bed Gasifier

In the fluidized bed gasifier represented in Fig.2.7 (a), the fluidized bed is the most promising

technology in biomass gasification in which a fluidized bed of fine inert material (sand, char,

etc. and/or catalyst) is used as a heat transfer medium (Shahbaz et al., 2017). Biomass is fed

directly into the sand and kept in a fluidized state using a gasification agent/fluidization

medium at the appropriate velocities. The operating temperature of a fluidized bed reactor is

kept constant and typically ranges between 800 °C and 900 °C depending on the melting

point of the bed material. Fluidized bed gasifiers are further categorized into bubbling

fluidized bed, circulating fluidized bed and dual fluidized bed gasifiers (Liu et al., 2016c,

Gwak et al., 2017, Yan et al., 2016a). Hydrocarbon content such as tar and particle content in

the gas produced from fluidized bed gasifiers are higher as compared to downdraft gasifiers

and lower than that of the updraft gasifiers. However, such systems are less sensitive to fuel

variations but produce larger amounts of tar and dust. They are more compact but also more

complex, and usually used at major scales. Fluidized bed gasifiers were operated with

significantly higher gas flow velocities than fixed bed gasifiers. The carbon conversion

efficiency of fluidized bed gasifiers is comparatively higher than fixed beds and lower than

entrained flow gasifier reported to be up to 95% (Bronson et al., 2016, Yan et al., 2016b).

2.5.2.3 Entrained flow gasifier

In the entrained flow gasifier represented in Fig.2.7 (b), the fine feed fuel (0.1–1 mm

particles) and the gasifying agents (air or oxygen and/or steam) are introduced at the top of

the reactor, and solid fuel is entrained by the gasifying agents in the reactor. Entrained–flow

gasifiers operate at high temperatures (1000–1400 °C) and pressures (2–8 MPa) and at

extremely turbulent flow conditions which cause rapid feed conversion and allows high

throughput. The gasification reactions take place at very high reaction rates and very low

residence time, typically few seconds (0.5–4.0 s). The product gas (syngas) leaves the

reactor at the bottom together with a low percentage of molten slag (Billaud et al., 2016).

Then, the product gas can be cleaned by a cyclone separator and bag filter, and cooled, most

commonly by either heat exchanger or quenching the gas with a water scrubber (Göktepe et

al., 2016). Entrained–flow gasifiers have the ability to handle practically any type of

biomass; coal, water slurries or dry feeds can be used as raw material and produce a clean,

tar–free syngas. However, solid fuel with lower moisture and ash contents are favored to

45

reduce oxygen consumption in the gasification process (Feng et al., 2016). The tar, bio–oil,

and other liquids produced from the devolatilisation of biomass inside the gasifier are

cracked into H2, CO and small amounts of light hydrocarbon gases. It requires a high

operating temperature to ensure a good carbon conversion of 98–99.5% due to the short

residence time of less than 5 s. Entrained flow reactors can be further classified into slagging

and non–slagging entrained flow gasifiers; in the former, ash leaves the reactor as a liquid

slag, and in the latter, slag is not produced (Duan et al., 2016). Compared with fixed bed and

fluidized bed gasifiers, entrained flow gasifiers operating at high capacity, high temperature

and high carbon conversion with small particle sizes can produce almost a tar–free, clean

product gas via a short residence time (Sripada et al., 2017).

Fig.2.7. (a) Fluidized bed reactor (b) Entrained flow reactor (Molino et al., 2016)

2.5.3 Effects of operating parameters

Since the gasification of biomass is a complex thermochemical process and has been reported

to be very sensitive to process parameters and operating conditions, a small change in process

conditions may affect the overall performance of the system and the quality of end product

(Liu et al., 2017a). In this section, the performance of the gasifier for the production of

product gas with precise composition is evaluated and the significant variables such as

46

temperature, equivalence ratio, steam to biomass ratio and particle size on the individual gas

yields are discussed based on research findings (Lopez et al., 2017). These parameters play

an important role in the performance of a gasifier in terms of the quality of produced gas and

gasification efficiency. Many experimental works were conducted to optimise the quality of

product gas and gasification efficiency. The effects of biomass characteristics and process

parameters on the quality of gas are discussed in the following section.

2.5.3.1 Temperature

Temperature is a crucial factor and plays a significant role in the overall biomass gasification

process and controlling tar concentration, char and ash formation. At low temperatures, CO2,

CH4, and un–burnt carbon is present with the syngas (Chiang et al., 2016). As the temperature

increases, the carbon is expected to react with O2 and form carbon monoxide (CO) by partial

oxidation reaction (Eq. 2.5). According to the Boudouard reaction, as the gasifier temperature

increases the CO mole fraction increases and that of CO2 decreases (Eq. 2.8). The Boudouard

reaction is a highly endothermic reaction; consequently, the heating value of syngas

improves. Water gas reaction suggests that as temperature increases the production of both

CO and H2 growth (Eq. 2.9). This reaction also accounts for the increase in the heating value

of syngas. Methane reacts with the steam present in the biomass and gets converted into

hydrogen indicating that a higher reaction temperature is favorable for steam reforming and

cracking of CH4. These results suggest an increase in the temperature of the gasifier favors

the production of CO and H2, such that CH4 and CO2 concentration in the syngas decreases

(Eq. 2.11) (Ali et al., 2017). Studies reported in the literature suggested a similar explanation

for gasification reactions. They found that, with increasing temperature, the concentrations of

H2 and CO increased while that of CO2 and CH4 decreased (Safari et al., 2017). For example,

Safari et al. (2016) investigated the effect of gasification temperature on the gas product yield

of sugarcane bagasse in a laboratory scale fixed bed gasifier with temperatures ranging from

400 to 1000 °C. They reported that H2 yield increased drastically from 2 to 48% (molar

fraction) within this range of temperature, while CO and CH4 remained approximately

constant and CO2 increased slightly (Safari et al., 2016). Narnaware et al. (2017)

experimentally studied the fixed bed downdraft gasification of vegetables waste. They

reported a syngas with a calorific value, cold gas efficiency and the hot gas efficiency of 4.71

MJ Nm–3, 74.11% and 79.87% respectively, at the gasification temperature between 889 °C

and 1011 °C. Ayas and Esen (2016) studied a fixed bed updraft gasification of tea waste and

47

found that the H2 and CO contents of the product gas increase at a higher temperature while

the CH4 yield firstly increase and then decrease at 650 and 850 °C, respectively.

2.5.3.2 Equivalence ratio

Equivalence ratio (ER) is defined as the ratio of the actual air supply to the stoichiometric air

required for complete combustion (Niu et al., 2014). ER indicates the amount of oxygen feed

in gasification, and it is a crucial factor that can affect the syngas composition. With an

increase in the ER, tar is cracked/oxidised during reactions, leading to high carbon

conversion. Hence, the mole fractions of CO and H2 (syngas) increase initially with

increasing ER but a further increase in ER leads to a decrease in the CO and H2 concentration

due to the potentially unfavorable combustion reaction. The concentration of CO2 increases

sharply with increasing ER due to complete combustion (reaction 3). CH4 combustion would

be favoured at high ER and hence the concentration of CH4 should decrease at higher oxygen

flow rates. Similar trends are observed throughout the literature (Assima et al., 2017). Guo et

al. (2014) investigated the impact ER on the gasification of dried corn stalk in a fixed bed

downdraft reactor. They found that with an increase in ER the volume fractions of CO and H2

increased until attaining the highest values of 12.89 and 19.41%, respectively at an ER of

0.25. Increasing ER past this point, showed decreased CO and H2 concentration due to

combustion reactions occurring. Rodriguez (2016) studied the effect of ER on the syngas

yield of peach pits from canneries and marcs and stalks from the wine industry in fixed bed

downdraft gasifiers. They reported that all the gas constituents except CO2 decrease with

increasing ER due to a shifting of the process. Lin and Weng (2017) pointed that an ER

increase from 0.2 to 0.4 provided more oxygen, so that the CO2 production increased and the

outputs of H2, CO, and CH4 decreased while improved the burning efficiency and reduced the

total heating value and gas yield.

2.5.3.3 Steam/biomass ratio

Steam/biomass ratio (SBR) is one of the most important parameters that affect the

performance of the gasification process. The influence of SBR on syngas gas composition

was investigated while keeping all other conditions (temperature, ER, particle size etc.)

constant for agricultural biomasses (Kumar et al., 2009). It was evident from the literature

that the introduction of steam significantly improved product gas yield and LHV. The CO

48

and H2 concentrations increase with increasing SBR and reach a maximum then decrease over

a higher SBR range (He et al., 2013). Consequently, the product gas yield and LHV also drop

beyond a critical value due to the potential occurrence of water gas shift reaction (Eq 2.10).

Also, the steam reforming of CH4 is also expected to take place at higher SBR, which

explains the decrease in CH4 concentration with an increase in SBR. Higher H2/CO ratios are

favorable for biofuel production. Such results are obtained mainly due to the fact that higher

SBR leads to greater H2 generation due to potential water gas shift reaction (Jangsawang et

al., 2007). Das et al. (2017a) investigated the hydrogen rich syngas production in a fixed bed

gasifier from cotton bolls. They reported that as the steam increased at a flowrate of 2–8

mL/min, H2, and CH4 generation increased and CO decreased due to water gas shift reaction.

However, the maximum hydrogen yield of 67.42% (v/v) was achieved at 700 °C at a steam

flowrate of 7 mL/min. Niu et al. (2013) studied the effect of SBR on the syngas yield of

municipal solid waste in bubbling fluidized bed gasifier. They found that, with increasing

SBR, the concentrations of H2 and CO2 increased remarkably while that of CO smoothly

decreased.

2.5.3.4 Particle size

Particle size is also one of the most significant parameters that influence the gasification rate

and the syngas yield of agricultural biomass (Wei et al., 2006). More controlled gasification

is achieved if temperatures remain uniform throughout the feedstock. It has been shown that

as particle size decreases, the concentrations of CO and H2 in the product gas increase

whereas CO2 and CH4 concentration slightly diminishes (James R et al., 2016). Furthermore,

it is accepted that a decrease in particle size improves syngas efficiency and reduces ash

yields as well as condensate yields (Hernández et al., 2010). As the particle size decreases,

the particle external surface area/volume increases. In addition to this, the mass and heat

transfer properties are much better for particles of smaller dimensions. Entrained flow

gasifiers are more sensitive to particle size (< 0.5 mm) than fixed and fluidized bed gasifiers.

Therefore, from the viewpoint of fuel conversion efficiency, smaller particles should be fed.

However, from technical and economic points of view, using small particles may increase the

difficulty and cost in comminuting or grinding (Deng et al., 2009, Kuo et al., 2014). As a

result, the pros and cons should be under consideration when smaller biomass particles are

employed. It is known that devolatilization accomplishes the carbon conversion, diffusion

controlled heterogeneous reaction and by char heterogeneous reactions in the gasifier.

49

Table 2.4. A list of operating conditions and performance of agricultural biomass gasification

Feed stock

Operating condition Conversion

(vol.%)

Gas compositions

(vol.%)

CGE

(%)

LHV

(MJ/Nm3) References

Gasifier Temp.

(°C)

Gasifying

agent ER SBR

Switchgrass Fixed bed 1000 Air – – – CO = 38, H2= 31,

CO2= 13, CH4= 7.5 – –

(Madadian et al.,

2017)

Pinewood

Bio–char Fixed bed 950 Steam – 0.5 –

CO = 18, H2= 62.4,

CO2= 20, CH4= 1 – – (Jia et al., 2017)

Tomato

Residue Fluidized bed 877 Air–steam 0.05 0.05 –

CO = 31.45, H2= 52.19,

CO2= 12.80, CH4= 0.39 84.03 7.22

(Kocer et al.,

2017)

Chicken

Manure Fixed bed 900 Air–steam 0.12 0.34 –

CO = 18.88, H2= 22.34,

CO2=16.24, CH4= 2 – 6.21

(Ngamchompoo

and

Triratanasirichai,

2017)

Wheat Straw Fixed bed 750 Steam – – 94.1 CO = 2, H2= 68,

CO2= 24, CH4= 6 94.1 8.55 (Cao et al., 2017)

Rice Husk Fluidized bed 950 Steam 0.5 0.7 79.3 CO = 26.24, H2= 33.1,

CO2= 30.43, CH4= 10.2 – –

(Doranehgard et

al., 2017)

Wood Residue Fluidized bed 900 Air–steam 0.17 0.17 93.65 CO = 23.04, H2= 42.52,

CO2= 18.1, CH4= 11.47 71.6 –

(Peng et al.,

2017)

Citrus Peel

Residue Fluidized bed 850 Air–steam – 0.5 88

CO = 17, H2= 25,

CO2= 16, CH4= 0 65 5.27

(Prestipino et al.,

2017)

Coffee Bean Fluidized bed 900 Steam – 0.2 – CO = 11.29, H2= 68.64, – – (Pala et al., 2017)

50

Husks CO2= 18.88, CH4=0.0003

Poultry Litter

Wastes Fixed bed 900 Steam – – –

CO = 22.2, H2= 57.3,

CO2= 18.9, CH4= – – –

(Perondi et al.,

2017)

White Oak

(Quercus

Alba)

Fluidized bed 850 Air–steam 0.05 0.9 95 CO = 31, H2= 31,

CO2= 22, CH4= 11 – –

(Bates et al.,

2017)

Pine Sawdust Fixed bed 800 Steam – – – CO = 33.57, H2= 28.52,

CO2= 25.49, CH4=12.42 – 11.77

(Zeng et al.,

2017)

Corn Straw. Fixed bed 850 – – – – CO = 15.90, H2= 13.25,

CO2= 14.72, CH4=2.62 75 –

(Fortunato et al.,

2017)

Cotton Stalks Fixed bed 800 Air 0.25 – 84 CO = 36.8, H2= 39.4,

CO2= –, CH4=4.1 – –

(Hamad et al.,

2016)

Sugar Cane

Bagasse Fixed bed 950 Steam – – 95

CO = 15, H2= 77,

CO2= 7.5, CH4= 0 – –

(Waheed et al.,

2016)

Oil Palm

Wastes

Briquette

Fixed bed 950 Air 0.29 – – CO = 14.12, H2= 9.11,

CO2= 19.58, CH4=1.4 72.32 3.21

(Sasujit et al.,

2017)

Coconut

Shells Fixed bed 800 Air – – 90

CO = 25.5, H2= 10.8,

CO2= 8.5, CH4=2 95 5.4

(Inayat et al.,

2016)

Neem Tree

Leaves Fixed bed 900 Oxygen–steam – – –

CO = 26.07, H2= 28.12,

CO2= 35.62, CH4= – – –

(Memon et al.,

2016)

51

2.6 Summary

Information related to torrefaction, pyrolysis, thermochemical liquefaction and gasification of

agricultural biomass wastes such as PSC of Karanja, Sorghum, and Mahua is not widely

available in the literature. Thus, this thesis focuses on the thermochemical conversion of PSC

by torrefaction, pyrolysis, liquefaction, and gasification technologies.

52

Chapter 3 Torrefaction Assessment and Kinetics of Karanja Press Seed Cake

53

3 Torrefaction Assessments and Kinetics of Karanja Press Seed Cake

Summary

The work presented in this chapter investigates the efficiency of torrefaction process in

upgrading the higher heating value (HHV) of lignocellulosic Karanja press seed cake (PSC).

Torrefaction was manually carried out in an inert or nitrogen environment using a

temperature range of 200–300°C for several minutes to several hours (i.e. 10 min to 90 min).

Elemental analysis of products clearly shows that there is a significant reduction of H/C and

O/C ratio. It was observed that mass loss is higher than the carbon loss which eventually

leads to energy densification. For an average weight loss of 30–35 %, the HHV was found to

be in the range of 19.5–21.5 MJ/kg and the total energy that remained in the fuel was to be

around 80–85%. Torrefaction kinetics of Karanja PSC was investigated to study the

thermochemical decomposition. The activation energies for thermal degradation,

carbonisation and volatilization were calculated to be 10.55, 3.16 and 30.39 kJ/mol,

respectively and the pre–exponential factors for the three processes were found to be and

0.341, 8.728 and 0.446 min-1, respectively. This work shows that torrefaction improves

greatly the biomass properties such as density, heating value, and grindability. Moreover,

torrefied product is hydrophobic which makes storage and transport of it more convenient.

3.1 Introduction

As mentioned in Chapters 1 and 2, lignocellulosic biomass has the potential to deliver cost–

effective renewable technology, which not only minimises the transition toward a low–carbon

economy but also provides sustainability and security. Biomass ranks as the fourth energy

reserve, providing approximately 14% of the world’s energy needs (Demirbas, 2004).

Biomass can be converted into energy resource via thermochemical conversions and

biochemical conversions (Van der Stelt et al., 2011, Saxena et al., 2009, Nabi et al., 2009,

Zhang et al., 2010). The typical characteristics that make biomass as an exceptional fuel are

its plentiful availability and the climate neutral carbon cycle in the form of CO2 (Van der

Stelt et al., 2011). The major obstacles that need to be addressed are the inherent

characteristics of biomass such as high moisture content, low energy density, and the

difficulty in its storage and production as per seasonal variation. These parameters hinder the

large–scale use of biomass (Arias et al., 2008). The other crucial parameters that affect its

54

usage in bioenergy generation are its heterogeneous composition and high oxygen content

which have to be addressed before biomass can be converted into an efficient fuel.

One of the methods used for the up–gradation of low–grade biomass is torrefaction which is a

slow and mild pyrolysis of biomass in the absence of oxygen to obtain bio–char. The major

advantage of torrefaction is that it increases the energy density of the biomass by increasing

its carbon content while decreasing its oxygen and hydrogen contents. Therefore, it is the

most efficient way to utilise biomass as fuel. Torrefaction produces a carbon–rich solid char

from biomass and the gas and liquid parts do not form a part of the product. Research has

shown that a woody biomass, on torrefaction, loses approximately 30% of the mass in the

form of torrefied gas, which contains only 10% of the energy of biomass, while the 90% of

energy is retained in 70% of the solid product. The mass yield during torrefaction decreases

rapidly during the first hour of treatment and thereafter slowly with increasing residence time.

The torrefaction temperature improves the grindability of the bio–char, which produces a

more energy–dense solid fuel with higher fixed carbon content and improved calorific value

(Hu et al., 2015, Matali et al., 2016). During the torrefaction process, oxygen is eliminated as

H2O, CO2, and CO gases while the volatile species in smaller quantities are removed as

water, acetic acid, formaldehyde, carbon dioxide, carbon monoxide, methanol, and furfural

(Prins et al., 2006). Researchers have also studied the wet torrefaction in hydrothermal media,

where they have converted a wide range of biomass into energy–dense solid biofuels, which

has greater physical, chemical, and fuel properties compared to the raw biomass. Further

improvements noticed in hydro–char include higher heating value, upgraded hydrophobicity,

and easier pelletability (Chen et al., 2015). Although a number of studies were carried out to

investigate the effects of residence time and torrefaction temperature on the torrefaction of

woody biomass, not many studies are available on the torrefaction of inedible seed cakes

(Tran et al., 2016, Aniya et al., 2015, Koullas et al., 1998, Chen et al., 2011).

Karanja plant, a native family of leguminosae, provides a rich source of Karanja oil from its

kernel. Karanja oil is used widely in India for biodiesel production. The Karanja tree grows in

a broad range of agro–climatic conditions (Saadon et al., 2014). About 2 tons of Karanja

press seed cake (PSC) is generated for every ton of biodiesel produced; therefore, an efficient

utilisation of non–edible Karanja PSC needs to be found. In this work, Karanja PSC, which is

a lignocellulosic biomass material, is subjected to torrefaction to upgrade its energy content

in terms of heating value and energy densification, which are otherwise achieved only using

55

techniques such as gasification and combustion. The importance of torrefaction as a pre–

treatment step for gasification has been reviewed by Van der Stelt et al. (2011).

Torrefaction of Karanja PSC was carried out in this work in a fixed–bed reactor at different

temperatures and residence times to determine the best set of operating conditions. The %

solid residue left after the decomposition of hemicellulose and cellulose contents of Karanja

PSC was analysed. Furthermore, the thermal degradation kinetics of Karanja PSC was

investigated. A kinetic model was developed for the temperature range of 200–300 °C, and

used to determine the kinetic weight loss parameters. The torrefaction kinetic parameters are

important for the prediction of biomass behaviour under different pre–treatment conditions.

The kinetic model developed in this work, although applicable to lignocellulosic biomasses in

general, will help to design, develop, and simulate the torrefaction of Karanja PSC.

3.2 Experimental

3.2.1 Materials

The Karanja press seed cake (PSC) was obtained from a local market in Hyderabad, India and

sun dried for 4–6 days before use. The biomass was sieved through Taylor series mesh to

obtain 0.2 mm (70 sieve sizes) particles of uniform size and stored in an air–impermeable

plastic container to assure constant moisture content. The elemental analysis of powdered

Karanja PSC was performed using CHNSO Analyser (Model: Element Vario Microcube,

Germany). The higher heating value (HHV) of Karanja PSC was determined in a static bomb

calorimeter (Model: C 2000 basic IKA–bomb calorimeter, Germany) as per the procedure

described by Naik et al. (2010) and Ahmed et al. (2016). The elemental analysis, higher

heating value, and the bulk density of the cake are reported in Table 3.1.

Table 3.1. Properties of Karanja Press Seed Cake.

Sample Elemental analysis HHV

(MJ/kg)

Bulk density

(g/cm3) C H N O S

Karanja PSC 44.2 6.5 4.3 45 ND* 18.37 0.382

ND* = Not detected

56

3.2.2 Equipment: Fixed–Bed Reactor

The torrefaction of PSC was carried out in a laboratory scale vertical stainless–steel (SS)

fixed–bed reactor (Fig. 3.1). The inner and outer diameters of the reactor are 38 and 42 mm,

respectively, with a reactor height of 465 mm. The fine–wire mesh was placed at both the

ends of the reactor to contain the biomass during the experiment. A co–axial cooler was

provided along the length of the reactor and the heating was supplied by a split furnace,

which had three thermocouples (T) to monitor the reactor temperature. The thermocouples

were connected to a proportional controller. An inert atmosphere was maintained within the

reactor by passing nitrogen at a fixed rate of 0.3 L/min from the top of the reactor. The outlet

of the reactor was connected to a conical flask, which acted as a glass trap. The temperature

of the cooling system was maintained at 20 °C using cooling water.

3.2.3 Experimental Procedure

In each experiment, the reactor was charged with Karanja PSC of known weight and the

remaining space in the reactor was filled with ceramic beads to ensure complete packing.

Wire meshes were placed at both ends of the reactor to avoid the displacement of the biomass

during the experiment, and the reactor was then fixed manually into the split furnace. Inert

atmosphere was maintained inside the reactor, sweeping gas (N2) was passed through the

packed bed at a flow rate of 0.2–0.3 L/min. The heating was supplied to the reactor by split

furnaces. The biomass was heated from ambient temperature to the desired torrefaction

temperatures using a slow heating rate at 5 °C/min. During the heating phase, the reactor

jacket was empty. The difference between furnace wall temperature and the temperature

inside the reactor was found to be a maximum of 5°C. The temperature in our experiments

was varied from 200 to 300 °C and the reaction time was varied from 10 to 90 min. The

temperature was recorded using Ni–Cr thermocouples. The thermocouples were located

inside the reactor. During the torrefaction process, the Karanja PSC samples decomposed

partly generating volatiles (liquids and gases). At the end of the experiment, the reactor was

cooled using cold water circulation until the reactor temperature reached 35 °C. Nitrogen

flow was maintained during the cooling process. The tubular reactor outlet was connected to

the glass trap located in a cooling water bath to condense the volatiles flowing out of the

reactor. As the liquid was collected in the glass trap, the gases evolved from the reactor were

scrubbed and vented out. The leftover solid bio–char was then removed from the reactor and

57

weighed. The experiments were conducted in triplicate at each temperature for checking the

reproducibility.

Fig. 3.1. Schematic experimental setup for torrefaction unit

3.2.4 Experimental Analysis

The elemental analysis of torrefied Karanja PSC was performed by CHNSO Analyser–

Elementer Vario micro cube model (Germany). On the basis of the elemental results, the

higher heating value (HHV) of the material was calculated according to the correlation

proposed by Channiwala and Parikh (2002) and shown in Eq.3.1.

𝐻𝐻𝑉 = 0.3491𝐶 + 1.1783𝐻 − 0.1034𝑂 − 0.0151𝑁 (3.1)

where C, H, O and N are the mass % of carbon, hydrogen, oxygen and nitrogen, respectively.

The energy yield (Yenergy), solid product yield (Ysoild) and energy density (Denergy) were

determined according to Eqs. (3.2–3.4), respectively.

𝑌𝑠𝑜𝑙𝑖𝑑 (%) = 𝑀𝑡𝑜𝑟𝑟𝑖𝑓𝑖𝑒𝑑 𝑝𝑟𝑜𝑑𝑢𝑐𝑡

𝑀𝑑𝑟𝑦𝑏𝑖𝑜𝑚𝑎𝑠𝑠 × 100 (3.2)

𝐷𝑒𝑛𝑒𝑟𝑔𝑦 = 𝐻𝐻𝑉𝑡𝑜𝑟𝑟𝑖𝑓𝑖𝑒𝑑 𝑝𝑟𝑜𝑑𝑢𝑐𝑡

𝐻𝐻𝑉𝑑𝑟𝑦 𝑏𝑖𝑜𝑚𝑎𝑠𝑠 (3.3)

𝑌𝑒𝑛𝑒𝑟𝑔𝑦(%) = 𝑌𝑠𝑜𝑙𝑖𝑑 × 𝐷𝑒𝑛𝑒𝑟𝑔𝑦 (3.4)

58

where Mtorrified product is weight of the product after torrefaction, Mdry biomass is the dry weight of

initial biomass, HHVtorrified product is the higher heating value of the product after torrefaction,

HHVdry biomass is the higher heating value of initial biomass.

3.3 Results and Discussion

The thermal degradation of Karanja PSC was carried out at different temperatures using

different residence times. The results obtained are presented in the sections below.

3.3.1 Thermal Degradation of Solid Product

The experimental results for the thermal degradation of Karanja PSC in the fixed–bed reactor

are presented in Table 3.2 for the temperature range of 200–300 °C. The data indicate a

constant decrease in the % solid residue with an increase in temperature and residence time.

The effect of temperature is clearer for the residence time of 90 min where the solid yield is

45.03% at 300 °C as compared to 75.33% at 200 °C. Compared to temperature, residence

time has lower effect on the yield of % solid residue. The variation in solid residue is only

about 4–7% as the residence time varies between 40 and 90 min. The change in % solid

residue can be ascribed to the decomposition of hemicellulose between 200 and 250 °C, and

the decomposition of cellulose at a higher temperature range of 240–350 °C (Prins et al.,

2006, Li et al., 2012). A longer residence time is required for the lignin to decompose (Li et

al., 2012). A weight loss map based on the data reported in Table 3.2 is shown in Fig 3.2 to

determine the best–operating conditions. As is evident from the plot, an increase in weight

loss is observed with increases in both temperature and residence time. The weight loss is

significant in the initial 30–40 min mainly due to the dehydration and decarboxylation

reactions (Prins et al., 2006). It is also due to the decomposition of hemicelluloses part of the

biomass (Windeisen et al., 2007, Yildiz et al., 2006, Gaur and Reed, 1998). On the other

hand, the slow and steady weight loss after 30–40 min is due to the slow decomposition of

cellulose which occurs after the complete decomposition of hemicellulose (Li et al., 2012). It

can be observed from the experimental results that the fraction of carbon in the residue is

higher than that of the mass loss rate. In this work, temperature is found to be the dominant

parameter compared to the residence time, and this finding is in agreement with those of

Bridgeman et al. (2010) and Medic et al. (2012). Figure 3.2 shows an optimum zone that will

lead to 28–34% of weight loss of Karanja PSC. The torrefied Karanja PSC was found to be

59

more brittle with better milling properties than raw Karanja PSC. In fact, the grindability of

the solid products improved greatly after torrefaction. After heating Karanja PSC from 200 to

300°C, the weight % of the solid products that were smaller than 150 µm increased while that

of the solid products larger than 450 µm decreased. After torrefaction at 200°C,

approximately 70 wt% of the solid products were larger than 450 µm and approximately 20

wt% of the solid products were smaller than 150 µm. After torrefaction at 250°C,

approximately 40 wt% of the ground particles were larger than 450 µm while approximately

50 wt% of the particles were smaller than 150 µm. After torrefaction at 300°C, approximately

20-30 wt% of the ground particles were larger than 450 µm while approximately 70-80 wt%

of the particles were smaller than 150 µm.

Table 3.2. Effect of temperature on thermal degradation of Karanja PSC at 200–300°C

Time

(min)

Temperature (°C)

Solid residue @

200 °C (%)

Solid residue @

220 °C (%)

Solid residue @

250 °C (%)

Solid residue @

270 °C (%)

Solid residue

@ 300 °C (%)

10 86.67 83.02 79.24 76.46 71.56

20 83.46 78.72 72.56 68.12 63.53

30 81.24 76.3 69.32 66.01 55.86

40 79.77 74.01 67.11 63.14 52.46

50 78.56 73.45 65.04 60.36 50.02

60 77.92 71.26 63.64 58.43 48.06

70 76.25 70.71 61.25 57.41 46.93

80 75.65 69.46 60.52 56.13 45.63

90 75.33 68.93 60.15 55.94 45.03

Fig. 3.2. Weight loss map as a function of temperature and residence time (weight loss of 30 %:

solid (●) symbol)

200 220 240 260 280 300

10

20

30

40

50

60

Residence Time : 10 20 30 40 50

60 70 80 90

Average 30 % weight loss :

Wei

ght l

oss

(%)

Temperature ( C)

60

3.3.2 Characteristics of Torrefied Karanja PSC

3.3.2.1 Elemental Analysis

The elemental analysis of Karanja PSC after torrefaction is presented in Table 3.3. From the

analysis, it can be inferred that the carbon and oxygen contents change significantly

compared to changes in the amounts of nitrogen and hydrogen with increasing temperature.

In the elemental analysis, sulphur is not observed. Based on the elemental analysis, an

attempt was made to visualise the changes in chemical composition of Karanja PSC after

torrefaction, and the results are presented in Fig.3.3 as van Krevelen diagram. The elemental

O/C and H/C ratios for different fuels such as biomass, peat, lignite, coal, and anthracite are

also shown in the figure for the sake of comparison. The diagram establishes the fact that

torrefaction of Karanja PSC leads to the reduction of its O/C and H/C ratios thereby

increasing its calorific value. A detailed characterisation (scanning electronic microscope

analysis, powder X–ray diffraction, electro spray ionization mass spectrometry, TGA and

DTA analysis, FTIR analysis) of the raw biomass and the bio–char obtained after torrefaction

has been reported in our previous work (Naik et al., 2015).

Table 3.3 Percentage elemental composition analysis for torrefied Karanja PSC

Temp.

(ºC)

20 min 40 min

C H O N C H O N

200 47.35 6.39 42.11 4.15 48.02 6.31 41.47 4.2

220 49.36 6.12 40.48 4.04 50.52 6.02 39.56 3.9

250 51.81 5.93 38.72 3.54 52.45 5.93 38.08 3.54

270 56.01 5.43 35.03 3.53 57.2 5.35 34.32 3.13

300 58.52 5.12 33.15 3.21 60.52 4.87 31.7 2.91

60 min 90 min

200 48.93 6.23 40.83 4.01 50.21 6.08 39.8 3.91

220 51.92 5.71 38.61 3.76 52.99 5.57 37.92 3.52

250 55.62 5.02 35.75 3.61 56.62 4.96 35.08 3.34

270 58.89 4.93 33.17 3.01 59.91 4.62 32.51 2.96

300 61.21 4.89 31.17 2.73 62.11 4.79 30.29 2.81

61

Fig. 3.3. Van Krevelen diagram of torrefied Karanja PSC

3.3.2.2 Heating Values of Torrefied biomass and Energy Yield

The higher heating value (HHV) of the torrefied Karanja PSC increases with increases in

temperature and residence time. The effects of residence time and temperature on HHV are

presented in Fig.3.4 (a). The decrease in the hydrogen content and the increase in the carbon

fraction in the remaining solid with increasing temperature and residence time lead to higher

values of HHV. The increase in HHV of torrefied Karanja PSC is also due to the formation of

CO by decarbonylation reactions and CO2 formation due to the cleavage in hemicelluloses

(Ponder and Richards, 1991). At higher temperature and residence time, these gases

combinedly remove more oxygen from the torrefied PSC and therefore increase its HHV.

The energy yield in the torrefaction process provides an assessment of the efficiency of the

process in terms of the ratio of HHV of the torrefied biomass to that of the original biomass.

The energy yield as a function of residence time and temperature is depicted in Fig.3.4 (b). It

can be clearly seen from the figure that energy yield decreases with increases in both

temperature and residence time. The main reason behind the decreases in the energy yield is

probably the increase in the extent of reaction between the oxygen and the biomass when the

residence time is longer. The rapid decomposition of biomass during the initial 30–40 min

leads to a significant decrease in the energy yield. The effect of the mass loss of Karanja cake

on HHV and energy yield is presented in Fig.3.5 (a) and Fig.3.5 (b), respectively. As weight

loss increases, the energy yield decreases but the HHV increases. The weight loss% plotted

62

on the x–axis of Fig.3.5 (a) and Fig 3.5 (b) increases with increase in temperature for a

constant residence time. This is because of the decrease in the mass of the torrefied solid

produced with an increase in temperature for a constant residence time. As the mass of the

torrefied product decreases, its HHV increases because of the decrease in hydrogen content

and increase in carbon content in the remaining solid. Also, the increase in the generation of

CO and CO2 gases with an increase in temperature also leads to an increase in HHV because

these gases remove more oxygen from the biomass (Eq 3.1). The energy yield and HHV

results at elevated temperatures are mainly due to the higher degree of decomposition of

biomass and the removal of oxygen and hydrogen. For a typical weight loss of 30–35%, an

average energy yield is in the range of 75–78% whereas the HHV values are in the range of

19.5–21.5 MJ/kg. Based on all these findings, it can be concluded that the optimum

temperature and residence time are 270 °C and 20 min for achieving a higher HHV of

biomass after torrefaction.

Fig. 3.4. (a) HHV and (b) Energy yield as a function of residence time and reactor temperature

63

Fig. 3.5. (a) Energy Yield and (b) HHV as a function of weight loss at different

temperatures

3.3.3 Kinetic Model for Karanja PSC Torrefaction

Considering the complex reactions that occur during the torrefaction, a simplified kinetic

model very similar to those used by Bradbury et al., (1979), Reina et al., (1998), Carrasco et

al., (2014), and Carrasco et al., (2013) is investigated in this study. The reaction mechanism is

assumed to be an one–step process, wherein only the reactivity of hemicellulose is

considered. Since the cellulose and lignin contents of biomass are found to be the least

reactive during torrefaction, they are not considered (Windeisen et al., 2007, Yildiz et al.,

2006, Li et al., 2012).

In this study, the thermal degradation of PSC is considered to occur in two schemes as shown

below.

Scheme 1 ∶ 𝐾𝑎𝑟𝑎𝑛𝑗𝑎 𝑃𝑆𝐶 (𝐵𝑖𝑜𝑚𝑎𝑠𝑠) 𝑘→ 𝑇𝑜𝑟𝑟𝑒𝑓𝑖𝑒𝑑 𝑃𝑆𝐶(𝐶ℎ𝑎𝑟) + 𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑒𝑠

Scheme 2 ∶ 𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑒𝑠 (𝐵𝑣) 𝑘𝑣← 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 (𝐵𝑘)

𝑘𝑐→ 𝐶ℎ𝑎𝑟(𝐵𝑐)

The Karanja PSC is converted into torrefied biomass in Scheme 2 according to the reaction

with a carbonisation rate constant kc (min-1) whereas the volatiles consisting of tar and gases

are formed according to the reaction with a volatilization rate constant kv (min-1). The single

step reaction shown in Scheme 1 defines the overall reaction with a rate constant k (min-1).

Thus, the overall and parallel reaction rates can be written as follows:

64

− 𝑑(𝐵𝑘) 𝑑𝑡⁄ = 𝑘 . (𝐵𝑘) = (𝑘𝑐 + 𝑘𝑣 )𝐵𝑘 (3.5)

Here the rate of formation of char can be expressed as

𝑑(𝐵𝑐) 𝑑𝑡⁄ = 𝑘𝑐 𝐵𝑘 (3.6)

The solid residue (𝐵𝑆𝑅) at a given time (t) during torrefaction consists of torrefied biomass

i.e. char (𝐵𝑐), and un–torrefied matter (𝐵𝑘).

𝐵𝑆𝑅 = 𝐵𝑘 + 𝐵𝑐 (3.7)

Eq.3.7 can be modified by combining the solutions of Eqs.3.5 and 3.6 as follows:

𝐵𝑆𝑅 = 𝐵𝑘,𝑖𝑘𝑐 (𝑘𝑐 + 𝑘𝑣 )⁄ + 𝐵𝑘,𝑖𝑘𝑣 (𝑘𝑐 + 𝑘𝑣 ).⁄ exp(−(𝑘𝑐 + 𝑘𝑣 )𝑡) (3.8)

Eq. 3.8 can be also written as

𝐵𝑆𝑅 = 𝐵𝑆𝑅∞ +

𝑎 exp(−𝑘𝑡) (3.9)

with,

𝑙𝑛𝑘 = 𝑙𝑛𝐴 − 𝐸𝑎 𝑅𝑇⁄ (3.10)

𝑘 = 𝑘𝐶 + 𝑘𝑣 (3.11)

𝐵𝑆𝑅∞ = 𝐵𝑘,𝑖𝑘𝑐 𝑘⁄ (3.12)

𝑎 = 𝐵𝑘,𝑖𝑘𝑣 𝑘⁄ (3.13)

Eqs. 3.5 and 3.6 are non–linear type expressions as a function of time t (t = t1, t2 ----tn), which

is an independent variable, and k is a parameter. For the determination of kinetic parameters

𝐵𝑆𝑅∞, 𝑎, 𝑘, a regression analysis on the experimental data was carried out using a nonlinear

fit. The Levenberg–Marquardt algorithm was used to adjust the parameter values in the

iterative procedure. The chi–square minimisation was used to minimise the deviations of the

theoretical curve from the experimental points.

3.3.4 Fitting Weight Loss Curves

On the basis of the assumption of global one–step first–order reaction, Eq.3.5 was solved.

The logarithm of solid residue versus residence time is plotted in Fig. 3.6 on the basis of the

solution. Fig.3.6 shows the straight line fit to the experimental data at different temperatures.

At lower temperature ranges (200–270 °C), the reaction is found to fit the global one–step

reaction with first–order kinetics. However, an increase in temperature to 300 °C results in a

shift of the reaction mechanism to a two–step first–order reaction with a demarcation time.

65

This shift can be attributed to the gradual decomposition of cellulose as temperature

increases.

Fig. 3.6. Logarithm of nondimensional solid residue versus time (First–order fit)

3.3.5 Kinetic Parameters Evaluation

A nonlinear regression was carried out on experimental data using Eq. 3.9 and the resulting

regression analysis parameters of reduced Chi–square and adjusted R–square values are

presented in Table 3.4. Figure 3.7 shows an excellent fit between the experimental results and

the values predicted using the parameters that minimised the deviations. Accordingly, in the

mode predictions, the solid residue after infinite time (𝐵𝑆𝑅∞) and the overall rate constant are

found to change in proportion to the change in torrefaction temperature. Clearly, the changes

in the overall rate constant and 𝐵𝑆𝑅∞ are directly and inversely proportional, respectively to

the changes in torrefaction temperature (Figure 3.7). The carbonisation and volatilization

reactions are found to follow the first–order kinetics. Their rate constants are reported in

Table 3.5.

The equations for kinetic rate constants based on the Arrhenius equation (Eq. 3.10) are

presented in Eqs.3.14 to 3.16 for the thermal degradation, carbonisation, and volatilization,

respectively.

𝑘 = 0.3411 𝑒−10.5484/𝑅𝑇 (3.14)

𝑘𝑣 = 8.7282 𝑒−30.3901/𝑅𝑇 (3.15)

66

𝑘𝑐 = 0.4464 𝑒−3.1656/𝑅𝑇 (3.16)

The rate constants (k, kv, and kc) are expressed in min-1, the temperature in K, and the gas

constant R in kJ/mol K. The activation energy Ea values reported in the above equations have

confidence level of 95%. The adjusted R–square values are found to be 0.95, 0.91, and 0.98

for the overall rate constant, carbonisation rate constant, and volatilization rate constant,

respectively. The results discussed above confirm the validity of the overall model proposed

for the torrefaction of Karanja PSC. The proposed model represents volatilization kinetics

better (r2 = 0.98) than carbonisation kinetics (r2 = 0.91). The activation energy values

determined in this study are reported in Table 3.6 along with those reported in the literature

(Koullas et al., 1998, Carrasco et al., 2014, Carrasco et al., 2013). It should be noted that the

models, pathways, raw materials, and experimental devices used in these studies were

different from those of the present study.

Fig. 3.7. Weight loss of Karanja PSC at various temperatures (Experimental and model

prediction for different temperatures)

Table 3.4. Estimated modeled parameters for thermal degradation of Karanja PSC

Temperature

(°C)

𝐵𝑆𝑅∞

calculated

𝑎

Calculated

𝑘

calculated

Reduced Adj.

Chi–Square R–Square

200 73.438 ± 0.706 16.591 ± 0.520 0.024 ± 0.003 0.103 0.993

220 67.158 ± 0.852 20.196 ± 0.683 0.026 ± 0.003 0.195 0.991

250 58.180 ± 1.018 27.466 ± 1.002 0.029 ± 0.004 0.41 0.990

270 54.305 ± 1.137 29.684 ± 1.431 0.032 ± 0.005 0.72 0.984

300 43.979 ± 0.513 41.056 ± 1.031 0.039 ± 0.002 0.254 0.997

67

Table 3.5. Carbonisation and volatilization rate constant at different temperature

Temperature (°C) 𝑘𝑐 × 102 𝑘𝑣 × 103

200 2.0018 ± 0.362 3.982 ±0.622

220 2.0749 ±0.378 5.251±0.783

250 2.1035 ±0.538 7.965±1.389

270 2.2501 ±.649 9.499±1.942

300 2.2988±.322 16.012±1.223

Table 3.6. Comparison of present work with the literature reported

Researcher Raw material/

Reactor

Temperature

range (°C)

Activation

energy

𝐸𝑎 (𝑘𝐽/𝑚𝑜𝑙)

Frequency

factor

𝑘𝑜 (1/𝑚𝑖𝑛)

Rate

constant,

𝑘 at 300°C

Koullas et al.

(1998)

Wood saw

dust/

Horizontal

tubular reactor

200 – 390 95.9 1.92x107 0.035

Carrasco et al.

(2013)

Red Oak/

Fluidized bed

Reactor

230 – 330 11.9 2.57 0.212

Carrasco et al.

(2014)

Red Oak/Pilot

Rotatory kiln

Reactor

230 – 330 20.4 5.22 0.072

Present work

Karanja

PSC/Fixed bed

Tubular

Reactor

200 – 300 10.54 0.34 0.039

3.4 Conclusion

Torrefaction was identified to be an up–gradation technique for Karanja PSC, a

lignocellulosic material. A significant change in elemental composition is observed for the

torrefied Karanja PSC with the reduction in O/C and H/C values which lead to increases in

the calorific value and hydrophobicity of the torrefied biomass. The mass loss is found to be

higher than that of carbon loss, which eventually leads to energy densification. For an

average weight loss of 30–35%, the HHV of torrefaction product is found to be in the range

of 19.5–21.5 MJ/kg and the total energy that remained in the torrefied biomass is found to be

68

around 80–85%. The overall carbon content of torrefied biomass increases by 40% compared

to that in the feed biomass. Hovewer, its oxygen content decreases by 32.6% after

torrefaction at 300 °C. The results show that torrefaction leads to an apparent reduction in the

volume of the biomass while improving its higher heating value, thereby making it as a

suitable fuel for gasification and combustion. A single–step reaction mechanism is found to

describe the overall kinetics of the torrefaction.

69

Chapter 4 Pyrolysis of Mahua PSC and Sorghum Bagasse into Bio–char and Bio–oil

70

Summary

Chapter 3 discussed the torrefaction of lignocellulosic biomass, Karanja PSC, in a fixed–bed

reactor under nitrogen environment using relatively mild temperatures ranging from 200 to

300°C and various reaction times. While bio–char is the major product, light volatiles are

also obtained from the conversion. Torrefaction was found to be a useful pre–treatment

process for the lignocellulosic biomass to increase its calorific value (HHV), energy density

and hydrophobicity. All the above enhancements make the biomass more suitable as a feed to

other thermochemical conversion processes. One of them is pyrolysis which is carried out

mainly at a temperature range of 350 to 600°C to convert the biomass into products such as

bio–char, bio–oil, and bio–gas. Although the torrefied biomass can be used as the feed to the

pyrolysis process, it is beneficial to study the influence of pyrolysis on fresh lignocellulosic

biomass first to determine its efficiency in converting waste biomass into energy dense

materials. A study on the pyrolysis using torrefied biomass as the feed can be then carried,

and its results can be compared with those from the one with fresh biomass feed to evaluate

the overall energy efficiency and economic feasibility. Since the overall scope of the present

work does not allow the use of fresh lignocellulosic biomass as well as the torrefied biomass

as pyrolysis feed, the current study uses only the fresh lignocellulosic biomass. Since the

focus of the work is to investigate the influence of thermochemical conversion processes in

converting lignocellulosic waste biomass, regardless the type of feed, into energy–dense

products, this work uses mahua and sorghum biomasses, in addition to Karanja PSC, as the

feed to pyrolysis process.

However, this chapter presents and discusses the experimental results for only mahua and

sorghum biomasses. Pyrolysis experiments for Karanja biomass were indeed carried out

using similar methodology but the results for that part of the work are presented in Chapter 5,

which discusses the liquefaction of Karanja PSC. The main reason for presenting pyrolysis

results for Karanja PSC in Chapter 5 is that the Karanja bio–oil, which is the main product of

pyrolysis, is used as both catalyst and solvent in the liquefaction of Karanja PSC. Combining

the discussions on the pyrolysis and liquefaction of Karanja PSC presents an integrated

approach of converting the biomass into a product that has higher energy–density. Also, this

chapter arrangement helps to reduce the length and scope of this chapter, which already

presents very detailed information on the pyrolysis of mahua (including its economic

analysis) and sorghum biomasses.

71

This work investigated the effects of pyrolysis temperature, retention time and the inert gas

(i.e. N2) flow rate on the conversion of lignocellulosic biomass into bio–oil in a slow

pyrolysis fixed–bed batch reactor. The optimum operating conditions for the process were

obtained using a Response Surface Methodology (RSM). It was found that the highest bio–oil

yield (49.25 wt.%) can be achieved at a moderate temperature of 475 °C and a retention time

of 45 minutes. As expected, the bio–oil yield was found to be affected by the reaction

temperature. The mahua bio–oil was characterized by GC–MS analysis and the major

compounds in the bio–oil were found to be 6–octadecenoic acid, octadecanoic acid and free

fatty acids (FFAs). The raw biomass and bio–char were also characterised using bomb

calorimeter, elemental analysis, and Fourier Transform Infrared (FT–IR) spectroscopy. Bio–

gas analysis confirmed that, at higher temperatures, higher gas yield was observed with

increased CO and CH4. Finally, from the Mahua PSC material and energy balance and

economic analysis, it has been confirmed that it is feasible to produce bio–oil at the derived

optimum operating conditions. Similar conclusions are made for the pyrolysis of sorghum

biomass.

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4.1 Derivation of Optimum Operating Conditions for the Slow

Pyrolysis of Mahua Press Seed Cake in a Fixed–bed Batch Reactor for

Bio–oil Production

4.1.1 Introduction

Due to an increased consumption of fossil fuel and rise in the cost of diesel and petroleum

products, developing nations such as India, Sri Lanka and Bangladesh have been focussing

on alternative energy resources such as the production of biodiesel from biomass or related

wastes. The biodiesel can be generated from dedicated energy crops such as Karanja,

Jatropha, and Mahua. Among these feedstocks, Mahua biomass has been identified as a

promising feedstock to produce bio–oil due to its abundant availability, lower price,

renewable nature and higher calorific value (Pradhan et al., 2016). The Mahua (Madhuca

longifolia) is an Indian tropical tree, and it can grow easily in semitropical and subtropical

regions. It is a fast–growing tree and even grows on rocky, sandy and dry shallow soils. The

flowers of this Mahua tree can be used as a food item for tribals. It can also be used to

manufacture jam, alcohol (alcoholic drink and engine fuel) and syrup for medicinal purposes.

The Mahua seed contains a maximum of 35 % oil content, and it is mostly extracted using

various biochemical extraction methods (Shadangi and Mohanty, 2014a, Krishna and Sai,

2015). The extracted oil then can be upgraded to meet specifications of the biodiesel using an

advanced process called “transesterification.” In many parts of India and other tropical

countries, due to its abundance and high oil content, the use of Mahua seeds for extracting oil

is a tradition and also commercially feasible. However, this oil extraction leaves a solid

residual matter generally referred as “Press Seed Cake (PSC).” Currently, Mahua PSC has

several low–value applications such as food in aquaculture ponds, fertilizer for agricultural

land, and in the production of bio–gas for energy using anaerobic/aerobic digesters (Sahin,

2000). However, it is often observed that the Mahua PSC (constitute 60 % of the total

biomass) obtained as the residual matter after the extraction of bio–oil still has a tremendous

potential to recover significant amount of high–value bio–oil and improved quality of bio–

char from it. The current work, therefore, focuses on the above aims to find out the feasibility

of bio–oil production from Mahua PSC.

The methods available for Mahua PSC to bio–oil conversion can be again divided broadly

into two categories such as thermochemical and biochemical conversion processes. Both

processes have their respective advantages and disadvantages, and they are well reported in

73

the literature (Tripathi et al., 2016, Naik et al., 2017). The current paper focuses on the

thermochemical conversion process called “pyrolysis.” The pyrolysis is one of the most

promising methods to produce oil, gas, and char from the thermal degradation of biomass

under an inert atmosphere. Based on the heating rate, pyrolysis process can be defined as a

slow and a fast/flash pyrolysis. The slow pyrolysis (heating rate of 0.01 to 10 °C/s) is usually

adopted for producing char, oil, and gas with char being the predominant product while the

fast/flash pyrolysis (heating rate of 10 to 1000 °C/s) is used for mainly gas and oil

production. Various types of pyrolysis reactors such as fixed–bed, bubbling fluidized–bed,

vacuum, vortex, rotating cone, and free–fall reactors have been utilised for pyrolysis (Ly et

al., 2016). Among them, fixed bed reactors have been widely used, and the operating

temperature employed is in the range of 350 –550 °C for the bio–oil and bio–char production,

and it can rise to 900–1000 °C for the bio–gas production. The current study is limited to the

production of bio–oil from the Mahua PSC using the slow pyrolysis process in a batch type

fixed bed reactor.

The pyrolysis stoichiometry, in general, can be expressed as follows (Zaafouri et al., 2016).

𝐶𝑥𝐻𝑦𝑂𝑧𝑁𝑝𝑆𝑞 + 𝑄 (𝑒𝑛𝑒𝑔𝑦) + 𝑁2 → 𝐵𝑖𝑜 − 𝑐ℎ𝑎𝑟 + 𝐶𝑜𝑛𝑑𝑒𝑛𝑠𝑎𝑏𝑙𝑒𝑠 𝑙𝑖𝑞𝑢𝑖𝑑𝑠 (𝐵𝑖𝑜 − 𝑜𝑖𝑙) +

𝑁𝑜𝑛 − 𝑐𝑜𝑛𝑑𝑒𝑛𝑠𝑎𝑏𝑙𝑒 𝑔𝑎𝑠𝑒𝑠 (𝐶𝐻4 + 𝐶𝑂 + 𝐶𝑂2 + 𝐻2) + 𝐻2𝑂 + 𝑁2

The fixed bed slow pyrolysis batch reactor can have several advantages. Firstly, the design of

such reactor can be very simple and, being a batch process, it can be controlled more

accurately. Also, the capital cost associated with the fixed bed batch reactor is expected to be

lower compared to other types of continuous/batch reactors due to its simple design. The

slow pyrolysis in fixed bed batch reactor can be a promising valorisation route for oil, gas

and char production from Mahua PSC in a decentralised manner. Even though quality,

quantity and energy efficiency of the bio–oil produced from the slow pyrolysis will not be as

good as fast/flash pyrolysis, it still makes a strong business case to adopt the fixed bed slow

pyrolysis to process Mahua PSC in a decentralised manner in remote village areas. Apart

from the reactor design and heating rate, the composition of Mahua PSC also plays a

significant role in deriving the optimum operating conditions of the pyrolysis process. The

results of the fixed bed slow pyrolysis of different biomass feedstocks having similar

properties as Mahua PSC are well reported in the literature. For instance, a maximum oil

yield of 51.7 % was obtained from the slow pyrolysis of rapeseed at a temperature of 550 °C

and a heating rate of 30 °C min−1 (Onay, 2003). The bio–oil produced from the slow


74

pyrolysis of wheat straw, timothy grass, and pine wood in a bench–scale fixed bed reactor

was about 40–48 wt.% at a temperature of 450 °C and a heating rate of 2 °C min−1 (Nanda et

al., 2014). Bertero et al. (2012) found maximum pyrolytic bio–oil yields between 30 and 45

wt.% at the slow pyrolysis temperature of 550 °C from different feedstocks (pine wood,

mesquite wood, stalk, and wheat shell) in a fixed bed reactor. Volli and Singh (2012)

obtained 41.36 wt.% of bio–oil from the slow pyrolysis of Mahua PSC and they found the

optimum operating conditions of 550 °C, and 25 °C min-1 for temperature and heating rate,

respectively. Moreira et al. (2017) obtained 40 wt.% of bio–oil from the slow pyrolysis of

cashew nut shell biomass and through a comprehensive experimental program. They found

the optimum operating conditions of 400 °C, 500 mL/min and 25 °C min-1 for temperature,

N2 flow rate, and heating rate, respectively.

It can be seen that data on bio–oil yield from fixed bed slow pyrolysis reactor in the literature

varies significantly between 30–55% which suggests that an identification of the optimum

operating conditions for achieving the highest bio–oil yield is essential as it is directly linked

to the economics of the process. Mostly, derivation of optimum operating conditions in the

literature was attempted by adopting comprehensive experimental matrix generated from

varying number of critical process parameters. However, a large number of scientific

experiments could be sometimes tedious as well as unreliable as there are chances that not all

parameters are studied appropriately covering their wide desired range. The limitations of the

classical experimental design approach can be eliminated by optimising the process

parameters collectively by a statistical experimental design using software such as Response

Surface Methodology (RSM) (Moreira et al., 2017).

Thus, in the present work, RSM is used to derive optimised process conditions for the fixed

bed slow pyrolysis of Mahua PSC to produce bio–oil. The RSM is a powerful statistical and

mathematical method suitable for the modeling of various processes in real applications

(Sahu et al., 2010a). The RSM helps in the process of modeling and analysing the

engineering problem, and at the same time useful for optimising the response surface that is

influenced by various parameters (Makibar et al., 2015). In fact, these models approximate

the functional relationships between the input and output (response) variables of the process

using experimental data. Subsequently, the models were use for estimating the optimal

settings of input variables to maximise or minimise the response (response surface

optimisation of bio–oil production from Mahua PSC using important factors). This

multivariate statistical model simultaneously optimises the effects of many factors and the

75

interaction between the variables to achieve the best system performance (Sarve et al., 2015).

The main advantage of RSM model is that it requires fewer tests, is less time–consuming and

is more accurate and reliable compared to the full factorial design experimentation (Ellens

and Brown, 2012).

To the best of author’s knowledge, no comprehensive study has been conducted on the

derivation of optimal slow pyrolysis conditions for bio–oil production from Mahua PSC in a

fixed bed batch type reactor using RSM. The key operating parameters such as temperature,

retention time and sweeping gas flow rate (N2) were studied in detail. The major aim here

was to identify the optimum operating conditions for obtaining the highest bio–oil yield from

Mahua PSC. This work also elucidates the properties of different pyrolysis products

generated under optimised conditions. The FT–IR spectroscopy and the GC–MS techniques

were used to characterise the biofuels and bio–char obtained under the optimum conditions.

Moreover, to establish the economic feasibility on the use of Mahua PSC in the production of

bio–oil, an energy balance and an economic analyses were conducted.

4.1.2 Materials and Methods

4.1.2.1 Materials

The PSC sample, obtained after Mahua seed oil extraction, was collected from a small local

scale industry Maruti Agrotech and Fertilizers Ltd. in Hyderabad city of Telangana state of

India. The PSC was milled into smaller pieces using a ball mill (Model PM100, Retsch

solutions) where 500 mL stainless steel jar and 20 mm diameter balls were used. The milled

product was further sieved using Taylor series sieves to obtain particles of size varying from

1 to 2 mm. Then, the samples were dried for 24 h at 105 °C in an oven before the

experiments. The ultimate, proximate analysis (dry basis) results and the calorific value of

Mahua PSC are presented in Table 4.1.1.

4.1.2.2 Experimental Design and Statistical Analysis

The RSM regression method employed in this study was first developed by Box and Wilson

in 1951. It is described as a collection of mathematical and statistical techniques for

developing, improving and optimising processes by finding the actual relationship between

the response and a set of independent variables (Ismail et al., 2013, Myers et al., 2016). The

76

response surface methodology consists of a sequential experimentation as it provides

considerable information with less number of experiments (Sareekam et al., 2016).

Based on the literature [9] and our previous work [6], three critical parameters namely,

temperature (X1), retention time (X2) and nitrogen gas flow rate (X3) were identified as

significant factors that may influence the pyrolysis of PSC in a fixed bed slow pyrolysis batch

reactor. Based on the literature, the X1 was varied between 400 and 550 °C, X2 between 30

and 60 min, and X3 between 0.1 and 0.5 L/min. The temperature and retention time are

considered because of their effects on the extent of reaction and heat transfer rate (Ghani et

al., 2011). In addition, nitrogen (sweeping gas) flow rate was also selected as a variable

because of its influence on vapor residence time, which is known to be positively correlated

with char yields (Bridgwater, 2012). Following that, a Box–Behnken design (BBD) of

experiments was conducted to determine both the minimum number of experiments and

optimum operating conditions for efficient production of bio–oil from Mahua PSC in a slow

pyrolysis fixed bed tubular reactor (Antal and Grønli, 2003).

Optimisation of pyrolysis, i.e., extraction of maximum bio–oil yield, could be either a

maximum/minimum function of design parameters based on the RSM. As mentioned earlier,

temperature, retention time, and N2 flow rate in this study were chosen as variables/design

parameters, and each considered at three levels: the high level ‘+1’, the low level ‘–1’ and the

centre points ‘0’. Following that, an Analysis of Variance (ANOVA) of the BBD was

employed. This method has been widely used in the literature for fitting a second–order

model (Box and Behnken, 1960). Two values named F–value and p–value were derived in

ANOVA analysis. The F–value is the ratio of variation between sample mean and variation

within the sample, while p–value is a measure of significance assuming the null hypothesis is

true. Low p–values (<0.05 or 0.01) are indications of strong evidence against the null

hypothesis. The prediction and verification of the model equation were carried out with this

method. It was performed at an alpha level of 0.05 using Minitab 17 statistical software

package (Minitab Inc.,) which includes experimental design, data analysis, and quadratic

model building and graph (three–dimensional response surface).

Three–dimensional response surface plots were obtained based on the effect of the three

factors mentioned above. From these three–dimensional plots, the simultaneous interaction of

the three factors (temperature, retention time, and N2 flow rate) on the responses was studied.

The model coefficients, F–values, p–values, significant probabilities, and R2–values were

evaluated. The F statistic and the lack–of–fit test were used to examine whether the

77

regression models were adequate to describe the observed data. The probability of the lack–

of–fit was required to be insignificant (p–value > 0.05) for all fitted models. For most of the

responses, the quadratic model was manually modified by eliminating the insignificant terms

to obtain a better model. No transformation was performed in any instance to produce the

insignificant lack–of–fit. The R2– statistics were also analysed for the percentage variability

of the optimisation parameter that is explained by the model. Finally, the normal probability

plots of the residuals and the plots of the residuals versus the predicted responses were

checked for the adequacy of the model. Surface and contour plots were used to show how a

response variable relates to two factors based on the model equation.

Two steps are mainly necessary for the optimisation process. The first step is to determine the

relationship between the response and the factors of the mathematical technique (Box and

Behnken, 1960). The equation that was used to investigate the effects of independent

variables on the bio–oil yield (%) is as follows:

𝑦𝑖 = 𝑓(𝑋1, 𝑋2, 𝑋3, … … . … . , 𝑋𝑛) − − − − − −(4.1.1)

where the term 𝑦𝑖 is called as the response (bio–oil yield %) of equation (4.1.1), 𝑓 is the

unknown function of response, 𝑋1, 𝑋2, 𝑋3, … … . … . , 𝑋𝑛are called as variables (temperature,

retention time, and N2 flow rate) and 𝑛 is the number of the independent variables. It is

assumed that the independent variables are continuous and controllable by experiments with

negligible errors. It is required to find a suitable approximation for the true functional

relationship between independent variables and the response surface (Abnisa et al., 2011).

The second step is estimating the coefficients in a mathematical model and predicting the

response. The model used for such response prediction is a quadratic equation or second–

order regression model. This model was used to approximate the responses based on a

second–order Taylor series approximation (Sahu et al., 2010b). The regression model can be

written as follows:

𝑦𝑖 = 𝛽0 + 𝛽1𝑋𝑖1 + 𝛽2𝑋𝑖2 + ⋯ + 𝛽𝑘𝑋𝑖𝑘 + 𝛽1𝑋𝑖1 + 𝛽11𝑋𝑖1 2 + 𝛽22𝑋𝑖2

2 + ⋯ +

𝛽𝑘𝑘𝑋𝑖𝑘 2 + 𝛽12𝑋𝑖1 𝑋𝑖2 + 𝛽13𝑋𝑖1 𝑋𝑖3 + ⋯ + 𝛽𝑘−1,𝑘𝑋𝑖,𝑘−1 𝑋𝑖𝑘 + ∈𝑖− − − − − − − (4.1.2)

𝑦𝑖 = 𝛽0 + ∑ 𝛽𝑗𝑋𝑖𝑗

𝑘

𝑗=1

+ ∑ 𝛽𝑗𝑗𝑋𝑖𝑗 2

𝑘

𝑗=1

+ ∑ ∑ 𝛽𝑗𝑗′

𝑘

𝑗′>𝑗

𝑘−1

𝑗=1

𝑋𝑖𝑗′ 𝑋𝑖𝑗′ + ∈𝑖− − − − − −(4.1.3)

78

The model above contains k+1 terms from the strict first–order model, k pure

quadratic 𝑋𝑖 2 terms, and (

𝑘

2) two–factor interactions. Therefore, the regression model contains

a total of (𝑘 + 1) + 𝑘 + (𝑘

2) =

(𝑘+1)(𝑘+2)

2 terms.

This means that for a given k value of 3, the regression model contains 10 terms which

consist of three coefficients for main effects, three coefficients for pure quadratic main effects

and three coefficients for two–factor interaction effects. In the equation above, 𝑦𝑖 is the

predicted response, 𝛽0 is the constant regression coefficient 𝛽𝑗, 𝛽𝑗𝑗 and 𝛽𝑗𝑗′ are the

coefficients for the linear, quadratic and interaction effects, respectively. The terms 𝑋𝑖𝑗 and

𝑋𝑖𝑗′ are the coded independent factors and ∈𝑖 is the error.

4.1.2.3 Apparatus and Procedure

All pyrolysis experiments were carried out in a lab–scale vertical fixed bed reactor. The

experimental setup is shown in Fig. 4.1.1, which consists of the stainless steel (SS) reactor

with an inner diameter of 38 mm and a height of 500 mm (heating zone height = 400 mm)

covered with an electrically heated split furnace. The reactor temperature was controlled by

varying the heating rate using a proportional–integral–derivative (PID) controller of the

furnace. The electric current was supplied to the split furnace attached to the reactor using the

PID controller, which controlled the voltage as required. Three temperature probes

(thermocouples) located in between the heating element and the reactor at different heights

(i.e. top, middle, and bottom) was used for recording and controlling the temperature.

The reactor was filled with 12 g of Mahua PSC only up to the upper level (400 mm reactor

length) of the split heater. The remaining space was filled with porcelain beads to ensure

complete packing. A stainless wire mesh was placed on top and bottom of the beads to hold

them in place and ensure that Mahua PSC does not get displaced inside the reactor during the

pyrolysis. During the experiment, a sweep gas (N2) was sparged through the reactor. A

rotameter was used to regulate the flow rate of nitrogen. The nitrogen flow ensured an inert

atmosphere within the reactor by displacing air and pyrolysis gases from the reaction zone.

The experimental set–up used in this work is similar to the one reported by Ellens and

Brown, (2012). The outlet of the reactor was connected using a tube to a bubbler that was

kept in cooling water to condense the vapor coming out of the reactor. The liquid product

containing the bio–oil, and water was collected in the bubbler and sent for further analysis.

79

The uncondensed gases exiting the bubbler were sent to a water scrubber (glass beaker filled

with water) before vented off. The main purpose of the water scrubber was to ensure the

removal of SOx, NOx and particulate matter before vapor is vented off to the environment.

Also, the water scrubber acted as an additional trap to collect the bio–oil that might not have

been condensed in the bubbler. However, the amount of bio–oil found in the scrubber was

negligible in all experiments.

Before each experiment, Mahua PSC samples were dried in an oven for 24 hours at 105 °C to

remove any moisture present in the samples completely. During the pyrolysis, heating of

Mahua PSC was started at ambient temperature and continued at a slow heating rate of rate of

20 °C/min until the desired temperature in the range of 450–550 °C was reached. The

temperature was then maintained for a chosen retention time which varied between 30 and 60

min. The flow rates of N2 was varied in the range of 0.1 to 0.5 L/min. At the end of the

experiment, the heating was stopped, and the temperature of the reactor was allowed to drop

gradually until it reached below 50 °C. Nitrogen flow was maintained during the cooling. At

the end of the run, the solid bio–char was removed from the reactor for further analysis. Also,

the liquid product (bio–oil) was separated and weighed. Further, the liquid product was

centrifuged at 10,000 rpm for 10 min to separate the residual solid particles from the bio–oil

(pyrolysis oil). The bio–oil was then subjected to GC–MS analysis. The non–condensable

vapor (bio–gas) yield was calculated by determining the difference between the total mass of

feed and the masses of bio–char and bio–oil.

All experiments were performed in triplicate for checking the reproducibility. The bio–oil

and bio–gas yields were determined using Eq. (4.1.4) and (4.1.5).

𝐵𝑖𝑜 − 𝑜𝑖𝑙 𝑦𝑖𝑒𝑙𝑑 (𝑤𝑡. %) =𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑜𝑖𝑙 (𝑔)

𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑏𝑖𝑜𝑚𝑎𝑠𝑠 (𝑑𝑟𝑦 𝑏𝑎𝑠𝑖𝑠) (𝑔) × 100 − − − −(4.1.4)

𝐵𝑖𝑜 − 𝑔𝑎𝑠 𝑦𝑖𝑒𝑙𝑑 (𝑤𝑡. %) = 100 − (𝐵𝑖𝑜 − 𝑜𝑖𝑙 𝑦𝑖𝑒𝑙𝑑 + 𝐵𝑖𝑜 − 𝑐ℎ𝑎𝑟) − − − −(4.1.5)

The amount of water produced during pyrolysis was found to be very small and therefore it

was difficult to separate it from bio–oil collected in the bubbler. Therefore, water yield is not

calculated separately and, in fact, it is added to bio–oil yield. It is understood that this will

lead to some inaccuracy in the data reported related to bio–oil yield. But the author believes

firmly that this will not create any significant difference to bio–oil yield.

80

Fig. 4.1.1 Pyrolysis experimental setup including the bench–scale fixed bed reactor

4.1.2.4 Feedstock and Product Characterisation

The carbon, hydrogen, nitrogen, sulfur, and oxygen (CHNSO) elemental analyser (model:

Elementar Vario Microcube made in Germany) was used for determining the CHNSO

percentage in raw PSC, bio–char and bio–oil samples. To determine the amount of fixed

carbon, volatile matter, moisture, and ash content of Mahua PSC samples, a TGA/DTA

system was used according to ASTM D 5142–04 method (Naik et al., 2015). The TGA

analysis was carried out under nitrogen atmosphere by varying the temperature from 30 to

1000 °C at a heating rate of 10 °C/min. The change in weight percentage of biomass due to

the change in temperature from 110 to 600 °C was denoted as volatile content, and the weight

percentage of the remaining substance was denoted as the fixed carbon content. The ash

content was measured by heating the biomass at 1000 °C under an oxidative atmosphere. The

calorific values of the raw PSC, bio–char, and bio–oil samples were determined using a bomb

calorimeter (model: C 2000 basic IKA–bomb calorimeter made in Germany).

81

The bio–oil samples were analysed by GC–MS on an Agilent 6890 gas chromatograph

(Agilent Technologies, Palo Alto, CA, USA) equipped with the 5973N mass selective

detector. An HP–5MS capillary column was used to determine the chemical compounds

present in the bio–oil. The length and inner diameter of the capillary column were 30 m and

250 μm, respectively. The column was initially held at 50 °C for 2 min, and then it was

heated up to a final temperature of 280 °C using 20 °C min-1 ramp where it was held for 5

min (total run time was 30 min). Helium gas of 99.99% purity was used as the carrier gas at a

flow rate of 1.0 mL min-1. 1 l of the clear organic sample was injected into the GC–MS

under split injector at 250 °C. The column inlet and GC–MS temperatures were kept at 250

°C and 280 °C, respectively. The sources of electron ionization and quadrupole analyser were

held at 230 °C and 150 °C, respectively. The mass spectrometer was in the full scan mode

scanning from 29 to 600 mz-1. The chemical compounds detected in the current work were

identified using MS library database.

FTIR spectra of raw PSC and bio–chars were recorded on a Thermo Nicolet Nexus 670

Spectrometer using KBr windows. A total of 40 scans were made to get a better signal–to–

noise ratio. The spectra were registered at 4 cm-1 resolution in the range of 400–4000 cm-1.

The gases produced from the pyrolysis system were analysed using an online compact

Micro–GC (Agilent, USA) equipped with two columns and detectors in parallel using N2 as a

carrier gas. The product gases were analysed for primary gases CO, H2, CH4, and CO2 using

an on–line GC gas analysis system. In the first column (Porapak q, 3 m, 0.125 mm), CO,

CH4, CO2, and N2 were separated at the oven temperature of 100 °C and analysed by thermal

conductivity detector (TCD). In the second column (molecular sieve 5a, 2 m, 0.125 mm),

mainly H2 was separated at the same oven temperature and analysed by TCD detector.

Table 4.1.1. Proximate, ultimate and calorific value of Mahua PSC

Proximate analysis (wt.%)

Moisture Ash Volatile matter Fixed carbon

8.2±0.2 3.5±0.15 80±0.5 8.8±0.25

Ultimate analysis (wt.%)

C H N S O

50.54±0.5 6.92±0.27 2.07±0.03 0.1±0.02 36.87±0.5

Calorific value (MJ/kg)

21.56±0.12

http://www.chromatographyonline.com/introduction-electron-impact-ionization-gc-ms

82

4.1.3 Results and Discussion

4.1.3.1 Elemental Analysis for the Mahua PSC, Bio–char, and Bio–oil

Produced from the Pyrolysis Process

The results of the ultimate analysis (elemental composition) of raw Mahua PSC, bio–char and

bio–oil products obtained from the pyrolysis are shown in Table 4.1.2. The weight

percentages of C (= 50.54 wt. %) and O (=40.37 wt. %) in raw PSC were found to be

significantly higher than those of H (=6.92 wt. %), N (=2.07 wt. %), and S (=0.1 wt. %). The

ultimate analysis of the bio–char produced from the pyrolysis is significantly different from

that of raw PSC. The carbon content of the bio–char increases with increasing reactor

temperature and corresponding hydrogen and oxygen contents decrease with an increase in

the carbon content. These results are qualitatively in agreement with those reported in the

literature (Chutia et al., 2014, Kim et al., 2012, Huang et al., 2015, Kotaiah Naik et al., 2017).

This observation indicates that the cleavage and cracking of weak bonds occur within Mahua

PSC structure with increasing pyrolysis temperature (Cascarosa et al., 2011). The removal of

oxygen from the bio–char indicates an increase in their energy density with increasing

pyrolysis temperature (Chen et al., 2012, Chun et al., 2004).

The elemental analysis for the bio–oil (dry basis) obtained at the optimum temperature of 475

°C and a retention time of 45 min is presented in Table 4.1.2. From the analysis, it can be

inferred that the carbon and hydrogen contents of bio–oil are higher compared to those of raw

PSC. Hence, the oxygen content is found to decrease. The effect of sulphur is not observed in

the bio–oil. The dry basis moisture content was measured by weighing 5g of raw PSC sample

in an uncovered porcelain crucible. The raw PSC sample was then placed in an oven at 105 ±

5 °C to dry until its weight remained constant. The sample was then removed from the oven

and stored in a desiccator before weighing.

The calorific values (CV) of raw PSC and other pyrolysis products, i.e., bio–char, and bio–oil

products are presented in Table 4.1.2. The CV of bio–char samples is found to be directly

proportional to the pyrolysis temperature. The CV of bio–char increases from 24.65 to 25.18

MJ/kg as temperature increases from 400 to 550 °C. The CV of bio–oil is higher (31.53

MJ/kg) compared to those of raw PSC (21.59 MJ/kg) and bio–char (25.18 MJ/kg). This

increase in CV can be attributed to the production of CO and CO2 gases at higher

temperatures during the pyrolysis which removes more oxygen from the bio–char and bio–oil

83

(Naik et al., 2017). As the pyrolysis temperature increases from 400 to 550 °C, the C/H molar

ratio also increases from 0.608 to 2.27 as a result of dehydration and decarboxylation

reactions (Jindo et al., 2014).

Table 4.1.2. Ultimate analysis and calorific value of bio–char and bio–oil co–produced from

pyrolysis of Mahua PSC

Temperature

(°C)

Ultimate analysis (ash–free wt.%) C/H

Molar

ratio

Calorific

value (MJ/

kg) C H N S O

Bio–char

400 61.06±0.5 5.21±0.2 2.98±0.36 0.05±0.07 26.66±0.35 0.98±0.02 24.65±0.44

475 67.88±0.4 3.26±0.22 2.99±0.24 0.03±0.02 25.75±0.6 1.73±0.1 24.83±0.61

550 70.25±0.25 2.58±0.25 3.4±0.12 0.1±0.1 23.82±0.42 2.27±0.22 25.18±0.36

Bio–oil

475 65.25±0.2 9.41±0.18 3.15±0.2 0.1±0.02 22.09±0.89 0.58±0.01 31.53±0.29

4.1.3.2 RSM Modelling and Experimental Observations

Based on three factors and three levels for each factor, one can estimate that the total number

of experiments required mathematically would be 27 (i.e., for a number of factors (k) = 3, 33

=27). However, when the information related to these factors and their levels were fed into

BBD with an objective of achieving higher bio–oil yield, the total number of experiments

required was reduced to 15 including 3 centre points (i.e., reference points).

Following that, all 15 experiments were carried out, and their results are tabulated in Table

4.1.3. As mentioned above, experiments were repeated three times for ensuring the reliability

of the data obtained from the experiments and their mean average values are reported in

Table 4.1.3. From Table 4.1.3, it can be observed that for variables used in this work, the bio–

oil yield varies from 39 to 49 wt. %, bio–char yield ranges from 27 to 40 wt. % and gas yield

varies from 17 to 28 %.

84

Table 4.1.3. Box–Behnken design matrix and response of bio–oil yield for the pyrolysis of PSC

Run

Actual level of factors Experimental yield (wt. %)

Temperature

(°C)

Retention

time (min)

Nitrogen

flow rate

(L/min)

Bio–char

yield

Bio–oil

yield

Bio–gas

yield

1 475 45 0.3 33.55±1.1 49.25±0.8 17.2±2.5

2 475 30 0.5 32.17±1.2 44.26±1.5 23.57±2

3 475 45 0.3 33.55±1.1 49.25±0.8 17.2±2.5

4 475 60 0.5 31.56±1.52 46.25±1.91 22.19±1.5

5 550 45 0.1 30.11±1.6 44.22±1.5 25.67±2.6

6 475 60 0.1 33.03±0.9 44.88±1.2 22.09±2.3

7 550 30 0.3 30.71±1.2 40.51±1.7 28.78±2.8

8 400 60 0.3 37.5±1.85 39.84±1 22.66±1.5

9 400 45 0.1 38.13±1.5 39.01±1.04 22.86±1.62

10 475 30 0.1 34.22±1.1 43.37±1.9 22.41±3.2

11 550 45 0.5 27.79±1.5 46.00±2.1 26.21±2.8

12 400 45 0.5 37.28±1.7 39.91±2.75 22.81±1.9

13 475 45 0.3 33.55±1.1 49.25±0.8 17.2±2.5

14 400 30 0.3 39.5±1.8 39.45±1.6 21.05±2.3

15 550 60 0.3 27.26±1 46.13±1.7 26.61±3.4

Bio–gas yield = 100– (bio–oil + bio–char yield)

This information derived from all 15 experiments was then fed to BBD which helped in

deriving the final mathematical model Eqs (4.1.6) and (4.1.7), which represent the quadratic

and linear model expressions in terms of actual factors for two responses namely, bio–oil

yield and bio–char yield. The methodology adopted here is similar to what was reported by

Gan and Yuan (2013).

𝐵𝑖𝑜 − 𝑜𝑖𝑙 = −174.9 + 0.8334 𝑋1 + 0.588 𝑋2 − 0.000904 𝑋12 − 0.01192 𝑋2

2 − 46.97 𝑋32

+ 0.001162 𝑋1𝑋2 − − − − − − − − − − − (4.1.6)

𝐵𝑖𝑜 − 𝑐ℎ𝑎𝑟 = 69.1 − 0.1045 𝑋1 + 0.156 𝑋2 + 14.4 𝑋3 − − − − − −(4.1.7)

85

where 𝑋1, 𝑋2, 𝑎𝑛𝑑 𝑋3 represent the actual factors given in equations (4.1.6) and (4.1.7) related

to the experimental variables of pyrolytic temperature (°C), retention time (min), and

nitrogen gas flow rate (L/min), respectively as shown in Table 4.1.3. 𝑋1, 𝑋2, 𝑎𝑛𝑑 𝑋3 are the

linear terms of variables; 𝑋1𝑋2 stand for interaction terms of variables; 𝑋12, 𝑋2

2, 𝑎𝑛𝑑 𝑋32

represent quadratic terms of variables (Asmadi et al., 2011). The above equations (4.1.6 and

4.1.7) can be used to identify the response (bio–oil yield and bio–char yield) for given levels

of each factor. The +1, –1, and 0 represent the high, low, and centre levels of the factors.

However, it must be noted that the statistical models as developed above are precise only in

specific conditions, e.g., applicable only to the specific reactor that was used in this study for

the specific biomass studied. If different reactor or conditions are investigated, results or

conclusion could be different. Keeping this limitation in mind, the models developed in this

study are still useful to understand the effect of each significant term and their interactions on

the target variables.

The results from the model are validated with experimental data of bio–oil and bio–char

yields by regression analysis and are presented in Fig. 4.1.2. From Figs. 4.1.2(a) and 4.1.2(b),

it can be seen that actual yield values are best fitted by the predicted values of bio–oil and

bio–char yields. Moreover, it can be seen that a majority of yield values are within the range

of 44–47 wt.% for bio–oil and 30–35 wt.% for bio–char. The yields of bio–oil and the bio–

char are determined from the equations 4.1.6 and 4.1.7, respectively where the bio–oil and

bio–char yields are given as a function of temperature, retention time, and nitrogen flow rate.

The quality of the proposed model can be judged based on the values of correlation

coefficients obtained. The correlation coefficients (R2) for bio–oil and bio–char yield are

found to be 0.9855 and 0.9893, respectively, which indicates that 98.54 and 98.93 % of the

total variations in the product yield can be attributed to the experimental variables studied.

86

Fig. 4.1.2 The relationships between the predicted and actual yields of (a) bio–oil, (b) bio–char

4.1.3.3 Effect of Operating Variables on the Bio–oil and Bio–char Yield

The effects of three variables on the bio–oil yield can be found out from three–dimensional

(3D) surface plots and two–dimensional (2D) contour plots of the empirical model (Fig.

4.1.3). The 3D surface plots demonstrate the classification of the surface shape for different

experimental variables studied, i.e., changing two variables at the same time by fixing the

third variable at a constant level. The coded value of factors in RSM method ranges from –1

to +1. In this study, the factorial levels of all variables vary as 400 to 550 °C for temperature,

30 to 60 min for retention time, and 0.1 to 0.5 L/min for nitrogen gas flow rate.

The regression model for total bio–oil yield has a high R2 value of 0.9854 indicating the best

fit for the experimental data considered in the given range. These findings suggest that the

model developed in this work estimates the effect of the independent variables

(𝑋1, 𝑋2, 𝑎𝑛𝑑 𝑋3) reliably in the slow batch pyrolysis of Mahua PSC. The statistical

significance of the RSM quadratic model for bio–oil yield is confirmed via analysis of

variance (ANOVA) (Table 4.1.4). This suggests that the pyrolytic bio–oil yield depends on at

least one of the variables. The quadratic model has obtained 37.53 and 0.00 for F–value and

p–value, respectively implying the significance of the mathematical model developed. The

insignificant lack–of–fit (p–value > 0.05) relative to the pure error further justifies the

adequacy of the model. The model F–value is a test for comparing model variance with error

variance; the p–value represents the probability of obtaining the observed values of F if the

87

null hypothesis is true (Jung et al., 2016). The model F–value expresses the statistical

significance; the p–value is used to check the significance of the corresponding coefficient (if

p–value < 0.05, then the model is significant). Thus, the coefficients of were X1, X2, X1X2,

and the quadratic terms X12, X2

2, X32 are significant. According to F–values, the linear model

term of temperature (𝑋1) is highly significant for the bio–oil yield. The interaction model

term temperature and retention time (𝑋1𝑋2) has a significant effect on the yield, whereas the

effects of temperature and nitrogen gas flow rate (𝑋1𝑋3), and retention time and nitrogen gas

flow rate (𝑋2𝑋3) are not significant hence not shown in the table. It can be seen that higher F

values are obtained for the quadratic terms (second–order terms 𝑋12, 𝑋2

2, 𝑋32) along with the

pyrolysis temperature (𝑋1) showing them as the parameters that affect mostly (larger

significance) the bio–oil production while the remaining terms are not seen to have much

influence. The results obtained in the present study are in accordance with other studies

reported in the literature (Brown and Brown, 2012).

Fig. 4.1.3 depicts the 3D response surface graphs and contour plots of interaction

factors 𝑋1𝑋2, 𝑋2𝑋3 and 𝑋1𝑋3 for the response chosen, i.e., bio–oil yield. From the 3D surface

plots, it can be inferred that the interactive combination 𝑋1𝑋2 has significant effect towards

the response whereas 𝑋2𝑋3 and 𝑋1𝑋3 have insignificant effect towards the response (bio–oil

yield) and therefore the insignificant factors were manually deleted from Fig. 4.1.3 and

ANOVA analysis (Table 4.1.4). Thus, the interaction effects of temperature (400–550 °C)

and retention time (30–60 min) on the bio–oil yield at a constant N2 flow rate (0.3 L/min) are

represented in the form of three–dimensional response surfaces and two–dimensional contour

line plots as shown in Fig. 4.1.3. The results show that the bio–oil yield increases with an

increase in temperature up to 475 °C and the maximum bio–oil yield obtained is 49.25 % at

475 °C, 45 min of retention time and 0.3 L/min of N2 flow rate. The bio–oil yield decreases

with further increase in temperature above 475 °C which can be attributed to the thermal

cracking, depolymerization, and recondensation of secondary reactions that could be

probably due to the formation of additional amounts of non condensable gases/volatiles

comprising mainly of CO, H2, CO2, CH4, and N2 (Xu and Lancaster, 2008, Xu and Etcheverry,

2008, Aktaş et al., 2009). Similar conclusions can be drawn from the preliminary

investigations that showed a maximum bio–oil yield as ~ 49 % obtained at 450 °C in single

variable experiments.

The regression models for complete bio–char yield have a very high R2 value of 0.9893,

indicating an excellent fit of the quadratic model to the experimental data. In the regression

88

model, the temperature (𝑋1) has the major negative coefficient followed by N2 flow rate (𝑋3)

and then the retention time (𝑋2). Temperature seems to be the most important variable that

significantly affects the bio–char yield. Increases in temperature, N2 flow rate and retention

time lead to a decrease in the bio–char yield. The bio–char yield (Table 4.1.3) ranges from

38.0 to 27.0 wt.% with the maximum and minimum yields occurring at 400 and 550 °C, 30

and 60 min retention times, and 0.1 and 0.5 L/min N2 flow rate, respectively. The decrease in

bio–char yield can be attributed to the degradation of hemicellulose, cellulose, and lignin due

to changes in these operating variables. The occurrence of a secondary reaction at the third

stage of pyrolysis is another reason for the decrease in bio–char yield. The effect of operating

variables on bio–char formation in the present study is in agreement with the findings of

other studies (Putun et al., 2009, Mante et al., 2013).

The regression model of bio–char yield obtained a R2 value of 0.9712 indicating the best fit

for the experimental data considered in the given range. According to the F–values,

temperature (𝑋1) is highly significant for its influence on the bio–char yield than the retention

time (𝑋2) and nitrogen flow rate (𝑋3). Due to the highest F–value found amongst the linear

terms, the pyrolysis temperature (𝑋1) is established as the most effecting parameter (with

larger significance) that affects the bio–char yield significantly. The statistical significance of

the RSM linear model for bio–char yield was confirmed via analysis of variance (ANOVA).

This suggests that the pyrolytic bio–char yield depends on at least one of the variables. The

linear model has obtained 123.55 and 0.00 for F–value and p–value, respectively implying

the significance of the mathematical model generated. The model F–value is a test for

comparing model variance with error variance; the p–value represents the probability of

seeing the observed values of F if the null hypothesis is true (Jung et al., 2016). Thus, the

linear model coefficients of 𝑋1, 𝑋2, 𝑋3 are significant and reported in ANOVA Table 4.1.5.

89

Fig. 4.1.3 Mahua bio–oil yield in 3D response surface (left) and contour plots (right),

respectively: effect of pyrolysis temperature (X1) and retention time (X2) at 0.3 L/min N2 flow

rate

Table 4.1.4. ANOVA for the response surface quadratic model and respective model term for

bio–oil yield

Source DF Sum of squares Mean square F–Value P–Value Remarks

Model 9 186.464 20.7183 37.53 0.000 Significant

Linear 3 57.833 19.2778 34.93 0.001

𝑋1 1 43.478 43.4778 78.77 0.000 Significant

𝑋2 1 11.305 11.3050 20.48 0.006 Significant

Square 3 121.542 40.5139 73.40 0.000

𝑋12 1 95.520 95.5198 173.05 0.000 Significant

𝑋22 1 26.544 26.5444 48.09 0.001 Significant

𝑋32 1 13.033 13.0327 23.61 0.005 Significant

2–way interaction 3 7.089 2.3631 4.28 0.076

𝑋1 × 𝑋2 1 6.838 6.8382 12.39 0.017 Significant

Error 5 2.760 0.5520

Lack–of–Fit 3 2.760 0.9200 230.2 <0.001 Significant

Pure Error 2 0 0.000 0.0000

Total 14 189.224

R2 = 98.54 %, Adj R2 = 95.92 %, Pred R2 = 76.66 %, Mean (s) = 0.742950, DF = Degrees of freedom

90

Table 4.1.5. ANOVA for response surface linear model and respective model term for bio–char

yield

4.1.3.4 GC–MS Analysis of Bio–oil

The bio–oil characterisation was done using GC–MS technique for optimal conditions (i.e.,

the temperature of 475 °C, the retention time of 45 min and N2 flow rate of 0.3 L/min)

derived using the RSM. The objective here is to identify what are the compounds that exist

under this optimal operating condition and determine the applicability of the oil produced as a

bio–fuel.

In this study, more than 100 organic compounds were identified with 15 being the major ones

based on their peak area percentages. The GC–MS chromatogram is shown in Fig. 4.1.4 and

details of the compounds are listed in Table 4.1.6. The compounds having peak areas around

or greater than 1.0 % are the ones identified in this work.

From Table 4.1.6, it can be observed that the major compound in bio–oil derived from PSC is

6–octadecenoic acid with the highest peak area of 15.23 %. The octadecenoic acid is used

an emollient and excipient in pharmaceuticals, and it is used as an emulsifying or solubilising

agent in aerosol products. The second major compound found is an octadecanoic acid with a

peak area of 11.48 %. It also has a wide range of applications, i.e., in the production of soaps,

cosmetics, and release agents and as non–drying oil for surface coatings. Also, free fatty

acids such as hexadecanoic acid, cyano–8–pentadecane, n–tetradecane, tetradecanamide,

Source DF Sum of squares Mean square F–Value P–Value Remarks

Model 3 179.061 59.687 123.55 0.000 Significant

Linear 3 179.061 59.687 123.55 0.000

𝑋1 1 166.896 166.896 345.48 0.000 Significant

𝑋2 1 6.570 6.570 13.6 0.004 Significant

𝑋3 1 5.595 5.595 11.58 0.006 Significant

Error 11 5.314 0.483

Lack–of–Fit 9 5.314 0.590 1.25 0.001 Significant

Pure Error 2 0.000 0.000

Total 14 184.375

R2 = 97.12 %, Adj R2 = 96.33 %, Pred R2 = 93.91 %, Mean (s) = 0.695049, DF = Degrees of freedom

https://en.wikipedia.org/wiki/Emollient

https://en.wikipedia.org/wiki/Excipient

91

1,3,5–triphenylbenzene, benzamide, N, and N–diundecyl–3–methyl are also found at

reasonable levels (i.e., representing total 30–35 % of the total peak area). Free Fatty acids

(FFAs) can be converted into alkyl esters via transesterification process. Therefore, it can be

stated that higher amount of FFAs present in the bio–oil can be better for achieving higher

transesterification efficiency. Also, biodiesel produced from bio–oil obtained from the Mahua

PSC can be an attractive alternative to the conventional petroleum and diesel fuels (Cardoso

et al., 2008, Lu et al., 2009). The results obtained here are in good agreement with those

found in the published literature on Mahua PSC (Pradhan et al., 2016).

Fig. 4.1.4 Gas chromatography–mass spectrometry chromatograms of Mahua bio–oil obtained

by pyrolysis process at 475 °C

4.1.3.5 FT–IR Analysis of Bio–char

The FT–IR spectra of the raw PSC and bio–char were analysed and the results are presented

in Fig. 4.1.5, and the several functional groups (peaks) with strong and medium intensities of

various bond types present in the samples are listed in Table 4.1.7. The bio–char samples

studied here were derived at three different temperatures where the bio–oil yield was found to

be the highest (for details see run numbers 1, 8 and 15 in Table 4.1.3). The FT–IR analysis is

used to investigate the chemical structure of the Mahua PSC and pyrolytic bio–char product

which might include water/hydroxyl, alkane, aromatic, alkane, acid, primary alcohol, and an

alkyl halide in the 500–4000 cm−1 regions.

The most prominent peaks in the spectrum that originate from 3200–3600 cm–1 is attributed

to the O–H group (e.g., water, alcohol, and phenol) of the mineral compounds present in the

Mahua PSC and bio–char product. The peaks in Fig. 4.1.5 are clearly visible in the spectra.

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92

However, the intensities of the each component (band) progressively decreases with

increasing pyrolysis temperature, i.e., raw PSC (major peak) > bio–char at 400 °C > bio–char

at 475 °C > bio–char at 500 °C (peak is nearly absent). This may be attributed to the fact that,

with increasing pyrolysis temperature, the dehydration reaction occurs in the PSC (Chen et

al., 2012). The aliphatic C–H bonds generally represented by the absorbance peak at 2850

and 3000 cm−1 correspond to the symmetric and asymmetric stretching vibrations present in

both raw PSC and bio–char samples indicating the presence of alkanes. It is observed that the

band intensities of the aliphatic components, which are similar to those of O–H group

gradually decrease with the increase in the temperature (Kotaiah Naik et al., 2017).

The weaker bond frequencies, for example, C=C stretching (aromatic) vibration at 1400–

1600 cm−1 is also observed in the spectra which, with an increase in temperature, are found to

be increasing. This suggests an increase in the aromaticity and rise in the degree of

condensation. The 1050–1150 cm−1 stretching vibrations suggest the presence of C–O

(primary alcohol), which is found to be disappearing with an increase in the temperature.

These vibrations are expected from the degradation of hemicelluloses, cellulose, and lignin of

the PSC and are in agreement with the results reported by Arazo et al. (2017).

Fig. 4.1.5 Fourier transforms infrared spectra of (a) raw PSC (b) bio–char @ 400 °C (c) bio–

char @ 475 °C (d) bio–char @ 550 °C.

93

Table 4.1.6. Chemical constituents of bio–oil identified by GC–MS

Sl. No. Retention time

(min)

Peak Area

(%) Compound name

Molecular mass

(g/mol)

Molecular

formula

1 14.83 1.89 Dodecane 170.34 C12H26

2 17.19 0.92 2,2,2-Trifluoroacetic

acid 114.02 C2HF3O2

3 19.37 5.81 n-Tetradecane 198.39 C14H30

4 19.58 6.23 Cyano-8-pentadecene 235.39 C16H29N

5 20.17 10.82 Hexadecanoic acid 256.424 C16H32O2

6 21.16 3.50 Benzamide, N,N-

diundecyl-3-methyl- 443.747 C30H53NO

7 21.30 5.23 Heptadecanenitrile 251.45

C17H33N

8 21.36 11.48 Octadecanoic acid 284.477 C18H36O2

9 21.94 15.23 6-Octadecenoic acid 282.47 C18H34O2

10 22.18 5.37 Tetradecanamide 227.392 C14H29NO

11 23.18 1.20 N-methyldodecanamide 213.359 C13H27NO

12 23.28 4.26 1,3,5-Triphenylbenzene 306.40 C24H18

13 23.68 1.46 3-(4-methylphenyl)

cyclopentanone

174.243 C12H14O

14 24.0 0.76

1,2,4-

Benzenetricarboxylic

acid 4-dodecyl 1,2-

dimethyl ester

406.512 C23H34O6

15 25.8 0.83 Pyrrolo[3,2-c]carbazole 204.22 C14H8N2

http://www.chemspider.com/Chemical-Structure.11436.html

http://www.chemicalbook.com/ChemicalProductProperty_EN_CB12273799.htm




94

Table 4.1.7. FT–IR functional groups and frequency of raw PSC and bio–char

Frequency

(Wavenumbers/ cm-1) Type of vibration

Name of the

functional group

3200–3600 O–H (stretch, H–bonded)

strong, broad

Water

2850–3000 C–H stretch strong Alkane

1400–1600

C=C stretch medium–

weak, multiple bands

Aromatic

1050–1150 C–O Primary alcohol

4.1.3.6 Gas Composition

Investigations on the bio–gas production are not included in the scope of the current study.

However, some general observations made are highlighted in this section. The gases

produced during pyrolysis of Mahua PSC are presented in Fig. 4.1.6 for different

temperatures where retention time and N2 flow rate have been kept constant as 30 min and

0.3 L/min, respectively. The gas yields are presented in Table 4.1.3. The results indicate that

gas phase includes CO2, CO, a small amount of CH4, and traces of H2. Due to the trace

amount of H2 present in the gas phase, it has not been shown in the figure. The CO2 and CO

are expected to mainly form due to decarboxylation and depolymerization reactions while

CH4 are expected to form from the cracking and depolymerization reactions (Vieitez et al.,

2012, Encinar et al., 1996, Klinger et al., 2014). Since these reactions are favored at higher

temperatures, an increase in temperature leads to an increase in the gas production rate or

bio–gas yield (See Table 4.1.3). The yield of bio–gas is found to be directly proportional to

the pyrolysis temperature, i.e., the volume percentages of bio–gas components increase with

an increase in the reactor temperature. In the temperature range studied, CO and CH4

concentrations are found to increase while CO2 concentration seems to decrease slightly with

an increase in the pyrolysis temperature. These observations are found to be similar to those

reported in the literature (Klinger et al., 2014).

95

Fig. 4.1.6 The effect of reaction temperature on the composition bio–gas produced from the

pyrolysis of Mahua PSC

4.1.3.7 Mass Balance

Fig. 4.1.7 shows input and output streams in the experimental batch pyrolysis process. The

input stream contains mahua biomass. According to the separation procedure (described in

the Materials and Methods Section 4.1.2), the output is made of three streams: bio–char, bio–

oil and bio–gas. In this study, the non–condensable vapour (bio–gas) yield was calculated by

determining the difference between the total mass of feedstock and the masses of the bio–

char and bio–oil. The mass balance for pyrolysis is shown Table 4.1.8. The temperature

significantly affects mass yield and characteristics of the bio–char.

Fig. 4.1.7. Mass balances for pyrolysis of mahua biomass.

96

Table 4.1.8. Mass distribution in the pyrolysis of mahua

Temperature

(°C)

Mass in (g) Mass out (g)

Mahua

biomass (g) Bio–char (g) Bio–oil (g) Bio–gas (g)

400 12 4.5 4.7808 2.7192

475 12 3.7872 5.55 2.6628

550 12 3.2712 5.5356 3.1932

4.1.3.8 Energy Balance

The energy balance is carried out to identify energy requirements for the pyrolysis. The basis

here is 1 kg of Mahua PSC. The energy balance model was developed in Matlab 7.10

platform. Pyrolysis process is an endothermic process. Therefore, the energy requirement for

this process includes heating of feed materials (i.e., QB +QM + QN2) and energy supply to

conduct pyrolytic reactions (i.e., Qr). Part of this energy can be directly or indirectly

recovered from the product streams (i.e., bio–char, bio–gas and bio–oil) and recycled back to

the process (i.e., QCBO, QBC, QBG) as shown in Fig. 4.1.8 to ensure that no external energy is

required for the process (Daugaard and Brown, 2003, Zhao et al., 2011, Crombie and Mašek,

2015).

The general assumptions used for this model are:

• The pyrolysis reactor is in a steady–state mode with uniform temperature and

pressure.

• The pyrolysis reactor is in isothermal mode (heat losses are zero).

• The initial moisture content of the Mahua PSC is considered as 8.2 wt.%.

• The bio–oil, bio–char and bio–gas production values per kg of Mahua PSC is used

from experimental results as highlighted above.

• The products such as bio–char and bio–gas are combusted to provide the energy

required for the pyrolysis. The excess energy available after that is used for electricity

generation.

• The bio–oil is condensed up to 200 °C using a heat exchanger, and 70 % of the heat

recovered from the heat exchanger due to the bio–oil condensation is used for heating

primary feed streams such as Mahua PSC/N2.

97

The detailed energy balance of the process is presented in Table 4.1.9. As can be seen in

Table 4.1.9, ~2965 kJ energy is required for the pyrolysis of 1 kg of Mahua PSC (i.e., QB

+QM + QN2 + Qr). The energy recovered from condensing bio–oil (i.e., 0.7 X QCBO) is

estimated to be ~231 kJ (i.e., 8% of the total energy required for pyrolysis) per kg of Mahua

PSC. The energy generated from bio–char (i.e., QBC) and bio–gas (i.e., QBG) combustion is

found be 7489 kJ and 543 kJ, respectively for 1 kg of Mahua PSC. This would leave 5297 kJ

(i.e., QBC+ QBG + 0.7 QCBO – QB – QM – QN2 – Qr) of excess energy for electricity

generation per kg of Mahua PSC. Therefore, it can be stated that the designed pyrolysis

process as highlighted in Fig. 4.1.8 would not require any external energy apart from the ones

that will be needed during plant start–up. These values are further used in the economic

analysis.

Fig. 4.1.8 Energy balance for the process

Table 4.1.9. Energy management for the process

Details of the energy streams Energy (kJ) Formula / Source

Energy Requirement for Pyrolysis Process

Biomass temperature increase from

25 to 475 °C, QB 497 mbCp,bΔT

Moisture temperature increase from

25 to 475 °C, QM 266

mwCp,waterΔT+ mwλ+

mwCp,steamΔT

Nitrogen temperature increase from

25 to 475 °C, QN2 563 mN2Cp,N2ΔT

98

Energy supply to proceed the

endothermic pyrolysis reactions, Qr 1640

1.64 MJ/kg, (Daugaard and

Brown, 2003)

Total energy requirement 2965

Energy Recovery

Bio–oil temperature decrease from

475 to 200 °C, QCBO 330 mboCp,boΔT

Factor considered for energy

recovery 0.7

Total energy recovery 231 Equated from 0.7QCBO

Energy production from the products

Energy from bio–char combustion,

QBC 7489

Chemical energy of bio–char,

22.3 MJ/kg, (Crombie and

Mašek, 2015)

Energy from bio–gas combustion,

QBG 543

Chemical energy of CH4 and

CO, CH4: 49.93 MJ/kg;

CO:10.08 MJ/kg, (Zhao et al.,

2011)

Total energy production 8031

Excess energy available for power

generation 5297

4.1.3.9 Economic Analysis

The economic analysis is carried out for 1500 kg/h of Mahua PSC employing fixed bed

reactor found in the thermochemical conversion facilities. This economic analysis has been

conducted employing experimental data of a bench–scale setup as outlined in the earlier

sections and data available in the literature (Tews et al., 2014, Mignard, 2014, Wright et al.,

2010, Brown et al., 2013, Anex et al., 2010).

Assumptions considered for this techno–economic assessment are detailed in Table 4.1.10.

Again economic analysis code was established in Matlab 7.10 platform.

The total capital expenditure (CAPEX) and operational expenditure (OPEX) of pyrolysis

process was calculated using following equations:

99

Pyrolysis reactor cost ($)

= Feed Rate of Mahua PSC (kg

s) ∗ Calorific Value of Mahua PSC (

kJ

kg) ∗ Gas Engine Eff. %

∗ Pyrolysis Reactor Unit cost ($

kW) − − − (4.1.8)

Gas Engine cost ($)

= Feed Rate of Mahua PSC (kg

s) ∗ Calorific Value of Mahua PSC (

kJ

kg) ∗ Gas Engine Eff. %

∗ Gas Engine Unit cost ($

kW) − − − (4.1.9)

Nitrogen Plant cost ($) = Feed Rate of Nitrogen (m3

h) ∗ Nitrogen plant unit cost (

$

𝑚3/ℎ) − −(4.1.10)

Total CAPEX ($) = Pyrolysis reactor cost ($) + gas engine cost ($) + nitrogen plant cost ($) − − −

(4.1.11)

Operating labour cost ($

yr) = Numberof labours ∗ Labour hourly cost (

$

h) ∗

Annual operating hours (h

yr) − − − (4.1.12)

Maintenance labour cost ($

yr) = 0.015 ∗ Total CAPEX − − − − − − − − − − − (4.1.13)

Overheads cost ($

yr) = 0.01 ∗ Total CAPEX − − − − − − − (4.1.14)

Maintenance materials cost ($

yr) = 0.01 ∗ Total CAPEX − − − − − − − (4.1.15)

Taxes, insurance cost ($

yr) = 0.01 ∗ Total CAPEX − − − − − − − (4.1.16)

Other fixed cost ($

yr) = 0.01 ∗ Total CAPEX − − − − − − − (4.1.17)

Mahua PSC Feedstock cost ($

yr)

= Mahua PSC cost ($

kg) ∗ Mahua PSC annual feed rate (

𝑘𝑔

ℎ) − − − − − −(4.1.18)

Nitrogen production cost ($

yr)

= Nitrogen flow rate (m3

h) ∗ 𝑁𝑖𝑡𝑟𝑔𝑒𝑛 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑢𝑛𝑖𝑡 𝑐𝑜𝑠𝑡 (

$

𝑚3)

∗ 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 (h

yr) − − − − − − − − − −(4.1.19)

Interest rate cost ($

yr) = Flat Interest rate % ∗ Total CAPEX − − − − − − − (4.1.20)

Total OPEX ($

yr) = Fixed operating costs (

$

yr) + Variable operating costs (

$

yr) − −(4.1.21)

100

The annual crude bio–oil production and electricity generation was calculated as follows:

𝐴𝑛𝑢𝑎𝑙 𝑐𝑟𝑢𝑑𝑒 𝑏𝑖𝑜 − 𝑜𝑖𝑙 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 (𝐿

𝑦𝑟)

=(𝑃𝑙𝑎𝑛𝑡 𝑓𝑒𝑒𝑑𝑠𝑡𝑜𝑐𝑘 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑖𝑛𝑔 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 (

𝑘𝑔ℎ

) ∗ 𝐵𝑖𝑜 − 𝑜𝑖𝑙 𝑦𝑖𝑒𝑙𝑑 (𝑖𝑛 𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛) ∗ 𝐴𝑛𝑛𝑢𝑎𝑙 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 ℎ𝑜𝑢𝑟𝑠 (ℎ

𝑦𝑟) )

(𝐵𝑖𝑜 − 𝑜𝑖𝑙 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 (𝑘𝑔𝐿

))

− − − (4.1.22)

Electricity generation (kwh

yr)

= ((Execess energy from energy balance (𝑘𝐽

𝑘𝑔) ∗ Gas engine efficiency (%)

∗ 𝑃𝑙𝑎𝑛𝑡 𝑓𝑒𝑒𝑑𝑠𝑡𝑜𝑐𝑘 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑖𝑛𝑔 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 (𝑘𝑔

ℎ) ∗ 𝐴𝑛𝑛𝑢𝑎𝑙 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 ℎ𝑜𝑢𝑟𝑠 (

ℎ

𝑦𝑟))

/ (4.18 (𝑘𝐽

𝑘𝑐𝑎𝑙) ∗ 860 (

𝑘𝑐𝑎𝑙

𝑘𝑊ℎ)) − − − (4.1.23)

Total revenue generation was calculated as follows:

Total revenue generation ($

yr)

= (𝐴𝑛𝑛𝑢𝑎𝑙 𝑏𝑖𝑜 − 𝑜𝑖𝑙 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 (𝐿

𝑦𝑟) ∗ Crude bio − oil cost (

$

L))

+ (𝐴𝑛𝑛𝑢𝑎𝑙 𝑒lectricity generation (kwh

yr) ∗ Electricity captive price (

$

kwh)) − − − −

− − − − − − − − − − − − − − − − − −(4.1.24)

Annual savings and payback period were calculated as follows:

Annual Savings ($

yr) = Total revenue generation (

$

yr) − Total OPEX (

$

yr) − −(4.1.25)

Payback Period (𝑦𝑟) =Total CAPEX ($)

Annual savings ($yr

)− − − − − −(4.1.26)

From the above calculations, the simple payback period of 5.6 years (Table 4.1.11) is

estimated for the production of crude bio–oil and electricity from Mahua PSC. Such payback

period seems attractive given the fact that by performing pyrolysis of Mahua PSC,

environmental burden of landfilling can be reduced. More scenario modelling will be carried

out in the future to identify critical parameters that can further improve the overall cost

economics.

101

Table 4.1.10. Assumptions for 1500 kg/h bio–oil production from slow pyrolysis of Mahua

PSC

Stream

No. Parameter Value Unit Source

A Pyrolysis process capacity

1 Plant capacity (Mahua PSC feedstock) 1500 kg/h

B General assumptions

(from experiments, analysis and energy balance of this paper and literature)

1 Mahua conversion during pyrolysis 100 % experiments

2 Gas engine efficiency 25 %

(Tews et al.,

2014, Anex

et al., 2010)

3 Nitrogen flow rate (calculated based on

1.25 m3/ kg of biomass) 1875 m3/h experiments

4 Calorific value of Mahua PSC 21.56 MJ/kg analysis

5

Other utility costs of plant ( natural gas,

water, and electricity) are assumed to be

negligible

− − −

6 Bio–oil density 1200 kg/m3 (Yu et al.,

2007)

7

Excess energy available for electricity

generation after providing heat required

for pyrolysis (refer energy balance)

5297 kJ/kg energy

balance

8 Annual operating hours of the plant 7680 h (Tews et al.,

2014)

9 Labour hourly rates 40 $/h (Tews et al.,

2014)

C Assumptions on details of the utility cost (from literature)

1 Mahua PSC raw material cost 0 $/tonne

2 Natural gas cost 0.35 $/m3 (Brown et

al., 2013)

3 Crude bio–oil sale price ($60/barrel) 0.56 $/L (Tews et al.,

2014)

102

4 Captive price for electricity 0.06 $/kW (Brown et

al., 2013)

5 Nitrogen production cost 0.045 $/m3

(Tews et al.,

2014,

Brown et

al., 2013)

D Pyrolysis yield assumptions (from experiments highlighted in this paper)

1 Bio–oil yield 49.5 wt.% experiments

2 Bio–char yield 33.0 wt.% experiments

3 Bio–gas yield 17.5 wt.% experiments

E Pyrolysis CAPEX assumptions (from literature)*

1 Pyrolysis reactor cost 1100 $/kW (Wright et

al., 2010)

2 Gas engine cost 650 $/kW (Wright et

al., 2010)

3 Nitrogen plant cost 750 $/m3 (Wright et

al., 2010)

Note: * Factors such as currency conversion, plant index, and scale have been included

while considering CAPEX

Table 4.1.11. Economic analysis of the pyrolysis

Stream No. Parameter Value Unit

F Total CAPEX

1 Pyrolysis reactor cost 2473990 $

2 Gas engine cost 1461903 $

3 Nitrogen plant cost 1406250 $

Total CAPEX 5342144 $

G Total OPEX

Fixed operating costs

1 Operating labour cost (function of plant size,

3 shifts assumed, 2 persons per shift)

614400 $/yr

2 Maintenance labour cost (1.5 % of CAPEX assumed) 80132 $/yr

3 Overheads (2 % of CAPEX assumed) 53421 $/yr

103

4 Maintenance materials (2 % of CAPEX assumed) 53421 $/yr

5 Taxes, insurance (2 % of CAPEX assumed) 53421 $/yr

6 Other fixed costs (1 % of CAPEX assumed) 53421 $/yr

Variable operating costs

7 Mahua PSC feedstock cost 0 $/yr

8 Other utility cost (natural gas, water and electricity) 0 $/yr

9 Nitrogen production cost 648000 $/yr

10 Interest Rate on CAPEX (Flat 7.5 %)

400661

$/yr

Total OPEX 1956879

$/yr

H Production of bio–oil

1 Crude bio–oil annual production 4752000 L/yr

2 Electricity generation 4243730 kW/yr

I Total revenue generation

1 Revenue from crude bio–oil 2661120 $/yr

2 Revenue from electricity generation 254624 $/yr

Total revenue generation 2915744 $/yr

J Annual savings and payback period

1 Annual savings 958865 $/yr

2 Payback period 5.6 yr

4.1.4 Conclusions

In the current study, slow pyrolysis of a Mahua PSC was investigated in a fixed bed batch

reactor. A Response Surface Method (RSM) was employed to derive the optimum operating

conditions for achieving the highest bio–oil yield in which the effects of three critical

parameters such as temperature, retention time and the N2 flow rate were studied. Using Box–

Behnken Design, a requirement of total 15 experiments was identified. Following that,

experiments were performed in the fixed–bed batch reactor where a constant heating rate of

20 °C/min (representing slow pyrolysis condition) was maintained for all experiments. The

details of the experimental results were incorporated in the model equation of response

surface method in order to identify optimum operating conditions for the highest bio–oil

production. The regression equation was finally established based on the statistical analysis,

104

and the optimum operating conditions such as pyrolysis temperature of 475 °C, the retention

time of 45 min, and N2 flow rate of 0.3 L/min were obtained where bio–oil yield as high as

49.25 % was achieved. The high F–value of 37.53 and low p–value of 0.00 of the predicted

model for bio–oil yield proved that the RSM model used in this work is significant. The

equation obtained fitted well with the experimental data, which is shown by a parity plot

between the actual versus predicted yield. The correlation coefficients (R2) obtained for all of

the responses of the bio–oil and bio–char are 0.985 and 0.989 justifying an excellent

correlation between the independent variables. The bio–oil, bio–char and bio–gas generated

at optimal conditions were characterised by GC–MS, FT–IR and Micro–GC. The major

compounds found in the bio–oil are 6–octadecenoic acid, octadecanoic acid, and FFAs. The

presence of higher FFAs suggests the applicability of PSC bio–oil as a biofuel. FTIR analysis

of bio–char revealed that with an increase in the temperature, hydroxyl, and aliphatic

compounds gets reduced with an increase in the aromaticity. Bio–gas analysis confirmed that,

at higher temperatures, higher gas yield with increased CO and CH4 is observed. Mass and

Energy balance suggest that payback period of 5.6 years can be achieved for producing crude

bio–oil and electricity from Mahua PSC.

105

4.2 Pyrolysis of Sorghum Bagasse Biomass into Bio–char and Bio–oil

Products: A Thorough Physicochemical Characterisation

The transformation of renewable biomass into valuable products as alternatives to fossil fuels

is essential for sustainable energy production. This work systematically investigates the

pyrolysis of sorghum bagasse biomass (SBB) into bio–char and bio–oil and studies the effect

of temperature (350–550 °C) on the conversion of sorghum bagasse and products yields. The

physicochemical properties of bio–char were thoroughly studied using powder X–ray

diffraction (XRD), elemental analysis (CHNSO), scanning electronic microscope (SEM), and

calorific value (CV), and Fourier transform infrared (FT–IR) spectroscopy techniques. Also,

gas chromatography mass spectrometry (GC–MS), CV, and FT–IR were used to understand

the properties of bio–oil. The results obtained indicate that an increase in the pyrolysis

temperature from 350 to 550 °C leads to a decrease in the bio–char yield from 42.55 to

30.38%. On the other hand, the maximum bio–oil yield of 15.94% is obtained at 450 °C. The

bio–char obtained at 400 and 500 °C is found by FT–IR analysis to be composed of a highly

ordered aromatic carbon structure. The calorific value of bio–oil, which contains a greater

amount of acidic compounds, is found to be 28.17 MJ/kg. The GC–MS analyses reveals the

presence of octadecenoic acid, p-cresol, 2, 6-dimethoxy phenol, 4-ethyl 2-methoxy phenol,

phenol, o-guaiacol and octadecanoic acid in the bio–oil obtained from the pyrolysis of

sorghum bagasse biomass. The present study provides useful information for understanding

the quality of bio–oil and bio–char obtained from sorghum bagasse.

4.2.1 Materials and Methods

The feedstock for the pyrolysis experiment was high sorghum bagasse, which was collected

from ICRISAT [International Crops Research Institute for the Semi–Arid Tropics],

Hyderabad, India. The biomass was cut into particles with an average size of 1–5 mm and

oven dried for 12 h. The dried sample was used as the feed in all pyrolysis experiments. The

detailed experimental setup and procedure used in this study were explained in section 4.1.

4.2.1.1 Characterisation of Raw Biomass and Bio–char Obtained from

Pyrolysis Process

SEM images of raw biomass and torrefied biomasses obtained at a various temperature

ranging from 350 to 550 °C with 60 min holding time. Which was taken using scanning

106

electron microscope (Hitachi S–3000N) having an acceleration voltage of 15 kV. SEM

analysis was done to compare the surface morphologies of the raw biomass and bio–char.

Other characterisation techniques are explained in section 4.1.2.3.

4.2.2 Results and Discussion

4.2.2.1 Mass loss during Pyrolysis of High Sorghum Biomass

The bio–fuels produced from the pyrolysis mainly depend on the reaction temperature and the

type of fed stock. The results obtained from the pyrolysis process are presented as the mass

percentage yield for 60 min retention time as a function of reaction temperature for bio–char,

bio–oil and gases in Fig. 4.2.1. The yield of bio–char depends on temperature only. Bio–char

decreases from 42.5 to 30.4% with an increase in temperature from 350 to 550 °C. This is

due to the degradation of hemicellulose (which occurs from 200 to 350 °C), cellulose (occurs

from 300 to 400 °C), and lignin (starts slowly from 200 to 550 °C and reaches a constant

value and then increase above 550 °C). The occurrence of a secondary reaction at the third

stage of pyrolysis is another reason for the decrease of bio–char yield. The results are in

accordance with those reported previously by other researchers (Di Blasi, 2009). The yield of

the liquid (pyrolysis oil) product increases from 3.80 to 15.94% with an increase in

temperature from 350 to 450 °C, which is due to the degradation of cellulose in this

temperature range. Thereafter, the yield of the liquid (pyrolysis oil) product decreases due to

the formation of non–condensable gases/volatiles comprising mainly of CO, H2, CO2, CH4,

and N2. The yield of gaseous products was estimated by subtracting the yield of bio–char,

pyrolysis oil and water layer from the total yield. It is about 37.5% at 350°C and it decreases

with an increase in temperature, reaches a minimum value of about 20% at 450°C and then

increases to reach a maximum (35.89%) at 550 °C. Since the yield of gas was determined by

mass balance, it has a trend that is opposite to that of bio–oil. The higher yield of gases at

higher temperatures can be attributed specifically to the secondary decomposition of char,

producing some non–condensable gaseous products.

107

Fig. 4.2.1 Effect of temperature on products yields in the pyrolysis of sorghum biomass.

4.2.2.2 Characterisation of Bio–char

The ultimate analyses of the raw biomass and bio–char are presented in Table 4.2.1. The

elemental composition of the raw bio–char is significantly different from that of biomass. The

biochar has high–carbon, low–oxygen, and low–hydrogen compounds. The literature cites

studies on elemental analyses of different raw biomasses which concur with the results

obtained in the present study (Huang et al., 2015). The elemental analyses of bio–char

produced from the pyrolysis at various temperatures are also listed in Table 4.2.1. As is

evident from the Table 4.2.1., the mass percentage of carbon in the bio–char increases with

an increase in the pyrolysis temperature (17.68 wt.% at 350 °C and 64.96 wt.% at 550 °C). In

contrast, the mass percentages of oxygen and hydrogen decrease with increasing temperature,

while the sulphur content remains the same. The decrease in hydrogen and oxygen contents

with increasing pyrolysis temperature can be attributed to the cracking of weak bonds within

the bio–char structure. It is clear that the emission of CO2, CO, and H2O will result in the

reduction of H and O contents. The formation of CO by decarbonylation reaction and that of

CO2 due to the cleavage in hemicelluloses, or the formation of H2O can be ascribed to the

decrease in oxygen and hydrogen contents of biomass. The production of CO and CO2 gases

at high temperatures and residence time removes more oxygen from pyrolytic products thus

leading to an increase in their calorific value. These results indicate that the energy density of

the sorghum biomass increases with increasing pyrolysis temperature due to the removal of

108

oxygen. From Table 4.2.1, a small increase in the nitrogen content can be observed with

increasing temperature. The calorific value of raw biomass is 12.0 MJ kg−1, whereas those for

bio–char and bio–oil are 15.24 and 28.17 MJ kg−1, respectively. The C/H molar ratio of bio–

char increases with increasing pyrolytic temperature. The C/H molar ratio of the raw biomass

and bio–char at 450 °C is found to be 0.604 and 1.437, respectively.

Table 4.2.1 Elemental analyses of bio–char co–produced from pyrolysis of high sorghum

biomass and standard deviations for C, H, N, S, and O are ± 9.4, 1.1, 0.27, 0.005, and 8.7/%,

respectively.

Properties SBB/ mass %

(dry basis)

bio–char/ mass % (dry basis)

350°C 400°C 425°C 450°C 475°C 500°C 550°C

C 43.72 51.45 56.79 61.78 63.45 65.18 69.74 72.12

H 6.03 4.53 4.29 3.77 3.68 3.31 2.91 2.65

N 0.30 0.32 0.35 0.41 0.78 0.82 0.86 0.91

S 0.04 0.03 0.04 0.03 0.03 0.04 0.03 0.04

O 49.91 43.67 38.53 34.01 32.02 30.65 26.46 24.28

C/H Molar

ratio 0.604 0.946 1.103 1.366 1.437 1.641 1.997 2.268

Pyrolysis oil/

mass% – 3.8 8.442 13.308 15.935 12.587 12.124 4.277

Bio–char

calorific

value/MJ kg-1

12.0 – – – 15.24 – – –

Bio–oil calorific value/ MJ kg-1 28.17

The overall composition of sorghum bagasse biomass is summarised in Table 4.2.2. Besides

water extractives and ethanol extractives, there are significant amounts of cellulose,

hemicellulose, and lignin. Fibre analysis such as cellulose, hemicellulose, and lignin was

carried out using Gerçel (2002) method. The average contents of cellulose, hemicellulose

(xylan, arabinan, and acetic acid), and lignin (acid soluble lignin and acid insoluble lignin) in

sorghum bagasse feed stock were measured to be 37, 25.7, and 19.7%, respectively. Ash

content of the feedstock was determined by thermogravimetric analysis (TG) to be 2.43

mass%. The ash contains major inorganic components such as calcium, magnesium, sodium,

potassium, phosphate, and silica (Piskorz et al., 1998).

109

Table 4.2.2 Composition of high sorghum biomass

The surface morphologies of the raw biomass and bio–char obtained from pyrolysis were

investigated using SEM and the images obtained are shown in Fig. 4.2.2. The surface of the

raw biomass is comparatively smooth without cracks, pores, and crevices [Fig. 4.2.2(a)].

However, after the pyrolysis, the surface develops cracks and few pores. At mild pyrolysis,

the pore size is small because the volatile matter and ash are still blocking them [Fig.

4.2.2(b)]. With an increase in pyrolysis temperature, however, the porosity of bio char

increases [Figs. 4.2.2(a) – (f)] which agrees with literature findings (Evans and Milne, 1987).

Pyrolysis at higher temperature degrades the cellulose, hemicellulose, and lignin of the

biomass samples. It can be observed from Figs. 4.2.2(b)–(f) that the bio–char samples

obtained at different temperatures have a porous structure. It is also clear that the bio–char

exhibits a surface morphology that is very different compared to the raw biomass. The SEM

images also show that an increase in the pyrolysis temperature influences the size and shape

of a particle through a general increase in size and proportion of voids. It can also be seen

that the shell wall becomes highly riddled and thinner with increasing temperature. The fast

release of volatile during pyrolysis generates substantial internal overpressure to form the

open structures. Nikzad et al. (2014) examined the morphology of untreated and pre–treated

sweet sorghum biomass using SEM. They also reported an intact morphology and smooth

Composition

Component/ mass %

(dried basis 100 g)

Water extractives 11.66

Ethanol Extractives 3.62

Cellulose 36.96

Xylan 20.49

Arabinan 2.36

Acetic Acid 2.82

Acid soluble lignin 4.36

Acid insoluble

lignin 15.3

Ash 2.43

110

surfaced compact structure for untreated biomass and increased porosity for pretreated

biomass.

Fig. 4.2.2 SEM images of (a) raw biomass (b) bio–char at 350 °C (C) bio–char at 400 °C (d) bio–

char at 450 °C (e) bio–char at 500 °C (f) bio–char at 550 °C

The results of X–ray diffractometer (XRD) analysis for raw biomass and bio–char are shown

in Fig. 4.2.3 for various pyrolysis temperatures. The diffraction peaks at line counts 100, 200,

and 500 are assigned to 2 theta = 5.2, 16.0, and 22.1º, respectively. Biswas et al., (2017)

reported the peak at 2 theta around 21º can be assigned to the crystallographic planes of

cellulose. They also reported that the peak decreased with an increase in pyrolysis

temperature thus forming amorphous bio-char that is rich in carbon content. Results obtained

in this work are also similar to those reported in other previous studies (Ding et al., (2016),

Mohan et al., (2014)). As can be observed from the Fig. 4.2.3 the peaks for the original

111

cellulose of un–carbonised sorghum biomass form up to 300 °C. In other words, the

carbonisation of the sorghum biomass did not occur below 300 °C. However; the cellulose

crystalline substance becomes non–crystallised when the sorghum biomass is carbonised at

350 °C. At 400 °C, the crystalline substance seems to be almost destroyed and disappears due

to the complete thermal decomposition of the cellulose crystal structure.

The above results agree well with the findings of Kwon et al. (2009), which showed the

crystalline region of the cellulose starts getting destroyed at around 300 °C. Nishimiya et al.

(1998) conducted experiments with Cryptomeria japonica in the temperature range of 300–

2000 °C and reported similar results. They also concluded that no definite reflection was

detected from X–ray diffraction profiles at carbonisation temperatures between 600 and 800

°C. Zickler et al. (2007) conducted experiments with spruce wood in the temperature range of

300 to 360 °C. They studied the kinetics of thermal decomposition of crystalline cellulose

using in–situ X–ray diffraction. They reported that the thermal decomposition of crystalline

cellulose in wood occurs mainly via a thermally activated decrease of the fibril diameter and

they also obtained an estimate of the activation energy.

Fig. 4.2.3 Powder XRD patterns of sorghum biomass and bio–char obtained at temperatures

from 350 to 550 °C

The Fourier transform infrared spectroscopic analysis was used to investigate the chemical

structure of raw biomass and bio–char samples. Table 4.2.3 shows the frequency of

functional groups that are found in raw biomass and bio–char whereas Fig. 4.2.4 shows FTIR

spectra of raw biomass and bio–char samples. The spectra of raw biomass and the bio–chars

in the figure are characterised by respective principal bands. The band in the region of 3200–

112

3600 cm–1 is attributed to the O–H group (e.g., water, alcohol, and phenol) (Wei et al., 2015).

The aliphatic bands C–H (alkenes) stretching between 2850 and 3000 cm−1 are present for

both raw biomass and bio–char samples. The N–H stretching (amide) vibration at 1550–1640

cm−1 is also observed in the spectra indicating that N–containing protein has been converted

mostly into bio–oil products. The weaker bond frequencies, for example 1400–1620, 1000–

1300, 600–1000 and 500–600 cm–1 for the stretching vibrations of C=C, C–O, =C–H and C–

Br groups, indicate the presence of aromatic, ether, alkene and alkyl halide functional groups,

respectively (Varma and Mondal, 2016). These vibrations are expected due to the presence of

hemicelluloses, cellulose, and lignin and are in agreement with the results reported by Yang

et al., (2007) and Joshi et al., (2015).

Table 4.2.3 FT–IR functional groups and frequency of bio–char

Frequency

Wavenumbers/cm-1 Type of vibration

Name of the

functional group

3200–3600 O–H (stretch, H–bonded) strong, broad Alcohol


1550–1640 N–H bending Amide

1400–1620 C=C stretch medium–weak, multiple bands Aromatic

1000–1300 C–O stretch strong Ether

600–1000 =C–H bending strong Alkene

600–800 C–Cl stretch strong Alkyl Halide

500–600 C–Br stretch strong Alkyl Halide

Fig. 4.2.4 FT–IR spectra of (a) raw biomass (b) bio–char at 350 °C (c) bio–char at 400 °C (d)

bio–char at 450 °C (e) bio–char at 500 °C (f) bio–char at 550 °C

113

4.2.2.3 Characterisation of Bio–oil

FT–IR is a fast, accurate, reliable and non–destructive method for bio–oil analysis. The

functional groups that are present in the spectra for the pyrolytic bio–oil are similar to those

for other bio–oils reported in the literature and they are shown in Fig. 4.2.5 and Table 4.2.4.

The FT–IR analysis of pyrolytic oil confirms that the oil extract from the raw sorghum may

be used as a bio–fuel. The broad vibrational frequency of O–H group in the range of 3200–

3600 cm–1 indicates the presence of alcohol group. The presence of alkanes is identified with

a range of frequency of 2850–3000 cm–1 for C–H stretch strong vibrations. The C=O stretch

strong vibration that appears in the range of 1700–1725 cm–1 signifies the presence of acid

groups in the bio–oil. The presence of amide is detected by N–H bending vibrations range of

1550–1640 cm–1 and the presence of aromatic group is detected by C=C stretch medium–

weak, multiple bands vibrations range of 1400–1600 cm–1. Alcohol functionality is detected

by C–O (stretch) strong vibrations in the range of 1050–1150 cm–1. Alkene is detected by

=C–H banding strong vibrations in the range of 675–1000 cm–1 and alkyl halide is detected

by the C–Cl stretch strong vibrations range of 600–800 cm–1. The compounds present in the

liquid product are identified by comparing the chromatogram of high sorghum pyrolysis oil

with standard chromatographic data from NIST library.

Fig. 4.2.5 FT–IR transmittance spectra of (a) bio–oil at 350 °C (b) bio–oil at 400 °C (c) bio–oil at

450 °C (d) bio–oil at 500 °C (e) bio–oil at 550 °C.

114

Table 4.2.4 FT–IR functional groups and frequency of bio–oil

Frequency


Name of the

functional group

3200–3600 O–H (stretch, H–bonded) strong, broad Alcohol


1700–1725 C=O stretch strong Acid



1350–1480 –C–H bending variable Alkane

1210–1320 C–O (stretch) strong Acid

1050–1150 C–O (stretch) strong Alcohol



As the bio–oil yield is higher at 450 °C, the sample at this temperature was selected for GC–

MS analysis. In the chemical composition analysis of the bio–oil, many active compounds

that are reported to be found in other naturally occurring oils were identified. The total ion

current chromatogram (TICC) obtained from GC–MS analysis of the water–insoluble liquid

fraction at 450 °C is shown in Fig. 4.2.6.

It can be noticed from Fig. 4.2.6 that the oil sample has a number of peaks due to different

chemical constituents. The peaks appeared in the TICC were identified by searching their EI

mass spectra against Wiley 9/NIST 11 library. The compounds identified are listed in Table

4.2.4, where the peak area values for each identified peak are also included. In an earlier

study, bio–oil and bio–char obtained from sweet sorghum bagasse subjected to a fast

pyrolysis with fractional condensers were characterised by GC–MS (Yin et al., 2013). The

compounds obtained from high sorghum bagasse used in the present study are very much

different from the compounds reported for sweet sorghum bagasse. Only a few compounds of

high sorghum bagasse have been found to be common with those for the sweet sorghum

bagasse as shown in Table 4.2.5.

As is evident from Table 4.2.5, the peak areas were relatively higher for unsaturated/saturated

fatty acids and their methyl esters when compared to other components. The octadecenoic

acid was found to be the major compound (peak area % 100). The other compounds like p-

115

cresol (63.10%), 2,6-dimethoxy phenol (61.98%), 4-ethyl-2-methoxy phenol (61.45%),

phenol (52.97%), o-guaiacol (51.07%), octadecanoic acid (36.23%), o-cresol (31.02%) were

next to the octadecenoic acid in percentage wise. In addition, 2-cyclopentenone, acetonyl

acetate, 2-methylcyclopentenone, 3-methylcyclopentenone, imidazole, 2-hydroxy-3-methyl

cyclopententanone 2,3,5-trimethoxytoluene, etc., were also present in a considerable

percentage.

One of the components of the bio–oil collected at 450 °C is phenol, whose derivatives are

important for making polycarbonates, epoxies, bakelite, nylon, detergents, phenoxy

herbicides, and numerous pharmaceutical drugs. Another important compound is p-cresol,

which is mainly used in the production of antioxidants, e.g., butylated

hydroxytoluene (BHT). The 2,6-dimethoxy phenol (syringol), a naturally occurring dimethyl

ether of pyrogallol, is the main chemical responsible for the smoky aroma, while guaiacol

contributes mainly to taste. Octadecenoic acid (oleic acid) is a naturally occurring fatty acid

present in various animal/vegetables; it is a major component of soap and used as

an emulsifying agent. Similarly, the octadecanoic acid (stearic acid) is mainly used in the

production of detergents, soaps, and cosmetics such as shampoos and shaving

cream products. O-cresol is an important intermediate in the manufacture of pesticides, epoxy

resins, dyes and pharmaceuticals; also, it is also an important component used in the

production of disinfectants and cleaning agents. 2, 4-Dimethylphenol is a naturally occurring

compound derived from the cresol fraction of petroleum or coal tars by fractional distillation,

and this compound is used in making pharmaceuticals, insecticides, fungicides, and also

plastics.

Fig. 4.2.6 GC–MS total ion current chromatogram of the liquid fractions/ bio–oils obtained by

flash pyrolysis of bio–mass at 450 °C

https://en.wikipedia.org/wiki/Derivative_(chemistry)

https://en.wikipedia.org/wiki/Phenoxy_herbicide

https://en.wikipedia.org/wiki/Phenoxy_herbicide

https://en.wikipedia.org/wiki/Pharmaceutical_drug

https://en.wikipedia.org/wiki/Antioxidant

https://en.wikipedia.org/wiki/Butylated_hydroxytoluene

https://en.wikipedia.org/wiki/Butylated_hydroxytoluene

https://en.wikipedia.org/wiki/Pyrogallol

https://en.wikipedia.org/wiki/Aroma

https://en.wikipedia.org/wiki/Taste

https://en.wikipedia.org/wiki/Fatty_acid

https://en.wikipedia.org/wiki/Animal_fat

https://en.wikipedia.org/wiki/Vegetable_fats_and_oils

https://en.wikipedia.org/wiki/Emulsifying_agent

https://en.wikipedia.org/wiki/Shampoo

https://en.wikipedia.org/wiki/Shaving_cream

https://en.wikipedia.org/wiki/Shaving_cream

116

Table 4.2.5. Chemical constituents of bio–oil (at 450 °C) identified by GC–MS

Sl. No. Retention

time/ min

Peak

Area/% Compound name

Molecular

mass

Molecular

formula

1 4.3 10.91 2-Cyclopentenone 82 C5H6O

2 4.83 18.04 Acetonyl acetate 116 C5H8O3

3 5.49 8.60 2-Methylcyclopentenone 96 CH3C5H5(=O)

4 6.53 20.03 3-Methylcyclopentenone 96 C6H8O

5 6.86 52.97 Phenol 94 C6H6O

6 7.18 4.79 Imidazole 71 C3H4N2

7 7.57 24.36 2-Hydroxy-3-methyl

cyclopententanone 112 C6H8O2

8 8.06 31.02 o-Cresol 108 C7H8O

9 8.40 63.10 p-Cresol 108 C7H8O

10 8.57 51.07 o-Guaiacol 124 C7H8O2

11 9.047 22.33 3-Ethyl-2-hydroxy

cyclopentenanone 126 C7H10O2

12 9.55 25.49 2,4-Dimethyl phenol 122 C8H10O

13 10.19 21.89 2-Methoxy-4-methylphenol 138 C8H10O2

14 10.87 13.48 4-Ethyl,2-methyl phenol 136 C9H12O

15 11.45 61.45 4-Ethyl,2-methoxy phenol 152 C9H12O2

16 11.97 14.72 2-Methoxy-4-vinylphenol 150 C9H10O2

17 12.46 61.98 2,6-Dimethoxy phenol 154 C8H10O3

18 13.71 18.94 4-Methyl-syringol 168 C9H12O3

19 13.78 22.48 cis-Isoeugenol 164 C10H12O2

20 14.31 11.49 1-(3-Hydroxy-4-

methoxyphenyl)ethanone 166 C9H10O3

21 14.71 31.91 2,3,5-Trimethoxytoluene 182 C10H14O3

22 15.65 7.64 Methoxyeugenol 194 C11H14O3

23 16.37 7.89 4-Hydroxy-3,5-dimethoxy

benzaldehyde 182 C9H10O4

24 18.79 3.86 Pentadecanenitrile 223 C15H29N

25 19.02 7.67 Methyl hexadecanoate 270 C17H34O2

26 19.40 59.38 Hexadecanoic acid 256 C16H32O2

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27 20.57 20.47 Oleanitrile 263 C18H33N

28 20.72 13.14 Methyl octadecenoate 296 C19H34O2

29 20.78 7.31 Heptadecanenitrile 251 C17H33N

30 20.93 4.17 Methyl octadecanoate 298 C19H38O2

31 21.09 100 Octadecenoic acid 282 C18H34O2

32 21.28 36.23 Octadecanoic acid 284 C18H36O2

33 23.08 9.44 Eicosanoic acid 312 C20H40O2

34 24.28 8.56 Nonadecane nitrile 279 C19H37N

35 24.35 7.02 Methyl docosanoate 354 C23H46O2

36 24.62 12.91 Docosanoic acid 340 C22H44O2

37 25.21 23.11 2-Phenyl-furo[b]benzopyrane-

4[4H]-one 262 C17H10O3

38 27.57 11.62 Nonacosane 408 C29H60

4.2.3 Conclusions

The effect of pyrolysis temperature on the conversion of biomass and the yields of bio–oil

and bio–char was investigated in this work. The results showed that physicochemical and

structural characteristics of the biochar are significantly influenced by pyrolysis temperature.

The degree of carbonisation of bio–char is accelerated with increasing temperature from 350

to 550 °C. As the temperature increases, oxygen and hydrogen are removed from the

biomass, thus leaving the remaining carbons to form aromatic carbon bonds. The

experimental results of FT–IR, SEM, and XRD indicate that the formation of highly ordered

aromatic structures in the bio–char begin at 400 °C. The maximum yield (pyrolysis oil) of the

oil obtained at 450 °C is 15.93 % by mass. It was found that the bio–oil contains around 38

types of compounds having carbon chain length ranging from C2 to C23. The calorific value

of sorghum bagasse oil is significantly different from those of other oils. Consequently, it can

be used as an alternative to fossil fuel after effective treatment. GC–MS analyses of bio–oil

reveal that it is mainly composed of a high percentage of octadecenoic acid, p-cresol, 2, 6-

dimethoxy phenol, 4-ethyl 2-methoxy phenol, phenol, o-guaiacol and octadecanoic acid. The

liquid product obtained can also be used as a valuable chemical feedstock.

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Chapter 5 Experimental Investigations on the Effect of Pyrolytic Bio–oil During the Liquefaction of Karanja Press Seed Cake

119

5 Experimental Investigations on the Effect of Pyrolytic Bio–oil during

the Liquefaction of Karanja Press Seed Cake

Summary

In Chapter 4, it was demonstrated that bio–oil can be obtained from the pyrolysis of

lignocellulosic biomasses such as mahua and sorghum using a lab–scale vertical fixed bed

stainless steel reactor. This chapter presents the results of pyrolysis of another lignocellulosic

biomass, Karanja PSC, to produce pyrolytic bio–oil (PBO) using the experimental set up and

operating procedure that was discussed in Chapter 4. The reason for including the discussion

on the pyrolysis of Karanja PSC in this chapter is that it provides an integrated study of

converting Karanja PSC into liquefied bio–crude using PBO as a catalyst.

This chapter discusses the liquefaction of Karanja PSC in the presence of PBO produced

from the slow pyrolysis of the same feedstock (Karanja PSC). The effects of PBO mass and

temperature were studied with an aim of achieving the highest conversion Karanja PSC into

bio–crude in liquefaction experiments. Also, conversion of Karanja PSC achieved in the

presence of PBO is compared with those obtained from conventional solvent such as phenol

and acid catalysts such as sulphuric acid. A detailed chemical analysis of liquefied product

(bio–crude) was carried out using FT–IR, and GC–MS techniques. The results showed that

the Karanja PSC could be directly liquefied in the presence of PBO at moderate reaction

conditions. A maximum liquefaction conversion of 99% was obtained at a reaction

temperature of 240 °C, a residence time of 160 min and a Karanja PSC to PBO ratio of 1:6.

In contrast, ~ 94% conversion was obtained for the same residence time but at significantly

lower temperature of 160 °C when phenol and sulphuric acid were used in the ratio of

Karanja PSC, phenol and H2SO4 as 1:2:0.6. It was observed that aromatic structure with less

oxygen was evident in bio–crude compared to that in PBO.

5.1 Introduction

Biomass sources such as forestry products, marine products, energy crops, agricultural crops,

aquatic plants, pulp derived black liquor, wood and wood waste, municipal solid waste,

sewage waste, and animal waste are considered as potential resources for the production of

biofuels and biochemicals (Shuping et al., 2010). The biomass currently provides

approximately 14% of the world’s total energy demand (Xu et al., 2012). Among these

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feedstocks, Karanja biomass has been identified as a promising feedstock for the production

of renewable fuels due to its higher heating value and its abundant availability in many of the

developing countries such as India, Sri Lanka, Nepal, Pakistan and China (Bobade and

Khyade, 2012). Karanja biomass is used to produce biodiesel using a transesterification

process (Thiagarajan et al., 2013). However, during this process, it leads to a generation of a

Press Seed Cake (PSC) as a solid residue, which could weigh up to 60% of the original

Karanja biomass weight. This PSC is normally treated as a waste and mainly landfilled

without any further treatment. It was recently established in the literature that PSC, due to its

organically rich structure, has the potential to be used as a feedstock for liquefaction (Bobade

and Khyade, 2012, Shadangi and Mohanty, 2014b).

In liquefaction, relatively low temperatures but high pressures can be used for the valorisation

of waste biomass compared to pyrolysis (Vardon et al., 2012, Wang et al., 2016).

Additionally, liquefaction yields high–quality liquid products (bio–crude) with higher

calorific value and lower water/oxygen content. The bio–crude obtained from the direct

liquefaction process is a dark–colored, semi–liquid with the smoke–like the smell, with

viscosity 10–10,000 times greater than that of diesel or biodiesel. Bio–crude contains a

significant amount of carboxylic acids such as acetic acid and formic acid as well as phenolic

fractions and carbohydrates (Jena and Das, 2011, Demirbaş, 2005).

Liquefaction of solid biomass involves a complex mechanism controlled by several

parameters. To carry out an efficient liquefaction process, which is measured in terms of

product quality, conversion, cost–effectiveness, and energy efficiency, the selection of

appropriate solvents and catalysts are very critical. Solvent has a considerable effect on the

liquefaction reaction. Water and various organic solvents such as phenols, alcohols, glycols,

ketones, etc., are established solvents that help in producing low viscosity heavy oil by

effectively breaking down the heterogeneous macromolecular structure of the biomass into

the light to moderate hydrocarbons. In addition, various co–solvents and acid catalysts such

as sulphuric acid, hydrochloric acid, phosphoric and oxalic acid are also used for increasing

bio–crude yields (Liu and Zhang, 2008, Li et al., 2009). Akalın et al. (2012) have obtained a

bio–crude yield of 28 wt.% by direct liquefaction of cornelian cherry samples with an acid

catalyst and water at 200–300 °C. Huang et al. (2011) reported bio–crude yields of 35.4 and

45.3 wt.% by employing supercritical ethanol as solvent along with acetone and phenol as

co–organic solvents, respectively. Cheng et al. (2010) investigated the liquefaction of wood

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at 300 °C and obtained a bio–crude yield of 65 wt.% using a solvent mixture containing

methanol and water.

The current work focuses on the use of pyrolytic bio–oil (PBO) produced from Karanja PSC

as a solvent for the liquefaction of Karanja PSC itself. Along with high–energy footprint, the

use of solvents and catalysts with high toxicity, low vapor pressure, and the inflated cost has

become one of the major barriers in the successful commercialisation of liquefaction

processes. To answer to this, the use of PBO produced from pyrolysis as a solvent in the

liquefaction has been proposed recently in the literature (Alma et al., 2016). The PBO is

generally non–toxic. Moreover, it has higher vapor pressure compared to the conventional

solvents, and it does not need to be separated from bio–crude for further up–gradation

(Radhakumari et al., 2014). Therefore, the losses associated with solvent/catalysts can be

greatly reduced. Also, costs associated with the use of conventional solvents, such as

production and transport (to the site), are also very high. It is hypothesised that PBO will

have lower production and transport cost compared to the conventional solvents because it

will be produced on site from the same feedstock which is available as a waste.

The literature on the use of PBO as a solvent in the liquefaction is scarce (Bobade and

Khyade, 2012, Shadangi and Mohanty, 2014b). Moreover, the studies conducted were on

different biomasses, and the PBO was mainly produced from fast pyrolysis. To the best of

author’s knowledge, the study of Karanja PSC liquefaction with PBO produced from slow

pyrolysis of the same feedstock is non–existent in the literature. Fig. 5.1 highlights the

process flow diagram proposed in this work. In the present work, the liquefaction of Karanja

PSC is carried out in the presence of PBO produced from the same feedstock. The effects of

PBO amount and its temperature are studied with an aim to achieve the highest conversion in

liquefaction experiments. Also, some comparison is made with the use of PBO and

conventional solvents and acid catalysts, such as phenol and sulphuric acid, respectively for

achieving the highest liquefaction conversion. A detailed chemical analysis and comparison

of PBO and liquefied product (bio–crude) is also carried out using FT–IR, and GC–MS

techniques.

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Fig. 5.1. Block diagram of the combined pyrolysis–liquefaction process

5.2 Materials and Methods

5.2.1 Wet Biomass Feedstock Source and its Properties

All experiments were performed using Karanja PSC that was collected from Indian Institute

of Chemical Technology, Hyderabad, India. The Karanja PSC was sun–dried for 5–10 days

and broken down into small pieces, which were then sieved in Tylor screens to obtain a

particle size of up to 1 mm. The sieved biomass sample was stored in airtight packages at

room temperature prior to pyrolysis and liquefaction studies. The elemental analysis of

powdered Karanja PSC was performed using CHNSO Analyser (Model: Element Vario

Microcube, Germany). The calorific value (CV) of Karanja PSC was determined in a static

bomb calorimeter (Model: C 2000 basic IKA–bomb calorimeter, Germany) as per the

procedure described by Naik et al. (2010) and Ahmed et al. (2016). To determine the amount

of fixed carbon, volatile matter, moisture, and ash content of the Karanja PSC samples, a

TGA/DTA system was used according to ASTM D 5142–04 method (Gerçel, 2002).

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The results of the elemental (ultimate and proximate) analysis and CV of Karanja PSC are

shown in Table 5.1. The chemical compositions of Karanja PSC such as extractives, protein,

and lignin contents are shown in Table 5.2. Concentrated H2SO4 (99 wt.%) and phenol (both

purchased from Aldrich Chemical Company, India) were used as an acid catalyst and

liquefaction reagent, respectively. All other chemicals used were analytical grade reagents.

Table 5.1. Elemental analysis of Karanja PSC feedstock

Ultimate analysis (dry wt. %) Proximate analysis (dry wt. %) Calorific value

(MJ/kg) C H O N S Moisture

content

Volatile

matter

Fixed

carbon Ash

44.2 6.5 45 4.3 ND* 7.27 71.73 17 4 18.37

ND* =Not Detected

Table 5.2. Compositional assay of Karanja cake (Radhakumari et al., 2014)

Sl. No. Component % wt. of components in Karanja cake

PSC & dried (100 g) Extractive free (60 g)

1 Ash 4.0 6.7

2 Extractives 40.0 –

3 Protein 13.4 22.3

4 Lignin 17.4 29.0

5 Carbohydrates 25.2 42.0

5.2.2 Apparatus and Experimental Procedure for Pyrolysis

The detailed description of apparatus and experimental procedure for pyrolysis process was

given in section 4.1.2.3.

5.2.3 Apparatus and Experimental Procedure for Liquefaction

The liquefaction experiments were performed in stainless steel (316) stirred batch autoclave

reactor with a maximum volume of 50 mL as shown in Fig. 5.2. The reactor was equipped

with a heater, pressure gauge, stirrer for mixing the biomass slurry, cooler, temperature

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controller, stirrer speed controller, and thermocouple for controlling the inner temperature. In

a typical experiment, the reactor was filled with a mixture of 4 g of Karanja PSC (dry), 8–32

g of PBO derived from pyrolysis experiments, 8–12 g of phenol and 0.12–0.48 g of sulphuric

acid. The reactor was closed tightly, and a leak test was done by passing nitrogen gas through

the reactor several times, which also helped in removing any oxygen present in the reactor.

The stirrer was turned on, and the number of its rotations was set between 500 and 600 rpm.

The reactor tube was then heated up to the chosen reaction temperature which varied between

160 and 240 °C. The reactions were carried out under autogenous pressure, which increased

up to 2.26 MPa depending on the reaction operating parameters, mainly the temperature. The

reaction was allowed to run for 120 min in all experiments after the desired temperature and

pressure were attained. At the end of the reaction, the heater was turned off, and the

temperature of the reactor was allowed to drop gradually below 50 °C by maintaining the

cooling water flow, while the vapors within the reactor condensed and cooled down. The

liquefaction product mixture was then removed from the reactor in a glass beaker and

dissolved in acetone. In addition, the residue on the reactor walls was washed with acetone

twice, and the mixture was added to the reaction product in the beaker. The experimental

parameters varied in the liquefaction process are temperature and Karanja PSC–to–PBO ratio.

Reaction time was kept constant throughout all experiments.

The black liquid mixture resulting at the end of the liquefaction experiments consisted of bio–

crude, free phenol, and unconverted Karanja PSC residue, which was dissolved in acetone for

an easy filtration. The product liquid mixture that results after the acetone wash was filtered

using a filter paper with a pore size of 0.2 μm to determine the acetone–insoluble part (i.e.,

solid residue). Finally, the weight of residue present on the filter paper was determined after

drying the filter paper in an oven at 105 °C, cooling it and then weighing it. The filtrate (i.e.,

black liquid products of the process) was washed with acetone and removed by using a rotary

evaporator and stored in a closed glass beaker.

The Karanja PSC conversion percentage was determined according to the Equation (5.1):

𝐾𝑎𝑟𝑎𝑛𝑗𝑎 𝑃𝑆𝐶 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 (%) =𝑊𝐾 − 𝑊𝑟

𝑊𝐾𝑥100 − − − − − −(5.1)

where, 𝑊𝐾 is the oven dry weight (g) of Karanja PSC particles before liquefaction and 𝑊𝑟 is

the amount (g) un–liquefied Karanja PSC residue after the liquefaction process. The acetone–

insoluble part of the liquefied residue (r %) was calculated by subtracting the value of

Karanja PSC conversion from 100 %.

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The liquid product recovery percentage was calculated according to the Equation (5.2):

𝑃𝑟𝑜𝑑𝑢𝑐𝑡 𝑟𝑒𝑐𝑜𝑣𝑒𝑟𝑦 (%) =𝑊𝑙𝑝

𝑊𝑟𝑒𝑎𝑐𝑥100 − − − − − −(5.2)

where, 𝑊𝑙𝑝 is the weight (g) of the total liquid products obtained after liquefaction reactions

and 𝑊𝑟𝑒𝑎𝑐 is the total amount (g) of reactants fed into the reactor.

The results presented in this work are the mean values of three identical trials of each

experiment. The GC–MS analysis of bio–crude samples was carried out using the same

procedure that was used for characterising PBO samples as mentioned above.

Fig. 5.2 Schematic diagram of the reaction tube used in the liquefaction of Karanja PSC

5.2.4 Sample Workup and Analysis

The Fourier Transform Infrared Spectroscopy (FT–IR) spectra of the PBO and liquefaction

product (bio–crude) were obtained using Thermo Nicolet Nexus 670 Spectrometer using KBr

windows. Each spectrum was an average of 40 scans. The spectra were recorded at 4 cm-1

resolution in the IR range of 400–4000 cm-1. The complete GC–MS analysis procedure was

described by Naik et al. (2017).

The water insoluble PBO obtained from pyrolysis of Karanja PSC at 450 °C was taken in a

fresh vial, and 1 ml of dichloromethane (DCM) was added to it. The solid particles in the

organic extract (PBO) can be removed either by filtration or centrifugation. In the present

work, the extract was centrifuged at 10,000 rpm over a period of 10 min leading to the

settling of residual solid particles at the bottom and the appearance of a clear supernatant

126

containing the organic extract. The supernatant was then transferred in to a new vial and

concentrated to 200 µL using speed vacuum concentrator. The liquefied product (bio–crude)

and PBO samples were analysed by GC–MS on Agilent 6890 gas chromatograph (Agilent

Technologies, Palo Alto, CA) equipped with the 5973N mass selective detector and DB–5ms

column (30 m, 250 μm). The column was maintained initially at 50 °C for 2 min and then

heated up at a heating rate of 20 °C/min to the final temperature of 280 °C, where it was held

for 5 min. High purity helium gas was passed through the column as a carrier gas at the rate

of 1 ml min-1 during the measurement.

5.3 Results and Discussion of Liquefaction

5.3.1 Pyrolysis Results and Characterisation

Fig. 5.3 shows the yield of liquid, bio–char and gaseous products of the slow pyrolysis (i.e.

achieved with a heating rate of 20 °C/min and 60–min retention time) of Karanja PSC at

elevated temperatures. The pyrolysis mainly depends on the reaction temperature and the

type of feedstock. It can be observed that the yield of the liquid product, also called as PBO,

increases with increase in temperature and reaches a maximum of 47.8% at 450 °C, which is

due to the degradation of cellulose in this temperature range. Thereafter, the yield of the

liquid product decreases due to the formation of non–condensable gases/volatiles comprising

mainly of CO, H2, CO2, CH4, and N2. As the temperature increases, the char yield decreases.

Fig. 5.3. Effect of temperature on liquid yield from pyrolysis process

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It is clear that the char yield decreases from 37.13 to 26.59% by weight with the increase in

temperature from 350 to 550 °C. This is due to the degradation of hemicellulose, cellulose,

and lignin. The occurrence of a secondary reaction at the third stage of pyrolysis is another

reason for the decrease in bio–char yield. The yield of gaseous products was estimated by

subtracting the yield of bio–char, pyrolysis oil and water layer from the total yield. The

gaseous products yield is found to increase with increasing temperature. These results are in

agreement with those reported in the literature (Nayan et al., 2012). Nayan et al. (2012)

obtained a maximum liquid yield from rape seeds as 57% at 500 °C. Bertero et al. (2014)

reported 550 °C as the temperature for a maximum liquid yield of 47% from chanar fruit

(Geoffroea decorticans). Agrawalla et al. (2011) obtained a maximum liquid yield of 50% for

groundnut PSC at a temperature of 450 °C with a slow heating rate of 20 °C/min.

FT–IR and GC–MS analyses were carried out only for PBO obtained at an optimum

temperature (i.e. 450 °C) where the highest PBO yield (i.e. ~ 47.8%) was achieved. The

purpose of FT–IR and GC–MS analyses was mainly to identify relevant functional groups

and chemical compounds, respectively.

The FT–IR results of PBO obtained from pyrolysis at 450 °C are presented in Table 5.3.

When compared with the results at 350 °C (Fig. 5.4), the FT–IR results for 450 °C shows that

the broadband obtained in the range of 3422–3443 cm–1 for O–H absorbance decreases

sharply with increasing temperature. This is in agreement with those reported in the literature

(Agrawalla et al., 2011). The C=O stretching vibrations at frequency 1708 cm–1, N–H

stretching vibrations at 1580–1589 cm–1, –C–H bending vibrations at 1400–1402 cm–1, and

C–Cl stretching vibrations at 653–655 cm–1 at 450 °C are similar to those at 350 °C and, in

general, indicates the presence of acid, amide, alkanes, and alkyl halide, respectively.

It is believed that the presence of both acid and phenolic groups in PBO makes it as an

effective solution for liquefaction and an one that can replace both conventional solvent (i.e.

phenol) and acid catalyst (i.e. H2SO4). The presence of acidic and phenolic groups can be

demonstrated from FT–IR results presented in Table 5.3 where C=O stretch appears at 1700–

1725 cm-1, and C–O stretch strong appears at 1050–1150 cm-1. Similar hypothesis has also

been suggested in the literature (Bobade and Khyade, 2012, Shadangi and Mohanty, 2014b).

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Fig. 5.4 Fourier transforms infrared spectra of PBO at (a) 350 °C, (b) 450 °C

Table 5.3. FT–IR functional groups and the corresponding wavenumbers (frequency) for PBO

Frequency (Wavenumber, cm-1) Type of vibration Name of the

functional group

600–800 C–Cl stretch strong Alkyl halide


1050–1150 C–O stretch strong Alcohol

1350–1480 –C–H bending variable Alkane



3200–3600 O–H (stretch, H–bonded) strong, broad Water

The results of GC–MS analysis of PBO obtained at the optimum temperature of 450 °C,

where the PBO yield is the highest, is shown in Table 5.4 where the chemical compounds

detected in the PBO are listed (Ucar and Ozkan, 2008, Agrawalla et al., 2011, Ahmed et al.,

2016). The major compounds in the PBO are phenols, carboxylic acids, and carbonyls. In

more detail, the highest peaks of total ion current chromatogram (TICC) observed in Table

5.4 are for unsaturated and saturated fatty acids and a few methyl esters of fatty acids.

Especially, oleic acid has the highest peak area of 66.99% of TICC. The other free fatty acids

(FFAs) like hexadecanoic acid (10.76%), tetradecanoic acid, octadecanoic acid, eicosanoic

129

acid, docosanoic acid, and fatty acid methyl esters (FAME) like methyl esters of

octadecadienoic acid and hexadecanoic acid are also found in considerable amounts.

The presence of FFAs is good for biodiesel production via transesterification (Cardoso et al.,

2008). Additionally, FFAs, which are present in copious quantities in the PBO, can be

efficiently converted into alkyl esters (especially methyl, ethyl, and propyl) of fatty acid

(FAs) by transesterification (Vieitez et al., 2012). Hexadecanoic acid has a very wide range

of applications in the production of soaps, cosmetics, and release agents. FAMEs can be used

as biofuels, raw materials for emulsifier production, oiling agents for foods, spin finishes, and

textiles, lubricants for plastics, additives to paints and inks, surfactants, and base materials for

perfumery (Nayan et al., 2013).

The presence of amides in PBO as seen in Table 5.3 can be attributed to the hydrolysis of co–

existing proteins with the residual amount of water and free ammonia or amine group

containing compounds in the biomass during pyrolysis. The condensation of ammonia or

amine groups with FAs leads to the generation of primary and secondary amides,

respectively. The amides have many commercial applications. Many amides can be converted

into hydrocarbons ranging from C14–C20 (pentadecane and hexadecane), which are similar to

those found in crude oils and petroleum based fuels such as kerosene. Another important

class of compounds found in PBO is nitrile compounds, which include octadecane nitrile,

hexadecane nitrile, and oleanenitrile, which can be used as chemical intermediates for the

production of fatty amines and their derivatives. The water insoluble liquid in PBO also

contains a range of non–polar compounds like a mixture of diesel and gasoline. Another low

molecular weight and a naturally occurring compound called 4H–pyran–4–one 3–hydroxy–

2–methyl, which is also known as larixinic acid or maltol and used as a flavor enhancer, is

found in the PBO (Chiaberge et al., 2013).

Table 5.4. Chemical constituents of PBO (obtained at 450°C) determined by GC–MS

Sl. No. RT

(min)

Peak

area% Compound name M.W.

Molecular

formula

1 7.76 0.34 Cyclopenten-1-one-

2hydroxy-3methyl 112.12 C6H8O2

2 9.19 0.92 4H-pyran-4-one 3-hydroxy-

2-methyl or Larixic acid 126.11 C6H6O3

130

3 13.08 0.24 Tetradecene 196.37 C14H28

4 13.3 0.82 Phenol, 2-methyl- 108 C7H8O

5 13.5 3.34 Phenol, 4-methyl- 108 C7H8O

6 13.67 0.43 β-methoxy cinnamic acid 178.18 C10H10O3

7 14.88 0.30 Benzoic acid-4ethoxy-

ethyl ester 194.22 C11H14O3

8 15.57 0.20 Hexadecene 226.44 C16H34

9 16.77 0.37 Heptadecene 238.45 C17H34

10 17.33 0.52 Tetradecenoic acid 228.37 C14H28O2

11 17.82 0.18 Octadecene 252.48 C18H36

12 18.97 0.24 Hexadecanenitrile

(palmitic acid nitrile) 237.42 C16H31N

13 19.20 0.29 Hexadecanoicacid-methyl ester 270.45 C17H34O2

14 19.74 10.76 Hexadecanoicacid 256.42 C16H32O2

15 19.86 0.54 Eicosene-3 280.53 C20H40

16 20.76 1.12 Oleanitrile 263.46 C18H33N

17 20.86 1.34 Octadecadienoic acid (9,12)

(ZZ) Methyl ester 294.47 C19H34O2

18 20.92 3.19 Octadecanoic acid

methyl ester 9(Z) 296.49 C19H36O2

19 21.68 66.99 Oleic acid 282.46 C18H34O2


21 23.08 0.93 Cis-13-eicosenoic acid 310.51 C20H38O2

22 23.28 1.62 Eicosanoic acid 312.53 C20H40O2

23 23.85 0.21 9-octadecenamide N,N-dimethyl 311.55 C20H41NO

24 24.47 1.57 Octadecanenitrile 265.47 C18H35N

25 24.54 0.50 Methyl-20-methyl

ecosanoate 326.55 C21H42O2

26 24.89 2.20 Docosanoic acid 340.58 C22H44O2

27 25.59 3.76 Karanjin 292.29 C18H12O4

RT=retention time; MW=molecular weight.

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5.3.2 Liquefaction Results and Characterisation

As explained earlier, the liquefaction experiments were conducted in a batch where the

autoclave temperature was maintained constant, and the reaction was allowed to proceed over

a chosen period. The changes in the reactor pressure with increasing time are shown Fig. 5.5

for different reaction temperatures. The pressure within the reactor was found to increase

with time and reached a steady value during liquefaction. The reactor pressure was found to

increase from ambient pressure up to a high value of 2.26 MPa depending on the temperature.

The final steady state reactor pressures were 0.605, 0.855, 1.365, 1.652, 1.891 and 2.264 MPa

for temperatures 160, 180, 200, 220, 230 and 240 °C, respectively.

Table 5.5 highlights the results of liquefaction experiments 1 to 16. The product recovery and

conversion values for each experiment are shown. In experiments 1 to 3, Karanja PSC was

liquefied in the presence of phenol and acid catalyst (i.e., H2SO4). More specifically, the

effect of H2SO4 was studied while keeping the phenol and Karanja PSC ratios constant in

these experiments. The temperature for all three experiments were also kept constant at 160

°C. As expected, it is found that with an increase in H2SO4 amount, due to increase in the pH

value of the reactant mixture, the overall liquefaction yield is found to increase significantly.

A relatively high 89.12% conversion is obtained for Karanja PSC: Phenol: PBO: the H2SO4

ratio of 1:2:0:0.6. Similar findings were observed for different biomass samples in the

literature. For example, Alma et al. (1996) studied liquefaction of the birch wood powder

with phenol and oxalic acid or its mixture with hydrochloric acid (HC1). It was reported that

the conversion of wood increased dramatically at higher acid: biomass ratio and reaction

temperature.

The conditions for experiments 4 to 6 were kept the same as those for experiments 1 to 3

except the addition of PBO, which was absent in experiments 1 to 3. The aim here was to see

the effect of PBO addition. More or less in all experiments, the conversion is found to

increase by 10–15%. This suggests that PBO can play a very important role in the Karanja

PSC liquefaction.

Yet experiments 4 to 6, did not confirm the direct effect of PBO on the liquefaction.

Therefore, in experiment 7, the phenol was removed and Karanja PSC:Phenol:PBO:H2SO4

ratio was kept as 1:0:3:0.3. The temperature used was 160 °C, which is similar to those in

previous experiments. This experiment led to the formation of a highly viscous and nearly

solid product with very little liquefaction of Karanja PSC. Due to the solid product formation,

132

no further analyses were carried out for experiment 7. The reason for such solidification is

unknown. It is believed that, in the absence of phenol as a solvent and the presence of H2SO4

as an acid catalyst, the recondensation reactions at lower pH might be dominating resulting in

the formation of the agglomerated/solidified product. Similar findings were reported in the

literature (Alma et al., 2016).

As explained above, the use of phenol as a solvent and H2SO4 as an acid catalyst has been

criticised on a number of occasions in the literature mainly due to their toxicity, low vapor

pressure, and inflated cost. The liquefaction using expensive solvent and catalyst was

generally found to make the liquefaction process techno–economically unviable. Therefore,

in experiments 8 to 10, both phenol and H2SO4 were removed, and only PBO was added as

the solvent in three different ratios. The reaction temperature was maintained at 160 °C. It

was confirmed from experiments 8 to 10 that PBO can act as an efficient solvent in the

liquefaction process. A conversion as high as 70.85% was found to occur for Karanja PSC:

Phenol: PBO: H2SO4 ratio of 1:0:6:0. It can be seen that phenol and H2SO4 combination still

works better than PBO when evaluated on the basis of % conversion. However, the PBO

based process is better because PBO is generated locally from the same feedstock, which

decreases the costs related to raw material, transport, and purification steps greatly. The

overall economics of the liquefaction of Karanja PSC is therefore significantly better when

PBO is used as the solvent.

Lastly, the effect of temperature was studied in experiments 10 to 15 where the reaction

temperature was gradually increased from 160 to 240 °C. The % conversion of Karanja PSC

is found to increase with increasing reaction temperature. It reaches a high value of 99.02%

at 240 °C. Thus; it is clear that temperature is one of the important parameters in the

liquefaction of Karanja PSC.

It is believed that the PBO obtained from the pyrolysis of Karanja PSC contains various

chemical bonds originated from the depolymerization and disintegration of cellulose,

hemicellulose, and lignin. Consequently, the liquid product formed during liquefaction is

expected to include free radicals, which increase the probability of repolymerization of the

fragmented molecular compounds thereby increasing the overall % conversion of Karanja

PSC.

The bio–crude formed during liquefaction consists of two fractions. i.e., a high percentage of

the organic phase and relatively a very low percentage of the aqueous phase. Considerable

133

amounts of phenolic compounds are found to be present in the organic phase. Similar

observations were also reported in the literature (Demirbaş, 2000, Czernik and Bridgwater,

2004, Bridgwater, 2012).

By comparing the results of experiments 14 and 15 with those for experiment 6, it can be

seen that using PBO as the only solvent needs a reaction temperature higher than 230 °C for

achieving >=95 % conversion while using phenol and H2SO4 needs a temperature as low as

160 °C for obtaining similar conversion. It can be concluded that % of solid residue (ash)

obtained in our study is very low which is around 0.1 to 1.5% and therefore Ash % cannot

influence the liquefaction rate.

To compare the effects of PBO and phenol directly, experiment 16 was carried out in which

only phenol and Karanja PSC were used. The Karanja PSC: phenol ratio chosen for this

experiment, which occurred at 220 °C, was 1:6. These conditions are similar to those for

experiment 15, but the only difference is the type of solvent used (PBO or phenol). A

relatively lower conversion of ~ 78.3% achieved in experiment 16 in comparison to that

achieved in experiment 15 can be attributed to the absence of an acid catalyst. However,

these findings suggest that PBO can be an effective solvent compared to phenol because the

acid group presents inherently in PBO can catalyse the liquefaction reaction.

Following this, GC–MS and FTIR analyses were carried out for the products of experiment

15 for which relatively higher conversion values were obtained. The purpose of these

analyses is to identify functional groups and chemical compounds in the liquefaction

products.

Fig. 5.5. Effects of temperature and reaction time on the liquefaction pressure

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Table 5.5. Liquefaction experiment results (Operating condition for these reactions are reported in Table 5.3)

Exp. No.

Biomass:

phenol: PBO:

catalyst

Temperature

(°C)

Pressure

(MPa)

Total amount

of reactants

(g)

Amount of solid

residue in the

product (g)

Amount of liquid

in the product

(bio–crude) (g)

Product

recovery

(wt. %)

Karanja PSC

conversion

(wt. %)

1 1:2:0:0.15 160 0.26 12.12 1.7 10.42 85.97 57.5

2 1:2:0:0.3 160 0.28 12.24 0.97 11.26 92.04 75.64

3 1:2:0:0.6 160 0.32 12.48 0.43 12.04 96.51 89.12

4 1:2:2:0.15 160 0.18 20.12 1.06 19.05 94.69 73.32

5 1:2:2:0.3 160 0.38 20.24 0.70 19.54 96.54 82.47

6 1:2:2:0.6 160 0.53 20.48 0.23 20.25 98.87 94.25

7 1:0:3:0.3 160 0.31 16.24 16.15 – – –

8 1:0:3:0 160 0.28 16 1.49 14.51 90.69 62.75

9 1:0:4:0 160 0.32 20 1.38 18.61 93.07 65.35

10 1:0:6:0 160 0.60 28 1.26 26.73 95.48 68.35

11 1:0:6:0 180 0. 84 28 1.16 26.83 95.83 70.85

12 1:0:6:0 200 1.36 28 0.98 27.02 96.48 75.4

13 1:0:6:0 220 1.64 28 0.41 27.59 98.53 89.72

14 1:0:6:0 230 1.89 28 0.05 27.95 99.81 98.64

15 1:0:6:0 240 2.26 28 0.03 27.96 99.86 99.02

16 1:6:0:0 220 1.67 28 0.86 27.13 96.90 78.30

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5.3.3 Chemical Properties of the Liquefied Karanja PSC (Bio–crude)

Samples

The FT–IR results of bio–crude obtained from liquefaction experiments carried out at 240 °C

after a reaction time of 150 min are shown in Table 5.6. The characteristic absorption peaks

in Table 5.6 indicate the presence of special functional groups and compounds corresponding

to them. The O–H stretching vibrations at 3200–3600 cm–1. This is in agreement with those

reported in the literatures (Agrawalla et al., 2011 and Wei et al., 2015). C–H stretching

vibrations at 2850–3000 cm–1 (Kotaiah Naik et al., 2017), C=O stretching vibration at ~1700

cm–1, C=C stretching (multiple bonds) vibrations at 1400–1620 cm–1, C–O stretching

vibrations at 1000–1300 cm–1, and C–Cl stretching vibrations at 600–800 cm–1 indicate the

presence of water, alkane, acid, aromatic, ether, and alkyl halide (Varma and Mondal, 2016,

Yang et al., 2007 and Joshi et al., 2015). The compounds present in the bio–crude are

identified with standard chromatographic data from NIST library as listed in Table 5.6.

Finally, the GC–MS results of bio–crude obtained from experiment 15 are shown in Table

5.7. Some long chain heavy aromatic hydrocarbon compounds are found in the bio–crude.

Overall oxygen content seems to be lower in bio–crude compared to PBO. Most of the

chemical compounds found in both bio–crude and PBO are identical. Yet, some of the

compounds in bio–crude (Table 5.7) are different from those in the PBO (Table 5.4). The new

chemical compounds produced in the bio–crude in the presence of PBO include 1,1–

biphenyl,4–(1–methylethyl), 4–methoxyisophthalic acid, naphtelene,1–methyl–7–(1–

methylethyl), phenanthrene, and phenantrhrene,3,6–dimethyl. It should be noted that these

compounds were not present in PBO. It is evident from the results shown in Table 5.7 that

PBO and biomass particles react with each other leading to the formation of these new

chemical groups. The reaction mechanism during liquefaction involves the decomposition of

unstable and reactive light fragments of biomass components into smaller compounds by

dehydration, dehydrogenation, deoxygenation, and decarboxylation (Vardon et al., 2012,

Peterson et al., 2008, Xu et al., 2012, Vardon et al., 2011 and Jena et al., 2011). It also

involves the rearrangement of the intermediate elements through condensation, cyclization,

and polymerization reactions to produce new compounds (Alma et al., 2016, Liu and Zhang,

2008 and Li et al., 2009). Some major chemical compounds formed according to the aforesaid

mechanisms are octadecanoic acid (7.24%), octadecene (7.18%), hexadecanoic acid (6.65%),

benzoic acid–4ethoxy–ethyl ester (5.3%), and oleic acid (4.99%).

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Table 5.6. FT–IR functional groups and frequency of bio–crude

Frequency


Name of the

functional group

3200–3600 O–H (stretch, H–bonded) strong, broad water impurities




1000–1300 C–O stretch strong Ether


Table 5.7. GC–MS analysis results for the bio–crude obtained in the liquefaction of Karanja

PSC at 240°C using a retention time of 150 min

Sl. No. RT (min) Peak Area% Compound name MW Molecular

formula

1 7.76 3.34 Cyclopenten-1-one-

2hydroxy-3methyl 112.12 C6H8O2

2 9.19 0.92 1,1-biphenyl,4-(1-

methylethyl) 196.28 C15H16

3 13.08 0.24 4-methoxyisophthalic

acid 196.16 C9H8O5

4 13.67 0.43 Naphtelene,1-methyl-

7-(1-methylethyl) 184.27 C14H16

5 14.88 5.30 Benzoic acid-4ethoxy-

ethyl ester 194.22 C11H14O3

6 15.57 2.20 Hexadecene 226.44 C16H34

7 16.77 0.37 Heptadecene 238.45 C17H34

8 17.33 0.52 Tetradecenoic acid 228.37 C14H28O2

9 17.52 3.5 Phenanthrene 178.23 C14H10

10 17.65 4.2 Phenantrhrene,3,6-

dimethyl 206.28 C16H14

11 17.83 7.18 Octadecene 252.48 C18H36

12 19.65 6.65 Hexadecanoicacid 256.42 C16H32O2

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13 19.86 4.54 Eicosene-3 280.53 C20H40

14 21.29 4.99 Oleic acid 282.46 C18H34O2

15 21.61 2.84 Flouranthene 202.26 C16H10


17 23.08 0.93 Cis-13-eicosenoic acid 310.51 C20H38O2

18 23.28 1.62 Eicosanoic acid 312.53 C20H40O2

19 24.47 0.57 Octadecanenitrile 265.47 C18H35N

20 24.54 0.50 Methyl-20-methyl

ecosanoate 326.55 C21H42O2

21 24.89 0.20 Docosanoic acid 340.58 C22H44O2

RT=retention time; MW=molecular weight.

5.4 Conclusions

The liquefaction experiments of Karanja PSC in the presence of PBO obtained from the slow

pyrolysis of the same feedstock were conducted in a batch reactor (i.e., autoclave). The

effects of PBO amount and reaction temperature were studied. The residence time in the

reactor was kept constant as 160 minutes for all experiments. It was observed that both PBO

amount and reaction temperature has a significant effect on the liquefaction conversion.

Higher conversion value as high as ~ 99.02 was obtained at a temperature of 240 °C and a

Karanja PSC: PBO as ratio 1:6. The effect of PBO was also compared with standard solvent

and catalyst (i.e., phenol and H2SO4, respectively). It was observed that phenol and H2SO4

combination works better compared to PBO. For phenol and H2SO4, lower temperature (i.e.,

160 °C) and the lower solvent ratio (i.e., Karanja PSC: Phenol: H2SO4 as 1:2:0.6) was

required. Finally, while comparing PBO and bio–crude products, bio–crude was found to

have an aromatic structure with less oxygen as compared to PBO.

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Chapter 6 Experimental Investigations on Entrained Flow Gasification of Torrefied Karanja Press Seed Cake

139

6 Experimental Investigations on Entrained Flow Gasification of

Torrefied Karanja Press Seed Cake

Graphical Representation

Summary

Chapter 3 showed that torrefaction, which is a thermal pre–treatment technology, can

improve properties such as density, heating value, grindability and C/O ratio of Karanja PSC.

The resultant bio–char (torrefied biomass) obtained from Karanja PSC can be used as a

promising feed stock for producing high quality syngas using entrained flow gasification.

This chapter demonstrates that entrained flow gasification of Torrefied Karanja Press Seed

Cake (TKPSC) using on oxygen–steam mixture as entrainment medium leads to higher

syngas volumes. The effects of temperature, equivalence ratio (ER), steam to biomass ratio

(SBR) and particle size (Dp) were investigated on the syngas composition, lower heating

value (LHV), cold gas efficiency (CGE) and carbon conversion. The experimental work was

conducted in an entrained flow gasification unit using a feed rate of 1 kg/hr. The temperature

was varied from 600 to 1100 °C, ER was set at 0.3, SBR was varied from 0.1 and 1 and Dp

was varied from 0.5 to 3.0 mm. The aim of the study was to determine the optimum operating

conditions that enhance the efficiency of entrained flow gasification of the TKPSC. The

experimental results obtained show that temperature of 1100 °C, ER of 0.3, SBR of 0.4 and

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Dp of 0.5 mm were the optimum operating parameters. The optimum operating conditions

were found to lead to a LHV of ~12 MJ/Nm3, CGE of ~90% and carbon conversion of 98%

for TKPSC. These values are in agreement with the results reported in the literature for the

other lignocellulosic biomass waste materials.

6.1 Introduction

Annual global bioenergy demand was about 550 EJ in 2010, providing 10% of world’s

primary energy supply (Mathioudakis et al., 2017). Bioenergy plays a crucial role in many

developing countries including India and China. Bioenergy can be referred as electricity, heat,

bio–gas, synthetic gas, bio–char and bio–oil produced from different biomass feedstocks

using thermochemical conversion processes such as pyrolysis, gasification, and combustion

(Das et al., 2017b). Biomass, apart from its renewable nature, offers several environmental

benefits when compared to coal. For example, sulphur and mercury emissions from biomass

are relatively lower than those from coal (Gao et al., 2008, Shen et al., 2008).

India due to its tropical environment and fertile agricultural land is always seen as an

attractive place to grow biomass. However, extremely high population density, food scarcity,

shortage of cultivable land and poverty has made it impossible to produce biomass for

bioenergy. Therefore, non–edible biomass (i.e., Karanja, Mahua, Jatropha, Bamboo, etc.) and

waste (municipal solid waste, kitchen waste, saw dust, coffee husk, etc.) have been

considered to be the most favorable biomass for bioenergy production in India. For example,

60% of bio–oil in India has been currently produced from Karanja and Jatropha. Bio–oil

production from these feedstocks, however, generates a significant amount of residue called

“Press Seed Cake (PSC).” It is estimated that for every 1 ton of bio–oil production from these

feedstocks, roughly 2 tons of PSC is produced. PSC is treated as a waste and majority of it is

currently sent to landfill without any further use. Due to the shortage of land available for

landfilling, increased awareness of environmental concerns associated with landfilling and/or

incineration, attempts have been made recently to identify the use of PSC for the production

of bioenergy or value–added materials. Researchers at Indian Institute of Chemical

Technology (IICT), India and Royal Melbourne Institute of Technology (RMIT), Australia

have carried out a considerable amount of collaborative R&D work in this area where

thermochemical conversion of PSC based materials have been investigated in detail

(Dhanavath et al., 2017). This work highlights the continuing efforts in studying gasification

141

of the so–called waste material, i.e., Karanja PSC. The research published on the

thermochemical conversion of such domestic Indian wastes is very limited in the literature.

The focus of this work is to study the oxygen–steam gasification of Karanja PSC specifically.

The oxygen–steam gasification has a number of advantages over the conventional partial air

or air–steam gasification. Due to the elimination of nitrogen, the energy density of syngas is

expected to increase in oxygen–steam gasification. Also, in oxygen–steam gasification, steam

reforming allows the increase of H2/CO ratio in syngas which is quite favourable for 1)

improving the performance of power generation units such as gas engines or gas turbines and

2) making syngas suitable for bio–fuels or biochemical production because of its high H2

content. It is reported in the literature that syngas generated by air–steam gasification has a

lower heating value (LHV) ranging from 4–7 MJ/N m3 while that generated by oxygen–steam

gasification has a LHV up to 10–18 MJ/ Nm3 (Schuster et al., 2001).

The yield and composition of syngas generally depends on the feedstock material, gasifying

agent, operational conditions, and the design of the reactor (Farzad et al., 2016). The biomass

gasification has been normally carried using various reactor designs including fixed bed

reactors (Ongen et al., 2016, Kihedu et al., 2016, Wiinikka et al., 2017, Park et al., 2016a,

Khonde and Chaurasia, 2016, Al-attab and Zainal, 2017, de Diego et al., 2016), fluidized bed

reactors (Liu et al., 2016c, Woytiuk et al., 2017, Stendardo et al., 2016, Mota et al., 2015, de

Diego et al., 2016), and entrained flow reactors (Tremel et al., 2012, Weiland et al., 2013).

Fixed bed reactors require high residence time, and therefore footprint required for the reactor

can be relatively high. However, the advantage with the fixed bed reactor is that it can handle

relatively large size particles (>2 cm). Also, the tar generation in the fixed bed is usually low

due to the higher residence time used. In contrast, the footprint of fluidized bed reactors is

smaller footprint due to the lower residence time used in them. However, they need smaller

particles (2–5 mm), and tar generation is relatively higher compared to that produced in fixed

bed reactors. Catalysts have been used in both fixed and fluidized bed reactors for 1)

improving the gasification kinetics and 2) in–situ cracking of tars (Waheed et al., 2016).

Operating temperature for both fixed bed and fluidized bed reactors is generally between 700

and 900 °C. In terms of scale up, fixed bed gasifiers are generally available in sizes between

100 kW and 2 MW whilst fluidized bed reactors can range from 1 MW to 200 MW (Cao et

al., 2017).

Entrained flow reactors, in contrast to fixed and fluidized bed reactors, can operate at

extremely high temperatures (up to 900–1500 °C) but they need smaller particles (0.1–1 mm).

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Their footprint is smaller, and they involve shorter residence time. Normally, entrained flow

gasification systems are available only for large–scale operations (>50 MW) (Sripada et al.,

2017). Tar generation in entrained flow systems can be significantly lower due to the high

temperature employed. The only drawback with entrained flow system is the particle size

requirement because it requires a significant amount of energy to grind the particles to obtain

the smaller particles required.

The current work focuses on the experimental investigations of oxygen–steam gasification of

Karanja PSC in a pilot scale entrained flow reactor system. The reason for the selection of the

entrained flow system is the favorable particle size of Karanja PSC. The particle size of

Karanja PSC was found to be in the range of 0.5–3 mm. This is due to the fact that Karanja

biomass is normally being grinded before carrying out the oil extraction step. It is reported in

the literature that biomass, due to higher reactivity, can afford to have a larger particle size

(1–3 mm) compared to coal (0.1–1.0 mm) in the entrained flow gasifier (Hernández et al.,

2010). In the current paper, no grinding of Karanja PSC was performed, and particle size in

the range of 0.5–3.0 mm is used.

Very few experimental investigations have been reported in the literature on entrained flow

oxygen–steam gasification of waste materials. Hernandez et al. (2010) carried out an

experimental investigation on the entrained flow gasification of grapevine pruning, saw dust,

and marc of grape, and compared the results with those for a coal–coke blend. The effects of

the fuel particle size (Dp) and the space residence time (tr) on several gasification parameters

such as the syngas composition, heating value, yield and cold gas efficiency were

systematically investigated. It was confirmed that reduction in the fuel particle size leads to

an improvement in the syngas quality and thus to a higher heating value. Cold gas efficiency,

H2/CO ratio and fuel conversion were also enhanced. The maximum fuel conversion was

obtained for the smallest particle size used (0.5 mm). Zhao et al. (2009) investigated entrained

flow gasification of rice husk at 900–1000 °C to obtain optimal gas yield. The optimal

gasification temperature and ER value were found to be 900 °C and 0.25, respectively. The

gasification process was over in 1.42 s once the gasification temperature gone above 800 °C.

Zhao et al. (2009) studied syngas production from coal using an oxygen–blown two–stage

entrained flow gasifier. Arabloo et al. (2015) studied oxygen–steam entrained flow

gasification of coal for the production of syngas under elevated pressure (up to 101.3 kPa)

and temperature (up to1250 °C). Mota et al. (2015) investigated oxygen–steam entrained flow

gasification of lignite coal to produce hydrogen rich syngas and found that cold gas

143

efficiencies were in the range 80–90%. Qin et al. (2012) studied high–temperature steam

gasification of straw and compared its performance with that of wood as the fuel. Effects of

reaction temperature, steam/carbon molar ratio and excess air ratio on the solid, liquid and

gas products were investigated. The yield of syngas was found to increase to 72% with a rise

in reaction temperature from 1000 to 1350 °C. The H2/CO molar ratio in syngas was close to

1 at temperatures above 1200 °C with steam addition.

The study on entrained flow gasification of the Karanja PSC, which is a valuable waste

material available in Asian countries, is still scarce. Karanja PSC is a seasonal feedstock,

which is normally generated in a distributed manner at various locations. It is also hydrophilic

in nature and usually obtained in a wet form after the oil extraction step. Therefore,

torrefaction is considered as an attractive option to improve its storage and transport

characteristics (i.e., energy density, homogeneity, hydrophobicity, grindability) for

centralised operations. The current work has used torrefied Karanja PSC (TKPSC) as the feed

considering the above facts. The objective of this work is to perform comprehensive

experimental work on oxygen–steam gasification of TKPSC in an entrained flow gasifier to

understand the effect of various operating parameters such as temperature, equivalence ratio

(ER), steam to biomass ratio (SBR) and particle size on syngas yield, composition, and cold

gas efficiency. Moreover, the entrained flow gasification technology is commercially

available on large scale mainly for coal and liquid fuels. They can reach the highest efficiency

for coal in the production of syngas. However, large scale commercial entrained flow

gasification technology is not available for biomass and this work can provide useful

information for scale–up purposes.

6.2 Experimental

6.2.1 Raw Material and its Characteristics

Raw Karanja Press Seed Cake (PSC) was purchased from a local trader namely, Maruthi

Agro Fertilizers, India, and was used as a feedstock in the present research. It was sun dried

for two days, sieved according to Taylor norms to obtain particles in size range of 0.5 to 3.0

mm, which was used in experiments. The properties of Karanja PSC are commonly described

by its proximate analysis (percentages of moisture, volatile matter, fixed carbon, and ash) and

ultimate analysis (percentages of elemental compounds like carbon, hydrogen, nitrogen,

144

sulfur, and oxygen). The analysis for carbon (C), hydrogen (H), nitrogen (N), sulphur (S) and

oxygen (by subtraction) was carried out using a CHNS elemental analyser (model: Elementar

Vario Microcube, Germany Make) (Kotaiah Naik et al., 2017). ASTM standards were

followed to estimate the moisture (E871), volatile matter (D1102) and ash content (E872)

(Ahmed et al., 2016). The calorific value of the feedstock was determined using a bomb

calorimeter (Ravikiran et al., 2011). The composition and properties of Karanja PSC are

shown in Table 6.1 along with the properties of several other residual waste biomasses that

are commonly considered as gasification feedstocks. They include corn straw, municipal solid

and Mahua PSC.

Table 6.1. Proximate and Ultimate analysis of raw Karanja PSC

Analysis Karanja PSC Corn straw

(Gai and Dong, 2012)

Municipal solid waste

(He et al., 2009)

Rice husk

(Zhou et al., 2009)

Initial moisture

(Wet basis) 7.27 6.17 8.8 9.23

Fixed carbon 17 13.75 11.79 14.87

Volatile matter 71.73 75.95 82.8 58.69

Ash 4 5.93 5.98 17.21

C 44.2 43.83 51.81 37.18

H 6.5 5.95 5.76 4.26

N 4.3 0.97 0.26 0.68

S ND* 0.13 0.36 0.15

O 45 45.01 30.22 31.29

HHV (MJ/kg) 16.07 17.75 21.3 15,039

6.2.2 Experimental Setup and Procedure

Fig. 6.1 represents the process flow diagram of the torrefaction of Karanja PSC (TKPSC). A

vertical tubular fixed–bed reactor (Inner diameter (ID) = 38mm, Outer diameter (OD) =

42mm) of height 465mm, covered with a split furnace was used for torrefaction. N2 was

supplied from the top of the reactor at a flow rate of 0.3 L/min. The outlet of the reactor was

connected to a glass trap, which was located in a chilled water bath for the purpose of

condensing the volatiles coming out of the reactor. The apparatus used in this pre–treatment

method is discussed in detail in Chapter 3. The produced TKPSC was subjected to further

145

analysis and the results obtained are tabulated in Table 6.2. The calorific value (energy

density) and C/H molar ratio of TKPSC are higher than those Karanja PSC and they increase

with increasing torrefaction temperature (Table 6.1).

Fig. 6.2 shows a schematic diagram of an entrained flow gasification system and Fig. 6.3

shows the image of the real pilot scale unit established in CSIR–IICT, Hyderabad. The setup

mainly consists of a gasifier with a split furnace, pre–heater, hopper, with a metal bellow and

rotary valve, cyclone separator, heat exchanger, scrubber, gas liquid separator and gas

chromatography.

Fig. 6.1 Schematic process flow for torrefaction process

Table 6.2. Elemental analysis of torrefied biomass co–produced from torrefaction of Karanja

PSC

Temperature

(°C)

Ultimate analysis (ash–free wt.%) C/H Molar

ratio

C/N molar

ratio

% Oil

content

Calorific

value

(MJ/ kg) C H N S O

200 54.55 6.73 4.26 ND* 34.46 0.675 14.9393 9.47 23.34587

250 57.31 7.02 4.33 ND* 31.34 0.68 15.441 10.93 24.97265

300 66.0 6.79 5.44 ND* 21.76 0.81 14.1544 24.08 28.70913

*ND= Not detectable

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The diameter and height of the cylindrical gasifier made of Inconel alloy are 161.5 mm (ID)

and 600 mm, respectively making the H/D (height to diameter) ratio of 4.0. The 6.7 mm thick

gasifier wall enables it to withstand temperature as high as 1500 °C. The main core of the

reactor (i.e., 600 mm length) is surrounded by a split furnace. A gas residence time of 2–4

seconds was used during the gasification experiments. The residence time used in this study is

similar to the ones reported in the literature (Hernández et al., 2010). Three K–type

thermocouples located at lower (T1), medial (T2), and upper (T3) sections of the reactor as

shown in Fig 6.2 were used to measure and record the temperatures during gasification.

Karanja PSC was fed to the pre–heater from the hopper using a rotary valve system attached

to a screw feeder. The pre–heater is surrounded by electrical heating tape and insulation. The

rotary valve system used in the set–up ensured a continuous flow of the powdery Karanja

PSC into the pre–heater via the screw feeder mechanism. The motor of the rotary valve is

attached to a variable–frequency drive (VFD) which controlled the opening of the valve

thereby allowing particles from the hopper to be fed to the pre–heater. The feed rate of

Karanja PSC was measured using a digital weighing balance, which could record the changes

in the weight of the hopper continuously. The capacity of the hopper is 50 kg. The diameter

of a screw feeder, which connects the rotary valve to pre–heater, is 50 mm (OD).

The oxygen was supplied from a Size G cylinder through a mass flow controller (MFC)

having a capacity of up to 2000 ml/min. A steam generator was used to generate the required

steam flow, which was controlled by a steam flow controller and steam flow meter. The

feeding lines of steam and oxygen include a pressure regulator, isolation valve, non–return

valve and a filter. Both oxygen and steam were fed to the pre–mixer where they were heated

up to 200 °C using an electrical heating tape. The contact between steam–oxygen mixture and

Karanja PSC occur at the pre–heater where the solid–gas mixture is heated up to 400 °C.

The flow rate of steam and oxygen mixture was maintained such that that gas velocities in the

pre–heater and gasifier sections were always higher than the settling velocity of Karanja PSC

particles. This was to ensure that Karanja PSC particles were always entrained in an upward

direction. After gasification, the residual Karanja PSC particles will be converted into

mineral–rich ash having a higher density (means gas velocity will become lower than the

particle settling velocity), which will make them settle at the bottom of the pre–heater. The

ash collected at the bottom of the preheater was removed through a knife valve. The product

mixture leaving the top of the gasifier unit flows first to a cyclone separator then to a cooler, a

scrubber, a gas–liquid separator, a cooler and the gas vent. A tapping is available in the gas

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venting line to remove gas samples for analysis. The function of the cyclone is to

recover/separate solid particles from the hot gas stream. The particle–free gas is further

cooled down in the cooler to 500 °C and then sent to a scrubber. Water scrubber performs the

secondary wash and lowers the gas temperature to 100 °C. After the scrubber, the gas is sent

to an atmospheric gas–liquid separator where any water droplets carried by the gas is

removed. The dried gas is constantly flared from the top while a small volume is sent to GC

(Gas chromatography) for syngas analysis. To assure the reliability of the test results, each

experiment was repeated twice, and the results were in good agreement. The data presented

represent the average values of the two results.

Online GC (Agilent, USA) is used to analyse the syngas to detect the concentration of

primary gases like CO, H2, CH4, and CO2. CO, CH4, CO2, and N2 gases are separated and

analysed in the first column (Porapak q, 3 m X 0.125 mm) whereas H2 is analysed in the

second column (Molecular sieve 5a, 2 m X 0.125 mm).

The equation used for the calculation of LHV of syngas is as follows (He et al., 2009):

𝐿𝐻𝑉𝑆 (𝑀𝐽

𝑁𝑚3) = 𝑌𝐶𝑂𝐿𝐻𝑉𝐶𝑂 + 𝑌𝐻2

𝐿𝐻𝑉𝐻2+ 𝑌𝐶𝐻4

𝐿𝐻𝑉𝐶𝐻4− − − − − (6.1)

The LHV of the respective gas species (He et al., 2009) are determined as follows:

𝐿𝐻𝑉𝐶𝑂 = 13.1𝑀𝐽

𝑁𝑚3, 𝐿𝐻𝑉𝐻2

= 11.2𝑀𝐽

𝑁𝑚3, 𝐿𝐻𝑉𝐶𝐻4

= 37.1𝑀𝐽

𝑁𝑚3

Cold gas efficiency is defined as follows (Kwon et al., 2009):

𝐶𝐺𝐸𝑆 (%) =𝐿𝐻𝑉 𝑜𝑓 𝑡ℎ𝑒 𝑠𝑦𝑛𝑔𝑎𝑠 ∗

𝐿𝐻𝑉 𝑜𝑓 𝑡ℎ𝑒 𝑓𝑒𝑒𝑑 ∗

𝑆𝑦𝑛𝑔𝑎𝑠 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒

𝐹𝑒𝑒𝑑 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒∗ 100 − − − − − (6.2)

where S in the subscript represents the syngas and Y is the mole fraction of each gas species.

The syngas flow rate and yield were measured using a rotameter as shown in Fig. 6.2.

The carbon conversion is calculated using the following equation:

𝐶𝑎𝑟𝑏𝑜𝑛 𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛(%)

=𝑇𝑜𝑡𝑎𝑙 𝑚𝑜𝑙 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒 𝑜𝑓 𝐶 (%) 𝑖𝑛 𝑠𝑦𝑛𝑔𝑎𝑠

𝑀𝑜𝑙 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒 𝑜𝑓 𝐶(%) 𝑖𝑛 𝑇𝐾𝑃𝑆𝐶 (𝑜𝑏𝑡𝑎𝑖𝑛𝑒𝑑 𝑑𝑢𝑟𝑖𝑛𝑔 𝑢𝑙𝑡𝑖𝑚𝑎𝑡𝑒 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠)∗ 100

− −(6.3)

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Fig. 6.2 Process flow diagram of 1.0 kg/h entrained–flow gasification process. A) Hopper, B) Rotary valve attached with screw feeder, C) O2

Cylinder, D) Steam generator, E) Pre–mixer (electrically heated), F) Pre–heater (electrically heated), G) Gasifier (covered with split furnace), H)

Cyclone separator, I) Heat exchanger, J) Scrubber, K) Cooling water, L) Gas–liquid separator, M) Gas chromatography.

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Fig. 6.3 Entrained–flow biomass gasification facility (1.0 kg/h capacity)

The experimental conditions adopted for this work are shown in Table 6.3. For each run, an

equilibration time of 30 minutes used.

Table 6.3. Experimental Matrix

Details

Temperature 600 °C–1100 °C

Equivalence Ratio (ER) (mass basis) 0.1–1.0

Steam to Biomass Ratio (SBR) (mass

basis)

0.1–1.0

Particle Size 0.5–3.0 mm

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6.3 Results and Discussion of the Gasification Experiments

6.3.1 Effect of Temperature on Syngas Composition and Cold Gas

Efficiency

Fig. 6.4(a) shows the effect of reaction temperature on syngas composition. The parameters

those were kept constant for generating the results shown in this figure were ER = 0.3,

particle size = 0.5 mm and SBR = 0.4. The concentrations of CO and H2 significantly

increase whereas those of CO2 and CH4 decrease with increasing gasification temperature.

This observation is similar to the ones reported in the literature for the gasification other

biomasses (Nishimiya et al., 1998, Kumar et al., 2009). This suggests that Karanja PSC,

despite pre–processing (i.e., oil extraction), is expected to yield results that are similar to

those for other biomasses in entrained flow gasification. The reactions that might take place

during gasification are shown in Table 6.4.

Based on the trends of experimental results obtained from gasification, the mechanisms of

various reactions can be explained as follows: CO2 is mainly generated from the

decomposition of carboxyl groups at low temperature (below 800 °C) by oxidation and water

gas shift reactions (R4 and R9). Around 500 to 600 °C, the concentration of CO2 is very high.

However, it decreases progressively with further increase in the gasifier temperature and

reaches relatively a low value at 1100 °C. Boudouard reaction (R7) is the one most likely to

occur at high temperatures resulting in an increased CO concentration due to the reaction

between CO2 and carbon of the torrefied biomass. The steam also reacts with carbon at

elevated temperatures to generate additional moles of H2 according to reactions R8 and R9.

The CH4 concentration decreases progressively with increasing temperature, which might be

occurring due to cracking reaction (R10).

The effect of temperature on cold gas efficiency (CGE) is shown in Fig. 6.4(b). CGE denotes

the percentage of energy contained in the feedstock that is available for the production of

syngas. It can be seen from the Fig. 6.4(b) that CGE increases with increasing gasification

temperature. The highest value of CGE (~90%) is obtained at 1100 °C. Again, the trend and

values reported in the literature show good agreement with the experimental results obtained

in this work (Nishimiya et al., 1998, Wei et al., 2006).

Based on the above, the relative optimum temperature considered for oxygen–steam

entrained flow gasification of TKPSC is 1100 °C. This is slightly higher than the optimum

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temperature for Corn Straw and OC–MSW (i.e. 1000 °C) (Gai et al., 2012, He et al., 2009).

This difference might be attributed to different reactor set up or possibly differences in the

hydrocarbon structure of different biomass waste.

Table 6.4. Main gasification reactions (Nishimiya et al., 1998, de Sales et al., 2017, Jangsawang

et al., 2007, Kwon et al., 2009, Kumar et al., 2009)

Reaction Heat of reaction

(kJ/mol) Name Reaction

Biomass + Q ➔ bio–char + tars +

H2O + gases (CO2 + CO + CH4 + H2

+ CnHm)

> 0 Devolatilization R1

Tars + Q ➔ bio–char + gases (CH4

+ H2 + CnHm)

> 0 Secondary cracking and

reforming

R2

Tars + H2O CO + H2 > 0 R3

Carbon combustion reactions

C +O2 ➔ CO2 – 394 Complete combustion R4

C + ½ O2 ➔ CO – 111 Partial combustion R5

CO + ½ O2 ➔ CO2 – 283 Carbon monoxide

oxidation R6

Reduction Gasification reactions

C + CO2 2 CO +173 Boudouard reaction R7

C + H2O CO + H2 + 131 Synthesis gas reaction R8

CO + H2O CO2 + H2 – 41 Water gas shift reaction R9

C + 2H2 ➔ CH4 – 75 Methanation reaction R10

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Fig. 6.4 Effect of temperature on (a) syngas composition and (b) cold gas efficiency (CGE)

6.3.2 Effect of ER on the Syngas Composition

Fig. 6.5(a) shows the effect of ER on the composition of syngas produced from TKPSC. The

ER was varied in the range of 0.1 to 1.0 while keeping other conditions such as temperature

(1100 °C), particle size (0.5 mm), and SBR (0.4) constant in all experimental runs. ER is an

essential factor that affects the yield of syngas as well as the performance of the gasification

process. As ER increases from 0.1 to 0.3, the concentrations of both H2 and CO tend to

increase gradually and attain a maximum level of about 50% for ER = 0.3, and then start

decreasing sharply.

The rapid decrease in the CO concentration at higher ER (>0.3) can be explained based on

the higher concentration of O2 which favors CO2 formation over CO. Similarly, H2O

formation due to the higher concentration of O2 leads to the decrease in H2 content at higher

values of ER. CH4 content is also found to decrease slightly because of its increased

oxidation at ER > 0.3. However, compared to the reductions in CO and H2 contents, the

reduction in CH4 with an increase in ER is not significant. This might be due to the lower

equilibrium partial pressures and higher reactivities of CO and H2 compared to those of CH4.

Overall, the quality of syngas decreases beyond the optimal ER of 0.3. Similar observations

were reported in the literature (de Sales et al., 2017).

The effect of ER on LHV of syngas at various temperatures is shown in Fig. 6.5 (b). It can be

observed that the LHV of syngas increases with an increase in ER up to a value of 0.3. Then,

as ER exceeds 0.3, the LHV starts declining. The decrease in LHV is due to an increase in the

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% vol. of CO2 and decreases in % vol. of CO and H2 in the syngas. On the other hand, the

influence of CH4 on LHV is not significant. The maximum LHV of 12 MJ/Nm3 is obtained at

1000 °C at the optimum ER of 0.3. This LHV is 2 to 3 times higher than those obtained using

steam–air or air gasification (Kumar et al., 2009).

Based on the above, the optimum ER value considered for oxygen–steam entrained flow

gasification of TKPSC is 0.3.

Fig. 6.5 Effect of equivalence ratio (ER) on (a) syngas composition (b) lower heating value

(LHV)

6.3.3 Effect of Steam Addition on Syngas Composition

Fig. 6.6(a) shows the effects of SBR on syngas composition. The SBR plays a significant role

in maximising the syngas yield and improving the gas quality. It is important to find an

optimum value of SBR, as higher SBR does not always favor better syngas yield and quality,

and gasification efficiency. In the current work, SBR was studied in the range of 0.1 to 1.0

while keeping all the other parameters constant (temperature = 1100 °C, ER = 0.3 and Dp =

0.5 mm). The results of the syngas composition for different SBR values are presented in Fig.

6.6(a). It shows that the addition of steam improves CO % in syngas up to the SBR value of

0.4. Further increase in the SBR leads to decreasing CO % values. The H2 on the other hand

continuously increases with increasing SBR. The major reason for the increase in H2 content

over CO content with increasing steam flow rate might be due to the reactions such as water

gas shift, steam reforming of methane and char gasification (Rapagna and Latif, 1997).

H2%/CO% of ~1.0 can be achieved at SBR value of 0.5. The higher SBR values had a

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negative effect on the syngas LHV as shown in Fig. 6(b). The LHV of syngas first increased

slightly from ~10.2 to 11.8 MJ/Nm3 and then decrease significantly for SBR values higher

than 0.4. This is mainly because concentrations of CO, H2 and CH4 showed a tendency to

increase up to 0.4 SBR value and followed by that CO values declined sharply.

Based on the above, the relative optimum SBR value considered for oxygen–steam entrained

flow gasification of TKPSC is 0.4. This value is like the values reported in the literature for

Corn Straw and OC–MSW.

Fig. 6.6 Effect of steam to biomass ratio (SBR) on (a) syngas composition and (b) lower heating

value (LHV)

6.3.4 Effect of Biomass Particle size (Dp) on Syngas Composition and

Carbon Conversion

The effect of feedstock particle size on syngas composition and carbon conversion is shown

in Fig. 6.7 (a) and Fig. 6.7 (b), respectively. In this case, feedstock particle size (Dp) was

varied in the range of 0.5 to 3.0 mm, while other parameters (temperature =1100 °C, ER =

0.3 and SBR = 0.4) were held constant. Apart from temperature, ER, and SBR, the Dp of

feedstock is also considered as a critical parameter in achieving complete conversion,

improving syngas yield and composition and achieving higher overall gasification efficiency

(Rapagna and Latif, 1997). It can be observed from Fig. 6.7(a) that the concentration of main

components of the syngas such as CO and H2 decreases whereas CO2 and CH4 concentration

increases slightly as the fuel particle size increases. A similar observation is reported in the

literature (Hernández et al., 2010). The reason behind such trend is believed to be the

incomplete gasification.

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It can be observed from Fig. 6.7(b) that carbon conversion decreases with an increase in the

particle size. A relatively low carbon conversion of 65% is achieved for 3.0 mm particle size.

However, a carbon conversion of > 98% is achieved when the particle size is 0.5 mm. The

smaller particle size due to its increased surface area will have a better heat and mass transfer

rates which are expected to lead to higher overall reaction rates and therefore higher carbon

conversion. Similar findings are reported in the literature (Kuo et al., 2014, Babu and

Chaurasia, 2003, Deng et al., 2009).

Based on the above, the optimum Dp value considered for oxygen–steam entrained flow

gasification of TKPSC is 0.5 mm.

Fig. 6.7 Effect of particle size on (a) syngas composition and (b) carbon conversion

6.4 Conclusions

A comprehensive experimental study was carried out in an atmospheric oxygen–steam based

entrained flow gasification system, to systematically investigate the effect of temperature,

equivalence ratio (ER), steam to biomass ratio (SBR) and particle size (Dp) on the lower

heating value (LHV), cold gas efficiency (CGE) and carbon conversion. The torrefied version

of residual waste generated after oil extraction from the Karanja biomass, i.e., Torrefied

Karanja Press Seed Cake (TKPSC) was used in this study. This feedstock is of particular

interest to Asian countries due to its abundant availability in their region.

The main conclusions obtained are the following: With the increase in temperature, the CO

and H2 contents in syngas were found to increase while CH4 and CO2 contents were found to

decrease. The CGE as a function of temperature was systematically investigated, and it was

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found that CGE increases with an increase in the temperature and highest CGE value of 90%

were achieved at 1100 °C. For ER, an optimum value of 0.3 was obtained for which LHV of

syngas as high as 12 MJ/Nm3 was attained. For SBR = 0.4, an LHV as high as 11.8 MJ/Nm3

was obtained. Highest carbon conversion of 98% was achieved for the particle size of 0.5

mm. Overall it is concluded in this study that 95% pure syngas (i.e. composition of CO+H2)

is produced in entrained flow gasification of Karanja PSC at optimum operating conditions of

1100°C temperature, equivalence ratio (ER) of 0.3, steam to biomass ratio (SBR) of 0.4, and

biomass particle size (DP) of 0.5mm.

It should be also noted that the carbon conversion efficiency in entrained flow gasifier is

higher than that in the fluidized bed gasifier. The major advantage of entrained flow

gasification is its large–scale availability and the gasifier operates at high temperature (1000–

1500°C) and high pressure to achieve high level of carbon conversion efficiency and high

efficiency in the production of syngas.

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Chapter 7 Oxygen–Steam Gasification of Karanja Press Seed Cake: Fixed Bed Experiments, ASPEN Plus Process Model Development and Benchmarking with Saw Dust, Rice Husk and Sunflower Husk

158

7 Oxygen–Steam Gasification of Karanja Press Seed Cake: Fixed Bed

Experiments, ASPEN Plus Process Model Development and

Benchmarking with Saw Dust, Rice Husk and Sunflower Husk

Summary

Chapter 6 discussed gasification of torrefied Karanja PSC in an entrained flow gasifier using

oxygen–steam mixture to produce syngas. This chapter aims to demonstrate the gasification

of raw Karanja PSC in a fixed–bed gasifier using oxygen–steam mixture to produce syngas.

In addition to the experimental work, a process simulation model was developed using

ASPEN Plus and its predictions were validated using the experimental results. Furthermore,

the process simulation was extended to investigate the effects of key operating parameters

like temperature, equivalence ratio (ER) and steam to biomass ratio (SBR) on syngas

composition, lower heating value (LHV) and cold gas efficiency (CGE). For benchmarking

purpose, a detailed comparison of results obtained using Karanja PSC was made with those

for other biomass feedstocks such as rice husk, saw dust, and sunflower husk, which are not

yet available in the literature.

7.1 Introduction

The consumption of liquid fuels, such as diesel and petrol has increased in developing

countries including India over the past decade at a rate of 14% per annum (Xu et al., 2012).

Thus, it is vital to find alternative fuel sources to meet the increasing demand. For example,

biodiesel can be used instead of diesel in many applications. However, biodiesel is mostly

produced using plant–based biomass or edible oils produced from this biomass (Saksule and

Kude, 2012). Although these plants or plant–based edible oils can be used in the production

of biodiesel, it is preferred to use non–edible plants and/or oils especially in populous

countries like China and India to avoid issues of food scarcity (Guan et al., 2015). Among the

many plant–based biomass feedstock available, Karanja and jatropa are used as the most

important raw materials for biodiesel production in India. A voluminous literature exists on

the use of this plant–based biomass feedstock for biodiesel production using thermochemical

(i.e., pyrolysis, gasification, and liquefaction) and chemical processes (i.e.,

transesterification) (Önal et al., 2017, Clausen, 2017). However, the use of non–edible

feedstock such as Karanja seed cake (a residue left after edible oil extraction from Karanja

biomass) has not been studied extensively in the literature for biodiesel production. Despite

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its current application as soil alteration for agricultural land, it has a high potential to be used

as a feed for energy/fuel production. The current work, therefore, focuses on investigating the

potential of gasifying Karanja press seed cake to convert it into synthesis gas (syngas) which

can be further used for biodiesel production via Fischer–Trope (FT) synthesis.

Gasification is a thermochemical conversion process which is usually employed to produce

syngas from coal (Ding et al., 2015, ZHANG, 2008). This technology has now been applied

to a variety of biomass feedstocks over last two decades (Bobade and Khyade, 2012, Gao et

al., 2016, Kook et al., 2016, Han et al., 2013, Han et al., 2011). Biomass gasification has been

attracting significant attention recently as a means of producing renewable energy from

biomass and thereby reducing overall greenhouse gas emissions (Naveed et al., 2009). As

explained earlier, the process essentially converts carbonaceous materials into combustible

gas or synthesis gas using gasifying agents such as air or oxygen and/or steam. The syngas

produced from the gasifier mainly contains carbon monoxide (CO) and hydrogen (H2) with

small quantities of carbon dioxide (CO2), methane (CH4), and nitrogen (N2) as well as light

hydrocarbons (Sadhwani et al., 2016). This process is highly flexible, and the end product

(syngas) can be used to produce electricity, hydrogen and liquid fuels (such as biodiesel and

bio–oil) or chemicals (using methods such as Fisher–Tropsch synthesis and dimethyl ether

synthesis) (Heidenreich and Foscolo, 2015). The biomass gasification has been carried out in

different types of gasifiers such as fixed bed reactors (He et al., 2013), moving bed reactors,

fluidized bed reactors (Nilsson et al., 2012), and entrained flow reactors (Zhou et al., 2009).

Among them, fixed bed reactor has many advantages such as simple design, low operating

conditions, and high yield of syngas.

Three types of gasification processes are carried out in fixed bed reactors such as air–

gasification, oxygen–rich air gasification, and oxygen–steam gasification. Among them, the

air gasification technology generates syngas with lower quality and higher heating value

(HHV) in the range of 4–6 MJ/m3 (Lv et al., 2004). The oxygen–rich air gasification leads to

a relatively better yield of syngas. However, this technology generates a gas with average

HHV in the range of 10 to 16 MJ/N m3. Compared to the above two methods, the oxygen–

steam gasification technology is relatively an effective way of producing high–quality syngas

with HHV in the range of 12 to 18 MJ/Nm3 (Molino et al., 2016, Farzad et al., 2016, Önal et

al., 2017, Kuo et al., 2017, Lv et al., 2007, Gao et al., 2008). It is hypothesised that despite

their high capital and operating costs due to oxygen and high–temperature steam

requirements, the oxygen–steam gasification may still be the most favorable option for

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biofuel production via FT process because of its ability to produce high–quality synthesis gas

with an improved H2/CO ratio.

Many previous studies on coal and biomass conversion involved the simulation of the process

using Aspen Plus simulator (Xiangdong et al., 2013). For instance, Kirsanovs and Zandeckis

(2015) have used the equilibrium model in Aspen Plus to stimulate the conversion of wood

chips in a downdraft fixed bed gasifier and reported that the HHV of product gas decreased

from 5.97 to 3.70 MJ/Nm3. Begum et al. (2013) have developed an integrated fixed bed

gasifier model using Aspen Plus simulator for the gasification of municipal solid waste and

food waste. They found that the simulation results agreed with experimental results within ±

4%. Nikoo and Mahinpey (2008) developed an Aspen Plus model for the gasification of

babul wood, neem wood, mango wood and bagasse in an atmospheric fixed bed reactor.

However, they mentioned that the experimental results agree with the simulation results only

to a limited extent. Ramzan et al. (2011) developed an Aspen Plus model for the gasification

of food and municipal solid waste in a hybrid biomass gasifier and found that the model

predictions were in good agreement with experimental results. Chang et al. (2014) developed

Aspen Plus model for an industrial–scale pressurised Lurgi fixed–bed gasifier involved in the

gasification of coal and found the model predictions agreed well with the results of lab–scale

gasification.

Despite growing interest in oxygen–steam gasification of biomass in recent times, only a

limited number of publications are available in the literature on oxygen–steam gasification of

low–cost waste materials such as Karanja PSC. Some of the reported studies on this topic

were based on kinetic modeling. There has been no recent work involving experimental and

Aspen Plus simulation studies on oxygen–steam gasification of Karanja PSC. Also, there has

been no attempt in comparing the results of Aspen plus simulation involving an equilibrium

model for different types of biomass feedstocks. Therefore, this work aims at investigating

the performance of oxygen–steam gasification of Karanja PSC using experimental and

modeling studies. The main objectives of this study are to 1) conduct oxygen–steam

gasification experiments using Karanja PSC in a fixed–bed reactor, 2) develop Aspen Plus

process simulation model for oxygen–steam gasification and validate the simulation results

with experimental results and 3) perform benchmarking the results of Karanja PSC against

those for other feedstocks such as rice husk, saw dust, and sunflower husk. Given the nature

of the gasification process (i.e., fixed bed reactor), an equilibrium model was used in the

development of ASPEN Plus simulation model. After validation, the simulation model was

161

used to investigate the effects of key operating parameters such as temperature, equivalence

ratio (ER) and steam to biomass Ratio (SBR) on syngas composition, lower heating value

(LHV) and cold gas efficiency (CGE).

7.2 Experimental

7.2.1 Raw Materials

Four types of biomass namely Karanja Press Seed Cake (PSC), rice husk, sawdust, and

sunflower husk were used as the feedstocks in this work. The biomass samples, collected

from Maruthi agro fertilisers, Hyderabad, were dried under sunlight for two days and sieved

using Taylor sieves to obtain particles in size range of 1–2 mm. The sieved biomass was then

used for gasification studies. According to the standard norms, proximate, carbon (C),

hydrogen (H), nitrogen (N), sulfur (S) and oxygen analyses of biomass were carried out. The

CHNSO elemental analyser (model: Elementar Vario Microcube made in Germany) was used

for determining the CHNSO percentage in raw biomass. The moisture, volatile matter and

ash contents were measured according to the ASTM International standard test method

numbers E871, D1102, and E872, respectively (Chen et al., 2003, Bakar and Titiloye, 2013,

Nayan et al., 2012, Yorgun et al., 2001). The calorific value of the biomass was determined

using a bomb calorimeter. The analytical results are presented in Table 7.1.

Table 7.1 Results of proximate and ultimate analyses of biomass feedstocks used in this work

Property Karanja PSC Rice husk Sawdust Sunflower husk

Proximate analysis ( w/w%, dry basis)

Volatile matter 71.73 63.07 71.23 78.4

Fixed carbon 17 18.23 17.81 12.15

Ash 4 12.27 0.98 5.31

Ultimate analysis (%, w/w, dry basis)

C 47.2 42.235 51.6 69.8

H 6.5 5.71 4.86 8.8

O 42 51.29 42.1 12.3

N 4.3 0.665 1.38 9.1

S – 0.10 0.06 –

Moisture (%, w/w) 7.27 6.43 9.98 4.14

Calorific value (MJ/kg) 18.37 15.34 17.58 18.7

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7.2.2 Experimental Investigations

A schematic diagram of the gasification system employed in the current study is shown in

Fig. 7.1. The assembly consists of a reactor with a split furnace, product gas cooling and

cleaning units, and a gas analysis unit (chromatography). The reactor is a cylinder made of

Inconel alloy with an internal diameter and length of 161 and 600 mm, respectively. The

heating was provided by external furnace equipped with electrically controlled heaters that

can heat the reaction zone up to 1000°C. Three K–type thermocouples located at lower (T1),

medial (T2), and upper (T3) sections of the reactor were used to record the temperature during

gasification.

In a typical experiment, the biomass was transferred to the reactor from a hopper via a screw

feeder mounted at the top of the gasifier. Oxygen and steam were supplied to the gasifier at

the bottom of the reactor through a nozzle using a mass flow controller. Gasification was

carried out at atmospheric pressure over a temperature range of 400–1000°C and atmospheric

pressure. The syngas produced during gasification mainly consisting of CO, H2 and small

traces of CO2, CH4, O2, N2, and water with dust particles was vented off from the top whereas

the ash was continuously removed from the bottom of the gasifier via a knife valve. The

syngas leaving reactor top entered into a cyclone separator where the dust particles carried

over by the gas were separated from the gas and collected at the bottom. The dust–free gas

was further cooled in a shell and tube heat exchanger and then scrubbed using a water

scrubber. Followed by this, a gas–liquid separator (GLS) was used to separate the dry gas and

carry–over liquid. The relatively dry syngas obtained this way was then sent to a gas

chromatography (GC) for analysis.

Fig. 7.1. Schematic diagram of the fixed–bed gasification unit

T

T

T

Drying

Pyrolysis

Reduction

Oxidation

Raw syngas

Cyclone

seperator

Heat

exchanger

Water

scrubber

Dust particles

Went

Gas-Liquid

seperator

Water outWashed

water

Feed biomass

Ash outlet

Oxygen Steam

Hopper

Pure syngas

Gas chromatography

163

7.2.3 Gas Analysis

The online compact GC (Agilent, USA) equipped with two columns and detectors in parallel

using N2 as a carrier gas was employed for syngas analysis. In the first column (Porapak q, 3

m X 0.125 mm) CO, CH4, CO2, and N2 were separated at the oven temperature of 100°C and

analysed by thermal conductivity detector (TCD). In the second column (Molecular sieve 5a,

2 m X 0.125 mm) mainly H2 compound was separated at 100°C and analysed by TCD.

7.3 Modelling of Biomass Gasifier

A simulation model was developed using Aspen Plus as shown in Fig. 7.2. The overall

gasification process was modeled in different stages including decomposition of the feed

followed by gasification steps based on the ultimate, proximate, and sulphur analyses of the

feedstock using the nonconventional components in the Aspen Plus simulation. Components

in Aspen plus are classified as either conventional or nonconventional. Conventional

components are the ones with property data contained in the Aspen plus component database.

Nonconventional components (NC) are non–homogeneous substances that do not have a

consistent composition and are not contained in the Aspen plus component database. These

components, which would include coal and biomass, must be given physical attributes, such

as those defined by the ultimate, proximate, and sulfur analyses. The conventional

components C, H2, O2 and N2 are also used in the simulation study.

NC properties: HCOALGEN and DCOALIGT were selected as enthalpy and density models,

respectively, for feed, dry–feed and ash which are non–conventional components.

HCOALGEN is the general coal/biomass model for computing enthalpy in the Aspen

Physical Property System which includes a number of different correlations for estimating

heat of combustion, heat of formation and heat capacity. The density model, DCOALIGT,

gives the true density of coal/biomass on a dry basis using ultimate and sulphur analyses.

The IDEAL property method was set for this simulation in which ideal behaviors are

assumed such as isomeric systems at low pressures. In the vapor phase, small deviations from

the ideal gas law are allowed. These deviations occur at low pressures (either below

atmospheric pressure, or at pressures not exceeding 2 bar) and very high temperatures. Ideal

behavior in the liquid phase is exhibited by molecules with either very small interactions or

interactions that cancel each other out. The IDEAL property method is generally used for

systems with and without non–condensable components. In this method, permanent gases can

164

be dissolved in the liquid. The property methods must also be chosen to calculate the

enthalpy and density of the substance. The methods chosen for these calculations are

mentioned above.

RYIELD block is an Aspen Plus yield reactor which was used to decompose the feed in the

simulation. The conversion is accomplished with an RYIELD block for the pyrolysis, labeled

DECOMP, which is a reactor model that generates products based on known yields from the

ultimate analysis (C = 47.2, H2 = 6.5, O2 = 42.0 and N2 = 4.3). The fuel feed stream enters in

RYIELD block where it is decomposed into its elemental constituents. A FORTRAN

calculator script interacts with the RYIELD block such that decomposition of the fuel is

calculated based on the proximate and ultimate analyses of the nonconventional feed

component. The carbon content of the feed is converted to solid carbon graphite. The

hydrogen, oxygen and nitrogen are converted to gaseous H2, O2, and N2. Finally the moisture

content is converted to liquid H2O. These species are now contained in an intermediate

stream called ELEMENTS, which then become the reactants for the RGIBBS block.

RGIBBS that calculates chemical and thermodynamic equilibrium based on minimizing the

Gibbs free energy of the system. The equilibrium reactor RGIBBS does not accept

nonconventional components as reactants. As a result, the fuel must be decomposed to

conventional components which can be used by the RGIBBS block. An O2/air and steam

streams representing the gasifying agents also enters RGIBBS reactor, where partial

oxidation and gasification reactions occur and a product stream exits it. Gibbs model

generally can be used to predict the process behavior of fixed bed gasifier. When the kinetics

of the reaction is not known, a rigorous reactor and a multiphase equilibrium based on the

total Gibbs free energy minimization of the product mixture (Gibbs reactor) are preferred to

predict the equilibrium composition of the produced syngas (Niu et al., 2014).

The sensitivity analysis feature of Aspen Plus is used to study the effect of various

combinations of O2/C and H2O/C ratios. For specified O2/C and H2O/C atomic ratios, the

ultimate analysis in terms of mass percentage of the elements carbon, hydrogen, and oxygen

is determined. In each case the total fuel flow rate is kept constant. For each case, a design

specification of carbon entering equal to carbon leaving in gaseous species is used to

determine the oxygen flow rate required for complete conversion of carbon.

165

Fig. 7.2. Aspen Plus flow sheet of the fixed bed gasification process

Table 7.2 contains the most common combustion and gasification reactions. However, as we

have taken the equilibrium approach, these reactions are not fed into the simulation. The

general assumptions used for this model are:

• The gasifier is in a steady–state mode with uniform temperature and pressure;

• The property method used for the simulation is “Ideal”;

• The gasification block is isothermal (heat losses are zero) (Rupesh et al., 2016,

Doherty et al., 2013, Yin et al., 2015);

• All gases are considered ideal, and volatile products include O2, N2, H2, CO, CO2,

CH4 and water;

• The reactions are at chemical equilibrium;

• The carbon conversion efficiency is 100%, and the drying and devolatilization of

feedstock are instantaneous (Ahmed et al., 2016);

• Heat balance between RYIELD and RGIBBS is maintained by Q–DEC;

• Heat balance for RGIBBS is obtained by extracting or providing energy generated or

required by the process using stream Q–GASFY.

The sensitivity analysis was performed to study the effects of various combinations of

O2/C and H2O/C ratio. The syngas composition was obtained for different operating

conditions. From that, LHV and CGE were calculated as follows.

The equation used for the calculation of LHV is as follows:

𝐿𝐻𝑉𝑆 (𝑀𝐽

𝑁𝑚3) = 𝑌𝐶𝑂𝐿𝐻𝑉𝐶𝑂 + 𝑌𝐻2

𝐿𝐻𝑉𝐻2+ 𝑌𝐶𝐻4

𝐿𝐻𝑉𝐶𝐻4− − − − − − − − − − − (7.1)

The LHV of the respective gas species are as follows:

RYIELD RGIBBS

ELEMENTS

OXYGEN

STEAM

SYNGAS

BIOMASS

Q-DEC

Q-GASFY

Q

166

𝐿𝐻𝑉𝐶𝑂 = 13.1𝑀𝐽

𝑁𝑚3, 𝐿𝐻𝑉𝐻2

= 11.2𝑀𝐽

𝑁𝑚3, 𝐿𝐻𝑉𝐶𝐻4

= 37.1𝑀𝐽

𝑁𝑚3

Cold gas efficiency is defined as follows [46, 47],

𝐶𝐺𝐸𝑆 (%) =𝐿𝐻𝑉 𝑜𝑓 𝑡ℎ𝑒 𝑠𝑦𝑛𝑔𝑎𝑠 ∗

𝐿𝐻𝑉 𝑜𝑓 𝑡ℎ𝑒 𝑓𝑒𝑒𝑑 ∗

𝑠𝑦𝑛𝑔𝑎𝑠 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒

𝑓𝑒𝑒𝑑 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒∗ 100 − − − − − − − −(7.2)

where S used in the subscript is the syngas and Y is the mole fraction of each gas species

(CO, H2, and CH4).

Table 7.2. Important biomass gasification reactions (Buragohain et al., 2010, Formica et al.,

2016, Liu et al., 2014, Kuo et al., 2014, González et al., 2008)

Reaction no. Reaction name Reaction chemistry ΔH

(kJ/mol)

R1 Carbon complete combustion C + O2 → CO2 – 392.5

R2 Carbon partial combustion C + 0.5 O2 → CO – 110

R3 Boudouard CO2 + C → 2CO +163.1

R4 Water gas C + H2O CO + H2 +120.6

R5 Carbon monoxide partial

combustion CO + 0.5 O2 CO2 – 283

R6 Hydrogen combustion H2 + 0.5 O2 H2O – 242

R7 Water gas shift CO + H2O CO2 + H2 – 41

R8 Methanation C + 2H2 CH4 – 80

R9 Steam–methane reforming CH4 +H2O CO + 3H2 +206

R10 H2S formation H2 + S H2S −

R11 NH3 formation N2 + 3H2 2NH3 −

7.4 Results and Discussions

7.4.1 Model Validation

Value or range used for ASPEN simulation is highlighted in Table 7.3. The experimental

validation of the model was carried out at two different temperatures (i.e., 800 and 900°C)

while all other parameters were kept constant. The experimental conditions used for model

validation is also highlighted in Table 7.3. The modelling and experimental results were

167

compared for syngas components such as H2, CO, CO2, and CH4 as shown in Table 7.4. The

variation between the measured and simulated results is found to be ±4% for all components

except for CH4 at 800°C. The considerable difference in the concentrations of CH4 at 800°C

obtained from experimental and Aspen Plus simulation studies is because CH4 is not found in

the simulated syngas stream which was obtained using the RGIBBS block in Aspen Plus

which involves a thermodynamic approach based on the Gibbs free energy minimization. The

significant reduction in the CH4 concentration at 900°C compared to that at 800°C in our

experimental results is due to the steam reforming reaction (R9 in Table 7.2) that occurs

between CH4 and steam present in the biomass which leads to the generation of H2 and CO.

Since the steam reforming is an endothermic reaction, the products of the reaction are

favoured with an increase in temperature. Therefore, the concentrations of CH4 and H2O

decrease and those of H2 and CO increase as the temperature rises from 800°C to 900°C

(ZHOU et al., 2009). Good agreements between experimental and Aspen plus simulation

studies have also been reported by other studies in the literature for fixed bed gasification

(Nikoo and Mahinpey, 2008, Ramzan et al., 2011). The validated simulation model was then

used for sensitivity analysis. Also, comparisons of Karanja PSC results were made with those

for rice husk, saw dust and sunflower husk.

Table 7.3. Feed input and operating conditions of the Aspen Plus simulation model

Parameter Type of variable

Unit

Value/range used

for ASPEN

Simulation

Experimental

conditions for

model validation

Fuel rate Constant kg/h 1 1

Oxygen flow rate Variable kg/h 0.1–1 0.2

Steam flow rate Variable kg/h 0.1–1 0.5

Moisture content Variable wt.% 4.5–13.5 8.2

Equivalence ratio Constant – 0.1–1.0 0.23

Steam–to–biomass ratio Constant – 0.1–1.0 0.3

Oxygen feed temperature Constant °C 30 30

Steam feed temperature Constant °C 150 150

Reactor temperature Variable °C 400–1000 800 and 900

Reactor pressure Constant bar 1 1

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Table 7.4. Comparison of experimental and simulation results by the gasification of Karanja

PSC in a fixed bed reactor at 800 °C and 900 °C

Measurement H2 CO CO2 CH4 Others

Temperature = 800°C

Experimental (%) 37.26 48.7 4.2 2.3 7.54

Model (%) 40.5 47 3.57 0.37 8.56

Difference (%) 3.24 –1.7 –0.63 –1.93 1.02

Temperature = 900°C

Experimental (%) 38.87 49.13 0.75 0.86 10.39

Model (%) 42.92 52.8 0.92 0.32 2.04

Difference (%) 4.05 3.67 0.17 –0.54 –8.35

7.4.2 Sensitivity Analysis

The sensitivity analysis was performed for three operating parameters including reactor

temperature, ER, and SBR. The carbon conversion and CGE for different feedstock were

investigated from the simulation model. In addition, LHV of syngas at various temperatures

were estimated for Karanja PSC, rice husk, saw dust and sunflower husk.

7.4.2.1 Effect of Temperature on Syngas Composition

The gasification temperature was varied from 400 to 1000°C at 100°C increment for an ER of

0.23 and SBR of 0.3. The effect of gasifier temperature on the produced syngas composition

for all four feedstocks is shown in Fig. 7.3. The molar composition of syngas produced from

Karanja PSC is presented in Table 7.5 for various temperatures.

At low temperatures, CO2, CH4 and unburnt carbon are present in the syngas. As the

temperature increases, the carbon is expected to react with O2 and get converted into carbon

monoxide (CO) in accordance with partial combustion reaction (R2). According to

Boudouard reaction, as the gasifier temperature increases the CO mole fraction increases and

that of CO2 decreases (R3). Boudouard reaction is a highly endothermic reaction;

consequently, the heating value of syngas improves. Water gas reaction suggests that as

temperature increases, the production of both CO and H2 concentration also increase (R4).

This reaction can also be attributed to the increase in the heating value of syngas. It is

169

observed from the Fig. 7.3, the concentration of CO increases continuously with increase in

the temperature from 400 to 1000°C. Methane is reacting with the steam present in the

biomass and converted into hydrogen indicating that higher reaction temperature is favorable

for steam reforming and cracking of CH4. These results indicate an increase in the

temperature of the gasifier further favors the production of CO and H2 and hence the

concentrations of CH4 and CO2 decrease (R9). Studies reported in the literature suggested a

similar explanation for gasification reactions. For example, Skoulou et al. (2008) investigated

the effect of reactor temperature on the syngas yield of olive kernels and olive tree cuttings in

laboratory fixed bed gasifier. They found that, with increasing temperature, the

concentrations of H2 and CO increased while that of CO2 and CH4 decreased. Begum et al.

(2013) studied the gasification of municipal solid waste (MSW) in fixed bed downdraft

gasifiers and found similar changes in gas composition with increasing temperature.

When comparing Karanja PSC with other three feedstocks, it can be stated that higher

hydrogen can be produced from Karanja PSC and sunflower husk while more CO can be

produced from the rice husk. These differences can be mainly attributed to their elemental

compositions.

The simulation results for the carbon conversion of the four–biomass feedstocks are shown in

Fig. 7.4(a). The gasifier temperature was varied from 400 to1000°C at atmospheric pressure.

As is evident from Fig. 7.4(a), at higher temperatures carbon conversion tends to increase; the

carbon is almost completely converted at 1000°C for Karanja PSC, rice husk, sawdust, and

sunflower husk. It can be concluded from the results that the favorable gasifier temperature

for syngas production for the feedstock used in this work should be 900–1000°C at the ER of

0.23 and SBR of 0.3.

The effect of gasification temperature on cold gas efficiency (CGE) is shown in Fig. 7.4(b).

The CGE for all biomasses is directly proportional to the gasifier temperature which means

that higher the gasification temperature, higher the CGE will be. This could be explained by

the continuous increase of the CO and H2 concentrations in the product gas with increasing

temperature. The CGE values obtained for Karanja PSC, rice husk, saw dust and sunflower

husk were found to vary in the ranges of 30–94%, 20–64%, 25–77% and 29–91%,

respectively about the temperature range of 400−1000°C. These values agree with the values

reported in the literature (Renganathan et al., 2012). Niu et al. (2013) reported the CGE from

39.9 to 87.6% for the high−temperature range of 650−1000°C and 39.9 to 87.6% for the

low−temperature range of 500−650°C.

170

Fig. 7.3. Effect of temperature on syngas composition for (a) Karanja PSC, (b) rice husk, (c)

sawdust, and (d) sunflower husk feedstock (ER=0.23 and SBR=0.3) (results obtained from

simulation)

Fig. 7.4. (a) Effect of temperature on carbon conversion (b) Effect of temperature on cold gas

efficiency for Karanja, rice husk, saw dust and sunflower husk feedstock (ER=0.23 and

SBR=0.3) (results obtained from simulation)

171

Table 7.5. The molar composition of syngas, carbon conversion and LHV values for Karanja

PSC obtained using ASPEN Plus model (ER=0.23 and SBR=0.3)

Temperature

(°C)

Syngas composition, molar fractions LHV

(MJ/Nm3) CO H2 CO2 CH4 Others Carbon

conversion

400 0.0028 0.059 0.17 0.08 6.88E-01 0.41 3.78

500 0.022 0.14 0.18 0.06 5.98E-01 0.44 4.23

600 0.101 0.25 0.17 0.04 4.39E-01 0.52 5.47

700 0.28 0.35 0.1 0.02 2.50E-01 0.72 8.23

800 0.47 0.405 0.036 0.004 8.50E-02 0.91 10.82

900 0.53 0.43 0.009 0.003 2.80E-02 0.98 11.77

1000 0.54 0.44 0.002 0.001 1.70E-02 0.99 12.01

7.4.2.2 Effect of ER on Syngas Gas Composition

The ER is defined as the ratio of the actual air supply to the stoichiometric air required for

complete combustion (Ravikiran et al., 2011, Niu et al., 2014). ER indicates the oxygen feed

to the gasification process, and it is a crucial factor that can affect the syngas composition.

ER in the current work was varied in the range of 0.1 to 1.0 by adjusting the oxygen feeding

rate during the experiments at 1000°C and constant SBR of 0.3. The results are presented in

Fig. 7.5 and Table 7.6.

With an increase in the ER, the mole fractions of CO and H2 (syngas) increase first and attain

the highest values of 0.55 and 0.43, respectively (~50% of syngas) at ER = 0.23. Further

increase in ER leads to decreases in CO and H2 concentrations due to potentially favorable

complete combustion reaction (R1). The composition of CO2 increases sharply with

increasing ER due to complete combustion and reaches a value of 0.2 to 0.3 at an ER value of

1. The change in the concentration of CH4 found to be negligible. Similar trends were

reported in other studies in the literature (Guo et al., 2014, Niu et al., 2013). Guo et al. (2014)

found that, with an increase in ER, the volume fractions of CO and H2 increase first and

attain the highest values of 12.89 and 19.41% at ER of 0.25 and 0.27, respectively. Further

increases in ER led to decreases in the volume fractions of CO and H2 due to combustion

reactions. Niu et al. (2013) studied the effect of ER on the syngas yield from municipal solid

waste in a bubbling fluidized bed gasifier. They reported that H2 content decreased from 22.7

172

to 7.9% with increasing ER while CH4 concentration decreased to very low values (3.0%).

They also reported that CO content initially increased slightly from 22.3 to 24.8% with

increasing ER but started to decrease thereafter continuously. Meanwhile, the CO2

concentration was found to increase from 6.8 to 15.4% due to complete oxidization of the

feedstock.

Fig. 7.5. Effect of equivalence ratio on product syngas gas composition for (a) Karanja, (b) Rice

husk, (c) Sawdust, and (d) Sunflower husk feedstock (Gasifier temperature = 1000°C, and

SBR=0.3) (results obtained from simulation)

173

Table 7.6. The molar composition of syngas produced by gasification, carbon conversion and

LHV calculated using the simulation model for Karanja PSC

ER


(MJ/Nm3) CO H2 CO2 CH4 Others Carbon

conversion

0.1 0.46 0.44 5.71E-05 1.74E-04 9.98E-02 0.843 10.9

0.2 0.526 0.44 7.00E-05 1.62E-04 3.38E-02 0.967 11.8

0.23 0.546 0.43 7.07E-04 1.64E-05 2.33E-02 1 12.0

0.3 0.53 0.41 0.014 6.62E-07 4.60E-02 1 11.5

0.4 0.51 0.36 0.03 1.92E-07 1.00E-01 1 10.7

0.5 0.481 0.31 0.06 8.04E-08 1.49E-01 1 9.87

0.6 0.46 0.27 0.08 3.75E-08 1.90E-01 1 9.07

0.7 0.43 0.23 0.11 1.81E-08 2.30E-01 1 8.25

0.8 0.39 0.19 0.15 8.72E-09 2.70E-01 1 7.43

0.9 0.36 0.17 0.18 4.09E-09 2.90E-01 1 6.61

1 0.32 0.14 0.22 1.83E-09 3.20E-01 1 5.78

The effect of ER on LHV of product gas was investigated for Karanja PSC at 800, 900, and

1000°C and the results are shown in Fig. 7.6(a). LHV follows a trend that is similar to that of

syngas composition. LHV increases with increasing ER up to the value of 0.23–0.3 and then

start decreasing dramatically. This again can be correlated with syngas composition.

Decreases in CO and H2 concentrations with increase in ER are found to be responsible for

such results. The effects of ER on LHV for Karanja PSC, rice husk, saw dust and sunflower

husk are compared in Fig. 7.6(b). The trends of LHV values for Karanja PSC, and sunflower

husk are nearly same because they have similar properties. Their LHV values are 11.5–12.5

MJ/Nm3 at 1000°C for an ER of 0.23. The LHV values of rice husk and saw dust are 12–13

MJ/Nm3 at 1000°C for an ER of 0.4. Niu et al. (2013) studied bubbling fluidized bed

gasification of municipal solid waste and found similar LHV in the range of 6–1 MJ/Nm3 for

ER in the range of 0.2 to 0.8.

LHV values for all biomasses were found to increase with increasing temperature at lower

values of ER. Again this is due to the fact that higher temperature leads to increased

production of CO and H2 (refer Fig. 7.3).

174

Fig. 7.6(a) Effect of equivalence ratio (ER) on lower heating value (LHV) of syngas at

temperature of 800, 900 and 1000°C for Karanja PSC and (b) Effect of ER on LHV of Karanja,

rice husk, saw dust and sunflower husk feedstock at 1000°C (SBR=0.3) (results obtained from

simulation)

7.4.2.3 Effect of Steam/biomass Ratio on Syngas Composition

SBR is one of the most efficient parameters that affect the performance of the gasification

process. The influence of SBR on syngas gas composition was investigated over the range 0.1

to 1.0 while keeping all other conditions constant (at 1000 °C, ER= 0.23) for Karanja PSC

and sunflower husk biomass. The results are presented in Table 7.7 and Fig. 7.7. It is evident

from Table 7.7 that the introduction of steam significantly improves syngas yield and LHV.

The CO and H2 concentrations increase with increasing SBR, reach maximum values and

then decrease at higher SBR values. Consequently, the production of syngas and heating

value also decrease due to the potential occurrence of water gas shift reaction (R7). Also,

steam reforming of CH4 is also expected to be viable at higher SBR, which can be confirmed

by the decrease in CH4 concentration with increasing SBR.

For the SBR range of 0 to 0.3, CO and H2 concentrations increase slowly, reach a maximum

whereas the CO2 and CH4 concentrations remain at low values (Fig. 7.7). Over the SBR range

from 0.3 to 1.0, the concentration of CO decreases gradually whereas that of CO2 increases

moderately. The H2 concentration does not decrease much as observed in the case of ER (Fig.

7.5). H2/CO ratio (Fig. 7.8) is found to be greater than one for SBR greater than 0.7. Higher

H2/CO ratio is favorable for biofuel production. Such results are obtained mainly due to the

fact that higher SBR leads to the generation of higher amounts of H2 due to potential water

175

gas shift reaction. He et al. (2013) investigated a commercial–scale pressurised Lurgi fixed

bed dry bottom coal gasifier using ShengLi coal and found similar changes in gas

composition with increasing SBR. Ramzan et al. (2011) studied gasification of food and

municipal solid waste and reported that increasing SBR (0.1–0.5) strongly degrades the

gasifier performance. Niu et al. (2013) studied the effect of SBR on the syngas yield of

municipal solid waste in bubbling fluidized bed gasifier. They found that, with increasing

SBR, the concentrations of H2 and CO2 concentrations increased remarkably while that of CO

concentration decreased.

Table 7.7. The molar composition of syngas produced by gasification and carbon conversion

and LHV determined using the simulation model for Karanja PSC

SBR


(MJ/Nm3) CO H2 CO2 CH4 Other

gases

Carbon

conversion

0.1 0.48 0.36 6.73E-05 1.25E-04 1.60E-01 0.783 10.4

0.2 0.52 0.41 7.08E-05 1.43E-04 6.98E-02 0.894 11.3

0.3 0.54 0.42 7.07E-04 1.64E-05 3.93E-02 1 12.0

0.4 0.49 0.43 0.015 6.03E-07 6.50E-02 1 11.3

0.5 0.46 0.42 0.027 2.74E-07 9.30E-02 1 10.7

0.6 0.42 0.41 0.037 1.64E-07 1.33E-01 1 10.1

0.7 0.39 0.39 0.045 1.11E-07 1.75E-01 1 9.61

0.8 0.36 0.38 0.052 7.97E-08 2.08E-01 1 9.15

0.9 0.34 0.38 0.057 5.99E-08 2.23E-01 1 8.73

1 0.32 0.37 0.062 4.64E-08 2.48E-01 1 8.35

Fig. 7.7. Effect of steam to biomass ratio (SBR) on syngas gas composition for (a) Karanja and

(b) sunflower husk feedstock (at 1000 °C and ER of 0.23) (results obtained from simulation)

176

Fig. 7.8. Effect of steam to biomass ratio on the H2/CO ratio (results obtained from simulation)

7.5 Conclusions

The ASPEN Plus simulation model developed for oxygen–steam gasification was validated

with fixed bed reactor experimental results. A general agreement was found between them

with a maximum variation of ± 4%. Additional sensitivity analysis was carried out to study

the influence of gasification temperature, ER, and SBR on synthesis gas composition.

Moreover, the effects of these parameters on LHV and CGE were investigated.

Following main findings were derived: CO and H2 concentration were found to increase with

an increase in the temperature for constant ER and SBR values of 0.23 and 0.3, respectively.

This has led to an increase in the LHV of the syngas with an increase in the temperature.

Carbon conversion was also found to increase with increasing temperature for constant ER

and SBR values of 0.23 and 0.3, respectively. The CGE was found to increase with

increasing temperature. Value as high as 95% was obtained for almost all feedstocks at

1000°C with ER and SBR values of 0.23 and 0.3, respectively. Increase in the ER led to

increases in CO and H2 concentrations up to an ER value of 0.23. Further increases in ER led

to decreases in CO and H2 concentrations as well as that of LHV. At lower values of ER,

LHV was found to increase with increasing temperature. An increase in SBR led to an

increase H2/CO ratio at an SBR value of >=0.7.

177

Chapter 8 Conclusions and Recommendations

178

8 Conclusions and Recommendations

On account of abundant protein, lipid and carbohydrate contents, industrially produced

lignocellulosic biomasses such as Karanja press seed cake (PSC) are becoming

promising feedstocks for producing second–generation biofuels. These feedstocks can be

used to produce solid, liquid and gaseous biofuels. This thesis aimed to investigate the

conversion of lignocellulosic biomass, Karanja PSC, into solid biofuel using torrefaction,

liquid biofuels such as bio–oil and bio–crude using pyrolysis and liquefaction,

respectively, and gaseous biofuels such as syngas using gasification. All these biofuels

are renewable fuels which can replace fossil fuels and therefore can abate the effects of

greenhouse gas emissions on the atmosphere. Although the focus of the thesis is to

demonstrate that thermochemical conversions processes such as torrefaction, pyrolysis,

liquefaction and gasification can be employed to convert Karanja PSC into various forms

of biofuels, the underlying objective is to demonstrate that renewable biofuels can be

produced from any lignocellulosic biomass which otherwise is considered as waste. In

order to reinforce this notion, a few other lignocellulosic materials such as mahua PSC,

sorghum bagasse, rice and sunflower husks and saw dust were also subjected to some of

the above–mentioned thermochemical conversion processes and their results were

presented in the thesis. The additional biomasses were used only in some of the studies

to maintain the scope of the thesis within the expected levels of the PhD candidature. The

major conclusions of this study are summarised below. To summarise the findings of the

thesis on a unified theme, conclusions are presented below first from those studies which

involved Karanja PSC, which was key biomass used in this work. Subsequently, conclusions

for studies involving other lignocellulosic materials of sorghum bagasse and mahua PSC are

presented as further validation of conclusions made based on Karanja PSC studies.

8.1 Thermochemical conversion of Karanja PSC

8.1.1 Torrefaction of Karanja PSC

Torrefaction was identified as an up–gradation/pre–treatment technique for Karanja PSC. A

significant change in elemental composition was observed with the reduction in O/C and H/C

thereby increasing the calorific value, energy density and hydrophobicity of the torrefied

biomass (bio–char). An average weight loss of about 30–35 % was found after torrefaction

179

whereas the total energy remained in the fuel (bio–char) was found to be 80–85% of the

original energy. The HHV of the torrefied biomass was in the range of 19.5–21.5 MJ/kg.

The higher weight loss found with Karanja PSC as compared to those usually found with

other woody biomass can be ascribed to the difference in the elemental composition of

Karanja PSC. The overall carbon content of solid biofuel increased by 40%, while the oxygen

content decreased by 32.6% for the biomass torrefied at 300 °C. The results indicated an

apparent reduction in volume with improved higher heating values, which are the favourable

properties of torrefied biomass for its application as a fuel for gasification and combustion.

Finally, to account for the thermochemical phenomena, a single step reaction mechanism was

found to describe the overall kinetics of the torrefaction process. The information on torrefied

bio–char yield and its elemental composition, the products distribution and the torrefaction

kinetics is needed for the reactor design, scale–up and commercialisation of industrial

torrefaction processes.

8.1.2 Pyrolysis and liquefaction of Karanja PSC

Pyrolysis of Karanja PSC was investigated using a fixed bed batch reactor. The effect of

pyrolysis temperature on the conversion of biomass and the yield of pyrolytic bio–oil (PBO)

were studied. Physicochemical and structural characteristics of the remaining bio–char were

significantly influenced by pyrolysis temperature. The degree of carbonisation of bio–char

was accelerated with increasing temperature from 350 to 550 °C at a constant heating rate of

20 °C/min. As the temperature increased, the oxygen and hydrogen were removed from the

biomass, thus leaving the remaining carbons to form aromatic carbon bonds. The highest

PBO yield of ~ 47.8% was found to occur at the optimum temperature of 450 °C. FT–IR and

GC–MS analyses carried out at 450 °C showed the presence of relevant functional groups

and chemical compounds in PBO.

The liquefaction experiments of Karanja PSC were carried out in the presence of Karanja

PBO in a batch reactor (i.e., autoclave). The effects of PBO amount and reaction temperature

were studied. The residence time in a batch reactor was kept constant at 160 minutes for all

experiments. It was observed that both PBO amount and reaction temperature had a

significant effect on the liquefaction conversion. Higher % conversion value as high as ~

99.02% was obtained at a temperature of 240 °C and a Karanja PSC: PBO ratio of 1:6. The

effect of PBO was also compared with standard solvent and catalyst (i.e., phenol and H2SO4

respectively). It was observed that phenol and the H2SO4 combination worked better

180

compared to PBO. For phenol and H2SO4, lower temperature (i.e., 160 °C) and the lower

solvent ratio (i.e., Karanja PSC: Phenol: H2SO4 as 1:2:0.6) was required to achieve higher %

conversion. Finally, while comparing PBO and bio–crude products, it was observed that

aromatic structure with less oxygen was evident in bio–crude compared to PBO.

8.1.3 Gasification of Karanja PSC

8.1.3.1 Entrained flow gasification of Karanja PSC

Comprehensive experimental matrix was employed in an atmospheric oxygen–steam based

entrained flow gasification system to systematically investigate the effect of temperature,

equivalence ratio (ER), steam to biomass ratio (SBR) and particle size (Dp) on the lower

heating value (LHV), cold gas efficiency (CGE) and carbon conversion. The torrefied version

of residual waste generated after oil extraction from the Karanja biomass, i.e., torrefied

Karanja press seed cake was used in this study. With increasing temperature, the CO and H2

contents in syngas were found to increase while CH4 and CO2 contents were found to

decrease. The CGE as a function of temperature was systematically investigated, and it was

found that CGE increases with an increase in the temperature and highest CGE value of 90%

occur at 1100 °C. For ER, an optimum value of 0.3 was obtained for which LHV of syngas as

high as 12 MJ/Nm3 was attained. For an SBR value of 0.4, an LHV as high as 11.8 MJ/Nm3

were obtained. Highest carbon conversion of 98% was achieved for a particle size of 0.5 mm.

8.1.3.2 Fixed bed gasification of Karanja PSC

An ASPEN Plus simulation model developed for oxygen–steam gasification of Karanja PSC

in a fixed bed reactor. The simulation results were validated with experimental results that

were obtained from a fixed bed reactor using steam–oxygen mixture as the gasification

medium. A general agreement was found between the simulation and experimental results

with a maximum variation of ± 4%. Additional sensitivity analysis was carried out to study

the influence of gasification temperature, ER, and SBR on synthesis gas composition.

Moreover, the effect of these parameters on LHV and CGE were investigated. The

simulation study was further validated using additional three feedstocks namely rice husk,

sunflower husk and saw dust.

181

CO and H2 concentrations were found to increase with increasing temperature for constant

ER and SBR values of 0.23 and 0.3, respectively. This has led to an increase in the LHV of

the syngas with an increase in the temperature. Carbon conversion was also found to increase

with increasing temperature for constant ER and SBR values of 0.23 and 0.3, respectively.

The CGE was found to increase with an increase in the temperature. CGE value as high as

95% was obtained for almost all feedstocks at 1000°C with ER and SBR values of 0.23 and

0.3, respectively. An increase in the ER led to increases in the CO and H2 concentrations until

an ER value of 0.23. Following that further increase in ER led to decreases in CO and H2

concentrations as well as LHV. At lower values of ER, LHV was found to increase with

increasing temperature. An increase in SBR led to improve H2/CO ratio at an SBR value of

>=0.7.

8.2 Thermochemical Conversion of other lignocellulosic biomasses –

Pyrolysis of Mahua PSC and Sorghum bagasse

Pyrolysis process of two additional lignocellulosic biomasses Mahua PSC and Sorghum

bagasse, were investigated in a fixed bed batch reactor. The effect of pyrolysis temperature

on the conversion of biomass and the yields of bio–fuels (bio–oil, bio–char and gases) were

studied. Physicochemical and structural characteristics of the bio–char were significantly

influenced by pyrolysis temperature. The degree of carbonisation of bio–char was accelerated

with increasing temperature from 350 to 550 °C at a constant heating rate of 20 °C/min. As

the temperature increased, the oxygen and hydrogen were removed from the biomass, thus

leaving the remaining carbons to form aromatic carbon bonds.

The maximum yield of bio–oil obtained from Sorghum bagasse biomass at 450 °C was 15.93

% by mass. A Response Surface Method (RSM) was employed for Mahua PS C to derive the

optimum operating conditions for achieving the highest bio–oil yield. They included three

critical parameters namely temperature, retention time and N2 flow rate. Using Box–Behnken

Design, a requirement of total 15 runs using the above experimental parameters was

identified. The details of the experimental results were incorporated in the model equation of

response surface method to identify optimum operating conditions for the highest amount of

bio–oil production. The regression equation was finally established based on the statistical

analysis. The optimum operating conditions to achieve a bio–oil yield as high as 49.25 %

were found to include the pyrolysis temperature of 475 °C, the retention time of 45 min, and

N2 flow rate of 0.3 L/min. The high F–value of 37.53 and the low p–value of 0.00 of the

182

predicted model for bio–oil yield proved that the RSM model derived in this work is

significant. The predictions of equation fitted well with the experimental data, which is

confirmed by a parity plot between the actual and predicted yields. The correlation

coefficients (R2) obtained for all of the responses of bio–oil and bio–char were 0.985 and

0.989 indicating an excellent correlation between the independent variables. Mass and energy

balances and economic analysis suggested that a payback period of 5.6 years could be

achieved for producing the crude bio–oil and electricity from Mahua PSC.

The bio–oil, bio–char and bio–gas generated from both biomasses at optimal conditions were

characterised by GC–MS, FT–IR, and Micro–GC. The major compounds found produced

from Sorghum bagasse were found to include 6–octadecenoic acid, p–cresol, 2,6–dimethoxy

phenol 4–ethyl 2–methoxy phenol, o–guaiacol, octadecanoic acid and phenol. On the other

hand, major compounds produced from Mahua PSC were 6–octadecenoic acid, octadecanoic

acid, and FFAs. The presence of higher FFAs suggests the applicability of PSC bio–oil as a

biofuel. In addition, an FT–IR analysis of bio–char revealed that the amounts of hydroxyl and

aliphatic compounds decreased with an increase in the aromaticity with an increase in the

temperature. Bio–gas analysis confirmed that the yield of gas increased with increased

volumes of CO and CH4 at higher temperatures.

8.3 Recommendations for Future Work

In future, full–scale process simulation model of oxygen–steam gasification of Karanja PSC

needs to be developed, and information related to net thermal–to–electrical or thermal–to–

hydrogen rich syngas for biofuel production needs to be obtained. Following that, a detailed

techno–economic assessment evaluating simple payback period, cash flow analysis and net

present value (NPV) needs to be performed. These details are very important to evaluate the

commercial viability of the process. The approach used in the design and operation of

entrained flow gasification system has the potential to be extended to large–scale syngas

production. The results found in this work can be used for the scale–up.

183

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