BIOENERGY POTENTIAL OF BIOMASS PRODUCED ON

MARGINAL LANDS

By

Muhammad Sajjad Ahmad

2006-GCUF-676-521

Thesis submitted in partial fulfillment of

the requirements for degree of

Doctorate of Philosophy

In

Biotechnology

Department of Bioinformatics and Biotechnology

Government College University Faisalabad

September 2018

DECLARATION

The work reported in this thesis was carried out by me under the supervision of Dr.

Muhammad Aamer Mehmood (Assistant Professor) Department of

Bioinformatics & Biotechnology Government College University Faisalabad,

Pakistan.

I hereby declare that the title of thesis: Bioenergy potential of biomass produced on

marginal lands and the contents of the thesis are the products of my own research

and no part has been copied from any published source (except the references,

standard mathematical or genetic models/equations /formulas/protocols etc.). I further

declare that this work has not been submitted for award of any other degree /diploma.

The University may take action if the information provided is found inaccurate at any

stage.

Signature of the Student/Scholar………………………………………

Name: Muhammad Sajjad Ahmad

Registration No: 2006-GCUF-676-521

iii

ACKNOWLEDGEMENTS

With humble and sincerest words, I am thankful to Almighty Allah, the Beneficent and

Merciful, who bestowed me the potential and ability to contribute a drop of material in

the existing ocean of knowledge. I pay humble but great admiration to the Holy Prophet

Hazrat Muhammad (PBUH) who is forever a torch of knowledge and guidance for

humanity.

I am greatly indebted to my honored research supervisor Dr. Muhammad Aamer

Mehmood Assistant Professor, Department of Bioinformatics and Biotechnology, for his

enthusiastic guidance, sympathetic attitude and enlightened supervision. His efforts, hard

work and maintenance of professional integrity enabled me to complete my research work

without any problem or hindrance. He is the person who explored my internal capabilities

to motivate myself towards the untouched research areas in Pakistan. I always feel proud

to be in his supervision.

I am highly obliged in paying deepest gratitude to my respected and dignified

teacher Dr. Muhammad Ibrahim Assistant Professor Department of Environment

Science and Engineering, for providing me necessary facilities available at his lab for the

completion of this work.

My heartfelt appreciation is must for the patience, generous help and countless

prayers of my father Mr. Allah Ditta (L), my caring mother Mrs. Shamshad Akhtar,

my beloved brother Muhamad Sarfraz Ahmad.

I am highly grateful to my all respectful teachers in the Department of

Bioinformatics and Biotechnology for their sincere guidance and great help.

I am particularly thankful to and all my lab fellows my juniors and staff for their

great cooperation throughout my research work.

Finally, as is customary, the errors that remained are mine alone.

MUHAMMAD SAJJAD AHMAD

iv

Dedication

I would like to dedicate this work to my Parents

Allah Ditta (Late)

Mrs. Shamshad Akhtar

&

My Brother

Muhammad Sarfraz Ahmad

TABLE OF CONTENTS

1 INTRODUCTION 1

1.1 Global Energy Scenario and potential of marginal lands ………………………… 1

1.2 Land use for Energy; Pakistani perspective ……………………………………… 1

2 REVIEW OF LITERATURE 10

3 MATERIALS AND METHODS 22

3.1 Selection of biomass …………………………………………………………….. 22

3.1.1 Urochloa mutica (Para Grass) …………………………………………………… 24

3.1.2 Pennesetum purpureum (Elephant Grass) ……………………………………….. 25

3.1.3 Pennesetum benthiumo (Mott Grass) ……………………………………………. 26

3.1.4 Parthenium hysterophorus (Carrot Grass) ……………………………………….. 27

3.1.5 Typha latifolia (Cattail grass) ……………………………………………………. 28

3.1.6 Eulaliopsis binata (Babui Grass) ………………………………………………… 29

3.1.7 Cymbopogon schoenanthus (Camel Grass) ……………………………………… 30

3.1.8 Dactyloctenium aegyptium (Egyptian Grass) ……………………………………. 31

3.1.9 Wolffia Arrihiza (Duck Weed) …………………………………………………… 32

3.2 Proximate analyses ……………………………………………………………….. 33

3.2.1 Total biomass yield ………………………………………………………………. 33

3.2.2 Total Solids ………………………………………………………………………. 33

3.2.3 Volatile Solids ……………………………………………………………………. 33

3.3 Evaluating the bioenergy potential ………………………………………………. 34

3.3.1 Thermogravimetric analysis ……………………………………………………… 34

3.3.2 Differential Scanning Calorimetry ………………………………………………. 36

3.4 Kinetic and thermodynamic analyses ……………………………………………. 36

3.4.1 Development of the mathematical model ……………………………………….. 36

3.5 Calculating the kinetic parameters ………………………………………………. 38

3.5.1 KAS Method …………………………………………………………………….. 38

3.5.2 FWO Method ……………………………………………………………………. 38

4 RESULTS AND DISCUSSION 41

4.1 Proximate analyses ……………………………………………………………… 41

4.2 Thermogravimetric analyses ……………………………………………………. 44

4.3 DTG Analyses ………………………………………………………………….. 55

4.3.1 Effect of heating rate on mass loss ……………………………………………… 55

4.4 Differential Scanning Calorimetry ……………………………………………… 69

4.5 Kinetic analyses …………………………………………………………………. 79

4.5.1 Activation energy of the selected grasses ……………………………………….. 79

4.5.2 Enthalpy of reaction ……………………………………………………………... 80

4.5.3 Pre-exponential factors ………………………………………………………….. 81

4.5.4 Gibbs free energy and entropy …………………………………………………… 81

4.6 Linear Fit Plot between Pre-Exponential Factor and Activation Energy ………… 100

4.7 Activation Energy and Conversion Points v/s Pyrolysis temperature ……………. 109

4.8 Comparison of Activation Energy and Enthalpy …………………………………. 134

CONCLUSION AND PROSPECTS 135

REFERENCES 136

PUBLICATIONS

LIST OF FIGURES

1.1 Biomass to energy conversion technologies ………………………………………… 06

1.2 Schematic representation of pyrolysis process ……………………………………… 07

1.3 Possible products obtained after pyrolysis ………………………………………….. 08

1.4 Scheme of study of the work conducted in this thesis ……………………………… 09

3.1 Para Grass (Urochloa mutica) ……………………………………………………… 24

3.2 Elephant Grass (Permisetum purpureum) ………………………………………….. 25

3.3 Mott Grass (Pennesetum benthiumo) ………………………………………………. 26

3.4 Carrot grass (Parthenium hysterophorus) ………………………………………….. 27

3.5 Cattail Grass (Typha latifolia) ……………………………………………………… 28

3.6 Babui Grass (Eulaliopsis binata) …………………………………………………… 29

3.7 Camel grass (Cymbopogon schoenanthus) …………………………………………. 30

3.8 Egyptian Grass (Dactyloctenium aegyptium) ………………………………………. 31

3.9 Duck Weed (Wolffia) ……………………………………………………………….. 32

3.9 NETZSCH TGA/DSC 409 instrument ……………………………………………... 35

4.1 Relationship between Mass percentage v/s Pyrolysis temperature of Para grass …………... 46

4.2 Relationship between Mass percentage v/s Pyrolysis temperature of Elephant grass ……… 47

4.3 Relationship between Mass percentage v/s Pyrolysis temperature of Carrot grass ………… 48

4.4 Relationship between Mass percentage v/s Pyrolysis temperature of Mott grass ………….. 49

4.5 Relationship between Mass percentage v/s Pyrolysis temperature of Egyptian grass ……… 50

4.6 Relationship between Mass percentage v/s Pyrolysis temperature of Babui grass …………. 51

4.7 Relationship between Mass percentage v/s Pyrolysis temperature of Camel grass ………… 52

4.8 Relationship between Mass percentage v/s Pyrolysis temperature of Cattail ………………. 53

4.9 Relationship between Mass percentage v/s Pyrolysis temperature of Wolffia………………. 54

4.10 DTG peaks of Para grass …………………………………………………………………….. 57

4.11 DTG peaks of Elephant grass ………………………………………………………………… 58

4.12 DTG peaks of Carrot grass …………………………………………………………………… 59

4.13 DTG peaks of Mott grass …………………………………………………………………….. 60

4.14 DTG peaks of Egyptian grass ………………………………………………………………… 61

4.15 DTG peaks of Babui grass ……………………………………………………………………. 62

4.16 DTG peaks of Camel grass ………………………………………………………………….. 63

4.17 DTG peaks of Cattail grass ………………………………………………………………….. 64

4.18 DTG peaks of Wolffia ……………………………………………………………………….. 65

4.19 DSC curves for the pyrolysis of Para grass ………………………………………………….. 70

4.20 DSC curves for the pyrolysis of Elephant grass ……………………………………………… 71

4.21 DSC curves for the pyrolysis of Carrot grass ………………………………………………… 72

4.22 DSC curves for the pyrolysis of Mott grass ………………………………………………….. 73

4.23 DSC curves for the pyrolysis of Egyptian grass ……………………………………………… 74

4.24 DSC curves for the pyrolysis of Babui grass ………………………………………………… 75

4.25 DSC curves for the pyrolysis of Camel grass ………………………………………………… 76

4.26 DSC curves for the pyrolysis of Cattail grass ………………………………………………… 77

4.27 DSC curves for the pyrolysis of Wolffia ……………………………………………………… 78

4.28 Regression plots to determine kinetic parameters of para grass ...……………………………. 82

4.29 Regression plots to determine kinetic parameters of Elephant grass …………………………. 84

4.30 Regression plots to determine kinetic parameters of Carrot grass ……………………………. 86

4.31 Regression plots to determine kinetic parameters of Mott grass ...……………………………. 88

4.32 Regression plots to determine kinetic parameters of Egyptian grass …………………………. 90

4.33 Regression plots to determine kinetic parameters of Babui grass .……………………………. 92

4.34 Regression plots to determine kinetic parameters of Camel grass ……………………………. 94

4.35 Regression plots to determine kinetic parameters of Cattail grass ……………………………. 96

4.36 Regression plots to determine kinetic parameters of Wolffia ...……………………………. 98

4.37 lnA vs Activation Energy of Para grass ……………………………………………………... 101

4.38 lnA vs Activation Energy of Elephant grass ………………………………………………... 102

4.39 lnA vs Activation Energy of Carrot grass ………………………………………………... 103

4.40 lnA vs Activation Energy of Mott grass ……………………………………………………... 104

4.41 lnA vs Activation Energy of Egyptian grass ……………………………………………... 105

4.42 lnA vs Activation Energy of Babui grass …………………………………………………... 106

4.43 lnA vs Activation Energy of Camel grass ………………………………………………... 107

4.44 lnA vs Activation Energy of Cattail grass ………………………………………………… 108

4.45 Plot between Activation energy v/s Pyrolysis temperature of Para grass …………………. 110

4.46 Relationship of Pyrolysis temperature and mass conversion points of Para grass 111

4.47 Plot between Activation energy v/s Pyrolysis temperature of Elephant grass ……………… 113

4.48 Relationship of Pyrolysis temperature and mass conversion points of Elephant grass …….. 114

4.49 Plot between Activation energy v/s Pyrolysis temperature of Carrot grass …………………. 116

4.50 Relationship of Pyrolysis temperature and mass conversion points of Carrot grass ……….. 117

4.51 Plot between Activation energy v/s Pyrolysis temperature of Mott grass …………………. 119

4.52 Relationship of Pyrolysis temperature and mass conversion points of Mott grass ………… 120

4.53 Plot between Activation energy v/s Pyrolysis temperature of Egyptian grass ……………. 122

4.54 Relationship of Pyrolysis temperature and mass conversion points of Egyptian grass ……… 123

4.55 Plot between Activation energy v/s Pyrolysis temperature of Babui grass …………………. 125

4.56 Relationship of Pyrolysis temperature and mass conversion points of Babui grass ………… 126

4.57 Plot between Activation energy v/s Pyrolysis temperature of Camel grass …………………. 128

4.58 Relationship of Pyrolysis temperature and mass conversion points of Camel grass ………… 129

4.59 Plot between Activation energy v/s Pyrolysis temperature of Wolffia ……………………… 131

4.60 Relationship of Pyrolysis temperature and mass conversion points of Wolffia …………….. 132

4.61 Comparison of Activation Energy and Enthalpy of all Biomass Samples …………………. 134

LIST OF TABLES

2.1 Activation energy values of already studied biomass samples …………………………... 18

2.2 High heating values of selected biomass samples ………………………………………. 19

2.3 Marginal lands crops ………………………………..…………………………………. 20

3.1 Characteristics of Selected Grasses ……………………………………………….. 23

4.1 Energy contents of all grasses …….………………………………………………….. 43

4.2 Pyrolysis temperature and heating rate characteristics association ……………………... 66

4.3 Zones and stages associated with heating rates …….………………………………….. 67

4.4 Kinetic and thermodynamic parameters of Para grass ……………………………. 83

4.5 Kinetic and thermodynamic parameters of Elephant grass ……………………….. 85

4.6 Kinetic and thermodynamic parameters of Carrot grass ………………………….. 87

4.7 Kinetic and thermodynamic parameters of Mott grass …………………………… 89

4.8 Kinetic and thermodynamic parameters of Egyptian grass ………………………. 91

4.9 Kinetic and thermodynamic parameters of Babui grass ………………………….. 93

4.10 Kinetic and thermodynamic parameters of Camel grass …………………………. 95

4.11 Kinetic and thermodynamic parameters of Cattail grass …………………………. 97

4.12 Kinetic and thermodynamic parameters of Wolffia ….. …………………………. 99

4.13

Relationship between Pyrolysis temperature, activation energies and product

formation upon pyrolysis of Para grass …………………………………………… 112

4.14 Relationship between pyrolysis temperature, activation energies and product formation upon

pyrolysis of Elephant grass ………………………………………………………………... 115

4.15 Relationship between pyrolysis temperature, activation energies and product formation upon

pyrolysis of Carrot grass ………………………………………………………………....... 118

4.16 Relationship between pyrolysis temperature, activation energies and product formation upon

pyrolysis of Mott grass ……………………………………………………………….......... 121

4.17 Relationship between pyrolysis temperature, activation energies and product formation upon

pyrolysis of Egyptian grass ……………………………………………………………….... 124

4.18 Relationship between pyrolysis temperature, activation energies and product formation upon

pyrolysis of Babui grass ………………………………………………………………......... 127

4.19 Relationship between pyrolysis temperature, activation energies and product formation upon

pyrolysis of Camel grass ………………………………………………………………......... 130

4.20 Relationship between pyrolysis temperature, activation energies and product formation upon

pyrolysis of Wolffia ………………………………………………………………................ 133

ABSTRACT

Biomass is believed as the only foreseeable feedstock for sustainable production of clean

energy. However, it cannot be produced using agricultural lands to avoid the competition with

the land for food crops. Hence, the plants/grasses adapted to poor soils (marginal lands) can be

exploited for energy production without causing any negative impact. Pakistan is a 6th most

populous country in the world, where the total population is even greater than Russia. So, the

country has huge energy demands and is passing through the worst energy crisis in its history.

Moreover, 64% of its energy requirements are being met through combustion of fossil fuels

which is alarming in terms of environmental stability. Hence, it is essentially required to

explore alternative, clean and sustainable energy sources. Biomass produced on marginal lands

offers a promising alternative. Pakistan has 9 million hectares of salt-affected soils which is

unfit for agricultural activity. The present study was focused to evaluate the bioenergy potential

of the grasses adapted to this salt-affected area. The thermodynamics, thermal degradation and

kinetics of selected grasses namely Para grass (Urochloa mutica), Elephant grass (Permisetum

purpureum), Babui grass (Eulaliopsis binate), Mott grass (Pennesetum benthiumo), Egyptian

grass (Dactyloctenium aegyptium), Carrot grass (Parthenium hysterophorus), Camel grass

(Cymbopogon schoenanthus) and Cattail (Typha latifolia) produced on marginal lands in

Pakistan without any fertilizers, pesticide and agriculture practices. All above biomass samples

were thermally degraded in Nitrogen environment under three different heating rates (10, 30

and 50) ºC min-1. The thermodynamic and thermokinetic parameters were investigated using

iso-conversion methods including KAS (Kissenger-Akahira-Sunose) and FWO (Flynn-Wall-

Ozawa). Whereas peak zones were also examined throughout the DTG curves under three

different heating rates. Degradation regions were also determined on the basis of degradation

of lignin, cellulose and hemicellulose components. High heating values were determined to

range from 15-18 MJ kg-1. Similarly, activation energy, Gibbs free energy and enthalpy values

of all biomass samples were determined which showed to be ranging from 166-267 kJ mol-1,

166-177 kJ mol-1and 161-262 kJ mol-1 respectively. All biomass samples had first order pre-

exponential factors. Our data showed all biomass samples had remarkable potential to adopt

as a low-cost biomass for bioenergy production through pyrolysis.

1

Chapter 1

INTRODUCTION

1.1 Global Energy Scenario and potential of marginal lands

In current global energy scenario different traditional resources contributed including

of with oil (35%), fossil fuel 88%, natural gas (24%) and coal (29%) as the major fuels total

primary energy consumption share (Brennan & Owende, 2010). However, their intensive

usage has resulted in reduction of fossil fuel reserve which has ultimately caused an increase

in price and demand of petroleum products across the globe. Transportation fuel demand is

protruded to increase up to 40% over 2010 to 2040 (Leite et al., 2013). World’s energy demand

is continuously increasing with the increase in population, and subsequent increasing mobility

and industrialization. On the other hand, natural resources that meet the energy need are

depleting side by side. So, it is estimated that after 88 years all reserve resources for fuel

would vanish and there must be some alternative in this situation. Approximately in 70 years,

all gas reserves would be consumed similarly coal reserves would be used within 60 years.

Globally, there is need to put efforts to cope the problem that is arising in the current scenario.

1.2 Land use for Energy; Pakistani perspective

Pakistan is the sixth most populated country of the world and has a huge and growing

energy demands, with increasing competition among resources. Pakistan is producing 12,000

MW annual energy from all resources. From total installed capacity, 64.2% is being produced

through fossil fuels, 29% is produced from hydropower sources and 5.8% is produced through

nuclear power plants. Recently, new nuclear power plants and solar energy parks are being

established to produce clean energy, but still, there is a gap in demand and supply. Moreover,

in future fossil energy sources should be replaced with other renewable and clean energy

sources. But problem is that the energy demand will be doubled by the end of 2030 that is the

very alarming situation the Country will face in upcoming years. Additionally, these all

reserves are limited and the country is not self-dependent through these resources.

The Pakistan’s energy demand is higher than its production that’s why new resources

are needed. It is estimated in the current demand would be doubled by 2030 due to increasing

2

population, and improving living standards. It is essential to establish new strategies and to

establish new energy resources to overcome the current energy crisis and future energy

demands of Pakistan. There is no standalone approach to fulfill the diverse energy requirement

of transport, industry and household sector. Hence, multiple energy sources including solar,

wind, hydro, thermal, nuclear and bioenergy should be exploited. Biomass-based energy offers

renewable and clean energy along with wind, water, and solar energy. So, there is urgent need

for renewable and environmentally friendly alternatives for energy production. Otherwise, the

energy crisis of Pakistan will become even worse in near future.

Pakistan contributes 0.79% of overall GHG emissions worldwide which is also an

alarming situation. The GHG emissions have been increased from 14,154 kt to 163,060 kt in

2012, which means that there has been 104% increase in 60 years and 70% increase in last 20

years. The FAO latest report that shows the GHG emission yearly from 1960-2014 in

Pakistan is shown below. According to FAO (Food and Agriculture Organization), there is 397

million Ha of saline soils present on the world map in which Pakistan has 9 million Ha of saline

soils that have a huge potential for energy generation in near future.

3

4

The term “marginal land” is on everyone’s lips yet there is no clear definition for it.

Marginal lands are described as drought, unproductive, substandard quality land area,

unfavorable climate conditions, low profitability for food crops. However, it has broader

definition in terms of usability and productivity by means of crops that could be grown on that

area for energy generation purpose. The field crops on that areas are extremely invasive and a

problem for the society due to no use of these crops (Smith et al., 2013). However, there are

unfavorable production condition, poor soil condition and harvesting problem these lands are

neglected all over the world but world contributed a huge area for these problem lands (Hill

et al., 2006; Nixon et al., 2001; Tilman et al., 2006).

Ilya Gelfand and his colleagues 2013 demonstrated the idea about the scope of marginal

lands for biofuel production in US Mid-West as if we produce lignocellulosic biomass on

marginal lands that lead to accomplishing our fuel needs in an efficient manner. It could have

positive environmental effects on the health of humans as well as animals. Biomass production

on marginal lands was highly appreciated as these crops could grow on marginal areas less

sustainable lands, areas where climate limitations were there. There was no completion of food

as only grasses and short rotation tree crops would grow there. Absorption of carbon dioxide

on these lands was another remarkable feature of their study. GHG (greenhouse gas) emission

mitigation due to displaced feed production and food when cellulosic crops on marginal lands

were grown without the risk of indirect carbon costs.

This thesis focuses on to estimate the energy potential of the biomass produced on the

salt-affected soils of Pakistan. Several grasses are adapted to these poor soils and produce

heavy biomass without any pesticide, fertilizers, minimum management practices, and

irrigation practices, hence offer abundant and low-cost biomass for bioenergy. We have

selected eight kinds of grasses from different salt-affected areas of Pakistan. These grasses

including Para Grass, Babui Grass, Mott Grass, Carrot Grass, Egyptian Grass, Camel grass,

Cattail, wolffia and Elephant Grass were selected based on their biomass productivity on

marginal lands. The selected grasses were subjected to thermogravimetric studies to evaluate

their energy potential through pyrolysis. While pyrolysis is one of the best methods for energy

generation (Fig. 1.1) stored in the biomass. A schematic representation of pyrolysis process

5

and possible product formation is reflected in diagrammatic sketches shown in Fig. 1.2 and

1.3, respectively. The plan of work used in this study is shown in Fig. 1.4.

6

Fig. 1.1 Biomass to energy conversion technologies

7

Fig. 1.2 Schematic representation of pyrolysis process

8

Fig. 1.3 Possible products obtained after pyrolysis

9

Fig. 1.4 Scheme of study of the work conducted in this thesis

10

Chapter 2

REVIEW OF LITERATURE

Pakistan is the 6th populous country and is one of the emerging economies in the world.

Therefore, it has a huge demand of energy which is being fulfilled from various resources

including oil, gas, wind, hydro and biomass. According to official government reports it was

dependent mainly on oil and gas that was alarming because both resources are traditional and

depleting rapidly. Other than this fact there is a limitation of tradition fuels in various

prospective including cost, GHG emission, non-reusability and limited availability. Currently,

Pakistan is mainly depending upon oil and gas to fulfill its energy requirements which is mainly

import from Middle East countries because indigenous resources are not sufficient to meet the

requirements. Two major sectors transport and power have increasing demands of energy

which is mainly fed through oil and gas consumption. It is estimated that transport and power

sector consumes 55 % and 35% total oil consumption, respectively in the year 2016. Whereas

natural gas is another alternative to fulfill 44% of the energy needs of the country which is

unfortunately restricted with its limited availability. All these resources had limitations as

described above, an alternative to these traditional resources is the use of renewable feedstocks

including e wind, solar, hydro and biomass ("Pakistan Economic Survey," 2015).

It was documented about the importance of TGA and combined TGA-MS analysis on

eucalyptus and fir food for its thermal degradation at different temperatures. They divided

biomass into four zones including water removal as a first step. Under different degradation

rated cellulose contents and volatile matter difference was calculated. They also studied in the

same experiment about the Pyrolysis kinetics by using multiple model steps (Sanchez-Silva et

al., 2012).

For instance, Eucalyptus biomass was subjected to study the impact of pre-treatments

namely hydrothermal and milling pre-treatment under controlled conditions using Ca(OH)2.

When ball milled sample was treated in 20% Ca(OH)2 at 170 ºC, 90% glucose yield was

obtained. It was also observed that through delignification with use of Ca(OH)2, the sample

was unaffected. Alkaline pre-treatments cause loosening of the bonds between hemicelluloses

11

and lignin that led to weakening the substrate structure (Ishiguro & Endo, 2014) for its

subsequent use in the production of 2G fuels.

Another study demonstrated the co-pyrolysis of sugar beet pulp in 50/50 w/w ratio to

reveal the thermal behavior. Kinetic and thermodynamic parameters were studied including

gaseous, liquid and solid products obtained from the special chamber designed at 600 ºC.

Proximate analyses were also performed to determine the ratio of Ash, volatiles and carbon

contents. It was confirmed that sugar beet pulp decomposed faster as compared to lignite. It

was also confirmed 600 ºC was the considerable temperature for the optimal production of

gasses (Yılgın et al., 2010).

Another study revealed the influence of Potassium on the gasses released during the

rice-husk pyrolysis in a fluidized bed reactor in two stages. The reaction chemistry was studied

through Model Fitting approach using Friedman method. It was confirmed that various gases

showed different phenomenon from start to end time of release means gasses had a different

mechanism for generating routes. Kinetic parameters were studied and it was shown that

mainly the kinetic energy was ranging from 10 kJ mol-1 to 19 kJ mol-1 for all selected samples

(Y. Liu et al., 2017), this is one of the lowest reported kinetic energy values of the studied

biomass.

In another study conducted for the investigation of pyrolysis of the sewage sludge and

microalgae separately, the DAEM model (Distributed Activation Energy Model) was

employed to calculate the activation energies and thermodynamic parameters of both samples.

Nine different experiments were conducted at different rates of heating to confirm the results

obtained from TGA experiments. The pre-exponential factor and activation energy were

ranging from 150-200 kJ mol-1 and 1010-1015 s-1 and 200-400 kJ mol-1 and 1015-1025 s-1 for

sewage sludge and C. vulgaris, respectively. These values were used to estimate the reaction

fraction with pyrolysis temperature through parabolic and exponential curves (Soria-Verdugo

et al., 2017).

Pyrolytic and kinetic study of blended and non-blended residue samples of Eucalyptus

benthamii and Pinus taeda were conducted at three different heating rates. Kinetic parameters

were studied by using Arrhenius equation model. Whereas peak zones were also examined

12

throughout the DTG curves under three different heating rates. It was determined that P. taeda

was more suitable as a bio-oil production feedstock as compared to Eucalyptus benthamii due

to a lower percentage of ash and a higher percentage of volatile contents (Costa et al., 2016).

M. Valix and his colleagues investigated the comparison of dry and thermochemical

pretreated sugarcane bagasse to determine their co-combustion behavior as compared to raw

baggase. Thermochemical torrefication was examined in the presence of Nitrogen environment

after treating with acid and dry torrefication was carried out without chemical pre-treatment.

The results showed that thermochemical pretreated baggase generated char that was closer to

various type of coal that was suitable for the co-firing purpose. Thermochemical torrefication

also influenced on densification of bagasse with a 335% rise in bulk density to 340 kg/ m3.

These features elucidated the potential of thermochemical torrefication as a considerable

strategy to produce biofuels from biomass (Valix et al., 2017).

Moreover, the thermal degradation of tobacco stem in Nitrogen Environment at four

different heating rates under experimental temperature was ranging from room temperature to

1000 ºC. Kinetic energy, Pre-exponential factors and reaction mechanism was studied through

Coats and Redfern Method. It was examined that different cellulose, hemicellulose, and lignin

were degraded from temperature ranges 180-540 ºC, whereas activation energy value

calculated from different methods were ranging from 150-230 kJ mol-1 (Polat et al., 2016).

Recently fast pyrolysis experiment was conducted to convert waste biomass to bio-oil

as a substituent to traditional fossil oil. Bubbling fluidized bed reactor was used to convert

cotton stalk into valuable bioproducts. However, several properties were investigated including

feed size, temperature conditions gas and char yield. It was determined the optimum pyrolysis

temperature was 490 ºC that gave maximum yield but here the char yield was minimum.

Chemical composition was also studied through FTIR and GC-MS analyses which reflected

that major constituents were phenols, ketones, furans, aldehydes, and sugars (Ali et al., 2015).

In another study, two Mediterranean species Silybum marianum and Dittrichia viscosa

were investigated on marginal lands as energy crops. Thermodynamic and kinetic parameters

and evolved gasses were analyzed to investigate the bioenergy potential and its comparison

with previouslu studied biomass samples. It was confirmed that average energy values were

13

295-300 kJ mol-1 that was also suitable for co-combustion and pyrolysis (Domínguez et al.,

2017).

Duckweed was investigated using the solid base as a catalyst in slow pyrolysis and was

subjected to thermogravimetric analysis. It was observed that 60% mass conversion remained

during slow pyrolysis. Energy parameters were also studied. Whereas direct pyrolysis resulted

in various hydrocarbon and industrial products including cyclobutanol, 3-methyl-butanal,

ethylbenzene, 2-pyrrolidinone, 4-ethyl-phenol and 2-methyl-1H-pyrrole were synthesized in

the presence of a solid base catalyst (C. Yang et al., 2017).

Another study revealed the thermogravimetric analysis and kinetic study of oil palm

biomass, Malaysian sub-bituminous coal, fruit bunches, and mesocarp fiber. Different blends

were made by mixing different amounts of biomass and palm oil in nitrogen and inert

environment. Thermogravimetric and DTG analysis were performed and determined various

characteristics of these blends using first-order reaction model (Idris et al., 2010).

Similarly, thermal degradation of two different Egyptian biomasses cotton stalk

powder and sugarcane baggase in Nitrogen Environment at three different heating rates under

experimental temperature was ranging from room temperature to 1000 ºC. Two different zones

were observed for biomass samples. Therefore, both samples showed the similar region of

major degradation from 200-500 ºC. Kinetic energy, Pre-exponential factors and reaction

mechanism were studied through Direct Arrehenis plot method. It was examined that

sugarcane baggase powder had activation energy values ranging from 45- 57 kJ mol-1, whereas

activation energy value calculated for cotton stalk was ranging from 67-100 kJ mol-1 (Saad A

El-Sayed & Mostafa, 2015).

Date palm waste was investigated through kinetic study, pyrolysis and combustion

process using TGA experiments. The biomass waste was including stem, leaf and leaf stem

both were examined. Proximate and ultimate analysis was done followed by the FTIR analysis

on all biomass samples. It was determined that date palm leaf and seed had high volatile and

high calorific values as compared to leaf stem. Therefore, it could be used as a source of energy

and biochar (Sait et al., 2012).

14

Pyrolytic and kinetic study of Potamogeton crispus and Sargassum thunbergii were

carried out in nitrogen environment under three different heating rates using thermogravimetric

analysis. Three pyrolysis stages were determined including dehydration, devolatization and

residual decomposition stage. Kinetic parameters and reaction mechanism were determined by

using three-dimensional diffusion model. Whereas peak zones were also examined throughout

the DTG curves under three different heating rates. Both samples had shown three

decomposition stages with two zones in the second region of degradation where major mass

was lost. The average activation energy of both samples was determined, Potamogeton crispus

had 140 kJ mol-1 whereas Sargassum thunbergii had 189 kJ mol-1 (Li et al., 2012).

Another study conducted for the investigation the pyrolysis of plant process by using

reaction model to determine the activation energies with thermodynamic parameters of

broadleaf plat sample including stem and leaf of the plant. It was determined that the activation

energies of leaf samples were ranging from 43-80 kJ mol-1 whereas twig samples had activation

energy ranging from 84-110 kJ mol-1. It was investigated that activation energy determined for

the degradation of hemicellulose, cellulose and lignin contents of both were in narrow ranges

(Tao et al., 2017).

Won and Choi et. Al. (2014) worked on NaOH-catalyzed optimization on empty fruit

bunch by using Response Surface Methodology under controlled conditions; 160 ºC and 11

min 20 sec for steam pre-treatment, where 3% NaOH was used, and enzymatic digestibility

and glucan recovery were both high. Furthermore, they obtained 78% and 93% yields of xylan

and glucan respectively under same conditions.

Pyrolysis characteristics of cellulose, hemicellulose, and lignin were investigated

through TGA coupled with DSC and a packed bed. Further FTIR analysis demonstrated the

major functional groups present in the biomass. It was determined that cellulose and cellulose

were degraded from 315–400 °C and 220–315 °C respectively. Lignin degradation was more

difficult from 160 to 900 °C with major compounds studied through FTIR analysis. Pyrolysis

process was endothermic in the beginning but after some time it was reverted to exothermic

reaction with negative results on the chart (H. Yang et al., 2007).

15

Another study revealed the combustion characteristics through TGA analysis coupled

with mass spectrometry of fir wood, pine bark and eucalyptus were investigated. Two main

steps devocalization and char oxidation were defined through combustion of the biomass.

Heating rate effect demonstrated that by increasing heating heat decomposition also increased.

However, combustion characteristics, model kinetic, model-based reaction order were also

investigated. Therefore, activation energy, best fitting experimental condition were also

determined from the combustion process (López-González et al., 2013).

In another study cellulose, lignin and xylan components of the eucalyptus, pine and fir

wood were calculated through coupled TGA and mass spectrometer analysis. Pyrolysis process

was done from ambient temperature to 1000 °C, remained char was also studied through

gasification of the material with 5% volume. The model fitting approach was used to correlate

the catalytic effect of the samples that had cellulose and pine dark were properly fitted with

the models. Semi-empirical model was also proposed for gasification rate and the catalytic

effect of ash components (López-González et al., 2013).

Another study was available for TGA-DSC pyrolysis and combustion of switchgrass,

corn stalks, and big bluestem. It was investigated integral reaction heats of the biomass under

controlled conditions of temperature. Furthermore, it was determined during pyrolysis from 30

to 700 °C integral heats were endothermic for bigstem and switchgrass but it was exothermic

from 30 to 587 °C. However, the exothermic and endothermic phenomenon was observed for

studied biomass samples. Differential reaction heat was also investigated; for switchgrass mass

loss was 0.0535-0.975 in fraction, it was 0.0885–0.850 and 0.736–0.919 for corn stalk and big

stem respectively (Shen et al., 2015).

It was quantified through kinetic analysis of torrefaction using DGA-TGA and FTIR

on lignocellulosic biomass in Canada. This technique improved the fuel properties under

different temperatures. They treated 200°C to 300°C as it is a reasonable residual time for the

samples. Using thermogravimetric analyzer with DSC and macro TGA thermal activities and

thermal degradation of wheat straw and Miscanthus for 45 minutes. The output of the above

experiment of micro TGA carried out into the FTIR analysis for gaseous products. (Acharya

et al., 2015).

16

Jade Little et al 2013 analyzed the importance of Bamboo as a biofuel resource as it is

naturally abundant and rapid growth in China. Bamboo is economically as well as technically

feasible for ethanol production in China. They used liquid hot water pre-treatment for this

purpose followed by enzymatic saccharification under temperature ranges between 170- 190

C for about 10-30 minutes. In their experiment, 69% sugars were released when standard

enzymes loaded. They also demonstrated that the cost of fuel per liter would be 0.484 dollars

per liter using Aspen Plus techno-economic model (Littlewood et al., 2013).

Idania Valdez et. al. 2010 studied the ethanol production 75.73 million tons from a dry

matter of 20 crops in Mexico as it was the third largest country throughout the world producing

biofuel from dry matter. Technologies used for this purpose were fermentation of crops and

the second is direct combustion because a lot of energy is released during this process used for

different purposes. In this (Valdez-Vazquez et al., 2010) study it was further elaborated that

63.13 million tons of biomass was produced from corn straw and sorghum straw, wheat straw

and leaves of sugar cane. In 75.73 million tons 15.60 million tons was the contribution of

corncobs, bagasse, coffee pulp and maguey bagasse as a secondary crop residue. In Mexico it

was estimated that 78 MW bioethanol production per year and 0.3 million m3 via anaerobic

fermentation is produced (Valdez-Vazquez et al., 2010).

Qiang Yang and Xuejun Pan (2012) determined the importance of flower stalks of

Agave americana for bioethanol production because in past it was not used a bioethanol

production due to its complex structure. But the study revealed when it was treated with dilute

acid sulfite and NaOH its cell wall composition changed as asses to the enzymatic digestibility

and adsorption of cellulases also investigated during their experimentation. Pre-treatments

with NaOH increased the enzymatic digestibility, as well as SPORL, increased sugar yields

and higher substrate (Q. Yang & Pan, 2012).

Cardona Eliana et. al. (2013) studied the impact of enzymatic hydrolysis and

fermentability of ethanol by pre-treatment. They grew elephant grass in farms of Antioquia

Columbia under reduced moisture contents by dried in air and ground to a particle size of 3

mm. Different experimentations were performed to determine its fermentability of cellulosic

fraction and enzymatic hydrolysis by physic-chemical and chemical processes as steam

explosion, alkaline delignification, alkaline peroxide, hydrolysis of dilute acid and aqueous

17

ammonia pre-treatment. Through those experimentations, they found the concentration of

reducing sugars is maximum when treated with NaOH. Highest ethanol yield was obtained by

performing pre-treatment conditions under 2%wt NaOH with solid to liquid ratio of 1:20 (wt)

at 120 ºC for 1 h. Furthermore, 88% lignin also removed through their experimentation (Eliana

et al., 2014).

18

Table 2.1: Activation energy values of already studied biomass samples

Biomass Samples Activation energy

(kJ mol-1) References

Coconut shell 180-216 (Tsamba et al., 2006)

Cashew 130-174

Cypress wood chips 168-210 (Vhathvarothai et al., 2014)

Macadamia nut shells 164-230

Milkweed 181-253 (S.-S. Kim & Agblevor, 2014)

Sugar cane 77-87 (Saad A. El-Sayed & Mostafa,

2014) Cotton stalk 98-100

Rice husk 74-80 (Maiti et al., 2006)

Pinewood sawdust 148-164 (Chen et al., 2015)

Fern stem 160-200

Loblolly pine hemicellulose 30-40 (Reza et al., 2013)

Loblolly pine cellulose 73-77

Bamboo 89-216 (Ma et al., 2017)

Tobacco stalk 46-85 (Cai et al., 2016)

Pinyon pine 43-160 (S.-S. Kim et al., 2014)

Polyester fiber 178-231 (Wen et al., 2017)

Cotton 144-243

Melon Seed husk (Citrullus

colocynthis L.) 145-300 (Nyakuma, 2015)

19

Table 2.2: High heating values of selected biomass samples

Biomass High Heating Value

(MJ kg-1) References

Corn cob 22.38 (Ravikumar et al., 2017)

Melon Seed husk

(Citrullus colocynthis L.) 19.07 (Nyakuma, 2015)

Date palm Rachies 17.88 (Bensidhom et al., 2018)

Miscanthus 18.38

(Corton et al., 2016) Willow 19.06

Rush sill 18.26

Brak sil 17.24

Lantana camara 30.03 (Mundike et al., 2017)

Mimosa pigra 31.01

Agro residue seed 25.6 (Tinwala et al., 2015)

Olive mill waste 26.4 (Benavente & Fullana, 2015)

Teak (Tectona grandis) 19.8 (Balogun et al., 2014)

Spruce wood 22.22 (Demirbas, 2017)

Hazelnut shell 21.23

Barley Straw 27.29 (Zhu et al., 2015)

20

Table 2.3: Marginal lands crops

Crop

Nature,

adapted

climate

Biomass

Yield

(tons ha-1

y-1)

Potential

characteristics

Energy

potential References

Bamboo

(Bamboosa

balcooa)

perennial,

adapted to

moderately

acidic loamy

soils, warm

humid

environment

40-50

One of the fastest-

growing plants,

alcoholic &

phenolic

compounds

41.85

GJ ha-1

(Kuehl, 2015; V.

Mishra, 2015;

Mullet et al.,

2014)

Cardoon

(Cynara

cardunculus)

Perennial,

adapted to

high

temperature

and low

rainfall

7.4 -14.6

artichoke oil,

feedstock for the

first bio-refinery

and biodiesel

138

GJ ha-1

(Angelini et al.,

2009;

Francaviglia et

al., 2016;

Mauromicale et

al., 2014)

Eucalyptus

(Eucalyptus

obliqua)

perennial,

adapted to

temperate,

tropical and

subtropical

poor soils

20

Source of essential

oils, phenolic

compounds,

medicinal

compounds, fast

growing

233-245

GJ ha−1

(Eijck et al.,

2012; Van Der

Hilst & Faaij,

2012)

Giant reed

(Arundo donax

L.)

Perennial,

adapted to

Mediterranean

environment

36

Resistant to

drought, used in

dissolving pulp,

durable yields,

impressive

bioenergy

feedstock

2281

CH4 kg-1

(o Di Nasso et

al., 2013)

21

Poplar

(Populus

tremula)

Circumpolar

subarctic and

cool

temperate

6-15.8

biofuels, carbon

mitigation

potential, and fast

growing

92

TW h-1

(Saha &

Eckelman, 2015)

Reed

canarygrass

(Phalaris

arundinacea L.)

Perennial,

adapted to

temperate

regions, wet-

soils, colder

climatic, flood

plains

15

drought-tolerant,

phytoremediation,

sources of drugs

97 GJ ha−1

(Lord, 2015;

Pociene et al.,

2013; Sanderson

& Adler, 2008)

Switchgrass

(Panicum

virgatum L.)

Perennial,

adaptive to

versatile

growth

conditions,

C4-pathway

1-22

ethanol, butanol,

biogas production,

thermal energy,

phytoremediation,

drought, and

flooding tolerant

60

GJ ha-1

(o Di Nasso et

al., 2015; Schmer

et al.,

2008{Gabrielle,

2014 #568)

Virginia

mallow

(Sida

hermaphrodita)

Perennial,

native to

sandy or

rocky river

shores,

temperate

9-20

source of food &

nectar for

honeybee rearing,

the potential for

bioenergy

production

219.5

GJ ha-1

(Borkowska et

al., 2009;

Franzaring et al.,

2014)

22

Chapter 3

MATERIALS AND METHODS

3.1 Selection of biomass

The present study demonstrated the bioenergy potential of biomass produced on poor, non-

productive and non-profitable soils which were affected by high salts present in the water table.

The grasses which were well-adapted to salt-affected soils were selected at the first stage based

on their easy availability and adaptation to the poor soils. These selected grasses were collected

from experimental fields of the Soil Salinity Research Institute (SSRI), Pindi Bhattian,

Pakistan. The following selection criteria were followed to select these grasses;

o The grass should be adapted to poor-soils to avoid competition with food or land for

food

o Should produce enough biomass without pesticide or fertilizers applications to get low-

cost biomass

o Should be abundantly available locally enabling efficient utilization of local resources

o Should have no negative impact on the growth to conserve the environment in the case

of mass scale production

The selected grasses were cultivated without any fertilizer and pesticide input. The

agronomic properties of the selected grasses shown in Table 3.1.

23

Table 3.1 Characteristics of Selected Grasses

Local name

of grass Scientific name Characteristics

Yield of grass

(thousands

Kg ha-1)

References

Para grass Urochloa mutica Invasive, fast

growth 57

(Kumar &

Abrol, 1984)

Elephant

grass

Permisetum

purpureum Fast growth, Tall 38

(Imhoff et al.,

2000; Wang et

al., 2002)

Mott grass Pennesetum benthiumo Palatable, tall,

broad leaves 199.20

(Bilal, 2009;

Larson et al.,

2017)

Carrot grass Parthenium

hysterophorus

Fast growth,

seed longevity,

drought

tolerance

55 (Laflamme,

1992)

Babui grass Eulaliopsis binate

Invasive, fast

growth, drought

tolerance

1.40 (El Bassam,

2010)

Camel grass Cymbopogon

schoenanthus

Adaptability,

High growth

capacity, adverse

species, long,

broad leaves

01.40

(Onadja, 2006;

Yentema et al.,

2007)

Cattail Typha latifolia

High

productivity, the

longevity of

seeds, can

spread through

the air.

0.76 (Mitich, 2000)

Egyptian

grass

Dactyloctenium

aegyptium

Invasive, high

productivity,

rapid growth

11.10 (Batanouny et

al., 1988)

24

3.1.1 Urochloa mutica (Para Grass)

Para grass is found in tropical and subtropical regions having extremely worst weeds

in US, Mexico, Central Asia and Mexico. It has fast growth, allelopathic abilities, high

productivity and invasive in nature (Kumar & Abrol, 1984). It can produce 57,000 kg of

biomass from one hectare per annum without management practices, fertilizers, and pesticides.

Being highly invasive in nature, it is a global problem. It may be used as bioenergy feedstock

due to its abundance and low-cost.

Fig. 3.1 Para Grass (Urochloa mutica)

Source: https://keyserver.lucidcentral.org/weeds/data/media/Html/urochloa_mutica.htm

25

3.1.2 Pennesetum purpureum (Elephant Grass)

Elephant grass occurs in tropical sub-Saharan and tropical Africa. It is found in

waterways, ponds, wetlands and stream edges. It is a high yielding tropical grass that can grow

in dry and wet conditions also have the ability to intercropped with banana and cassava (Kataria

et al., 2016; Machado et al., 2017). It may produce biomass up to 37,000 kg ha-1 y-1 without

any irrigation and pesticide application. This grass is also well-adapted to Pakistani salt-

affected soils is abundantly available for bioenergy production.

Fig. 3.2 Elephant Grass (Permisetum purpureum)

Source: http://www.feedipedia.org/node/395

26

3.1.3 Pennesetum benthiumo (Mott Grass)

Mott grass is abundantly found in salt-affected areas of Pakistan, Africa, Kenya, and

the USA. It is tall, palatable, erect, has nutritious broad leaves, fast growing, and high-yield

grass. It produces biomass up to 200,000 kg ha-1 y-1 without applying fertilizers and pesticides.

Hence, it may be a source of abundant low-cost feedstock for bioenergy (Larson et al., 2017).

Fig. 3.3 Mott Grass (Pennesetum benthiumo)

http://www.ibrahimfarms.com/articles/mott-grass

27

3.1.4 Parthenium hysterophorus (Carrot Grass)

Carrot grass has aggressively colonized throughout the world on roadsides, wetlands,

pastures and farmlands including Pakistan, USA, Africa, Australia, and India. It is fast

growing, invasive, stay longer in the soils due to seed-longevity, drought tolerance and has

high productivity and yield (Evans, 1997). It grows in many types of wild environmental

conditions. It yields 55,000 kg ha-1 y-1 without any management practices, fertilizers, and

pesticide application. It produces light-weight seeds that can travel through air, birds, vehicles,

water and animal traffic from one place to another that contributes to its rapid growth in various

areas of the world (Adkins & Shabbir, 2014). This grass is one of the best candidates for

bioenergy production because of its adaptability on poor soil and no competition with the food

products areas.

Fig. 3.4 Carrot grass (Parthenium hysterophorus)

http://msue.anr.msu.edu/news/controlling_wild_carrot_in_hay_fields_and_pastures

28

3.1.5 Typha latifolia (Cattail grass)

Cattail grass is found throughout the world in water ponds, Roadside water bodies,

wetlands, the tropical and subtropical areas of Asia Africa North America and Europe. It has

fast growth, invasive, allelopathic abilities, the longevity of seeds in soils, drought tolerance

and high productivity and yield. It can also favourable growth in any type of environment

(Morton, 1975). It produces biomass up to 55,000 kg ha-1 y-1 without management practices,

fertilizers, and pesticide applications. It has light seeds that can travel through air, birds,

vehicles, water and animal traffic from one place to another that contributes its rapid growth

in different habitats of the world (Mitich, 2000). This plant is the best candidate for bioenergy

production because of its high adaptability on poor soil and no competition with the food

products areas.

Fig. 3.5 Cattail Grass (Typha latifolia)

http://www.cattails.info/

29

3.1.6 Eulaliopsis binata (Babui Grass)

Babui Grass is a perennial specie present in tropical and subtropical areas of Pakistan,

India, China, Nepal, Bhutan and Southern Asia. It has two cropping seasons throughout the

year due to its low fertility. It has no competition with fodder as it is not used as animal food.

It is also suitable for cultivation on large scale in the hilly area for rapid ecological impact

(Dutt et al., 2004).

Fig. 3.6 Babui Grass (Eulaliopsis binata)

Source: http://www.wikiwand.com/sv/Eulaliopsis_binata

30

3.1.7 Cymbopogon schoenanthus (Camel Grass)

Camel grass is a perennial specie widely adapted nutritious soils and poor water

conditions of Pakistan, India, Malaysia, China and Northern Africa. The Cymbopogon genus

has almost 50 different species adapted as a low-cost biological resource. It has high

adaptability due to its growth capacity in municipal wastewater, poor soils, and deserts (Gupta

& Sharma, 1971). Hence, due to abundance and adaptable nature, this grass also offers a low-

cost biomass feedstock for bioenergy.

Fig. 3.7 Camel grass (Cymbopogon schoenanthus)

Source: http://zelenaya-lavka.ru/shop/travy/limonnaia-trava-lemongrass/

31

3.1.8 Dactyloctenium aegyptium (Egyptian Grass)

Egyptian grass grows rapidly in tropic and sub-tropic regions of Asia and Africa

including India, China, Pakistan, Malaysia, Japan, Morocco, Egypt, Sudan, Tunisia and Libya

(B. S. Naik et al., 2016). It does not act as hostile competitive in healthy vegetation, instead, it

thrives on disturbed and overgrazed lands (Navie et al., 1998). It is a common coarse grass

widespread mostly grown on waste rain falls, roadsides, wastelands, and fallows.

Fig. 3.8 Egyptian Grass (Dactyloctenium aegyptium) Source: http://www.feedipedia.org/node/465

32

3.1.9 Wolffia arrhiza (floating duckweed)

Wolffia arrhiza belongs to photosynthetic duckweeds often found floating in

wastewater. They help in water purification by removing excessive nutrients (nitrates and

phosphates) and heavy metals from the wastewater with concomitant biomass production and

atmospheric carbon fixation. The duckweed biomass may be rich in starch, proteins and some

other metabolites making the biomass suitable to be used as a feedstock for bioenergy and

biorefinery (Suppadit, 2011).

Fig. 3.9 A dense mat of Duck weed (Wolffia arrhiza) over water surface

Source: https://paulskillicorn.wordpress.com/2012/09/14/a-dense-wolffia-arrhiza-mat/

33

3.2 Proximate analyses

3.2.1 Total biomass yield

Grasses produced on the marginal land were harvested from an area of the one-meter-

square from three randomly selected locations in the field. The mass of fresh and air-dried

biomass was measured. The fresh mass per hectare is provided in Table 3.1.

3.2.2 Total Solids

Total solids in each sample biomass were measured through drying at 105 ºC for 24 h

in the oven. The known mass of each sample was put in the crucibles and put in the oven in

triplicate up to a constant mass. The analytical grade balance was used to determine the mass.

The difference in mass before and after drying gave us total solids. The percent solid and

moisture content was determined using formula.

Formula

Moreover, all samples were put at 105 ºC for 50 h for moisture removal. After 50 h,

each sample was pulverized using a plant disintegrator and finely ground to obtain a particle

size of 150-200 µm.

3.2.3 Volatile Solids

Volatile solids represent the organic part of the biomass. A known mass of oven-dried

biomass was put placed in crucibles at 550 ºC in a Muffle furnace (Thermo Fisher) in triplicate

for 4 h. The crucibles containing sample were put in a desiccator to cool down and were

weighed. The whole process was repeated twice and the mass was measured. The difference

between the initial and final mass gave us volatile content in the mass. Where the percent

volatile and ash content were calculated using following formulae.

Ash (%) = (𝑊2−𝑊3)

(𝑊2−𝑊1) × 100

Volatile solids (%) = 100 Ash

34

3.3 Evaluating the bioenergy potential

3.3.1 Thermogravimetric analysis

Thermogravimetric analysis (TGA) is a technique to determine chemical and physical

properties of the samples such as biomass, liquids are measured by increasing function of time

with rise in temperature. It is very effective to determine the degradation stages that occur in a

sample with an increase in temperature. TGA instrument monitors the mass of the sample as a

relevance of time with the continuous temperature rise, according to the provided conditions

of temperature, heating rate, and the environment. Almost 10 mg of each grass was put in

Alumina crucibles and was heated from ambient temperature to 1000 ºC that reflects the

degradation of the sample and loss in weight with increase in temperature (point to point).

Whereas results were simulated on the software side by side that makes peak by keeping the

temperature on X-axis and mass percentage on Y-axis. In current experimentations heating

rates including 10, 30 and 50 ºC min-1 were used as described previously (Li et al., 2012; Li,

Chen, Yi, et al., 2010; Li et al., 2011). Heating rate reflects the rise in temperature in a specified

period. The pyrolytic characteristics are determined using a thermal analyzer. All samples were

placed in a auminiu crucibles with lids and investigated the accuracy of the crucibles by

keeping observation throughout the experiment when heated in TG analyzer from room

temperature to maximum 1000ºC at rates described before in the furnace under a nitrogen flow

rate of 100 mL min-1 to maintain an inert environment.

It also provided the first derivative of the mass percentage with the rise in temperature

and drew the peaks on different heating rates as provided. The data were saved in a notepad

file that was further transferred to MS-Excel for observing the clear picture of the experimental

data. A photo of the equipment is shown in Fig. 3.9.

35

Fig. 3.10 NETZSCH TGA/DSC 409 instrument

https://www.netzsch-thermal-analysis.com/en/products-solutions/simultaneous-

thermogravimetry-differential-scanning-calorimetry/

36

3.3.2 Differential Scanning Calorimetry

The Differential Scanning Calorimetry (DSC) technique is used to measure the heat

flow towards and from the biomass in response to the parallel rise in temperature of the sample.

The DSC data provides information on the endothermic or exothermic reaction chemistry

during the thermal degradation of the biomass. The representation of the data is shown by the

type of DSC equipment used for the analysis. In the DSC curves obtained from the NETZSCH

(TGA/DSC 409 instrument), curves growing upwards indicate the exothermic and downward

curves represent endothermic reactions.

3.4 Kinetic and thermodynamic analyses

There are different methods to analyze the TGA-DSC data to calculate the

thermodynamic and kinetic parameter. In the current study the data were analyzed as described

below.

3.4.1 Development of the mathematical model

The Arrhenius equation was initiated to determine various characterization used in the

thermal degradation reaction. In this equation, the initial weight of the sample, 𝑚𝑜 was

degraded by programmed heating. After time, “t”, the mass changed to 𝑚𝑡 and the mass of

the residue as 𝑚∞. These values describe the decomposition rate as follows;

The initial equation is as follow

𝑑𝛼

𝑑𝑡= 𝑓1(𝑇)𝑓2(𝛼) (1)

Where 𝑓1(𝑇) is temperature dependent variable i.e. Arrhenius equation, and 𝑓2(𝛼) is function

of conversion with mass change. The 𝑓1(𝑇) is depend on temperature.

𝑑𝛼

𝑑𝑡= 𝑘(𝑇) 𝑓2(𝛼) (2)

𝛼 = (𝑚𝑜 − 𝑚𝑡)/ (𝑚𝑜 − 𝑚∞)

According to Arrhenius

37

𝑘(𝑇) = 𝐴 𝑒𝑥𝑝 (−𝐸

𝑅𝑇) (3)

Now substituting equation (3) into equation (2), we obtain equation (4)

𝑑𝛼

𝑑𝑡= 𝐴 𝑒𝑥𝑝 (−

𝐸

𝑅𝑇) 𝑓(𝛼) (4)

Manipulating equation (4) by multiplying numerator and denominator by dT/dT as:

𝑑𝛼

𝑑𝑡.

𝑑𝑇

𝑑𝑇= 𝐴𝑒𝑥𝑝 (−

𝐸

𝑅𝑇) 𝑓(𝛼) (5)

𝛽 =𝑑𝑇

𝑑𝑡, So equation (5) becomes as follows:

𝑑𝛼

𝑑𝑇=

𝐴

𝛽𝑒𝑥𝑝 (−

𝐸

𝑅𝑇) 𝑓(𝛼) (6)

or

𝑑𝛼

𝑓(𝛼)=

𝐴

𝛽𝑒𝑥𝑝 (−

𝐸

𝑅𝑇) 𝑑𝑇 (7)

If the differential coefficient mechanism function is written in general nth order form as

follows,

𝑓(𝛼) = (1 − 𝛼)𝑛 (8)

Then equation (6) can be written in a general way as:

𝑑𝛼

𝑑𝑇=

𝐴

𝛽𝑒𝑥𝑝 (−

𝐸

𝑅𝑇) (1 − 𝛼)𝑛 (9)

Rearranging equation (9) as follows:

𝑑𝛼 𝑓(1 − 𝛼)𝑛⁄ = (𝐴 𝛽⁄ )𝑒𝑥𝑝(−𝐸 𝑅𝑇⁄ )𝑑𝑇 (10)

Now, if equation (10) is integrated for unity reaction order and for the initial conditions, α =

0, at 𝑇 = 𝑇0 , to obtain the following experssion:

𝐺(𝛼) = ∫ 𝑑𝛼 𝑓(1 − 𝛼)⁄𝛼

0= (𝐴 𝛽⁄ ) ∫ 𝑒𝑥𝑝

𝑇

𝑇0(−𝐸 𝑅𝑇⁄ )𝑑𝑇 = (𝐴𝐸 𝛽𝑅⁄ )𝒑(− 𝐸 𝑅𝑇⁄ ) (11)

If the exponential term is written in its expanded form as follows;

𝑒−𝐸 𝑅𝑇⁄ = 1 − 𝐸 𝑅𝑇⁄ + (1 2!⁄ )(𝐸 𝑅𝑇⁄ )2 − (1 4!⁄ )(𝐸 𝑅𝑇⁄ )3+.. (12)

38

Upon integrating RHS of equation (11) and replacing the expansion of the exponential series,

equation (12), we obtain equation (13)

𝐺(𝛼) = ∫ 𝑑𝛼 𝑓(1 − 𝛼)⁄𝛼

0= 𝐴𝑅𝑇2 𝛽𝐸⁄ [1 − 2𝑅𝑇 𝐸⁄ ]𝑒𝑥𝑝 (−

𝐸

𝑅𝑇) (13)

Combining equations (11) and (13), and

𝐺(𝛼) = (𝐴𝑅𝑇2 𝛽𝐸⁄ [1 − 2𝑅𝑇 𝐸⁄ ])𝑒𝑥𝑝(−𝐸 𝑅𝑇⁄ ) (14)

Rearranging equation (14), and it is known that the quantity, 2𝑅𝑇 𝐸𝑎⁄ is negligible compared

with unity and hence can be ignored (Coats, 1964), then, we obtain equation (15)

𝐺(𝛼) = (𝐴𝑅𝑇2 𝛽𝐸⁄ )𝑒𝑥𝑝(−𝐸 𝑅𝑇⁄ ) (15)

3.5 Calculating the kinetic parameters

The kinetic parameters of the biomass sample were determined by using two different

methods to keep the accuracy of the experiments. First methods was the Kissinger-Akahira-

Sunose (KAS) that also influence the arrhenis equation and turn into Flynn-Wall-Ozawa

(FWO) (Ozawa,1965; Flynn and Wall; 1966). Whereas other parameters were also calculated

like Pre-Exponential Factor (As-1), Entropy, Enthalpy and Gibbs free energy with standard

equations.

3.5.1 KAS Method

Equation 15 was reaarranged to obtain following

ln (𝛽

𝑇2) = 𝑙𝑛(𝐴𝑅 𝐸𝐺(𝛼)⁄ ) − 𝐸 𝑅𝑇⁄ (16)

Now, if the left-hand side of above equation was plotted on the y-axis whereas, 1/T was plotted

on x-axis to obtain the kinetic parameters from the value of the slope and intercept as it will be

shown later.

3.5.2 FWO Method

In equation (11), the polynomial quantity i.e. 𝒑(𝐸𝑎 𝑅𝑇⁄ ) was expanded using Doyle’s

approximation for the integral that allows the quantity

ln (𝒑(𝐸 𝑅𝑇)⁄ ≅ −5.331 − 1.052 𝐸 𝑅𝑇⁄ (17)

39

Now if equation (10) was integrated with the initial conditions, α = 0, at 𝑇 = 𝑇0 , By

introducing Doyle’s approximation after some mathematical manipulations, we obtained

equation (18) also called FWO procedure.

𝐿𝑛𝛽 = 𝑙𝑛 (𝐴𝐸/𝑅𝐺(𝛼)) – 𝐸/𝑅𝑇 (18)

The same method was applied on above equation to calculate the kinetic parameters by plotting

LHS on y-axis and RHS on x-axis. At each point of mass conversion rate α was calculated

using MS Excel for the calculation of Activation energy and 𝐴(𝑠−1). With the conversion rate

points plots between 𝑙𝑛𝛽 and 𝑙𝑛𝛽/𝑇2 versus 1/T produced regression lines with linear

equation to obtain activation energy.

3.5.3 Ozawa–Flynn–Wall methods

The thermokinetic behavior of biomass was used to calculate the kinetic parameters

and energy values including activation energy, Gibbs free energy, entropy, enthalpy and pre-

exponential factors. It is independent of the reaction mechanism to determine the optimal

values of energy distribution through biomass conversion. The non-isothermal process was

simplest, accurate and most popular to determine the thermodynamic properties and kinetics

of the biomass. It was safer and alternative to reaction mechanism approach without knowing

the prerequisites of the reaction chemistry and chemical composition of the material. The

Ozawa–Flynn–Wall kinetics iso-conversional method Eq. was used as described below.

ln(𝛽) = 𝐶𝛼 − 𝐸 𝑅𝑇⁄ (19)

Thermodynamic parameters were calculated under three different heating rates 10, 30

and 50 ºC min-1 for observing curves of DTG and TG. These curves demonstrated the mass

loss conversion at each point of temperature. It was investigated whether kinetics of biomass

is changing with the conversion of mass or not. All energy values and thermodynamic

parameters were calculated at each conversion point from ambient to 1000 ºC temperature.

Enthalpy (∆𝐻), free Gibbs energy (∆𝐺) and the changes of entropy (∆𝑆) were also

calculated using standard equations (Y. S. Kim et al., 2010)

40

∆𝐻 = 𝐸 − 𝑅𝑇 (20)

∆𝐺 = 𝐸 + 𝑅𝑇𝑚ln (𝐾𝐵𝑇𝑚 ℎ𝐴)⁄ (21)

∆𝑆 = ∆𝐻 − ∆𝐺 𝑇𝑚⁄ (22)

Where:

𝐾𝐵 Boltzmann Constant (1.381 ∗ 10−23 𝐽/𝐾)

ℎ Plank Constant (6.626 ∗ 10−34𝐽𝑠)

𝑇𝑚 DTG peak temperature, K

41

Chapter 4

RESULTS AND DISCUSSION

4.1 Proximate analyses

The proximate analyses and higher heating values (HHVs) for all the biomass are

provided in Table 4.1. The moisture content of the biomass from all grasses shown to be

lesser than 10% on dry weight basis. While the biomass containing moisture content lower

than 10% is considered suitable for a pyrolysis in terms of efficient energy retrieval and quality

of the products. Higher volatile (VM %) content in a biomass is desired to obtain higher yields

of bio-oil and gasses. The VM content for all biomasses ranged from 64 % to 82 % which is

comparable with various previously studied biomass species including rice husk (Maiti et al.,

2006), tobacco waste (Wu et al., 2015), Lemna minor (C. Yang et al., 2017), Water hyacinth

(L. Huang et al., 2016), Switchgrass (Masnadi et al., 2014), Tall fescue (Hlavsová et al., 2016),

Tall oat grass (Hlavsová et al., 2016), Red top (Hlavsová et al., 2016), Sawdust (Masnadi et

al., 2014), Typha angustifolia (Singh et al., 2017), Cardoon leaves (Damartzis et al., 2011),

Soybean straw (X. Huang et al., 2016; Jeguirim et al., 2010) and Miscanthus (Jeguirim et al.,

2010).

The HHV for all biomasses were calculated using Eq. 23 and are shown in Table 4.1.

Among the grasses studied, the Mott grass showed highest value (HHV= 18.63 KJ kg-1) and

the Camel grass showed the lowest value (HHV = 15.00 KJ kg-1). Interestingly, the HHV

values of all the studied grasses were shown to be comparable with established energy crops

such as, Baggase (Eliche-Quesada et al., 2011), Sawdust (Das et al., 2017), Switchgrass (Biney

et al., 2015; Maia & de Morais, 2016), Coffee waste (Skreiberg et al., 2011), Miscanthus

(Collura et al., 2006), Melon seed husk (Nyakuma) and in accordance with several previously

studied biomass feedstocks including red peppers waste (Maia & de Morais, 2016), tobacco

waste (Wu et al., 2015), and rice husk (Maiti et al., 2006). These values indicate the suitability

of the studied grasses for bioenergy purpose.

However, inorganic content (ash) of biomass can demonstrate certain types of

reactions. The higher ash content had disadvantages when considered for large scale

production which may cause agglomeration to increase operational costs. Among the studied

42

grasses, Wolffia showed highest and Mott grass showed lowest ash content. Similarly, Carrot

grass and Mott grass had the highest moisture and fixed carbon contents and Elephant grass

and Para grass had lowest ash contents, respectively (Table 4.1)

𝐻𝐻𝑉 = 19.2880 − 0.2135𝑉𝑀

𝐹𝐶− 1.9584

𝐴𝑠ℎ

𝑉𝑀+ 0.0234

𝐹𝐶

𝐴𝑠ℎ

(23)

Where VM = Volatile matter (%), FC = Fixed carbon (%). The calculated HHV of all selected

samples is shown in table 4.1. When, proximate analyses of the Para grass and its HHV value

are compared with different energy crops as above it was revealed that all grass samples were

high potential for bioenergy utilization.

43

Table 4.1 Energy contents of all grasses

Grasses Moisture (%) Volatile

Matter (%) Ash (%)

Fixed Carbon

(%)

HHV

(MJ Kg-1)

Para grass 7.23 ± 0.18 79.45 ± 0.05 9.20 ± 0.06 4.21 ± 0.15 15.04 ±1.39

Cattail grass 6.69 ± 0.18 71.30 ± 0.55 8.8 ± 0.19 19.04 ± 0.11 18.32±1.15

Carrot grass 7.66 ± 0.15 69.03 ± 0.75 5.41 ± 0.08 17.90 ± 0.17 18.25±1.08

Mott grass 5.83 ± 0.15 70.03 ± 0.53 3.18 ± 0.17 20.96 ± 0.13 18.63±0.98

Egyptian grass 6.99 ± 0.18 71.04 ± 0.67 4.22 ± 0.14 17.75 ± 0.11 18.09±1.13

Babui grass 7.33 ± 0.15 82.62 ± 0.19 5.72 ± 0.21 4.33 ± 0.26 15.10±1.33

Camel grass 6.33 ± 0.15 82.67 ± 0.13 6.31± 0.39 6.08 ± 0.28 15.00±1.26

Elephant grass 5.33 ± 0.15 80.13 ± 0.02 4.32 ± 0.34 4.43 ± 0.55 17.06±1.09

Wolffia 4.76 ± 0.14 72.06 ± 0.60 10.74± 0.02 6.43 ± 0.34 17.77±0.12

44

4.2 Thermogravimetric analyses

Thermogravimetric (TG) analyses are widely used to understand the thermal behavior

of biomass under specific conditions of time, temperature, heating rate and particle size.

Different heating rates were used according to nature of the biomass. In current series of

experiments low and high heating, rates were used for equal weight (10 mg) and equal particle

size of the selected grasses.

Thermogravimetric analyses are often employed to calculate energy values including

activation energy, change in enthalpy, Gibbs free energy, and pre-exponential factors. These

preliminary studies are used to understand the reaction chemistry of the biomass and for reactor

modeling. Without a clear understanding of these parameters, it is almost impossible to

understand the pyrolysis reaction mechanism and technical feasibility of the pyrolysis reactor

to obtain desired products. The thermal degradation of the biomass into soil, liquid and gasses

parts is monitored through TG analyses.

In the present study, the selected biomasses were subjected to thermogravimetric

analysis showed in Materials and Methods section. Where, loss in biomass was monitored in

response to increasing temperature at three different heating rates including 10, 30, and 50

ºC/min. Which produced three curves of mass loss with the rise in temperature from ambient

to 1000 ºC. The TG curves of all samples are shown from Fig 4.1 to 4.9. Different biomasses

showed different thermal degradation pattern during pyrolysis, which was determined from the

curves obtained. The difference in degradation pattern may be due to the difference in chemical

composition of the selected biomasses. Moreover, the difference in pyrolysis behavior of the

biomasses also indicated the potential of grasses studied to produce a variety of products.

The selected biomass samples showed the same phenomenon but at different

temperature ranges. The Fig 4.1 indicated that from ambient temperature to 270 ºC there was

a simple relationship between mass loss and temperature at all heating rates but suddenly there

was a huge mass loss occurred from 270 ºC to 360 ºC. This major loss demonstrated due to the

cellulose, hemicellulose and pectin degradation. After that, from 360 ºC to 490 ºC there was a

tilt preceding to a straight line up to 1000 ºC, which indicates lignin degradation and char

formation. On a similar fashion, all selected biomass samples showed the same trend in

temperature vs weight degradation for which the curves are shown in Fig 4.1 to 4.9 at three

45

different heating rates. All biomass samples showed linear curves parallel to temperature axis

from ambient temperature to 155 ºC to 226 ºC maximum, however, there was a sudden drop

of major mass loss from minimum 155 ºC to 504 ºC for all biomass samples. This trend was

found similar when compare with cherry stones, cedar wood, (Özsin & Pütün, 2017; Tuly et

al., 2017), sugarcane mill waste (da Silva et al.), sewage sludge (Nozela et al.), bamboo and

fibre of coconut fruit (Rocha et al.), Columbian agriculture residue, (Millán et al., 2017), spoxy

composts (Ferdosian et al., 2016), tomato peels (Brachi et al., 2016), silane wood (S. Mishra

& Verma, 2016), date palm residue (Elmay et al., 2016).

46

Fig. 4. 1 Relationship between Mass percentage v/s Pyrolysis temperature of Para grass

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900 1000

Mass

(%

)

Temperature (°C)

10K

30K

50K

Heating Rate (ºC/min)

47

Fig. 4. 2 Relationship between Mass percentage v/s Pyrolysis temperature of Elephant grass

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900 1000

Mass

(%

)

Temperature (°C)

10K

30K

50K

Heating Rate (°C min-1)

48

Fig. 4.3 Relationship between Mass percentage v/s Pyrolysis temperature of Carrot grass

20

30

40

50

60

70

80

90

100

110

0 100 200 300 400 500 600 700 800 900 1000

Mass

(%

)

Temperature (°C)

10K

30K

50K

Heating Rate (°C min-1)

49

Fig. 4.4 Relationship between Mass percentage v/s Pyrolysis temperature of Mott grass

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900 1000

Mass

(%

)

Temperature (°C)

10K

30K

50K

Heating Rate (°C min-1)

50

Fig. 4.5 Relationship between Mass percentage v/s Pyrolysis temperature of Egyptian grass

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900 1000

Temperature (° C)

Mass

Loss

(%

)

10K

30K

50K

Heating Rate (°C min-1)

51

Fig. 4.6 Relationship between Mass percentage v/s Pyrolysis temperature of Babui grass

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900 1000

Mass

(%

)

Temperature (°C)

10K

30K

50K

Heating Rate (°C/min)

52

Fig. 4.7 Relationship between Mass percentage v/s Pyrolysis temperature of Camel grass

20

30

40

50

60

70

80

90

100

110

0 100 200 300 400 500 600 700 800 900 1000

Mass

(%

)

Temperature (°C)

10K

30K

50K

Heating Rate (°C min-1)

53

Fig. 4.8 Relationship between Mass percentage v/s Pyrolysis temperature of Cattail grass

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900 1000

Mass

(%

)

Temperature (°C)

10K

30K

50K

Heating Rate (°C min-1)

54

Fig. 4.9 Relationship between Mass percentage v/s Pyrolysis temperature of Wolffia

0 100 200 300 400 500 600 700 800 900 1000

0

20

40

60

80

100

120

Ma

ss (

%)

10K

30K

50K

Heating Rate (°C min-1)

55

4.3 DTG Analyses

Differential thermogravimetric analysis (DTG) or DTG curves are obtained when we

take derivative of the TG curves. These curves show the thermal degradation stages and

pyrolytic behavior with peak temperatures that are further used as parameters in energy

calculation equations.

The DTG curves are often divided into various stages (mostly three) according to major

mass loss and peak temperatures. Without DTG curves, the major mass loss stages cannot be

related to specific characteristic temperatures. The DTG curves obtained from the pyrolysis of

the selected grasses showed the same phenomenon at different heating rates but the

temperature and mass loss ranges were slightly different because each heating rate resulted a

slightly different set of reactions at a specific temperature point. Therefore, the best heating

rates for shown up for the specific biomass. Time is an important parameter when it comes to

industrial scale. While, at lower heating rates, it took more time to degrade the biomass but at

high heating rates less time was required for biomass degradation.

4.3.1 Effect of heating rate on mass loss

Effect of heating was not significantly different on the studied grasses. In the case of

Para grass, at a heating rate of 10 ºCmin-1, the first stage occurred between ambient temperature

to 195 ºC where 7.14% of the mass was lost. The second stage showed two zones and couple

of peaks. The first zone appeared from 195 ºC to 295 ºC with 13.23% mass loss, whereas zone-

II occurred from 295 ºC to 335 ºC where 20.73% mass was lost. The overall mass loss during

the second stage was 33.96% from 195 ºC to 335 ºC. This stage is also called major mass

degradation stage. The third stage ranged from 335 ºC to maximum temperature 1000 ºC where

54.03% mass was decomposed. The residual mass was 27.61% that referred as biochar of

biomass.

Similarly, at the heating rate of 30 ºC min-1, the first stage appeared from room

temperature to 165 ºC with 7.31% biomass loss. The second stage was observed with a drastic

mass loss in two zones (I & II). When temperature ranged from 165 ºC to 290 ºC, the zone-I

occurred with 13.16% mass loss whereas 20.73% mass was shown to be degraded from 290

ºC to 325 ºC in zone-II. During the second stage, the major mass loss was observed from 165

56

ºC to 325 ºC with overall 34.49% mass degradation during this stage. The third stage ranged

from 325 ºC to maximum temperature 1000 ºC where 59.57% mass was decomposed.

Similarly, three decomposition stages were shown at 50 ºCmin-1. While 7.41% loss in mass

was observed during the first stage (25 ºC to 175 ºC). Two degradation zones appeared during

the second stage, while zone-I was appeared between 175 ºC to 290 ºC with 13.71% mass loss

whereas zone-II was ranging from 290 ºC to 325 ºC with 28.24% mass loss. The overall mass

degraded during the second stage was 41.95% also called major mass degradation stage. The

remaining curve was lied in the third stage with a long tail from 325 ºC to 1000 ºC, mass loss

during this stage was 60.57%. All selected biomass samples showed the same phenomenon as

described below in Tables 4.2 and 4.3 with two zones in the second stage but Elephant grass

and Mott grass showed only one peak during the second stage of degradation shown in table

4.2 and 4.3.

At all heating rates, the mass loss during the first stage was < 8.0% which reflects the

removal of moisture trapped in inter and intracellular spaces. Moreover, the water content

<10% reflects the suitability of all biomass samples for combustion and pyrolysis (Braga et al.,

2014). The second stage indicated the thermal conversion of cellulose, hemicelluloses and

pectin components present in the biomass. Where zone-I indicate the conversion of smaller

sugars and zone-II indicates the conversion of larger polysaccharides into gasses. Because the

temperatures associated (Tables 4.2 and 4.3) with these zones are typically shown by this kind

of carbohydrates (Xu & Chen, 2013).

57

Fig. 4.10 DTG peaks of Para grass

0 100 200 300 400 500 600 700 800 900 1000

-60

-50

-40

-30

-20

-10

0

Temperature (°C)

DT

G (

% m

in-1

)

10K

30K

50K

Heating Rate (°C/min)

58

Fig. 4.11 DTG peaks of Carrot grass

-50

-40

-30

-20

-10

0

10

0 100 200 300 400 500 600 700 800 900 1000

DT

G (

% m

in-1

)

Temperature (°C)

10K

30K

50K

Heating Rate (°C min-1)

59

Fig. 4.12 DTG peaks of Egyptian grass

-50

-40

-30

-20

-10

0

10

0 100 200 300 400 500 600 700 800 900 1000

DT

G/(

% m

in-1

)

Temperature (° C)

10K

30K

50K

Heating Rate (°C min-1)

60

Fig. 4.13 DTG peaks of Babui grass

-50

-40

-30

-20

-10

0

0 100 200 300 400 500 600 700 800 900 1000

DT

G/(

% m

in-1

)

Temperature (°C)

Heating rate (ºC min-1)

10K

30K

50K

61

Fig. 4.14 DTG peaks of Camel grass

-50

-40

-30

-20

-10

0

0 100 200 300 400 500 600 700 800 900 1000

DT

G (

% m

in-1

)

Temperature (°C)

10K

30K

50K

Heating Rate (°C min-1)

62

Fig. 4.15 DTG peaks of Cattail grass

-60

-50

-40

-30

-20

-10

0

10

0 100 200 300 400 500 600 700 800 900 1000

DT

G (

% m

in-1

)

Temperature (°C)

10K

30K

50K

Heating Rate (°C min-1)

63

Fig. 4.16 DTG peaks of Mott grass

-40

-35

-30

-25

-20

-15

-10

-5

0

0 100 200 300 400 500 600 700 800 900 1000

DT

G/(

% m

in-1

)

Temperature (° C)

10K

30K

50K

Heating Rate (°C min-1)

64

Fig. 4.17 DTG peaks of Elephant grass

-50

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

0 100 200 300 400 500 600 700 800 900 1000

DT

G/(

% m

in-1

)

Temperature (° C)

10K

30K

50K

Heating Rate (°C min-1)

65

Fig. 4.18 DTG peaks of Elephant grass

-16

-14

-12

-10

-8

-6

-4

-2

0

2

0 100 200 300 400 500 600 700 800 900 1000

DT

G (

% m

in-1

)

Temperature (°C)

10K

30K

50K

Heating Rate (°C min-1)

66

Table 4.2 Pyrolysis temperature and heating rate characteristics association

Heating rate

(˚C min-1)

Temperature (˚C)

T1 T2 T3 T4 T5

Para Grass

10 195 275 295 310 335

30 165 265 290 310 325

50 175 265 290 310 325

Elephant grass

10 203 310 346 -- --

30 205 324 369 -- --

50 213 329 362 -- --

Carrot Grass

10 185 267 296 326 470

30 195 283 306 338 493

50 200 284 310 341 504

Mott grass

10 174 310 380 -- --

30 179 324 384 -- --

50 184 330 394 -- --

Egyptian Grass

10 151 268 297 331 369

30 157 279 309 346 380

50 169 282 318 340 372

Sabal Grass

10 135 250 295 325 375

30 145 280 300 262 385

50 160 285 305 340 400

Camel Gras

10 175 270 294 331 454

30 179 283 309 347 459

50 180 290 319 342 469

Cattail

10 212 265 295 333 371

30 226 282 311 348 381

50 222 283 318 350 387

Wolffia

10 155 271 294 321 551

30 159 272 306 336 560

50 170 282 309 344 569

67

Table 4.3 Zones and stages associated with heating rates

Stages Heating rate (˚C min-1)

10 30 50

Para grass

Stage-I, WL (%) 7.14 7.31 7.41

Stage-II, WL

(%)

Zone-I 13.23 13.16 13.71

Zone-II 20.73 21.33 28.24

Stage-III, WL (%) 54.03 59.57 60.57

Final residues at 800-1000 ºC

(%) 31.32-27.61 29.82-27.12 28.73-25.96

Elephant grass

Stage-I, WL (%) 7.40 7.05 7.33

Stage-II, WL (%) 45.78 47.92 47.64

Stage-III, WL (%) 18.85 16.57 16.92

Final residues at 800-1000 ºC

(%) 27.97 28.46 28.11

Carrot grass

Stage-I, WL (%) 5.96 7.07 7.57

Stage-II, WL

(%)

Zone-I 19.83 19.22 20.5

Zone-II 37.49 38.91 38.41

Stage-III, WL (%) 8.48 8.14 7.14

Residual mass (%) at 1000 ºC 28.23 26.66 26.38

Mott grass

Stage-I, WL (%) 6.93 7.09 7.8

Stage-II, WL (%) 49.12 48.53 49.54

Stage-III, WL (%) 15.04 17.92 14.5

Residual mass (%) at 1000 ºC 28.91 26.46 28.16

Egyptian grass

Stage-I, WL (%) 7.67 7.79 8.22

Stage-II, WL

(%)

Zone-I 23.76 23.18 24.07

Zone-II 27.03 27.74 25.07

Stage-III, WL (%) 17.85 16.99 17.82

Residual mass (%) at 1000 ºC 23.69 24.30 24.82

Babui grass

Stage-I, WL (%) 6.50 6.64 6.69

Stage-II, WL

(%)

Zone-I 12.34 19.14 19.34

Zone-II 26.84 25.32 26.00

Stage-III, WL (%) 59.77 60.88 61.71

Final residues at 800-1000 ºC

(%) 27.32-25.71 26.02-24.80 26.72-25.73

Camel grass

Stage-I, WL (%) 7.04 6.95 8.91

Stage-II, WL

(%)

Zone-I 21.22 22.11 23.48

Zone-II 38.12 36.18 35.15

Stage-III, WL (%) 8.81 9.59 8.87

68

Final residues 1000 ºC (%) 24.81 25.17 23.59

Cattail grass

Stage-I, WL (%) 7.45 7.46 8.32

Stage-II, WL

(%)

Zone-I 19.65 20.58 21.62

Zone-II 30.83 30.30 30.17

Stage-III, WL (%) 17.48 16.21 16.22

Final residues at 800-1000 ºC

(%) 24.59 25.45 23.67

Wolffia

Stage-I, WL (%) 7.77 8.18 10.9

Stage-II, WL

(%)

Zone-I 16.88 15.76 15.06

Zone-II 27.13 28.54 28.92

Stage-III, WL (%) 12.32 11.81 12.11

Final residues at 800-1000 ºC

(%) 35.90 35.71 33.01

69

4.4 Differential Scanning Calorimetry

Differential scanning calorimetry (DSC) technique applied to study the heat-transfer

from and towards the sample under study with rise in temperature function. The sample is

maintained at a nearly same temperature in comparison with a reference. Generally, the

temperature of sample holder increases linearly with time. It can be used to measure a few

characteristic properties of a sample such as fusion, crystallization, oxidation and other

thermochemical conversions.

In the present study, the direction of heat flow during the pyrolysis process of the

selected grasses was observed through DSC. Three different heating rates were used to obtain

DSC curves of each sample. The DSC curves obtained for Para grass showed a variation of

heat flow (Fig. 4.19). The heat flow increased at all heating rates. Endothermic peaks observed

up to around 100 °C which indicates the heat absorbed in the dehydration of the moisture from

the Para grass. After this point, the exothermic reactions were predominant during pyrolysis.

Almost similar patterns of heat flow were shown by all the selected grasses with slight changes

in the range of temperatures (Fig. 4.19 to Fig. 4.27)

70

Fig. 4.19 DSC curves for the pyrolysis of Para grass

-5

0

5

10

15

20

25

0 100 200 300 400 500 600 700 800 900 1000 1100

Hea

t F

low

DS

C (

mW

mg

-1)

Temperature (°C)

50K

30K

10K

Heating Rate (°C min-1)

71

Fig. 4.20 DSC curves for the pyrolysis of Elephant grass

-2

0

2

4

6

8

10

12

0 100 200 300 400 500 600 700 800 900 1000

Hea

t F

low

DS

C (

mW

mg

-1)

Temperature (° C)

10K

30K

50K

Heating Rate (°C min-1)

72

Fig. 4.21 DSC curves for the pyrolysis of Carrot grass

-2

0

2

4

6

8

10

12

14

0 100 200 300 400 500 600 700 800 900 1000

Hea

t F

low

DS

C (

mW

mg

-1)

Temperature (°C)

10K

30K

50K

Heating Rate (°C min-1)

73

Fig. 4.22 DSC curves for the pyrolysis of Mott grass

-5

0

5

10

15

20

25

30

0 100 200 300 400 500 600 700 800 900 1000

Hea

t F

low

DS

C (

mW

mg

-1)

Temperature (° C)

10K

30K

50K

Heating Rate (°C min-1)

74

Fig. 4.23 DSC curves for the pyrolysis of Egyptian grass

-2

0

2

4

6

8

10

12

14

16

0 100 200 300 400 500 600 700 800 900 1000

Hea

t F

low

DS

C (

mW

mg

-1)

Temperature (°C)

10K

30K

50K

Heating Rate (°C min-1)

75

Fig. 4.24 DSC curves for the pyrolysis of Babui grass

-2

0

2

4

6

8

10

12

14

16

18

0 100 200 300 400 500 600 700 800 900 1000

Hea

t F

low

DS

C (

mW

mg

-1)

Temperature (°C)

50K

10K

30K

Heating Rate (°C min-1)

76

Fig. 4.25 DSC curves for the pyrolysis of Camel grass

-2

0

2

4

6

8

10

12

0 100 200 300 400 500 600 700 800 900 1000

Hea

t F

low

DS

C (

mW

mg

-1)

Temperature (°C)

10K

30K

50K

Heating Rate (°C min-1)

77

Fig. 4.26 DSC curves for the pyrolysis of Cattail grass

-5

0

5

10

15

20

250 350 450 550 650 750 850 950 1050 1150 1250

Hea

t F

low

DS

C (

mW

mg

-1)

Temperature (K)

10K

30K

50K

Heating Rate (Kmin-1)

78

Fig. 4.27 DSC curves for the pyrolysis of Wolffia

-2

0

2

4

6

8

10

12

0 100 200 300 400 500 600 700 800 900 1000

Hea

t F

low

DS

C (

mW

mg

-1)

Temperature (°C)

10K

30K

50K

Heating Rate (°C min-1)

79

4.5 Kinetic analyses

Kinetic modeling is a key step to estimate the key parameters (energy values) to

understand the pyrolytic conditions of the biomass. These parameters including activation

energy (E), change in enthalpy (∆H), Gibbs free energy (∆G) and pre-exponential factors (A)

are helpful to investigate the nature of the material, chemical composition, reaction mechanism

and possible products to be obtained from pyrolysis. The minimum energy which is required

to break the bonds between molecules to release products is called activation energy

(Mehmood et al., 2017), while lower activation energy values indicate that lower energy will

be required for the thermal conversion of the sample to get various products. Generally, the

materials having low activation energy are considered best candidate for bio-oil and other

products owing to their easier thermal conversion. Alternatively, the biomasses with high and

low E-values can be used in co-pyrolysis in combination with various of biomasses. The E-

values have a physical importance that can be understood by molecular collision theory (White

et al., 2011).

4.5.1 Activation energy of the selected grasses

There are various approaches to estimate the activation energy values but the most

authentic approach is through kinetic modeling using Arrhenius’s equation. The KAS

(Kissenger-Akahira-Sunose) was used to compute the E-values which were then confirmed

through Flynn-Wall-Ozawa (Ozawa,1965; Flynn and Wall; 1966) methods. Linear fit plots

were subjected to obtain energy values through KSA and FWO methods, slopes were made for

each conversion point by keeping and 1/T on x-axis and 𝑙𝑛𝛽/𝑇2 (KAS) and 𝑙𝑛𝛽 (FWO) on

y-axis. Using the slope values activation energies, the pre-exponential factors (A s-1) and R2

values were obtained as shown in Figures 4.22 and 4.29. These energy values were calculated

at each conversion point from 0.1 to 0.9 by using equations 16 and 18.

The average E-values of all biomass samples were estimated by KAS and FWO

methods shown in Tables 4.4 to 4.11 with reference to the conversion (0.1-1.0), the E-values

were ranging from 79 kJ mol−1 to 322 kJ mol−1 and 91 kJ mol−1 to 315 kJ mol−1 calculated by

KAS and FWO, respectively. Biomass samples which showed lower E-values may be suitable

for the co-pyrolysis with other biomass samples in an energy efficient manner. Alternatively,

80

the Carrot grass showed the highest amount of activation energy which indicates that it had

complex chemical composition. The observed range of E is lower for Egyptian grass, Para

grass, Cattail grass and Mott grass and higher for Carrot grass and Elephant grass when

compared to tobacco waste (118-257 kJ mol-1) (Wu et al., 2015). Moreover, average E of

Egyptian, Para, and Mott grasses was shown to be litter lower when compared to the E of rice

husk (221-229 kJ mol−1), cellulose (191 kJ mol−1), Enteromorpha prolifera, Sargassum

pallidum, Laminaria japonica, sodium algenate, Plocamium telfairiae and Corallina pilulifera

(Braga et al., 2014; Li, Chen, Yi, et al., 2010; Li et al., 2011; Li, Chen, Zhao, et al., 2010;

Sanchez-Jimenez et al., 2013) and it was higher than switchgrass, Dunaliella tertiolecta, Para

grass and fresh plant Potamogeton crispus (Ahmad et al., 2017; Biney et al., 2015; Li et al.,

2012; Shuping et al., 2010). However, the E-values of Carrot grass shown to be the highest

activation energy range when compared to the above-mentioned biomass, this diversity in E-

values of all grasses indicates that their biomass may be used for co-firing with several types

of biomass.

4.5.2 Enthalpy of reaction

During a chemical reaction amount of energy exchanged is called enthalpy of the

reaction. If the difference in enthalpy and activation energy is lesser than 5 kJ mol-1, it indicates

that formation of activation complex is favored. While, favorable conditions for the formation

of activation complex reflect the production formation is favored (Vlaev et al., 2007).

The average difference between the activation energies (E) and enthalpies (∆H)

indicates the likelihood of the reaction to occur, where lower difference indicate that product

formation is being favoured. Among the E and ∆H values of all the grasses, a difference of ~5

kJ mol-1 was observed which indicated lower potential energy barrier between the reactants

and products to achieve the product formation (Vlaev et al., 2007) which was also supported

from the relevant studies (Ahmad et al., 2017; Maia & de Morais, 2016).

81

4.5.3 Pre-exponential factors

Values of pre-exponential factors (A) explicate the reaction chemistry, which is

significant to recognize while adjusting the pyrolysis reaction of biomass. While lower A-

values (<109 s-1) indicate mainly a surface reaction. However, if the reaction does not depend

on the surface area, then lower A-values also indicate a closed complex, whereas the higher A-

values (≥ 109 s-1) indicate a simple complex (Turmanova et al., 2008). The A-values for Carrot,

Mott and Egyptian grasses were shown to be ranging from 2.14x1015 1.63x1026, 4.82x1007

1.99x1018, and 6.80 x1004 6.46 x1014, respectively (Table 4). These values indicate that the

grasses have a complex biomass. Moreover, A-values of Carrot grass were higher when

compared to A-values of red paper waste (3.80 x 100 to 2.80 x1012), rice straw (1.70 x1007 to

9.35 x1012), rice bran (1.00 x1007 and 1.58 x1010). However, the A-values of all grasses were

shown to be closely ranging within the A-values of switchgrass (3.70 x1003­1.65 x1021) and

Para grass (1.42 x1007 2.26 x1019) (Ahmad et al., 2017; Biney et al., 2015; Maia & de Morais,

2016; Mehmood et al., 2017; Xu & Chen, 2013).

4.5.4 Gibbs free energy and entropy

Gibbs free energy is deposited amount of energy in the material that is considered as

a bioenergy potential when it comes to the biomass. Entropy shows the adsorption and affinity

of the sorbent depending upon the values of entropy, where positive values indicate high

affinity of the sorbent and negative values indicate that adsorption process is mainly driven by

entropy (Sotirelis & Chrysikopoulos, 2015). The present study showed biomass had both

negative and positive values of entropy that reflected it could be used as bioenergy product

formulation and co-pyrolysis with other products.

Gibbs free energy (∆G) imitates the energy which would become available upon

pyrolysis. Here, the ∆G values of Carrot, Mott, and Egyptian grasses were shown to be ranging

from 169-171, 168-171 and 173-177 kJ mol-1, respectively. These values were higher when

compared with the ∆G values of the rice bran (167.17 kJ mol-1), rice straw (164.59 kJ mol-1),

red pepper waste (139.4 kJ mol-1) (Maia & de Morais, 2016; Xu & Chen, 2013). However,

these values were in accordance when compared to the ∆G values of Para grass and Camel

grass (Ahmad et al., 2017; Mehmood et al., 2017).

82

Fig. 4.28 Regression plots to determine kinetic parameters of para grass

-11.5

-11

-10.5

-10

-9.5

-9

-8.5

-8

0.0011 0.00126 0.00142 0.00158 0.00174 0.0019 0.00206 0.00222

ln(β

/T2)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(A)

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

0.001110 0.001310 0.001510 0.001710 0.001910 0.002110

ln(β

)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(B)

83

Table 4.4 Kinetic and thermodynamic parameters of Para grass

Conversion

points (α)

E

(KJ mol-1) A (s-1) R²

∆H

(kJ mol-1)

∆G

(kJ mol-1)

∆S (J

mol-1)

KAS Method

0.1 144.35 5.93E+10 0.96 139.30 171.47 -52.912

0.2 180.18 8.87E+13 0.99 175.13 170.35 7.867

0.3 194.19 1.52E+15 0.99 189.14 169.97 31.531

0.4 212.45 6.19E+16 0.99 207.40 169.51 62.307

0.5 223.79 6.14E+17 0.98 218.74 169.25 81.391

0.6 231.52 2.93E+18 0.99 226.46 169.08 94.377

0.7 165.59 4.54E+12 0.99 160.54 170.77 -16.833

0.8 152.54 3.16E+11 0.99 147.49 171.19 -38.984

0.9 103.87 1.42E+07 0.99 98.82 173.13 -122.229

Average 178.72 -- -- 173.66 170.52

FWO Method

0.1 144.89 6.62E+10 0.99 139.84 171.45 -52.00

0.2 179.84 8.26E+13 0.99 174.78 170.36 7.27

0.3 193.48 1.32E+15 0.99 188.43 169.99 30.33

0.4 211.23 4.83E+16 0.99 206.18 169.55 60.25

0.5 222.26 4.51E+17 0.99 217.21 169.29 78.81

0.6 229.77 2.06E+18 0.99 224.71 169.12 91.43

0.7 167.39 6.55E+12 0.99 162.33 170.72 -13.80

0.8 156.08 6.52E+11 0.99 151.03 171.08 -32.98

0.9 112.05 7.72E+07 0.99 107.00 172.75 -108.15

Average 179.67 -- -- 174.61 170.48 --

84

Fig. 4.29 Regression plots to determine kinetic parameters of elephant grass

-11.5

-11

-10.5

-10

-9.5

-9

-8.5

-8

0.0011 0.0013 0.0015 0.0017 0.0019 0.0021 0.0023

ln(β

/T2)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(A)

2

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

0.0011 0.0013 0.0015 0.0017 0.0019 0.0021 0.0023

ln(β

)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(B)

85

Table 4.5 Kinetic and thermodynamic parameters of Elephant grass

Conversion

points (α)

Ea

(kJ mol-1) R²

ΔH

(kJ mol-1) A (s-1) ΔG (kJ mol-1) ΔS (J mol-1)

KAS Method

0.1 198.88 0.99 193.91 7.89E+15 166.81 45.31

0.2 154.94 0.99 149.97 8.92E+11 168.05 -30.24

0.3 201.71 0.99 196.73 1.41E+16 166.74 50.16

0.4 209.06 0.99 204.08 6.42E+16 166.56 62.74

0.5 216.53 0.98 211.56 2.99E+17 166.39 75.53

0.6 224.14 0.99 219.17 1.43E+18 166.22 88.54

0.7 235.79 0.99 230.82 1.57E+19 165.96 108.46

0.8 197.20 0.99 192.23 5.58E+15 166.85 42.43

0.9 165.18 0.99 160.21 7.46E+12 167.73 -12.58

Average 200.38 -- 195.41 -- -- --

FWO Method

0.1 196.64 0.99 191.66 4.97E+15 166.87 41.47

0.2 155.97 0.99 151.00 1.11E+12 168.02 -28.46

0.3 200.71 0.99 195.73 1.15E+16 166.77 48.44

0.4 207.85 0.99 202.88 5.01E+16 166.59 60.68

0.5 215.11 0.98 210.14 2.23E+17 166.42 73.11

0.6 222.50 0.99 217.53 1.02E+18 166.25 85.75

0.7 233.82 0.99 228.85 1.05E+19 166.01 105.09

0.8 198.23 0.99 193.26 6.90E+15 166.83 44.20

0.9 169.84 0.99 164.87 1.96E+13 167.60 -4.55

Average 200.08 -- 195.10 -- -- --

86

Fig. 4.30 Regression plots to determine kinetic parameters of Carrot grass

-11.5

-11

-10.5

-10

-9.5

-9

-8.5

-8

0.0011 0.0013 0.0015 0.0017 0.0019 0.0021 0.0023

ln(β

/T2)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(A)

2

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

0.0011 0.0013 0.0015 0.0017 0.0019 0.0021 0.0023

ln(β

)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(B)

87

Table 4.6 Kinetic and thermodynamic parameters of Carrot grass

Conversion

points (α)

Ea

(kJ mol-1) R²

ΔH

(kJ mol-1) A (s-1) ΔG (kJ mol-1) ΔS (J mol-1)

KAS Method

0.1 215.43 0.99 210.34 8.45E+16 170.66 64.84

0.2 251.47 0.98 246.38 1.17E+20 169.88 125.00

0.3 275.72 0.98 270.63 1.51E+22 169.41 165.39

0.4 295.92 0.98 290.83 8.60E+23 169.05 198.99

0.5 311.55 0.99 306.46 1.96E+25 168.79 224.96

0.6 322.18 0.99 317.10 1.63E+26 168.62 242.62

0.7 246.73 0.99 241.64 4.54E+19 169.97 117.10

0.8 221.58 0.99 216.50 2.91E+17 170.52 75.13

0.9 263.88 0.99 258.79 1.41E+21 169.63 145.68

Average 267.16 -- 262.07 -- -- --

FWO Method

0.1 197.18 0.99 192.09 2.14E+15 171.11 34.28

0.2 247.57 0.99 242.48 5.38E+19 169.96 118.51

0.3 271.02 0.99 265.93 5.90E+21 169.49 157.58

0.4 290.55 0.99 285.46 2.94E+23 169.14 190.06

0.5 305.63 0.99 300.55 6.00E+24 168.88 215.14

0.6 315.91 0.99 310.82 4.67E+25 168.72 232.20

0.7 244.44 0.99 239.35 2.87E+19 170.02 113.29

0.8 221.29 0.98 216.20 2.75E+17 170.53 74.64

0.9 263.27 0.99 258.19 1.25E+21 169.64 144.68

Average 261.87 -- 256.78 -- -- --

88

Fig. 4.31 Regression plots to determine kinetic parameters of Mott grass

-11.5

-11

-10.5

-10

-9.5

-9

-8.5

-8

0.0011 0.0013 0.0015 0.0017 0.0019 0.0021 0.0023

ln(β

/T2)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(A)

2

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

0.0011 0.0013 0.0015 0.0017 0.0019 0.0021 0.0023

ln(β

)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(B)

89

Table 4.7 Kinetic and thermodynamic parameters of Mott grass

Conversion

points (α)

Ea

(kJ mol-1) R²

ΔH

(kJ mol-1) A (s-1) ΔG (kJ mol-1) ΔS (J mol-1)

KAS Method

0.1 184.12 0.99 179.10 2.71E+14 168.72 17.22

0.2 186.55 0.99 181.54 4.46E+14 168.65 21.37

0.3 200.44 0.99 195.43 7.67E+15 168.29 45.01

0.4 211.19 0.99 206.17 6.88E+16 168.03 63.25

0.5 218.50 0.98 213.49 3.06E+17 167.86 75.67

0.6 225.95 0.99 220.93 1.40E+18 167.69 88.30

0.7 187.10 0.99 182.09 5.00E+14 168.64 22.31

0.8 168.70 0.99 163.68 1.15E+13 169.16 -9.07

0.9 116.46 0.99 111.45 2.36E+08 171.01 -98.79

Average 188.78 -- 183.77 -- -- --

FWO Method

0.1 185.96 0.99 180.95 6.59E+14 166.11 24.61

0.2 187.16 0.99 182.14 8.42E+14 166.07 26.65

0.3 201.43 0.99 196.42 1.56E+16 165.71 50.93

0.4 212.49 0.99 207.48 1.50E+17 165.44 69.71

0.5 220.01 0.98 215.00 6.95E+17 165.26 82.48

0.6 227.68 0.99 222.67 3.32E+18 165.09 95.48

0.7 186.53 0.99 181.52 7.41E+14 166.09 25.59

0.8 166.09 0.99 161.08 1.12E+13 166.67 -9.28

0.9 108.83 0.99 103.82 8.03E+07 168.79 -107.75

Average 188.47 -- 183.45 -- -- --

90

Fig. 4.32 Regression plots to determine kinetic parameters of Egyptian grass

2

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

0.0011 0.0013 0.0015 0.0017 0.0019 0.0021 0.0023 0.0025

ln(β

)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(A)

-11.5

-11

-10.5

-10

-9.5

-9

-8.5

-8

0.0011 0.0013 0.0015 0.0017 0.0019 0.0021 0.0023 0.0025

ln(β

/T2)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(B)

91

Table 4.8 Kinetic and thermodynamic parameters of Egyptian grass

Conversion

points (α)

Ea

(kJ mol-1) R²

ΔH

(kJ mol-1) A (s-1) ΔG (kJ mol-1) ΔS (J mol-1)

KAS Method

0.1 91.16 0.99 86.02 7.06 x1005 177.22 -147.3

0.2 143.59 0.99 138.44 2.95 x1010 174.88 -58.86

0.3 191.06 0.99 185.92 3.99 x1014 173.41 20.21

0.4 172.86 0.99 167.72 1.05 x1013 173.92 -10.02

0.5 174.63 0.99 169.48 1.49 x1013 173.87 -7.09

0.6 183.74 0.98 178.59 9.24 x1013 173.61 8.05

0.7 193.08 0.98 187.93 5.96 x1014 173.35 23.55

0.8 168.82 0.99 163.68 4.68 x1012 174.05 -16.75

0.9 182.12 0.99 176.97 6.68 x1013 173.66 5.36

Average 166.79 -- 161.64 -- -- --

FWO Method

0.1 79.80 0.99 74.66 6.80 x1004 177.90 -166.7

0.2 144.87 0.99 139.72 3.82 x1010 174.83 -56.72

0.3 190.35 0.99 185.21 3.46 x1014 173.43 19.03

0.4 173.49 0.99 168.34 1.19 x1013 173.90 -8.99

0.5 185.66 0.99 180.51 1.35 x1014 173.56 11.24

0.6 184.37 0.98 179.22 1.05 x1014 173.59 9.10

0.7 193.48 0.98 188.34 6.46 x1014 173.34 24.22

0.8 171.38 0.99 166.23 7.80 x1012 173.97 -12.50

0.9 185.90 0.99 180.76 1.42 x1014 173.55 11.64

Average 167.70 -- 162.55 -- -- --

92

Fig. 4.33 Regression plots to determine kinetic parameters of Babui grass

-11.5

-11

-10.5

-10

-9.5

-9

-8.5

-8

0.0012 0.0014 0.0016 0.0018 0.002 0.0022

ln(β

/T2)

Inverse of pyrolysis temperature (K-1 )

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(A)

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

0.0012 0.0014 0.0016 0.0018 0.002 0.0022

ln(β

)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(B)

93

Table 4.9 Kinetic and thermodynamic parameters of Babui grass

Conversion

points (α)

Ea

(kJ mol-1) R²

ΔH

(kJ mol-1) A (s-1) ΔG (kJ mol-1) ΔS (J mol-1)

KAS Method

0.1 114.80 0.99 109.71 1.12E+08 174.17 -105.16

0.2 152.46 0.99 147.36 2.40E+11 172.72 -41.38

0.3 202.35 0.99 197.25 5.67E+15 171.28 42.37

0.4 163.15 0.98 158.05 2.09E+12 172.38 -23.37

0.5 224.85 0.99 219.75 5.21E+17 170.74 79.95

0.6 232.61 0.99 227.52 2.47E+18 170.57 92.9

0.7 240.51 0.99 235.41 1.20E+19 170.40 106.06

0.8 226.22 0.99 221.12 6.86E+17 170.71 82.24

0.9 148.87 0.99 143.78 1.16E+11 172.84 -47.42

Average 189.54 0.99 184.44 -- 171.75 --

FWO Method

0.1 116.63 0.99 111.54 1.62E+08 174.09 -102.05

0.2 153.54 0.99 148.44 2.98E+11 172.69 -39.56

0.3 201.32 0.99 196.23 4.61E+15 171.31 40.64

0.4 164.26 0.98 159.16 2.61E+12 172.35 -21.51

0.5 223.18 0.99 218.09 3.73E+17 170.79 77.16

0.6 230.72 0.99 225.62 1.69E+18 170.62 89.74

0.7 238.38 0.99 233.28 7.85E+18 170.45 102.50

0.8 224.67 0.99 219.57 5.02E+17 170.75 79.64

0.9 153.72 0.99 148.63 3.09E+11 172.69 -39.25

Average 189.60 0.99 184.51 -- 171.75 --

94

Fig. 4.34 Regression plots to determine kinetic parameters of Camel grass

-11.5

-11

-10.5

-10

-9.5

-9

-8.5

-8

0.0011 0.0013 0.0015 0.0017 0.0019 0.0021 0.0023

ln(β

/T2)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(A)

2

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

0.0011 0.0013 0.0015 0.0017 0.0019 0.0021 0.0023

ln(β

)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(B)

95

Table 4.10 Kinetic and thermodynamic parameters of Camel grass

Conversion

points (α)

Ea

(kJ mol-1) R²

ΔH

(kJ mol-1) A (s-1) ΔG (kJ mol-1) ΔS (J mol-1)

KAS Method

0.1 96.98 0.99 91.83 2.25E+06 177.21 -137.71

0.2 148.91 0.99 143.76 8.19E+10 175.00 -50.39

0.3 163.45 0.99 158.30 1.51E+12 174.52 -26.16

0.4 178.67 0.99 173.51 3.16E+13 174.06 -0.88

0.5 177.33 0.98 172.17 2.42E+13 174.10 -3.10

0.6 183.42 0.98 178.26 8.15E+13 173.92 6.99

0.7 192.74 0.99 187.59 5.23E+14 173.67 22.45

0.8 191.03 0.99 185.88 3.72E+14 173.71 19.62

0.9 188.54 0.99 183.38 2.26E+14 173.78 15.48

Average 169.01 -- 163.85 -- -- --

FWO Method

0.1 84.59 0.99 79.44 1.77E+05 177.91 -158.83

0.2 150.09 0.99 144.93 1.04E+11 174.96 -48.43

0.3 164.30 0.99 159.15 1.79E+12 174.49 -24.74

0.4 179.16 0.99 174.01 3.49E+13 174.04 -0.06

0.5 178.13 0.98 172.97 2.84E+13 174.07 -1.78

0.6 184.07 0.98 178.92 9.28E+13 173.91 8.08

0.7 193.17 0.99 188.01 5.69E+14 173.66 23.15

0.8 192.21 0.99 187.06 4.70E+14 173.68 21.57

0.9 191.42 0.99 186.26 4.02E+14 173.70 20.26

Average 168.57 -- 163.42 -- -- --

96

Fig. 4.35 Regression plots to determine kinetic parameters of cattail grass

-11.5

-11

-10.5

-10

-9.5

-9

-8.5

-8

0.0011 0.0012 0.0013 0.0014 0.0015 0.0016 0.0017 0.0018 0.0019 0.002 0.0021

ln(β

/T2)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(A)

2

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

0.0011 0.0012 0.0013 0.0014 0.0015 0.0016 0.0017 0.0018 0.0019 0.002 0.0021

ln(β

)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(B)

97

Table 4.11 Kinetic and thermodynamic parameters of Cattail

Conversion

points (α)

Ea

(kJ mol-1) R²

ΔH

(kJ mol-1) A (s-1) ΔG (kJ mol-1) ΔS (J mol-1)

KAS Method

0.1 147.20 0.99 142.04 5.53E+10 175.37 -53.67

0.2 183.84 0.98 178.68 8.34E+13 174.22 7.18

0.3 189.93 0.99 184.76 2.80E+14 174.05 17.25

0.4 185.30 0.99 180.14 1.12E+14 174.18 9.60

0.5 201.89 0.99 196.73 3.02E+15 173.73 37.02

0.6 197.73 0.99 192.57 1.32E+15 173.84 30.16

0.7 198.37 0.99 193.21 1.50E+15 173.82 31.21

0.8 153.94 0.99 148.78 2.14E+11 175.13 -42.44

0.9 177.61 0.99 172.45 2.41E+13 174.40 -3.13

Average 184.58 0.99 179.42 -- 174.22 --

FWO Method

0.1 134.78 0.99 129.62 7.61E+09 173.18 -70.16

0.2 183.32 0.99 178.16 1.25E+14 171.59 10.57

0.3 189.12 0.99 183.96 3.98E+14 171.43 20.17

0.4 188.97 0.99 183.81 3.86E+14 171.44 19.92

0.5 200.18 0.99 195.02 3.59E+15 171.14 38.46

0.6 196.55 0.99 191.39 1.74E+15 171.23 32.45

0.7 196.39 0.99 191.23 1.69E+15 171.24 32.19

0.8 153.00 0.99 147.84 2.95E+11 172.53 -39.76

0.9 158.36 0.99 153.20 8.62E+11 172.35 -30.84

Average 182.67 0.99 177.51 -- 171.64 --

98

Fig. 4.36 Regression plots to determine kinetic parameters of Wolffia

-12

-11.5

-11

-10.5

-10

-9.5

-9

-8.5

-8

0.0009 0.0011 0.0013 0.0015 0.0017 0.0019 0.0021 0.0023 0.0025 0.0027

ln(β

/T2)

Inverse of pyrolysis temperature (K-1)

0.1 0.2

0.3 0.4

0.5 0.6

0.7 0.8

0.9

A

2

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

0.0011 0.0013 0.0015 0.0017 0.0019 0.0021 0.0023 0.0025 0.0027

ln(β

)

Inverse of pyrolysis temperature (K-1)

0.1 0.2

0.3 0.4

0.5 0.6

0.7 0.8

0.9

B

99

Table 4.12 Kinetic and thermodynamic parameters of Wolffia

α Ea

kJmol-1 R²

ΔH

kJmol-1

A

s-1

ΔG

kJmol-1

ΔS

Jmol-1

KAS method

0.1 136.53 0.99 131.47 1.14 x1010 172.06 -66.65

0.2 166.62 0.99 161.56 5.29 x1012 171.05 -15.59

0.3 157.47 0.99 152.40 8.20 x1011 171.34 -31.09

0.4 175.48 0.99 170.41 3.20 x1013 170.79 -0.62

0.5 157.48 0.98 152.41 8.21 x1011 171.34 -31.08

0.6 179.44 0.99 174.38 7.17 x1013 170.68 6.08

0.7 188.45 0.98 183.38 4.46 x1014 170.43 21.27

0.8 181.50 0.98 176.44 1.09 x1014 170.62 9.56

0.9 172.21 0.99 167.14 1.65 x1013 170.89 -6.14

Avg. 168.35 -- 163.29 -- -- --

FWO method

0.1 136.11 0.99 131.05 1.04 x1010 172.08 -67.37

0.2 166.56 0.99 161.50 5.23 x1012 171.05 -15.69

0.3 158.46 0.99 153.39 1.00 x1012 171.31 -29.42

0.4 176.05 0.99 170.98 3.60 x1013 170.77 0.35

0.5 159.36 0.98 154.30 1.21 x1012 171.28 -27.88

0.6 180.88 0.99 175.81 9.59 x1013 170.64 8.50

0.7 190.50 0.99 185.44 6.76 x1014 170.37 24.74

0.8 185.56 0.99 180.49 2.48 x1014 170.51 16.40

0.9 179.87 0.99 174.81 7.82 x1013 170.67 6.81

Avg. 170.37 -- 165.31 -- -- --

100

4.6 Linear Fit Plot between Activation Energy and Pre-Exponential Factor

Fig 4.33 to Fig 4.40 showed Linear fit plot between by taking pre-exponential factor

on the y-axis and Activation energy on y-axis were formed to confirm the energy values were

almost linear through both methods KAS and FWO of all biomass samples. The R values were

shown very good results. These were best-fit plots from KAS and FWO methods showed a

linear relationship between preexponential factor (A) and Activation Energy (E).

101

Fig. 4.37 lnA vs Activation Energy of Para grass

lnA = 0.203E - 4.650

R² = .99

15

20

25

30

35

40

45

100 120 140 160 180 200 220 240

Ln

A

Activation energy (kJ/mol)

KAS

lnA = 0.2035E - 4.5695

R² = .99

15

20

25

30

35

40

45

100 120 140 160 180 200 220 240 260

Ln

A

Activation energy (kJ/mol)

FWO

102

Fig 4.38 lnA vs Activation Energy of Elephant grass

lnA = 0.2064E - 4.4456

R² = .99

25

30

35

40

45

150 170 190 210 230

Ln

A

Activation energy (kJ mol-1)

KAS

lnA = 0.2063E - 4.442

R² = .99

25

30

35

40

45

150 170 190 210 230

Ln

A

Activation energy (kJ mol-1)

FWO

103

Fig. 4.39 lnA vs activation Energy of Carrot grass

LnA= 0.1971E - 4.9184

R² = .99

30

35

40

45

50

55

60

65

200 220 240 260 280 300 320 340

Ln

A

Activation energy (kJ mol-1)

KAS

LnA = 0.197E - 4.8982

R² = .99

30

35

40

45

50

55

60

65

180 200 220 240 260 280 300 320 340

Ln

A

Activation energy (kJ mol-1)

FWO

104

Fig. 4.40 lnA vs Activation Energy Mott grass

LnA = 0.2053E - 4.5901

R² = .99

15

20

25

30

35

40

45

50

100 120 140 160 180 200 220 240 260

Ln

A

Activation energy (kJ mol-1)

KAS

LnA = 0.2051E - 4.5616

R² = .99

15

20

25

30

35

40

45

50

100 120 140 160 180 200 220 240 260

Ln

A

Activation energy (kJ mol-1)

FWO

105

Fig. 4.41 lnA vs Activation Energy Egyptian grass

LnA = 0.197E - 4.8982

R² = .99

10

15

20

25

30

35

40

50 70 90 110 130 150 170 190 210 230 250

Ln

A

Activation energy (kJ mol-1)

FWO

LnA= 0.1971E - 4.9184

R² = .99

10

15

20

25

30

35

40

50 70 90 110 130 150 170 190 210 230 250

Ln

A

Activation energy (kJ mol-1)

KAS

106

Fig. 4.42 lnA vs Activation Energy of Babui grass

lnA = 0.2019E - 4.5943

R² = .99

17

22

27

32

37

42

47

110 130 150 170 190 210 230 250

lnA

Activation energy (kJ mol-1)

FWO

lnA = 0.2019E - 4.598

R² = .99

17

22

27

32

37

42

47

110 130 150 170 190 210 230 250

lnA

Activation energy (kJ mol-1)

KAS

107

Fig. 4.43 lnA vs Activation Energy Camel grass

LnA = 0.2015E - 4.9342

R² = .998

10

15

20

25

30

35

40

80 100 120 140 160 180 200

Ln

A

Activation energy (kJ mol-1)

FWO

LnA = 0.2011E - 4.8537

R² = .999

10

15

20

25

30

35

40

80 100 120 140 160 180 200

Ln

A

Activation energy (kJ mol-1)

KAS

108

Fig. 4.44 lnA vs Activation Energy of Cattail

LnA = 0.197E - 4.8982

R² = .99

23

25

27

29

31

33

35

130 140 150 160 170 180 190 200

Ln

A

Activation energy (kJ mol-1)

FWO

LnA= 0.1971E - 4.9184

R² = .99

23

25

27

29

31

33

35

130 140 150 160 170 180 190 200

Ln

A

Activation energy (kJ mol-1)

KAS

109

4.7 Activation Energy and Conversion Points v/s Pyrolysis temperature

Activation Energy (E) values with pyrolysis temperature were showed the energy

distribution throughout the experiment. These also demonstrated that how pyrolysis

temperature effect on activation energy (E) values with conversion (α). This phenomenon is

different for different biomass samples due to differences in chemical composition in each

sample. For Para grass, initially, Activation Energy E increased with the rise in temperature at

conversion points from 0.1 to 0.6 then there was a decrease in E values from 0.7 to 0.9. This

behavior showed that temperature was independent of the E values because at higher

temperature there was lower E values and vice versa but it could not be taken as a rule as in

other biomass experiments it had seen some different results. It could not say at higher

temperature E would be higher and at a lower temperature, it should be lower. It depends upon

the chemical composition of biomass and parameters of the experiment.

The plot between conversion (α) and Pyrolysis temperature at three different heating

rates were showed a linear relationship as with the rise in temperature conversion (α) was

increasing. This showed a uniformity of the experiment with the biomass consumption at

different heating rates. It was also confirmed the accuracy of an experiment by observing the

same phenomenon at different heating rates with a slight natural difference showed from Fig

4.41 to Fig 4.54.

110

Fig. 4.45 Plot between Activation energy v/s Pyrolysis temperature of Para grass

100

120

140

160

180

200

220

240

450 550 650 750 850 950

Act

ivati

on

en

ergy (

kJ m

ol-1

)

Pyrolysis temperature (K)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

111

Fig. 4.46 Relationship of Pyrolysis temperature and mass conversion points of Para grass

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

450 550 650 750 850

Con

ver

sion

(α

)

Pyrolysis Temperature (K)

10K

30K

50K

Heating Rate (ºC min-1)

112

Table 4.13 Relationship between pyrolysis temperature, activation energies and product

formation upon pyrolysis of Para grass

Conversion

range (α)

Temperature

range (˚C) Reactions

Activation energy

(E)

α ≤ 0.1 24-212

Release of moisture contents

and decomposition of smaller

and simpler sugar molecules

Amplified from initial

point to 147 kJ mol-1

0.1 ≤ α ≤ 0.4 212-318

Thermal transformation of

celluloses to products

Increased from 147 to

185 kJ mol-1

0.4 ≤ α ≤ 0.8 318-386

Thermal transformation of

hemicellulose/lignin to

products

Increased from 185 to

196 kJ mol-1

0.1 ≤ α ≤ 1.0 386-1000 Degradation of residual lignin

and char formation

Decreased from 196 to

177 kJ mol-1

113

Fig. 4.47 Plot between Activation energy v/s Pyrolysis temperature of Elephant grass

150

170

190

210

230

250

4 5 0 5 0 0 5 5 0 6 0 0 6 5 0 7 0 0 7 5 0 8 0 0 8 5 0

Act

ivati

on

en

ergy (

kJ m

ol-1

)

Pyrolysis temperature (K)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.9

114

Fig. 4.48 Relationship of Pyrolysis temperature and mass conversion points of Elephant grass

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

450 500 550 600 650 700 750 800 850 900

Cover

sion

(α

)

Pyrolysis temperature (K)

10K

30K

50K

Heating Rate (oC/min)

115

Table 4.14 Relationship between pyrolysis temperature, activation energies and product

formation upon pyrolysis of Elephant grass

Conversion

range (α)

Temperature

range (˚C) Reactions

Activation energy

(E)

α ≤ 0.2 0-191

Moisture content

decomposition and release of

sugars

Amplified from initial

point to 184 kJ mol-1

0.2 ≤ α ≤ 0.7 290-350

Thermal transformation of

cellulose/pectin/hemicellulose

Decrease from 184 to

from 152 and increase

to 233 kJ mol-1

0.1 ≤ α ≤ 1.0 350-1000 Thermal transformation of

lignin to char

Decreased from 233 to

165 kJ mol-1

116

Fig. 4.49 Plot between Activation energy v/s Pyrolysis temperature of Carrot grass

200

220

240

260

280

300

320

340

4 5 0 5 0 0 5 5 0 6 0 0 6 5 0 7 0 0 7 5 0 8 0 0 8 5 0

Act

ivati

on

en

ergy (

kJ m

ol-1

)

Pyrolysis temperature (K)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

117

Fig. 4.50 Relationship of Pyrolysis temperature and mass conversion points of Carrot grass

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

450 500 550 600 650 700 750 800 850

Cover

sion

(α

)

Pyrolysis temperature (K)

10K

30K

50K

Heating Rate (oC/min)

118

Table 4.15 Relationship between pyrolysis temperature, activation energies and product

formation upon pyrolysis of Carrot grass

Conversion

range (α)

Temperature

range (˚C) Reactions

Activation energy

(E)

Carrot grass

α ≤ 0.1 0-204

Release of moisture contents

and decomposition of sugar

molecules

Amplified from initial

point to 215 kJ mol-1

0.1 ≤ α ≤ 0.4 204-320

Thermal conversion of

hemicellulose, cellulose, and

pectin

Amplified from 215 to

295 kJ mol-1

0.4 ≤ α ≤ 0.6 320-345 Lignin degradation

Amplified from 295 to

322 kJ mol-1

0.6 ≤ α ≤ 1.0 345-1000 Thermal transformation of

lignin to char

lowered from 322 to 263

kJ mol-1

119

Fig. 4.51 Relationship of Activation energy V/S Pyrolysis temperature of Mott grass

100

120

140

160

180

200

220

240

4 5 0 5 0 0 5 5 0 6 0 0 6 5 0 7 0 0 7 5 0 8 0 0 8 5 0 9 0 0

Act

ivati

on

en

ergy

(k

J m

ol-1

)

Pyrolysis Temperature(K)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.9

120

Fig. 4.52 Relationship of Pyrolysis temperature and mass conversion points of Mott grass

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

450 500 550 600 650 700 750 800 850 900

Cover

sion

(α

)

Pyrolysis temperature (K)

10K

30K

50K

Heating Rate (oC/min)

121

Table 4.16 Relationship between pyrolysis temperature, activation energies and product

formation upon pyrolysis of Mott grass

Conversion

range (α)

Temperature

range (˚C) Reactions

Activation energy

(E)

Mott grass

α ≤ 0.1 25-195

Release of moisture contents

and decomposition of sugar

molecules

Amplified from initial

point to 185 kJ mol-1

0.1 ≤α ≤ 0.4 195-315

Thermal transformation of

hemicelluloses/celluloses/

pectin

Raised from 185 to 212

kJ mol-1

0.4 ≤α ≤ 0.6 315-340 Degradation of lignin

Elevated from 212 to

227 kJ mol-1

0.6 ≤α ≤ 1.0 340-1000 Lignin transformation and

char formation

Lowered from 227 to

108 kJ mol-1

122

Fig. 4.53 Relationship of Activation energy V/S Temperature of Egyptian grass

80

100

120

140

160

180

200

4 0 0 4 5 0 5 0 0 5 5 0 6 0 0 6 5 0 7 0 0 7 5 0 8 0 0 8 5 0

Act

ivati

on

en

ergy (

kJ m

ol-1

)

Pyrolysis temperature (K)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

123

Fig. 4.54 Relationship of Pyrolysis temperature and mass conversion points of Egyptian grass

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

400 450 500 550 600 650 700 750 800 850

Cover

sion

(α

)

Pyrolysis temperature (K)

10K

30K

50K

Heating Rate (oC/min)

124

Table 4.17 Relationship between pyrolysis temperature, activation energies and product

formation upon pyrolysis of Egyptian grass

Conversion

range (α)

Temperature

range (˚C) Reactions

Activation energy

(E)

Egyptian grass

α ≤ 0.1 25-155

Release of moisture contents

and decomposition of sugar

molecules

Raised from initial point

to 91 kJ mol-1

0.1 ≤α ≤ 0.4 155-310

Thermal conversion of

hemicellulose, cellulose, and

pectin

Increased from 91 to

172, and then decreased

from 202 to 163 kJ mol-1

0.4 ≤α ≤ 0.7 310-359 Conversion of lignin

Initially raised from 172

to 193 kJ mol-1

0.7 ≤α ≤ 1.0 359-900 Char production from lignin

degradation

Lowered from 193 to

168 kJ mol-1, and then

elevated from 168 to 182

kJ mol-1 for the last

conversion

125

Fig. 4.55 Relationship of Activation energy V/S Pyrolysis temperature of Babui grass

100

120

140

160

180

200

220

240

450 500 550 600 650 700 750 800

Act

ivati

on

en

ergy (

kJ m

ol-1

)

Pyrolysis temperature (K)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

126

Fig. 4.56 Relationship of Pyrolysis temperature V/S mass conversion points of Babui grass

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

450 500 550 600 650 700 750 800

Cover

sion

(α

)

Pyrolysis temperature (K)

10

30

50

Heating Rate (oC/min)

127

Table 4.18 Relationship between pyrolysis temperature, activation energies and product

formation upon pyrolysis of Babui grass

Conversion

range (α)

Temperature

range (˚C) Reactions Activation energy (Ea)

α ≤ 0.1 25-190

Release of retained moisture

and conversion of simpler

sugar

Elevated from initial

point to 114 kJ mol-1

0.1 ≤ α ≤ 0.4 190-315 Transformation of celluloses/

hemicelluloses/pectin

Elevated from 114-202

kJ mol-1, and then

lowered from 202 to

163 kJ mol-1

0.4 ≤ α ≤ 0.7 315-355 Lignin transformation

elevated from 163 to

240 kJ mol-1

0.7 ≤ α ≤ 1.0 355-900 Thermal transformation of

lignin to char

Decreased from 240 to

148 kJ mol-1

128

Fig. 4.57 Relationship of Activation energy V/S Pyrolysis temperature of Camel grass

80

100

120

140

160

180

200

4 5 0 5 0 0 5 5 0 6 0 0 6 5 0 7 0 0 7 5 0 8 0 0

Act

ivati

on

en

ergy (

kJ

mol-1

)

Pyrolysis temperature (K)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

129

Fig. 4.58 Relationship of Pyrolysis temperature and mass conversion points of Camel grass

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

450 500 550 600 650 700 750 800

Cover

sion

(α

)

Pyrolysis temperature (K)

10K

30K

50K

Heating Rate (oC/min)

130

Table 4.19 Relationship between pyrolysis temperature, activation energies and product

formation upon pyrolysis of Camel grass

Conversion

range (α)

Temperature

range (˚C) Reactions

Activation energy

(E)

α ≤ 0.1 25-205

Release of moisture contents

and decomposition sugar

molecules

Amplified from initial

point to 96 kJ mol-1

0.1 ≤ α ≤ 0.4 205-340

Thermal conversion of

cellulose and pectin

Elevated from 96 to 178

kJ mol-1

0.4 ≤ α ≤ 0.8 340-370

hemicelluloses and lignin

degradation

Elevated from 178 to

192 kJ mol-1

0.1 ≤ α ≤ 1.0 370-1000

lignin degradation and char

formation

Decreased from 192 to

188 kJ mol-1

131

Fig. 4.59 Relationship of Activation energy V/S Pyrolysis temperature of Wolffia

120

130

140

150

160

170

180

190

200

3 5 0 4 5 0 5 5 0 6 5 0 7 5 0 8 5 0 9 5 0 1 0 5 0 1 1 5 0

Act

iva

tio

n e

ner

gy

(k

J m

ol-1

)

Pyrolysis temperature (K)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

132

Fig. 4.60 Relationship of Pyrolysis temperature and mass conversion points of Wolffia

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

350 450 550 650 750 850 950 1050 1150

Co

ver

sio

n

(α)

Pyrolysis temperature (K)

10K 30K

50K

Heating Rate (oC/min)

133

Table 4.20 Relationship between pyrolysis temperature, activation energies and product

Conversion

range (α)

Temperature

range (˚C) Reactions

Activation energy

(E)

α ≤ 0.1 25-271

Release of moisture contents

and decomposition sugar

molecules

Amplified from initial

point to 136 kJ mol-1

0.1 ≤ α ≤ 0.6 271-377

Thermal conversion of

cellulose and pectin

Elevated from 136 to

180 kJ mol-1

0.6 ≤ α ≤ 0.8 340-370

hemicelluloses and lignin

degradation

Remain almost same

180-181 kJ mol-1

0.8 ≤ α ≤ 1.0 370-1000

lignin degradation and char

formation

Decreased from 181 to

172 kJ mol-1

134

4.8 Comparison of Activation Energy and Enthalpy

It was clearly seen from the Fig 4.55 all biomass samples had average activation energy

and enthalpy values ranging from 166-267 kJ mol-1 and 161-262 kJ mol-1 respectively. The

Egyptian grass showed the minimum average of activation energy (166 kJ mol-1) and enthalpy

(161 kJ mol-1) whereas carrot grass showed the maximum amount of activation energy (267 kJ

mol-1) and enthalpy (262 kJ mol-1) that showed that all samples were suitable for co-pyrolysis

to release different products. It was also showed that enthalpy was <5 in all biomass samples

from activation energy values.

Fig. 4.61 Comparison of Activation Energy and Enthalpy of all Biomass Samples

178

200

188

166

184

169

267

189

172

195

183

161

179

164

262

184

140

160

180

200

220

240

260

280

Comparison of Acivation Energy and Enthalpy

Activation energy (kJ mol-1)

Enthalpy (kJ mol-1)

135

CONCLUSION AND PROSPECTS

Technology is shifting from traditional fuels to biofuels due to the limitation of tradition fuels

in various aspects including cost, GHG emission, non-reusability, limited resources and unsafe

for the environment. The increasing energy demands have emphasized on the use of marginal

lands and their biomass to be exploited for bioenergy production. Pakistan is an emerging

economy and its energy requirements are exponentially increasing. With the increasing

population, the country has huge food requirements too. Hence, the agricultural lands cannot

be used to produce energy crops. Interestingly, a huge land is considered as poor or marginal

due to its unsuitability to produce food crops, which may be exploited to produce biomass for

bioenergy production. Aiming at this, the present study focused on the characterization of

biomass in terms of thermodynamics and kinetics parameters. The results showed that selected

biomass samples had remarkable potential for energy generation on large scale. Kinetic

parameters including kinetic energy, Gibbs Free energy, enthalpy and entropy were calculated

using KAS and FWO iso-conversional methods. It was examined that some biomass had more

potential to be adopted as a bio-oil production like Camel grass, Para grass, Cattail, Egyptian

grass, Mott grass as compared to Carrot grass and Elephant grass due to a lower percentage of

ash and a higher percentage of volatile contents. Because lower energy was required to break

the bonds of cellulose and hemicellulose. Therefore, these energy values demonstrated the

potential of selected energy crops in near future. It also pivoted on further advance analysis

could be done in the future study on these biomass samples. FTIR and GC-MS analysis could

be performed in the future to determine the evolved gasses during the pyrolysis process. It

would be essential to design reactor to obtain useful products. This study will be a milestone

in terms of technology shifting from traditional fuels to biofuels in Pakistan.

Moreover, before exploiting this biomass for pyrolysis, lab-scale pyrolysis experiments need

to be performed to study the fuel properties of the bio-oil produced from this biomass.

Moreover, more detailed studies including LCA and environmental impact of the large-scale

cultivation of these grasses on the biodiversity should also be conducted in the future.

136

References

Acharya, B., Pradhan, R. R., & Dutta, A. (2015). Qualitative and kinetic analysis of

torrefaction of lignocellulosic biomass using DSC-TGA-FTIR. AIMS Energy, 3(4),

760-773.

Adkins, S., & Shabbir, A. (2014). Biology, ecology and management of the invasive

parthenium weed (Parthenium hysterophorus L.). Pest Management Science, 70(7),

1023-1029.

Ahmad, M. S., Mehmood, M. A., Al Ayed, O. S., Ye, G., Luo, H., Ibrahim, M., . . . Qadir, G.

(2017). Kinetic analyses and pyrolytic behavior of Para grass (Urochloa mutica) for its

bioenergy potential. Bioresource Technology, 224, 708-713.

Ali, N., Saleem, M., Shahzad, K., & Chughtai, A. (2015). Bio-oil production from fast

pyrolysis of cotton stalk in fluidized bed reactor. Arabian Journal for Science and

Engineering, 40(11), 3019-3027.

Almodares, A., & Hadi, M. (2009). Production of bioethanol from sweet sorghum: A review.

African Journal of Agricultural Research, 4(9), 772-780.

Angelini, L. G., Ceccarini, L., Nassi o Di Nasso, N., & Bonari, E. (2009). Long-term evaluation

of biomass production and quality of two cardoon (Cynara cardunculus L.) cultivars

for energy use. Biomass and Bioenergy, 33(5), 810-816.

Aylott, M. J., Casella, E., Tubby, I., Street, N., Smith, P., & Taylor, G. (2008). Yield and spatial

supply of bioenergy poplar and willow short-rotation coppice in the UK. New

phytologist, 178(2), 358-370.

Balogun, A. O., Lasode, O. A., & McDonald, A. G. (2014). Devolatilisation kinetics and

pyrolytic analyses of Tectona grandis (teak). Bioresource Technology, 156(Supplement

C), 57-62. doi:https://doi.org/10.1016/j.biortech.2014.01.007

Batanouny, K. H., Stichler, W., & Ziegler, H. (1988). Photosynthetic pathways, distribution,

and ecological characteristics of grass species in egypt. Oecologia, 75(4), 539-548.

doi:10.1007/bf00776418

Benavente, V., & Fullana, A. (2015). Torrefaction of olive mill waste. Biomass and Bioenergy,

73(Supplement C), 186-194. doi:https://doi.org/10.1016/j.biombioe.2014.12.020

137

Bensidhom, G., Ben Hassen-Trabelsi, A., Alper, K., Sghairoun, M., Zaafouri, K., & Trabelsi,

I. (2018). Pyrolysis of Date palm waste in a fixed-bed reactor: Characterization of

pyrolytic products. Bioresource Technology, 247(Supplement C), 363-369.

doi:https://doi.org/10.1016/j.biortech.2017.09.066

Bhateria, R., & Dhaka, R. (2015). Algae as biofuel. Biofuels(ahead-of-print), 1-25.

Bilal, M. Q. (2009). Effect of molasses and corn as silage additives on the characteristics of

mott dwarf elephant grass silage at different fermentation periods. Pakistan Veterinary

Journal, 29(1).

Biney, P. O., Gyamerah, M., Shen, J., & Menezes, B. (2015). Kinetics of the pyrolysis of

arundo, sawdust, corn stover and switch grass biomass by thermogravimetric analysis

using a multi-stage model. Bioresource Technology, 179, 113-122.

Borkowska, H., Molas, R., & Kupczyk, A. (2009). Virginia fanpetals (Sida hermaphrodita

Rusby) cultivated on light soil; height of yield and biomass productivity. Polish Journal

of Environmental Studies, 18(4), 563-568.

Brachi, P., Miccio, F., Miccio, M., & Ruoppolo, G. (2016). Pseudo-component thermal

decomposition kinetics of tomato peels via isoconversional methods. Fuel processing

technology, 154, 243-250. doi:http://dx.doi.org/10.1016/j.fuproc.2016.09.001

Braga, R. M., Melo, D. M., Aquino, F. M., Freitas, J. C., Melo, M. A., Barros, J. M., & Fontes,

M. S. (2014). Characterization and comparative study of pyrolysis kinetics of the rice

husk and the elephant grass. Journal of Thermal Analysis and Calorimetry, 115(2),

1915-1920.

Brennan, L., & Owende, P. (2010). Biofuels from microalgae: a review of technologies for

production, processing, and extractions of biofuels and co-products. Renewable and

Sustainable Energy Reviews, 14(2), 557-577.

Cai, J., Li, B., Chen, C., Wang, J., Zhao, M., & Zhang, K. (2016). Hydrothermal carbonization

of tobacco stalk for fuel application. Bioresource Technology, 220(Supplement C),

305-311. doi:https://doi.org/10.1016/j.biortech.2016.08.098

Chen, Z., Hu, M., Zhu, X., Guo, D., Liu, S., Hu, Z., . . . Laghari, M. (2015). Characteristics

and kinetic study on pyrolysis of five lignocellulosic biomass via thermogravimetric

analysis. Bioresource Technology, 192(Supplement C), 441-450.

doi:https://doi.org/10.1016/j.biortech.2015.05.062

138

Collura, S., Azambre, B., Finqueneisel, G., Zimny, T., & Weber, J. V. (2006). Miscanthus×

Giganteus straw and pellets as sustainable fuels. Environmental chemistry letters, 4(2),

75-78.

Corton, J., Donnison, I. S., Patel, M., Bühle, L., Hodgson, E., Wachendorf, M., . . . Fraser, M.

D. (2016). Expanding the biomass resource: sustainable oil production via fast

pyrolysis of low input high diversity biomass and the potential integration of

thermochemical and biological conversion routes. Applied Energy, 177(Supplement

C), 852-862. doi:https://doi.org/10.1016/j.apenergy.2016.05.088

Costa, V. J., Vieira, R. M., Girotto, S. B. F. T., & Simioni, F. J. (2016). Pyrolysis and

thermogravimetry of blended and nonblended residues of pine and eucalyptus forestry

woods. Environmental Progress & Sustainable Energy, 35(5), 1521-1528.

da Silva, D. R., Crespi, M. S., Ribeiro, C. A., & Capela, J. M. Thermal decomposition kinetics

of sugarcane mills wastes. Journal of Thermal Analysis and Calorimetry, 1-12.

Damartzis, T., Vamvuka, D., Sfakiotakis, S., & Zabaniotou, A. (2011). Thermal degradation

studies and kinetic modeling of cardoon (Cynara cardunculus) pyrolysis using

thermogravimetric analysis (TGA). Bioresource Technology, 102(10), 6230-6238.

doi:https://doi.org/10.1016/j.biortech.2011.02.060

Das, P., Mondal, D., & Maiti, S. (2017). Thermochemical conversion pathways of

Kappaphycus alvarezii granules through study of kinetic models. Bioresource

Technology, 234, 233-242.

Davis, S. C., Dohleman, F. G., & Long, S. P. (2011). The global potential for Agave as a

biofuel feedstock. Gcb Bioenergy, 3(1), 68-78.

Demirbas, A. (2017). Higher heating values of lignin types from wood and non-wood

lignocellulosic biomasses. Energy Sources, Part A: Recovery, Utilization, and

Environmental Effects, 39(6), 592-598.

Domínguez, M. T., Madejón, P., Madejón, E., & Diaz, M. J. (2017). Novel energy crops for

Mediterranean contaminated lands: Valorization of Dittrichia viscosa and Silybum

marianum biomass by pyrolysis. Chemosphere, 186, 968-976.

Dragone, G., Fernandes, B. D., Vicente, A. n. A., & Teixeira. (2010). Third generation biofuels

from microalgae.

139

Dutt, D., Upadhyaya, J., Tyagi, C., & Malik, R. (2004). Studies on pulp and paper making

characteristics of some Indian non-woody fibrous raw materials-Part II.

Eijck, J., Smeets, E., & Faaij, A. (2012). The economic performance of jatropha, cassava and

Eucalyptus production systems for energy in an East African smallholder setting. Gcb

Bioenergy, 4(6), 828-845.

El-Sayed, S. A., & Mostafa, M. E. (2014). Pyrolysis characteristics and kinetic parameters

determination of biomass fuel powders by differential thermal gravimetric analysis

(TGA/DTG). Energy Conversion and Management, 85(Supplement C), 165-172.

doi:https://doi.org/10.1016/j.enconman.2014.05.068

El-Sayed, S. A., & Mostafa, M. E. (2015). Kinetic parameters determination of biomass

pyrolysis fuels using TGA and DTA techniques. Waste and Biomass Valorization, 6(3),

401-415.

El Bassam, N. (2010). Handbook of bioenergy crops: a complete reference to species,

development and applications: Routledge.

Eliana, C., Jorge, R., Juan, P., & Luis, R. (2014). Effects of the pretreatment method on

enzymatic hydrolysis and ethanol fermentability of the cellulosic fraction from

elephant grass. Fuel, 118(Supplement C), 41-47.

doi:https://doi.org/10.1016/j.fuel.2013.10.055

Eliche-Quesada, D., Martínez-García, C., Martínez-Cartas, M. L., Cotes-Palomino, M. T.,

Pérez-Villarejo, L., Cruz-Pérez, N., & Corpas-Iglesias, F. A. (2011). The use of

different forms of waste in the manufacture of ceramic bricks. Applied Clay Science,

52(3), 270-276. doi:http://dx.doi.org/10.1016/j.clay.2011.03.003

Elmay, Y., Jeguirim, M., Trouvé, G., & Said, R. (2016). Kinetic analysis of thermal

decomposition of date palm residues using Coats–Redfern method. Energy Sources,

Part A: Recovery, Utilization, and Environmental Effects, 38(8), 1117-1124.

Escamilla-Trevino, L. L. (2012). Potential of plants from the genus Agave as bioenergy crops.

BioEnergy Research, 5(1), 1-9.

Evans, H. C. (1997). Parthenium hysterophorus. a review of its weed status and the possibilities

for biological control. Biocontrol News and Information, 18, 89N-98N.

140

Ferdosian, F., Yuan, Z., Anderson, M., & Xu, C. C. (2016). Thermal performance and thermal

decomposition kinetics of lignin-based epoxy resins. Journal of Analytical and Applied

Pyrolysis, 119, 124-132. doi:http://dx.doi.org/10.1016/j.jaap.2016.03.009

Francaviglia, R., Bruno, A., Falcucci, M., Farina, R., Renzi, G., Russo, D. E., . . . Neri, U.

(2016). Yields and quality of Cynara cardunculus L. wild and cultivated cardoon

genotypes. A case study from a marginal land in Central Italy. European Journal of

Agronomy, 72, 10-19.

Franzaring, J., Schmid, I., Bäuerle, L., Gensheimer, G., & Fangmeier, A. (2014). Investigations

on plant functional traits, epidermal structures and the ecophysiology of the novel

bioenergy species Sida hermaphrodita Rusby and Silphium perfoliatum L. Journal of

Applied Botany and Food Quality, 87, DOI:

http://dx.doi.org/10.5073/JABFQ.2014.5087.5006.

Gabrielle, B., Bamiere, L., Caldes, N., De Cara, S. p., Decocq, G., Ferchaud, F., . . . Wohlfahrt,

J. (2014). Paving the way for sustainable bioenergy in Europe: technological options

and research avenues for large-scale biomass feedstock supply. Renewable and

Sustainable Energy Reviews, 33, 11-25.

Geyer, W., Argent, R., & Walawender, W. (1987). Biomass properties and gasification

behavior of 7-year-old Siberian elm. Wood and fiber science, 19(2), 176-182.

Gupta, R., & Sharma, S. (1971). Grasses of the rangelands in arid Rajasthan. Journal

d'agriculture tropicale et de botanique appliquée, 18(1), 50-99.

Hill, J., Nelson, E., Tilman, D., Polasky, S., & Tiffany, D. (2006). Environmental, economic,

and energetic costs and benefits of biodiesel and ethanol biofuels. Proceedings of the

National Academy of Sciences, 103(30), 11206-11210.

Hlavsová, A., Corsaro, A., Raclavská, H., Vallová, S., & Juchelková, D. (2016). The effect of

feedstock composition and taxonomy on the products distribution from pyrolysis of

nine herbaceous plants. Fuel processing technology, 144, 27-36.

Holou, R. A., & Stevens, G. (2012). Juice, sugar, and bagasse response of sweet sorghum

(Sorghum bicolor (L.) Moench cv. M81E) to N fertilization and soil type. Gcb

Bioenergy, 4(3), 302-310.

Holtum, J. A., Chambers, D., Morgan, T., & Tan, D. K. (2011). Agave as a biofuel feedstock

in Australia. GCB Bioenergy, 3(1), 58-67.

141

Huang, L., Liu, J., He, Y., Sun, S., Chen, J., Sun, J., . . . Kuo, J. (2016). Thermodynamics and

kinetics parameters of co-combustion between sewage sludge and water hyacinth in

CO2/O2 atmosphere as biomass to solid biofuel. Bioresource Technology, 218, 631-

642.

Huang, X., Cao, J.-P., Zhao, X.-Y., Wang, J.-X., Fan, X., Zhao, Y.-P., & Wei, X.-Y. (2016).

Pyrolysis kinetics of soybean straw using thermogravimetric analysis. Fuel, 169, 93-

98. doi:http://dx.doi.org/10.1016/j.fuel.2015.12.011

Idris, S. S., Rahman, N. A., Ismail, K., Alias, A. B., Rashid, Z. A., & Aris, M. J. (2010).

Investigation on thermochemical behaviour of low rank Malaysian coal, oil palm

biomass and their blends during pyrolysis via thermogravimetric analysis (TGA).

Bioresource Technology, 101(12), 4584-4592.

Imhoff, S., Da Silva, A. P., & Tormena, C. (2000). Spatial heterogeneity of soil properties in

areas under elephant-grass short-duration grazing system. Plant and Soil, 219(1-2),

161-168.

Ishiguro, M., & Endo, T. (2014). Addition of alkali to the hydrothermal–mechanochemical

treatment of Eucalyptus enhances its enzymatic saccharification. Bioresource

Technology, 153, 322-326.

Jeguirim, M., Dorge, S., & Trouve, G. (2010). Thermogravimetric analysis and emission

characteristics of two energy crops in air atmosphere: Arundo donax and Miscanthus

giganthus. Bioresource Technology, 101(2), 788-793.

Kataria, R., Mol, A., Schulten, E., Happel, A., & Mussatto, S. I. (2016). Bench scale steam

explosion pretreatment of acid impregnated elephant grass biomass and its impacts on

biomass composition, structure and hydrolysis. Industrial Crops and Products.

Kim, S.-S., & Agblevor, F. A. (2014). Thermogravimetric analysis and fast pyrolysis of

Milkweed. Bioresource Technology, 169, 367-373.

Kim, S.-S., Shenoy, A., & Agblevor, F. A. (2014). Thermogravimetric and kinetic study of

Pinyon pine in the various gases. Bioresource Technology, 156, 297-302.

Kim, Y. S., Kim, Y. S., & Kim, S. H. (2010). Investigation of thermodynamic parameters in

the thermal decomposition of plastic waste− waste lube oil compounds. Environmental

science & technology, 44(13), 5313-5317.

142

Kuehl, Y. (2015). Resources, Yield, and Volume of Bamboos Bamboo (pp. 91-111).

Switzerland: Springer.

Kumar, A., & Abrol, I. (1984). Studies on the reclaiming effect of Karnal-grass and para-grass

grown in a highly sodic soil. Indian Journal of Agricultural Sciences.

Laflamme, L. (1992). Carrot/grass silage as cattle feed. Canadian Journal of Animal Science,

72(2), 441-443.

Larson, S. R., Pearson, C. H., Jensen, K. B., Jones, T. A., Mott, I. W., Robbins, M. D., . . .

Waldron, B. L. (2017). Development and Testing of Cool-Season Grass Species,

Varieties and Hybrids for Biomass Feedstock Production in Western North America.

Agronomy, 7(1), 3.

Leite, G. B., Abdelaziz, A. E., & Hallenbeck, P. C. (2013). Algal biofuels: challenges and

opportunities. Bioresource Technology, 145, 134-141.

Li, D., Chen, L., Chen, S., Zhang, X., Chen, F., & Ye, N. (2012). Comparative evaluation of

the pyrolytic and kinetic characteristics of a macroalga (Sargassum thunbergii) and a

freshwater plant (Potamogeton crispus). Fuel, 96, 185-191.

Li, D., Chen, L., Yi, X., Zhang, X., & Ye, N. (2010). Pyrolytic characteristics and kinetics of

two brown algae and sodium alginate. Bioresource Technology, 101(18), 7131-7136.

Li, D., Chen, L., Zhang, X., Ye, N., & Xing, F. (2011). Pyrolytic characteristics and kinetic

studies of three kinds of red algae. Biomass and Bioenergy, 35(5), 1765-1772.

Li, D., Chen, L., Zhao, J., Zhang, X., Wang, Q., Wang, H., & Ye, N. (2010). Evaluation of the

pyrolytic and kinetic characteristics of Enteromorpha prolifera as a source of

renewable bio-fuel from the Yellow Sea of China. Chemical Engineering Research and

Design, 88(5), 647-652.

Littlewood, J., Wang, L., Turnbull, C., & Murphy, R. J. (2013). Techno-economic potential of

bioethanol from bamboo in China. Biotechnology for biofuels, 6(1), 173.

Liu, W., Mi, J., Song, Z., Yan, J., Li, J., & Sang, T. (2014). Long-term water balance and

sustainable production of Miscanthus energy crops in the Loess Plateau of China.

Biomass and Bioenergy, 62, 47-57.

Liu, Y., Wang, Y., Guo, F., Li, X., Li, T., Guo, C., & Chang, J. (2017). Characterization of the

gas releasing behaviors of catalytic pyrolysis of rice husk using potassium over a micro-

fluidized bed reactor. Energy Conversion and Management, 136, 395-403.

143

López-González, D., Fernandez-Lopez, M., Valverde, J., & Sanchez-Silva, L. (2013).

Thermogravimetric-mass spectrometric analysis on combustion of lignocellulosic

biomass. Bioresource Technology, 143, 562-574.

Lord, R. (2015). Reed canarygrass (Phalaris arundinacea) outperforms Miscanthus or willow

on marginal soils, brownfield and non-agricultural sites for local, sustainable energy

crop production. Biomass and Bioenergy, 78, 110-125.

Ma, P.-Y., Shi, S.-W., Fan, F.-Y., Wang, Y.-Q., Zhang, X.-W., & Xing, X.-J. (2017).

Determination of Combustion Kinetic Parameters and Fuel Properties of Hydrochar

Prepared from Hydrothermal Carbonization of Bamboo. BioResources, 12(2), 3463-

3477.

Machado, J. C., Carneiro, P. C. S., da Costa Carneiro, J., Resende, M. D. V., Vander Pereira,

A., & de Souza Carneiro, J. E. (2017). Elephant grass ecotypes for bioenergy

production via direct combustion of biomass. Industrial Crops and Products, 95, 27-

32.

Maia, A. A. D., & de Morais, L. C. (2016). Kinetic parameters of red pepper waste as biomass

to solid biofuel. Bioresource technology, 204, 157-163.

Maiti, S., Dey, S., Purakayastha, S., & Ghosh, B. (2006). Physical and thermochemical

characterization of rice husk char as a potential biomass energy source. Bioresource

Technology, 97(16), 2065-2070. doi:http://dx.doi.org/10.1016/j.biortech.2005.10.005

Masnadi, M. S., Habibi, R., Kopyscinski, J., Hill, J. M., Bi, X., Lim, C. J., . . . Grace, J. R.

(2014). Fuel characterization and co-pyrolysis kinetics of biomass and fossil fuels.

Fuel, 117, 1204-1214.

Mauromicale, G., Sortino, O., Pesce, G. R., Agnello, M., & Mauro, R. P. (2014). Suitability of

cultivated and wild cardoon as a sustainable bioenergy crop for low input cultivation

in low quality Mediterranean soils. Industrial Crops and Products, 57, 82-89.

Mehmood, M. A., Ye, G., Luo, H., Liu, C., Malik, S., Afzal, I., . . . Ahmad, M. S. (2017).

Pyrolysis and kinetic analyses of Camel grass (Cymbopogon schoenanthus) for

bioenergy. Bioresource Technology, 228, 18-24.

Millán, L. M. R., Vargas, F. E. S., & Nzihou, A. (2017). Kinetic Analysis of Tropical

Lignocellulosic Agrowaste Pyrolysis. BioEnergy Research, 1-14.

144

Mishra, S., & Verma, J. (2016). Thermal decomposition kinetics of silane treated wood: PVC

microcellular composites. International Journal of Plastics Technology, 20(1), 93-105.

doi:10.1007/s12588-016-9140-9

Mishra, V. (2015). Bamboo and Its Connectivity to the Different Fields of Economics: A

Potential Resource of Modern India. International Journal of Innovative Research and

Development, 4(2), 140-145.

Mitich, L. M. (2000). Common cattail, Typha latifolia L. Weed technology, 14(2), 446-450.

Morton, J. F. (1975). Cattails (Typha spp.)—weed problem or potential crop? Economic

Botany, 29(1), 7-29.

Mullet, J., Morishige, D., McCormick, R., Truong, S., Hilley, J., McKinley, B., . . . Rooney,

W. (2014). Energy Sorghum-a genetic model for the design of C4 grass bioenergy

crops. Journal of experimental botany, eru229.

Mundike, J., Collard, F.-X., & Görgens, J. F. (2017). Pyrolysis of Lantana camara and Mimosa

pigra: Influences of temperature, other process parameters and incondensable gas

evolution on char yield and higher heating value. Bioresource Technology,

243(Supplement C), 284-293. doi:https://doi.org/10.1016/j.biortech.2017.06.086

Naik, B. S., Dangi, N. B., Sapkota, H. P., Wagle, N., Nagarjuna, S., & Sankaranand, R. (2016).

Phytochemical screening and evaluation of anti-fertility activity of Dactyloctenium

aegyptium in male albino rats. Asian Pacific Journal of Reproduction, 5(1), 51-57.

Naik, S., Goud, V. V., Rout, P. K., & Dalai, A. K. (2010). Production of first and second

generation biofuels: a comprehensive review. Renewable and Sustainable Energy

Reviews, 14(2), 578-597.

Navie, S., Panetta, F., McFadyen, R., & Adkins, S. (1998). Behaviour of buried and surface-

sown seeds of Parthenium. Weed Res, 38, 335-341.

Nixon, D., Stephens, W., Tyrrel, S., & Brierley, E. (2001). The potential for short rotation

energy forestry on restored landfill caps. Bioresource technology, 77(3), 237-245.

Nozela, W., Braz, C., Almeida, S., Ribeiro, C., & Crespi, M. Mixture of biomass to energy

reuse. Journal of Thermal Analysis and Calorimetry, 1-5.

Nyakuma, B. B. Bioenergy Potential of Melon Seed Husks.

145

Nyakuma, B. B. (2015). Thermogravimetric and kinetic analysis of melon (Citrullus

colocynthis L.) seed husk using the distributed activation energy model. Environmental

and Climate Technologies, 15(1), 77-89.

o Di Nasso, N. N., Lasorella, M., Roncucci, N., & Bonari, E. (2015). Soil texture and crop

management affect switchgrass (Panicum virgatum L.) productivity in the

Mediterranean. Industrial Crops and Products, 65, 21-26.

o Di Nasso, N. N., Roncucci, N., & Bonari, E. (2013). Seasonal dynamics of aboveground and

belowground biomass and nutrient accumulation and remobilization in giant reed

(Arundo donax L.): a three-year study on marginal land. BioEnergy Research, 6(2),

725-736.

Onadja, Y. (2006). Physical characterization of essential oils: refractive index measuring of

the essential oil of Cymbopogon schoenanthus (L.) Spreng. DEA in Physics (LCOA),

University-Ouagadougou.

Owen, N. A., & Griffiths, H. (2014). Marginal land bioethanol yield potential of four

crassulacean acid metabolism candidates (Agave fourcroydes, Agave salmiana, Agave

tequilana and Opuntia ficusâ-indica) in Australia. GCB Bioenergy, 6(6), 687-703.

Özsin, G., & Pütün, A. E. (2017). Kinetics and evolved gas analysis for pyrolysis of food

processing wastes using TGA/MS/FT-IR. Waste Management, 64, 315-326.

doi:http://dx.doi.org/10.1016/j.wasman.2017.03.020

Pakistan Economic Survey. (2015). Government of Pakistan Statistics.

Perez, I., Perez, J., Carrasco, J., & Ciria, P. (2014). Siberian elm responses to different culture

conditions under short rotation forestry in Mediterranean areas. Turkish Journal of

Agriculture and Forestry, 38, doi:10.3906/tar-1311-3113.

Pociene, L., Šarunaite, L., Tilvikiene, V., Slepetys, J., & Åkadziuliene, Ž. (2013). The yield

and composition of reed canary grass biomass as raw material for combustion.

Biologija, 59(2).

Polat, S., Apaydin-Varol, E., & Pütün, A. E. (2016). Thermal decomposition behavior of

tobacco stem Part II: Kinetic analysis. Energy Sources, Part A: Recovery, Utilization,

and Environmental Effects, 38(20), 3073-3080.

Ravikumar, C., Kumar, P. S., Subhashni, S., Tejaswini, P., & Varshini, V. (2017). Microwave

assisted fast pyrolysis of corn cob, corn stover, saw dust and rice straw: Experimental

146

investigation on bio-oil yield and high heating values. Sustainable Materials and

Technologies, 11, 19-27.

Reza, M. T., Yan, W., Uddin, M. H., Lynam, J. G., Hoekman, S. K., Coronella, C. J., &

Vásquez, V. R. (2013). Reaction kinetics of hydrothermal carbonization of loblolly

pine. Bioresource Technology, 139(Supplement C), 161-169.

doi:https://doi.org/10.1016/j.biortech.2013.04.028

Rocha, E. P. A., Sermyagina, E., Vakkilainen, E., Colodette, J. L., de Oliveira, I. M., &

Cardoso, M. Kinetics of pyrolysis of some biomasses widely available in Brazil.

Journal of Thermal Analysis and Calorimetry, 1-10.

Runge, C. F., & Senauer, B. (2007). How biofuels could starve the poor. Foreign affairs, 41-

53.

Saha, M., & Eckelman, M. J. (2015). Geospatial assessment of potential bioenergy crop

production on urban marginal land. Applied Energy, 159, 540-547.

Sait, H. H., Hussain, A., Salema, A. A., & Ani, F. N. (2012). Pyrolysis and combustion kinetics

of date palm biomass using thermogravimetric analysis. Bioresource Technology, 118,

382-389.

Sanchez-Jimenez, P. E., Perez-Maqueda, L. A., Pereon, A., & Criado, J. M. (2013).

Generalized master plots as a straightforward approach for determining the kinetic

model: the case of cellulose pyrolysis. Thermochimica acta, 552, 54-59.

Sanchez-Silva, L., López-González, D., Villaseñor, J., Sánchez, P., & Valverde, J. (2012).

Thermogravimetric–mass spectrometric analysis of lignocellulosic and marine biomass

pyrolysis. Bioresource Technology, 109, 163-172.

Sanderson, M. A., & Adler, P. R. (2008). Perennial forages as second generation bioenergy

crops. International Journal of Molecular Sciences, 9(5), 768-788.

Schmer, M. R., Vogel, K. P., Mitchell, R. B., & Perrin, R. K. (2008). Net energy of cellulosic

ethanol from switchgrass. Proceedings of the National Academy of Sciences, 105(2),

464-469.

Searchinger, T., Heimlich, R., Houghton, R. A., Dong, F., Elobeid, A., Fabiosa, J., . . . Yu, T.-

H. (2008). Use of US croplands for biofuels increases greenhouse gases through

emissions from land-use change. Science, 319(5867), 1238-1240.

147

Shen, J., Igathinathane, C., Yu, M., & Pothula, A. K. (2015). Biomass pyrolysis and

combustion integral and differential reaction heats with temperatures using

thermogravimetric analysis/differential scanning calorimetry. Bioresource

Technology, 185, 89-98.

Shuping, Z., Yulong, W., Mingde, Y., Chun, L., & Junmao, T. (2010). Pyrolysis characteristics

and kinetics of the marine microalgae Dunaliella tertiolecta using thermogravimetric

analyzer. Bioresource Technology, 101(1), 359-365.

doi:http://dx.doi.org/10.1016/j.biortech.2009.08.020

Singh, Y. D., Mahanta, P., & Bora, U. (2017). Comprehensive characterization of

lignocellulosic biomass through proximate, ultimate and compositional analysis for

bioenergy production. Renewable Energy, 103, 490-500.

Skevas, T., Swinton, S. M., & Hayden, N. J. (2014). What type of landowner would supply

marginal land for energy crops? Biomass and Bioenergy, 67, 252-259.

Skreiberg, A., Skreiberg, Ø., Sandquist, J., & Sørum, L. (2011). TGA and macro-TGA

characterisation of biomass fuels and fuel mixtures. Fuel, 90(6), 2182-2197.

doi:http://dx.doi.org/10.1016/j.fuel.2011.02.012

Smith, S. L., Thelen, K. D., & MacDonald, S. J. (2013). Yield and quality analyses of

bioenergy crops grown on a regulatory brownfield. Biomass and Bioenergy, 49, 123-

130.

Soria-Verdugo, A., Goos, E., Morato-Godino, A., García-Hernando, N., & Riedel, U. (2017).

Pyrolysis of biofuels of the future: Sewage sludge and microalgae–Thermogravimetric

analysis and modelling of the pyrolysis under different temperature conditions. Energy

Conversion and Management, 138, 261-272.

Sotirelis, N. P., & Chrysikopoulos, C. V. (2015). Interaction between graphene oxide

nanoparticles and quartz sand. Environmental science & technology, 49(22), 13413-

13421.

Tao, J.-J., Wang, H.-H., Chen, S., & Zhu, F. (2017). Reality in the Kinetic Modelling of

Pyrolysis of Plant Fuels. Energy Procedia, 107, 85-93.

Tilman, D., Hill, J., & Lehman, C. (2006). Carbon-negative biofuels from low-input high-

diversity grassland biomass. Science, 314(5805), 1598-1600.

148

Tinwala, F., Mohanty, P., Parmar, S., Patel, A., & Pant, K. K. (2015). Intermediate pyrolysis

of agro-industrial biomasses in bench-scale pyrolyser: Product yields and its

characterization. Bioresource Technology, 188(Supplement C), 258-264.

doi:https://doi.org/10.1016/j.biortech.2015.02.006

Tsamba, A. J., Yang, W., & Blasiak, W. (2006). Pyrolysis characteristics and global kinetics

of coconut and cashew nut shells. Fuel processing technology, 87(6), 523-530.

doi:https://doi.org/10.1016/j.fuproc.2005.12.002

Tuly, S., Parveen, M., Islam, M., Rahman, M., & Haniu, H. (2017). Pyrolysis kinetics study of

three biomass solid wastes for thermochemical conversion into liquid fuels. Paper

presented at the AIP Conference Proceedings.

Turmanova, S. C., Genieva, S., Dimitrova, A., & Vlaev, L. (2008). Non-isothermal degradation

kinetics of filled with rise husk ash polypropene composites. Express Polymer Letters,

2(2), 133-146.

Valdez-Vazquez, I., Acevedo-Benítez, J. A., & Hernández-Santiago, C. (2010). Distribution

and potential of bioenergy resources from agricultural activities in Mexico. Renewable

and Sustainable Energy Reviews, 14(7), 2147-2153.

Valix, M., Katyal, S., & Cheung, W. (2017). Combustion of thermochemically torrefied sugar

cane bagasse. Bioresource Technology, 223, 202-209.

Van Der Hilst, F., & Faaij, A. P. (2012). Spatiotemporal cost-supply curves for bioenergy

production in Mozambique. Biofuels, Bioproducts and Biorefining, 6(4), 405-430.

Vhathvarothai, N., Ness, J., & Yu, Q. J. (2014). An investigation of thermal behaviour of

biomass and coal during copyrolysis using thermogravimetric analysis. International

journal of energy research, 38(9), 1145-1154.

Vlaev, L., Georgieva, V., & Genieva, S. (2007). Products and kinetics of non-isothermal

decomposition of vanadium (IV) oxide compounds. Journal of Thermal Analysis and

Calorimetry, 88(3), 805-812.

Wang, D., Poss, J., Donovan, T., Shannon, M., & Lesch, S. (2002). Biophysical properties and

biomass production of elephant grass under saline conditions. Journal of Arid

Environments, 52(4), 447-456.

149

Watkins, D., Nuruddin, M., Hosur, M., Tcherbi-Narteh, A., & Jeelani, S. (2015). Extraction

and characterization of lignin from different biomass resources. Journal of Materials

Research and Technology, 4(1), 26-32.

Wen, C., Wu, Y., Chen, X., Jiang, G., & Liu, D. (2017). The pyrolysis and gasification

performances of waste textile under carbon dioxide atmosphere. Journal of Thermal

Analysis and Calorimetry, 128(1), 581-591.

White, J. E., Catallo, W. J., & Legendre, B. L. (2011). Biomass pyrolysis kinetics: A

comparative critical review with relevant agricultural residue case studies. Journal of

Analytical and Applied Pyrolysis, 91(1), 1-33.

doi:https://doi.org/10.1016/j.jaap.2011.01.004

Wu, W., Mei, Y., Zhang, L., Liu, R., & Cai, J. (2015). Kinetics and reaction chemistry of

pyrolysis and combustion of tobacco waste. Fuel, 156, 71-80.

Xiang, Y., Xiang, Y., & Wang, L. (2017). Kinetics of the thermal decomposition of poplar

sawdust. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects,

39(2), 213-218.

Xu, Y., & Chen, B. (2013). Investigation of thermodynamic parameters in the pyrolysis

conversion of biomass and manure to biochars using thermogravimetric analysis.

Bioresource Technology, 146, 485-493.

Xue, S., Lewandowski, I., Wang, X., & Yi, Z. (2016). Assessment of the production potentials

of Miscanthus on marginal land in China. Renewable and Sustainable Energy Reviews,

54, 932-943.

Yang, C., Li, R., Cui, C., Wu, J., Ding, Y., Wu, Y., & Zhang, B. (2017). The pyrolysis of

duckweed over a solid base catalyst: Py-GC/MS and TGA analysis. Energy Sources,

Part A: Recovery, Utilization, and Environmental Effects, 39(2), 177-183.

Yang, H., Yan, R., Chen, H., Lee, D. H., & Zheng, C. (2007). Characteristics of hemicellulose,

cellulose and lignin pyrolysis. Fuel, 86(12), 1781-1788.

Yang, L., Lu, M., Carl, S., Mayer, J. A., Cushman, J. C., Tian, E., & Lin, H. (2015). Biomass

characterization of Agave and Opuntia as potential biofuel feedstocks. Biomass and

Bioenergy, 76, 43-53.

Yang, Q., & Pan, X. (2012). Pretreatment of Agave americana stalk for enzymatic

saccharification. Bioresource Technology, 126, 336-340.

150

Yentema, O., Alioune, O., & Dorosso, S. A. (2007). Chemical composition and physical

characteristics of the essential oil of Cymbopogon schoenanthus (L.) Spreng of Burkina

Faso. Journal of Applied Sciences, 7(4), 503-506.

Yılgın, M., Duranay, N. D., & Pehlivan, D. (2010). Co-pyrolysis of lignite and sugar beet pulp.

Energy Conversion and Management, 51(5), 1060-1064.

Zhu, Z., Toor, S. S., Rosendahl, L., & Chen, G. (2015). Subcritical Hydrothermal Liquefaction

of Barley Straw in Fresh Water and Recycled Aqueous Phase. In G. Dell & C. Egger

(Eds.), World Sustainable Energy Days Next 2014: Conference Proceedings (pp. 121-

128). Wiesbaden: Springer Fachmedien Wiesbaden.

Bioresource Technology 224 (2017) 708–713

Contents lists available at ScienceDirect

Bioresource Technology

journal homepage: www.elsevier .com/locate /bior tech

Kinetic analyses and pyrolytic behavior of Para grass (Urochloa mutica)for its bioenergy potential

http://dx.doi.org/10.1016/j.biortech.2016.10.0900960-8524/� 2016 Elsevier Ltd. All rights reserved.

⇑ Corresponding author.E-mail address: draamer@gcuf.edu.pk (M.A. Mehmood).

Muhammad Sajjad Ahmad a, Muhammad Aamer Mehmood a,⇑, Omar S. Al Ayed b, Guangbin Ye c,Huibo Luo c, Muhammad Ibrahim d, Umer Rashid e, Imededdine Arbi Nehdi f,h, Ghulam Qadir g

aBioenergy Research Centre, Department of Bioinformatics & Biotechnology, Government College University Faisalabad, Faisalabad 38000, PakistanbDepartment of Chemical Engineering, Al-Balqa’a Applied University, Amman, JordancCollege of Bioengineering, Sichuan University of Science and Engineering, Zigong 643000, People’s Republic of ChinadDepartment of Environmental Sciences & Engineering, Government College University Faisalabad, Faisalabad 38000, Pakistane Institute of Advanced Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, MalaysiafChemistry Department, College of Science, King Saud University, Riyadh 1145, Saudi Arabiag Soil Salinity Research Institute, Pindi Bhattian, PakistanhUR Physico-Chimie des Materiaux Solides, Chemistry Department, Science College, Tunis El Manar University, 2092 Tunis, Tunisia

h i g h l i g h t s

� Para grass is a source of low-cost and abundant biomass.� TGA-DSC analyses were performed to understand kinetics of pyrolysis.� Thermodynamics parameters indicate the bioenergy potential of this novel biomass.� The bioenergy potential of the biomass is comparable with established bioenergy crops.

a r t i c l e i n f o

Article history:Received 31 August 2016Received in revised form 26 October 2016Accepted 30 October 2016Available online 2 November 2016

Keywords:Low cost biomassTGA-DSC analysesPyrolysisBioenergy

a b s t r a c t

The biomass of Urochloa mutica was subjected to thermal degradation analyses to understand its pyroly-tic behavior for bioenergy production. Thermal degradation experiments were performed at three differ-ent heating rates, 10, 30 and 50 �C min�1 using simultaneous thermogravimetric-differential scanningcalorimetric analyzer, under an inert environment. The kinetic analyses were performed using isoconver-sional models of Kissenger-Akahira-Sunose (KAS) and Flynn–Wall–Ozawa (FWO). The high heating valuewas calculated as 15.04 MJ mol�1. The activation energy (E) values were shown to be ranging from 103through 233 kJ mol�1. Pre-exponential factors (A) indicated the reaction to follow first order kinetics.Gibbs free energy (DG) was measured to be ranging from 169 to 173 kJ mol�1 and 168 to 172 kJ mol�1,calculated by KAS and FWO methods, respectively. We have shown that Para grass biomass has consid-erable bioenergy potential comparable to established bioenergy crops such as switchgrass andmiscanthus.

� 2016 Elsevier Ltd. All rights reserved.

1. Introduction

Global mobility and heavy industrialization required huge con-sumption of natural energy resources in recent years, causing thedepletion of energy reserves. Moreover, the recent scenario of clea-ner production and consumption have raised serious questions ontheir usage due to emission of toxic and greenhouse gases associ-ated with their combustion. So, it is required to find renewable,

sustainable and environmental friendly alternative sources ofenergy (Mehmood et al., 2016). Moreover, energy obtained frombiomass not only has the substantial potential to replace thefossil-based energy but also can contribute to sequester the atmo-spheric carbon into biomass, hence mitigating it (Skevas et al.,2014). For these reasons, plant biomass is being considered asone of the most promising alternatives along with solar, wind,and hydropower energy (Saqib et al., 2013). Currently, 19% of theglobal energy demand is being met through the renewable sources,where biomass contributes up to 9%, with an increasing rate of2.5% per year (Edrisi and Abhilash, 2016).

M.S. Ahmad et al. / Bioresource Technology 224 (2017) 708–713 709

Solar energy stored in plant biomass can be utilized in variousways, from direct combustion to biological fermentation. Eachmethod has its own merits and demerits. Whereas, biomass mayalso be subjected to pyrolysis, which is a thermal conversion ofbiomass into energy or high value products under an inert environ-ment. Though the thermal conversion depends on the compositionand nature of the biomass, however, the process itself dependsupon several factors including heating rate, pressure and residencetime (Slopiecka et al., 2012). So a complete understanding of pyr-olytic behavior of a biomass and the process conditions is neces-sary, prior to utilization of a particular biomass for energypurpose (Maia and de Morais, 2016). Various biomass sources havebeen studied to understand their pyrolytic behavior including,sawdust (Heo et al., 2010), macroalgae (Li et al., 2010a,b), rice husk,elephant grass, (Braga et al., 2014), tobacco waste (Wu et al., 2015)and red pepper waste (Maia and de Morais, 2016). Though differ-ent feedstocks have been attempted to use for bioenergy produc-tion, but the most promising concept is the use of agriculturalresidues, grasses with low lignin contents, and biomass producedfrom marginal lands (Mehmood et al., 2016). Use of grasses pro-duced on marginal lands offer several advantages, including lowerlignin content, no direct competition with food crops or land forfood crops, and their large scale production may also enhance eco-logical biodiversity (Mehmood et al., 2016).

Para grass (Urochloa mutica) is a C4 perennial grass native totropical Africa, and is famous for its invasive dispersion, whichcauses several problems and is enlisted as a weed in at least 34countries. Thus, Para grass offer a low cost biomass for bioenergyproduction but its thermal conversion into energy requires tounderstand its pyrolytic characteristics. Aiming at this, the presentstudy was focused on the pyrolytic characterization of this low-cost and abundant biomass, for the very first time. Whole plantbiomass was subjected to TGA-DSC analyses and we have demon-strated that the Para grass biomass can be used as potential energysource through pyrolysis using mild temperature conditions.

2. Materials and methods

2.1. Sample preparation and physicochemical analyses

Para grass was washed with freshwater and left to air dry underthe sun for seven days. Sun dried biomass was put in oven at 105 �Cfor drying, ground and pass through 120 mesh to obtain a particlesize of �125 lm, and then stored in a desiccator for further use.

Proximate analyses of Para grass were carried out to estimatethe total solids (TS%), Volatile Matter (VM%), moisture (%) and ashcontent (%) following the standard protocols as described in ASTME871-82 (2006), ASTM E1755-01 (2007) and ASTM E872-82(2006). The fixed carbon content (%) was expressed as 100 � (ashcontent + volatile matter + moisture content). All samples wereoven-dried at 105 �C for 24–48 h until a constant mass wasobtained, and difference in masses before and after drying wereused to calculate moisture (%) and total solid (TS%) content. Simi-larly, known masses of all the samples were added into pre-weighed crucibles and put at 600 �C in a Muffle furnace for 4–5 hor until the constant mass was obtained. Difference in mass beforeand after heating, was used to calculate volatile matter (VM%) andash content (%). All experiments were performed in triplicates, andaverage values were used tomake calculations. The percent contentof C, H, S, N and O in the biomass was determined using an elemen-tal analyzer (Vario EL Cube, Germany) using Argon as a carrier gas.

2.2. TGA-DSC experiment

Almost, ten (10) mg of powdered biomass (<125 lm) of Paragrass was placed in alumina crucibles and was continuously heated

from room temperature to 1000 �C, at three different heating ratesof 10, 30 and 50 �C min�1 under nitrogen environment at the flowrate of 50 mL min�1 in a simultaneous TGA-DSC analyzer (STA-409,NETZSCH-Gerätebau GmbH, Germany).

2.3. Mathematical model development

For the analyses of the TGA-DSC data, a mathematical modelwas developed. In isoconversional method the rate of decomposi-tion of a material is given by:

dadt

¼ kf ðaÞ ð1Þ

where

a ¼ ðmo �mtÞ=ðmo �m1Þ ð2Þwhere, a is the conversion rate, mo is the initial mass, mt is thechange in mass and m1 is the residual mass.

Using Arrhenius temperature dependency of K, Eq. (1) is writtenas

dadt

¼ A exp � ERT

� �f ðaÞ ð3Þ

where A is the pre-exponential factor, E is the activation energy(kJ mol�1), R is the universal gas constant, T is the pyrolysis temper-ature (Kelvin).

Introducing the heating rate, b (�C min�1), and the conversionfunction, f(a) = (1 � a)we obtain Eq. (4)

dadT

¼ Abexp � E

RT

� �ð1� aÞ ð4Þ

Now, if Eq. (4) is integrated for the initial conditions, a = 0, atT = T0, and after mathematical manipulations, we obtain Eq. (5):

GðaÞ ¼Z a

0da=ð1� aÞ ¼ ART2

=bE½1� 2RT=E� exp � ERT

� �ð5Þ

where, G is the Gibbs free energy (kJ mol�1).Rearranging Eq. (5), and it is known that the quantity, 2RT/E is

negligible compared with unity and hence can be ignored (Coatsand Redfern, 1964), then, we obtain Eq. (6)

GðaÞ ¼ ðART2=bEÞ expð�E=RTÞ ð6Þ

2.4. Kinetic and thermodynamics parameters calculation

Kinetic parameters of a thermal reaction are necessary for accu-rate prediction of pyrolytic behavior and to optimize the processfor thermal degradation of a biomass. The kinetic and thermody-namic parameters of the sample were calculated using the KAS(Kissenger-Akahira-Sunose) and FWO (Flynn-Wall-Ozawa) meth-ods (Akahira and Sunose, 1969; Flynn and Wall, 1966; Ozawa,1965) using isoconversional standard equations, as describedbelow.

2.4.1. KAS methodUpon rearranging and taking logarithm of both sides of Eq. (6),

we obtained

lnb

T2

� �¼ lnðAR=EGðaÞÞ � E=RT ð7Þ

Now, the LHS of Eq. (7) was plotted on the y-axis, and inverse ofpyrolysis temperature was plotted on the x-axis to calculate thekinetic parameters from the value of slope and intercept.

710 M.S. Ahmad et al. / Bioresource Technology 224 (2017) 708–713

2.4.2. FWO methodIntegrating Eq. (4) with the initial conditions, a = 0, at T = T0,

and Doyle’s approximation (Doyle, 1961) was introduced, and aftersome mathematical manipulations, we obtained Eq. (8), the finalform used by FWO procedure:

lnðbÞ ¼ lnðAE=RGðaÞÞ � E=RT ð8ÞThe LHS of Eq. (8) was plotted on the y-axis, and inverse of

pyrolysis temperature was plotted on the x-axis, for selected avalue to calculate kinetic parameters from the value of the slopeand intercept. At each point of conversion rate value of a was cal-culated using MS Excel for calculation of A(s�1) with conversionrate points plotted between ln(b) and lnð b

T2Þ versus 1/T produced

a straight line with slopes of equations, which were used to calcu-late activation energy. The graph between pyrolysis temperatureand mass loss percentage was drawn that showed us the mass lossmechanism in response to increasing temperature and under whattemperature.

Thermodynamic parameters including pre-exponential factor(A) in Arrhenius equation, as well as enthalpy (DH), free Gibbsenergy (DG) and the changes of entropy (Ds) were also calculatedusing following equations.

A ¼ ½b:E expðE=RTmÞ�=ðRT2mÞ ð9Þ

DH ¼ E� RT ð10Þ

DG ¼ Eþ RTmlnðKBTm=hAÞ ð11Þ

DS ¼ DH � DG=Tm ð12Þ

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900 1000

-60

-50

-40

-30

-20

-10

0

Mas

s L

oss

(%)

Temperature (°C)

DT

G (

% m

in-1

)

10K

30K

50K

Heating Rate (°C/min)

Fig. 1. Percent mass loss of sample versus pyrolysis temperature (�C) and DTGcurves of Para grass sample versus pyrolysis temperature at different heating rates.

3. Results and Discussion

3.1. Physicochemical properties of Para grass

Proximate analyses were shown the biomass to contain mois-ture content (%) up to 7.23 ± 0.18, which reflects its suitability forpyrolysis and combustion because any biomass which containsmoisture content lesser than 10% is considered suitable for com-bustion. It is interesting to note that Para grass showed highervolatile matter (79.45%) and lower ash content (9.32%), when sub-jected to combustion. The percent content of carbon, hydrogen,oxygen, nitrogen, and sulphur were shown to be as 44.73, 6.88,46.84, 0.98 and 0.24% respectively, with 95% confidence. The lowersulphur and nitrogen content reflects the lower emission of toxicgases upon combustion. Taking together, these indicators makethe Para grass a valuable biomass for thermal conversion into var-ious products including gas or oil. The volatile matter is higherwhen compared to established bioenergy crops such as Ardunodonax and Miscanthus gigantus, which contain 68.4 and 78.8% vola-tile matter, respectively (Jeguirim et al., 2010). Moreover, it is veryimportant to measure the high heating value (HHV, MJ kg�1) of anybiomass while studying its bioenergy potential, because HHVreflects the amount of energy which may be released from a bio-mass when subjected to combustion. Unfortunately, the experi-mental methods used to estimate HHV are expensive, timeconsuming and may cause higher experimental errors (Nhuchhenand Salam, 2012). Alternatively, several correlation models havebeen established to estimate the HHV of biomass using the proxi-mate values of biomass. Here we used the most reliable correlationmodel developed to date, to calculate the HHV of Para grass(Nhuchhen and Salam, 2012).

HHV ¼ 19:2880� 0:2135VMFC

� 1:9584AshVM

þ 0:0234FCAsh

where VM = Volatile matter (%), FC = Fixed carbon (%). This cor-relation model can be used to calculate the HHV with least errors,when compared to other models published till to date. The calcu-lated HHV of Para grass was shown to be 15.04 MJ kg�1. So, Paragrass produced from one hectare can be used to produce 0.780–0.850 TJ (Tera Joule, 1 Tera = 1000 Mega) of energy, which reflectsthat Para grass has considerable potential for bioenergyproduction.

3.2. Analyses of TG and DTG curves

We used thermogravimetry (TG) and derivative thermo-gravimetry (DTG) analyses to elucidate the thermal conversion ofthe Para grass, which is a measure of percent mass loss of sampleas a function of the pyrolysis temperature. The mass loss curvesobtained during the TG and DTG analyses (Fig. 1) indicated thephysical and chemical structural changes occurring during thethermal conversion of biomass into products (Ceylan and Kazan,2015; Maia and de Morais, 2016). The mass loss percent was stud-ied at heating rates of 10, 30 and 50 �C min�1. Our data have shownthat the heating rate does not influence the mass loss percent(Fig. 1). Only, minor differences were shown in the percent massloss, between 10 �C min�1 and 50 �C min�1 which maybe ascribedexperimental measurement conditions. The curves have the typicalappearance of degradation of lignocellulosic biomass just like theTG curves obtained for the pyrolysis of cardoon leaves, red pepperwaste, rice husk and elephant grass (Braga et al., 2014; Maia and deMorais, 2016; Xu and Chen, 2013).

The mass loss pattern can be subdivided into three differentstages. Where, first stage ranged from room temperature to190 �C, with 6.41% of the total mass loss, which corresponds tothe loss of intracellular water retained in the biomass. Lowerretained moisture content has shown the suitability of Para grassfor combustion, because any biomass with retained water <10%is considered feasible for combustion (Braga et al., 2014). At secondstage, a drastic mass loss was observed at the temperature rangingfrom 240 to 350 �C. The percent mass loss was found to be 10% upto 240 �C and reached to 66% at 350 �C, indicating 56% mass lossoccurred with 110 �C temperature increment, which may be attrib-uted to the degradation of hemicellulose and cellulose content ofthe sample (Xu and Chen, 2013). Third mass loss stage wasobserved at the temperature range of 360–590 �C, where up to70% of the mass was decomposed, this stage corresponds to thelignin degradation (Braga et al., 2014). Most of the mass loss(70%) was observed up to 590 �C when compared to the maximummass loss (74%) which was achieved at a 1000 �C, indicating thatno appreciable mass conversion reactions took place at tempera-tures higher than 590 �C. So, the suitable temperature for the

-5

0

5

10

15

20

25

0 100 200 300 400 500 600 700 800 900 1000 1100

Hea

t Flo

w D

SC (

mW

mg-1

)

Temperature (ºC)

50K

30K

10K

Heating Rate (°C min-1)

Fig. 2. DSC curves of the Para grass at three different heating rates.

-11.5

-11

-10.5

-10

-9.5ln(

/T2 )

value0.1

0.2

0.3

0.4

0.5

0.6

(A)

M.S. Ahmad et al. / Bioresource Technology 224 (2017) 708–713 711

thermal conversion of biomass may be from 240 to 590 �C, whichmay vary according to the products required. The mass loss andtheir associated temperatures are shown in Tables 1 and 2.

Most of the thermal conversion happened at the temperaturerange of 240–590 �C. For instance for combustion purposes, lowertemperature would be required if we are interested in the volatileproducts during the thermal conversion. On the other hand, bio-char yield was measured as 31.5% at 600 �C, which is higher whencompared to the biochar yields of rice straw (23.68%) and rice bran(25.17%) even at 700 �C (Xu and Chen, 2013). This finding reflectsthe suitability of Para grass biomass for biochar production too.Interestingly, the Para grass showed higher volatility (72%) whencompared to elephant grass (65%) and rice husk (52%), whichmakes the Para grass a suitable for gasification or to produce bio-oil when compared to the elephant grass and rice husk (Bragaet al., 2014).

Although biomass showed same mass loss pattern at all heatingrates, however it can be observed from the DTG curves (Fig. 1) thatthere is a shift in pyrolysis temperature, where the maximum rateof mass loss occured at 330, 335 and 340 �C at heating rates of 10,30 and 50 �C min�1, respectively. This finding is in accordance tocurve-shift observed in the mass loss of red-pepper waste at threeheating rates (Maia and de Morais, 2016).

-9

-8.5

-80.0011 0.00126 0.00142 0.00158 0.00174 0.0019 0.00206 0.00222

Inverse of pyrolysis temperature (K-1)

0.7

0.8

0.9

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

40.001110 0.001310 0.001510 0.001710 0.001910 0.002110

ln(

)

Inverse of pyrolysis temperature (K-1)

value

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(B)

Fig. 3. Linear fit plots for determining activation energy of the Para grass calculatedby the KAS (A) and FWO (B). Where, ln(b/T2) and ln(b) were plotted against inverseof pyrolysis temperature (K�1), respectively.

3.3. DSC and heat flow measurement

The heat flow (mWmg�1) to Para grass sample was studied asfunction of temperature and as indicated (Fig. 2), the heat flowincreased linearly with pyrolysis temperature at all heating rates.It can be seen that at initial stages there is an increase in heat flow,and then depending upon the magnitude of the heating rate, adecreasing heat flow was observed at different corresponding tem-peratures. At all heating rates, there is an endothermic reaction andexothermic trend observed at temperatures below 100 �C, afterthat, the exothermic effect increased with the increase in temper-ature and was extended ln(b/T2) d up to 600, 730 and 770 �C for theheating rate of 10, 30 and 50 �C min�1, respectively. These findingsare in accordance with the DSC curves observed for the Sargassumthunbergii (a macroalga) and Potamogeton crispus (a freshwaterplant, previously (Li et al., 2012). Above these temperatures, itcan be observed from experimental data, that the reaction stopped,which may be attributed to depletion of reactants where sampleslost 95% (70/74) of its volatile mass at 600 �C or highertemperatures.

Table 1Temperature characteristics associated with pyrolysis temperature.

Heating rate (K min�1) Temperature (K)

T1 T2 T3 T4 T5

10 468 548 568 583 60830 438 538 563 583 59850 448 538 563 583 598

Table 2Mass loss during different stages of decomposition.

Stages Heating rate (K min�1)

10 30 50

Stage-I, WL (%) 7.14 7.31 7.41Stage-II, WL (%) Zone-I 13.23 13.16 13.71

Zone-II 20.73 21.33 28.24Stage-III, WL (%) 54.03 59.57 60.57Final residues at 800–1000 �C (%) 31.32–27.61 29.82–27.12 28.73–25.96

FWOln(A) = 0.2035E - 4.5695

R² = .99

KASln(A) = 0.2039E - 4.6501

R² = .99

15

20

25

30

35

40

45

00.05200.00200.05100.001

Ln(

A)

Activation energy (kJ mol-1)

FWO

KAS

Fig. 4. Linear fit plots for the compensation effects between the pre-exponentialfactors and the activation energy of Para grass by KAS and FWO methods, with R2

value of 0.9996, 0.9997, respectively.

100

120

140

160

180

200

220

240

450 550 650 750 850 950

Act

ivat

ion

ener

gy (

kJ m

ol-1

)

Pyrolysis temperature (K)

Conversion, , at = 50, 30, 10 (oC min-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Fig. 5. Para grass activation energy v/s pyrolysis temperature.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

450 550 650 750 850

Con

vers

ion

()

Pyrolysis Temperature (K)

10K

30K

50K

Heating Rate (ºC min-1)

Fig. 6. Relationship between the conversion and pyrolysis temperature (K).

712 M.S. Ahmad et al. / Bioresource Technology 224 (2017) 708–713

3.4. Kinetic analyses and thermodynamic parameters

For the determination of the kinetic parameters, namely, orderof reaction, preexponential factor and activation energy, and ln (b)were plotted against inverse of pyrolysis temperature as describedin Materials and Methods section. The straight lines slopes (Figs. 3and 4) allowed us to determine the corresponding activationenergy (E) of each point and the preexponential factor (A). It wasobserved that calculated E-values varied according to the conver-sion points and at higher values of conversion, the E was shownto be decreased as conversion increased further (Fig. 5). Moreover,the conversion rate (a) showed a linear relationship with thepyrolysis temperature (Fig. 6). The E of Para grass showed variationat different conversion points ranging from 103 to 223 and 117 to233 kJ mol�1, calculated by KAS and FWO methods, respectively(Table 3). Both ranges are lower when compared to range of E-val-ues of tobacco waste i.e. 118–257 kJ mol�1 (Wu et al., 2015). More-over, average E of Para grass (189.54 kJ mol�1) was shown to belower than the average activation energies of cellulose, rice husk,and elephant grass (Table 4), which were shown to be as 191,221–229, and 218–227 kJ mol�1, respectively (Braga et al., 2014;Sanchez-Jimenez et al., 2013). The lower E-values reflect that Paragrass biomass may also be used for co-firing with various bio-masses having either very lower or higher E-values. These findingsmake the Para grass suitable for thermal conversion into variousproducts and bioenergy.

When the E-values obtained from both methods, were com-pared with the values of enthalpies (DH), it was revealed that therewas a little difference (�5 kJ mol�1) at each conversion point. Thisfinding is in accordance with the previous studies (Maia and deMorais, 2016) and reflects that formation of activated complex isfavored, owning to the lower potential energy barrier (Vlaevet al., 2007). Moreover, enthalpy represents the total energy

consumed by the biomass during pyrolysis process, for its conver-sion to various products, including gas, oil or char (Daugaard andBrown, 2003).

The values of pre-exponential factors (A) for the Para grass ran-ged from 1.42 � 1007 to 2.9313 � 1018 and 2.55 � 1008 to2.26 � 1019, as calculated by KAS and FWO methods, respectively(Table 3). The lower pre-exponential factors (<109 s�1) indicatemainly a surface reaction, but if the reactions are not dependenton surface area, the low factor may also indicate a closed complex,whereas the higher A-values indicate a simple complex (P109 s�1)(Turmanova et al., 2008). Moreover, A-values ranging between 1010

to 1012 s�1 show that the activated complex was probablyrestricted in rotation compared to the initial reagent (Xu andChen, 2013). The A-values of Para grass showed that it is a complexbiomass, with multi-phasic degradation reaction chemistry. More-over, A-values of Para grass are higher when compared to A-valuesof red paper waste (3.80 � 100 to 2.80 � 1012), rice straw(1.70 � 1007 to 9.35 � 1012), and rice bran (1.00 � 1007 and1.58 � 1010) (Maia and de Morais, 2016; Xu and Chen, 2013).

Changes of entropies for the Para grass (Table 3) have both neg-ative (as low as �122.22 J mol�1) and positive values (as high as111.33 J mol�1). Where, negative values indicate the degree of dis-order of products was lower when compared to the biomass, andpositive values showed otherwise. The occurrence of the negativeand positive values reflects the complexity of the thermal conver-sion of biomass into variety of products, which may be furthercharacterized in future, using GC–MS or FTIR based analyses ofthe volatiles.

Gibbs free energy (DG) for the Para grass was measured to beranging from 169 to 173 kJ mol�1 and 168–172 kJ mol�1, calcu-lated by KAS and FWO methods, respectively. These values wereshown to be higher when compared with the DG values of the ricestraw (164.59 kJ mol�1) and rice bran (167.17 kJ mol�1) (Xu andChen, 2013). Moreover, the average DG-value of Para grass(170 kJ mol�1) was also higher from the average DG-value of redpepper waste (139.4 kJ mol�1) (Maia and de Morais, 2016). Thisfinding reflects the bioenergy potential of the Para grass biomass.

4. Conclusion

Thermal analyses of Para grass have revealed its bioenergypotential comparable with the established bioenergy crops.High volatile content, high heating value of the biomass(15.04 MJ mol�1), activation energy (103–233 kJ mol�1), Gibbs freeenergy (169–173 kJ mol�1), and lower N (0.98%) and S (0.24%) con-tent have shown that it can be used for to produce direct energythrough pyrolysis or may be converted to bio-oil or other gaseousproducts, in a cost efficient and eco-friendly manner.

Table 3Conversion points and the corresponding thermodynamic parameters values.

Conversion points (a) E (kJ mol�1) A (s�1) R2 DH (kJ mol�1) DG (kJ mol�1) DS (J mol�1)

KAS method0.1 144.35 5.93 � 1010 0.96 139.30 171.47 �52.9120.2 180.18 8.87 � 1013 0.99 175.13 170.35 7.8670.3 194.19 1.52 � 1015 0.99 189.14 169.97 31.5310.4 212.45 6.19 � 1016 0.99 207.40 169.51 62.3070.5 223.79 6.14 � 1017 0.98 218.74 169.25 81.3910.6 231.52 2.93 � 1018 0.99 226.46 169.08 94.3770.7 165.59 4.54 � 1012 0.99 160.54 170.77 �16.8330.8 152.54 3.16 � 1011 0.99 147.49 171.19 �38.9840.9 103.87 1.42 � 1007 0.99 98.82 173.13 �122.229Average 178.72 – – 173.66 170.52

FWO method0.1 152.37 3.06 � 1011 0.99 147.32 171.20 �39.280.2 189.12 5.45 � 1014 0.99 184.06 170.11 22.960.3 203.47 1.00 � 1016 0.99 198.41 169.74 47.170.4 222.13 4.39 � 1017 0.99 217.08 169.29 78.590.5 233.73 4.58 � 1018 0.99 228.68 169.04 98.090.6 241.62 2.26 � 1019 0.99 236.57 168.87 111.350.7 176.02 3.80 � 1013 0.99 170.97 170.47 0.820.8 164.13 3.37 � 1012 0.99 159.08 170.82 �19.310.9 117.83 2.55 � 1008 0.99 112.78 172.50 �98.22Average 188.93 – – 183.88 170.22 –

Table 4Comparison of activation energies of various biomass.

Sr. NO. Biomass Activation energy(E) kJ mol�1

References

1 Cellulose 200 (Orfao et al., 1999)2 Laminaria japonica 207.7 Li et al. (2010a,b)3 Enteromorpha prolifera 228.1 Li et al. (2010a,b)4 Pophyra yezoensis 157.2 Li et al. (2011)5 Tobacco waste 118–257 Wu et al. (2015)7 Rice husk 221–229 Braga et al. (2014)8 Elephant grass 218–227 Braga et al. (2014)9 Para grass 102–233 This study

M.S. Ahmad et al. / Bioresource Technology 224 (2017) 708–713 713

Conflict of interests

It is declared that authors have no conflict of interests.

Acknowledgements

We are thankful to Higher Education Commission, Pakistan, andInternational Foundation for Science, Sweden for their financialsupport. The authors would also like to extend their sincere appre-ciation to the Deanship of Scientific Research at King Saud Univer-sity to support this research through the Research Group Projectnumber RGP-048.

References

Akahira, T., Sunose, T., 1969. Transactions of Joint Convention of Four ElectricalInstitutes. 246.

Braga, R.M., Melo, D.M., Aquino, F.M., Freitas, J.C., Melo, M.A., Barros, J.M., Fontes, M.S., 2014. Characterization and comparative study of pyrolysis kinetics of the ricehusk and the elephant grass. J. Therm. Anal. Calorim. 115, 1915–1920.

Ceylan, S., Kazan, D., 2015. Pyrolysis kinetics and thermal characteristics ofmicroalgae Nannochloropsis oculata and Tetraselmis sp. Bioresour. Technol.187, 1–5.

Coats, A.W., Redfern, J.P., 1964. Kinetic parameters from thermogravmetric data.Nature 201, 68–69.

Daugaard, D.E., Brown, R.C., 2003. Enthalpy for pyrolysis for several types ofbiomass. Energy Fuels 17, 934–939.

Doyle, C.D., 1961. Kinetic Analysis of Thermogravimetric Data. V(15): 285–292.Edrisi, S.A., Abhilash, P., 2016. Exploring marginal and degraded lands for biomass

and bioenergy production: an Indian scenario. Renew. Sustain. Energy Rev. 54,1537–1551.

Flynn, J.H., Wall, L.A., 1966. A quick, direct method for the determination ofactivation energy from thermogravimetric data. J. Polym. Sci. B. 4, 323–328.

Heo, H.S., Park, H.J., Park, Y.-K., Ryu, C., Suh, D.J., Suh, Y.-W., Yim, J.-H., Kim, S.-S.,2010. Bio-oil production from fast pyrolysis of waste furniture sawdust in afluidized bed. Bioresour. Technol. 101, S91–S96.

Jeguirim, M., Dorge, S., Trouve, G., 2010. Thermogravimetric analysis and emissioncharacteristics of two energy crops in air atmosphere: Arundo donax andMiscanthus giganthus. Bioresour. Technol. 101, 788–793.

Li, D., Chen, L., Chen, S., Zhang, X., Chen, F., Ye, N., 2012. Comparative evaluation ofthe pyrolytic and kinetic characteristics of a macroalga (Sargassum thunbergii)and a freshwater plant (Potamogeton crispus). Fuel 96, 185–191.

Li, D., Chen, L., Yi, X., Zhang, X., Ye, N., 2010a. Pyrolytic characteristics and kinetics oftwo brown algae and sodium alginate. Bioresour. Technol. 101, 7131–7136.

Li, D., Chen, L., Zhang, X., Ye, N., Xing, F., 2011. Pyrolytic characteristics and kineticstudies of three kinds of red algae. Biomass Bioenergy 35, 1765–1772.

Li, D., Chen, L., Zhao, J., Zhang, X., Wang, Q., Wang, H., Ye, N., 2010b. Evaluation of thepyrolytic and kinetic characteristics of Enteromorpha prolifera as a source ofrenewable bio-fuel from the Yellow Sea of China. Chem. Eng. Res. Des. 88, 647–652.

Maia, A.A.D., de Morais, L.C., 2016. Kinetic parameters of red pepper waste asbiomass to solid biofuel. Bioresour. Technol. 204, 157–163.

Mehmood, M.A., Ibrahim, M., Rashid, U., Nawaz, M., Ali, S., Hussain, A., Gull, M.,2016. Biomass production for bioenergy using marginal lands. Sustain. Prod.Consump. http://dx.doi.org/10.1016/j.spc.2016.08.003.

Nhuchhen, D.R., Salam, P.A., 2012. Estimation of higher heating value of biomassfrom proximate analysis: A new approach. Fuel 99, 55–63.

Orfao, J., Antunes, F., Figueiredo, J.L., 1999. Pyrolysis kinetics of lignocellulosicmaterials-three independent reactions model. Fuel 78, 349–358.

Ozawa, T., 1965. A new method of analyzing thermogravimetric data. Bull. Chem.Soc. Jpn. 38, 1881–1886.

Sanchez-Jimenez, P.E., Perez-Maqueda, L.A., Pereon, A., Criado, J.M., 2013.Generalized master plots as a straightforward approach for determining thekinetic model: the case of cellulose pyrolysis. Thermochim. Acta 552, 54–59.

Saqib, A., Tabbssum, M.R., Rashid, U., Ibrahim, M., Gill, S.S., Mehmood, M.A., 2013.Marine macro algae Ulva: a potential feed-stock for bio-ethanol and biogasproduction. Asian J. Agri. Biol. 1, 155–163.

Skevas, T., Swinton, S.M., Hayden, N.J., 2014. What type of landowner would supplymarginal land for energy crops? Biomass Bioenergy 67, 252–259.

Slopiecka, K., Bartocci, P., Fantozzi, F., 2012. Thermogravimetric analysis and kineticstudy of poplar wood pyrolysis. Appl. Energy 97, 491–497.

Turmanova, S.C., Genieva, S., Dimitrova, A., Vlaev, L., 2008. Non-isothermaldegradation kinetics of filled with rise husk ash polypropene composites. Exp.Polym. Lett. 2, 133–146.

Vlaev, L., Georgieva, V., Genieva, S., 2007. Products and kinetics of non-isothermaldecomposition of vanadium (IV) oxide compounds. J. Therm. Anal. Calorim. 88,805–812.

Wu, W., Mei, Y., Zhang, L., Liu, R., Cai, J., 2015. Kinetics and reaction chemistry ofpyrolysis and combustion of tobacco waste. Fuel 156, 71–80.

Xu, Y., Chen, B., 2013. Investigation of thermodynamic parameters in the pyrolysisconversion of biomass and manure to biochars using thermogravimetricanalysis. Bioresour. Technol. 146, 485–493.

Seediscussions,stats,andauthorprofilesforthispublicationat:https://www.researchgate.net/publication/316240058

ThermogravimetricanalysesrevealedthebioenergypotentialofEulaliopsisbinata

ArticleinJournalofThermalAnalysisandCalorimetry·April2017

DOI:10.1007/s10973-017-6398-x

CITATION

1

READS

76

9authors,including:

Someoftheauthorsofthispublicationarealsoworkingontheserelatedprojects:

SpecialThematicIssueforCurrentOrganicChemistry:WasteBiomassUtilizationforValue-added

GreenProductsViewproject

HeterogeneousbasedcatalysedbiodieselproductionfromwastecookingoilViewproject

MuhammadSajjadAhmad

UniversityofWaterloo

9PUBLICATIONS27CITATIONS

SEEPROFILE

MuhammadAamerMehmood

GovernmentCollegeUniversityFaisalabad

58PUBLICATIONS251CITATIONS

SEEPROFILE

OmarAl-Ayed

Al-Balqa'AppliedUniversity

24PUBLICATIONS173CITATIONS

SEEPROFILE

MuhammadIbrahim

BrawijayaUniversity

81PUBLICATIONS952CITATIONS

SEEPROFILE

AllcontentfollowingthispagewasuploadedbyMuhammadSajjadAhmadon06October2017.

Theuserhasrequestedenhancementofthedownloadedfile.

Thermogravimetric analyses revealed the bioenergy potentialof Eulaliopsis binata

Muhammad Sajjad Ahmad1 • Muhammad Aamer Mehmood1 • Guangbin Ye2 •

Omar S. Al-Ayed3 • Muhammad Ibrahim4• Umer Rashid5 • Huibo Luo2 •

Ghulam Qadir6 • Imededdine Arbi Nehdi7,8

Received: 27 October 2016 / Accepted: 7 April 2017

� Akademiai Kiado, Budapest, Hungary 2017

Abstract The present study was focused on the thermal

degradation of Eulaliopsis binata biomass produced on a

salt-affected soil without any fertilizer or pesticide appli-

cations. The plant biomass was subjected to thermal

degradation experiments at three heating rates, 10, 30 and

50 K min-1. The kinetic analyses were performed through

isoconversional models of Kissinger–Akahira–Sunose and

Flynn–Wall–Ozawa, followed by the calculation of ther-

modynamic parameters of activation. The high heating

value was calculated as 15.10 MJ mol-1. The activation

energy values of the grass were shown to be ranging from

118 through 240 kJ mol-1. Energy difference of enthalpies

of activation between the reagent and the activated

complex was in accordance with activation energies. Pre-

exponential factors indicated the reaction to follow first-

order kinetics. Gibbs free energy for the grass was mea-

sured to be ranging from 171 to 174 kJ mol-1. Our data

have shown that E. binata biomass offers remarkable

potential as a low-cost biomass for bioenergy.

Keywords Low-cost biomass � Salinity � Thermal

analysis � Pyrolysis � Bioenergy

Introduction

Excessive usage of fossil-based liquid fuels has caused

depletion of these resources due to their non-renewable

nature. Moreover, the environmental concerns associated

with the emission of toxic (SOx, NOx) and greenhouse

gasses (CO2) have made their use questionable, under the

recent scenario of cleaner production and consumption.

Hence, the world needs sustainable, renewable and envi-

ronmentally friendly alternative sources of energy [1, 2].

The energy obtained from biomass often referred as

‘‘bioenergy,’’ is believed to be a promising alternative due

to its renewable and environmentally friendly nature [3].

Overall, among global energy sources, biomass contributes

up to 9%, with an increasing rate of 2.5% per year [4].

Our Sun is the most reliable source of energy in terms of

renewability, sustainability and abundance. A part of the

solar energy is stored in plant biomass via photosynthesis

along with the fixation of atmospheric carbon [5]. There

are various ways to utilize the solar energy stored in plant

biomass, ranging from direct combustion to biological

fermentation. Each method has its own advantages and

disadvantages. Biomass may also be subjected to pyrolysis,

which is the thermal conversion of biomass into three

& Muhammad Aamer Mehmood

draamer@gcuf.edu.pk

1 Bioenergy Research Centre, Department of Bioinformatics

and Biotechnology, Government College University

Faisalabad, Faisalabad 38000, Pakistan

2 College of Bioengineering, Sichuan University of Science

and Engineering, Zigong 643000, People’s Republic of China

3 Department of Chemical Engineering, Al-Balqa Applied

University, Amman 11134, Jordan

4 Department of Environmental Sciences and Engineering,

Government College University Faisalabad,

Faisalabad 38000, Pakistan

5 Institute of Advanced Technology, Universiti Putra Malaysia

(UPM), 43400 Serdang, Selangor, Malaysia

6 Soil Salinity Research Institute, Pindi Bhattian, Pakistan

7 Chemistry Department, College of Science, King Saud

University, Riyadh 1145, Saudi Arabia

8 UR Physico-Chimie des Materiaux Solides, Chemistry

Department, Science College, Tunis El Manar University,

2092 Tunis, Tunisia

123

J Therm Anal Calorim

DOI 10.1007/s10973-017-6398-x

different phases of products including solid, liquids and

gasses. Each component of the products depends upon the

conversion conditions and nature of the biomass. The

pyrolysis process is subdivided into six categories

depending on the reaction conditions, which includes slow

pyrolysis [6], fast pyrolysis [7, 8], intermediate pyrolysis

[9], vacuum pyrolysis [10], flash pyrolysis [11] and abla-

tive pyrolysis [12]. Particle size distribution, pyrolysis

conditions and nature of the biomass are the key aspects of

influencing the products obtained [13]. Fast pyrolysis may

be used to get higher liquid yields, and alternatively, slow

pyrolysis produces charcoal as the major product. Various

products can be obtained from the fast pyrolysis of biomass

where bio-oil is the major product. It can be directly used

to replace the fuel oil or diesel in static boilers, turbines or

engines to produce electricity or may be converted into

transportation fuels or other high-value biochemicals via

hydroprocessing using hydrogen and a catalyst [14].

Upgradation of biofuel into transportation fuels via fast

pyrolysis and hydroprocessing has been extensively studied

[15–17].

Along with environmental and sustainability, the cost of

production is another key concern for the commercial

production of biomass energy. According to estimations,

the fast pyrolysis of willow and miscanthus costs US$

12–26 per GJ of energy [18] depending upon nature of

feedstocks and size of the plant used. The production cost

varies with the hydrogen source used in the process and

[19] and depends on the price of the biomass used [1], so

the grasses produced on marginal lands without any fer-

tilizer or pesticide input can provide low-cost biomass with

additional benefits of turning the non-profit lands into

profitable lands. Use of grasses produced on marginal lands

offers several advantages such as no direct competition

with food crops or land for food crops, and their large-scale

production may enhance biodiversity too [1]. Though the

thermal conversion depends on the process conditions itself

including heating rate, pressure and residence time [20], it

also depends upon the nature and composition of biomass.

Hence, the pyrolytic behavior of the biomass should be

well understood prior to its utilization for energy purpose

[21]. Previously, sawdust [22, 23], macroalgae [24], rice

husk, elephant grass [25], tobacco waste [26] and red

pepper waste [21], textile waste [27], vegetables oil [28],

coals [29], pine needle [30] and bagasse of sugarcane and

cassava [31] have been studied to understand their pyrol-

ysis behavior. Among various feedstocks, agricultural

residues, grasses produced from marginal lands offer

promising feedstock [1] due to their short growth cycles

and reduced input of energy and chemicals [32].

Babui grass or Sabai grass (Eulaliopsis binata) is a

perennial grass which is widely distributed in southern and

central China, India and Pakistan and is renowned for its

potential to conserve water and soil [33]. It grows well on

soils with poor fertility and has two cropping seasons in a

year. Moreover, it is not eaten by the cattle, so its use for

bioenergy production does not cause any competition with

the animal fodder. In the present study, Babui grass was

selected for the thermal characterization for the very first

time. The grass was grown on salt-affected soils, without

neither any fertilizer nor pesticide applications. Biomass of

this grass was subjected to TG, DTG and DSC analyses

aimed to understand its pyrolysis behavior. We have shown

that the Babui grass biomass can be used as a potential

energy source through pyrolysis using mild temperature

conditions.

Experimental

Biomass production and sample preparation

Babui grass was grown in five different plots (5 9 5 m

square each) of salt-affected soil [pH 8.4, electrical con-

ductivity 4.23 dS m-1, sodium adsorption ratio

18.56 (mmol L-1)1/2] at Soil Salinity Research Institute,

Pindi Bhattian, Pakistan, during the summer season without

any management practices, fertilizer or pesticide applica-

tions using underground irrigation water. The grass was

harvested from one meter square area from the center of

each plot, and fresh biomass weight obtained from each

plot was measured separately. Biomass was washed with

freshwater and left to air dry under the Sun for 7 days. Sun-

dried biomass was pulverized in a plant disintegrator to

pass through 120 mesh and then stored in a desiccator for

further use.

Proximate analyses and high heating value (HHV)

Proximate analyses of Babui grass were carried out to

estimate the total solids (TS %), volatile matter (VM %),

moisture (%) and ash content (%) following the standard

protocols as described in ASTM E871-82 (2006), ASTM

E1755-01 (2007) and ASTM E872-82 (2006). The fixed

carbon (FC %) was expressed as 100 - (ash con-

tent ? volatile matter ? moisture content). All samples

were oven-dried at 378 K for 24–48 h until a constant mass

was obtained, and the difference in masses before and after

drying was used to calculate moisture (%) and total solid

(TS %) content. Similarly, known masses of all the samples

were added into pre-weighed crucibles and put at 873 K for

4–5 h until the constant mass was obtained. The difference

in mass before and after heating was used to calculate

volatile matter (VM %) and ash content (%).

Moreover, it is very important to measure the high

heating value (HHV, MJ kg-1) of any biomass while

M. S. Ahmad et al.

123

carrying out its energy analysis, because HHV reflects the

amount of energy which may be released from a biomass

when subjected to combustion. Unfortunately, the experi-

mental methods used to estimate HHV are expensive, time-

consuming and may cause higher experimental errors [34].

Alternatively, several correlation models have been estab-

lished to estimate the HHV of biomass using the proximate

values of biomass. Here we used the most reliable corre-

lation model developed to date, to calculate the HHV of

Babui grass [34].

HHV ¼ 19:2880 � 0:2135VM

FC� 1:9584

Ash

VM

þ 0:0234FC

Ash

where VM = volatile matter (%), FC = fixed carbon (%).

This correlation model can calculate the HHV with the

least errors when compared to other models published till

to date. All experiments were performed in triplicates, and

average values were used to make calculations.

TG-DSC experiment

Almost, ten (10) mg powdered biomass of Babui grass was

placed in alumina crucibles and was continuously heated from

room temperature to 1275 K, at three different heating rates of

10, 30 and 50 K min-1 under nitrogen environment at the

flow rate of 50 mL min-1 in a simultaneous TGA-DSC ana-

lyzer (STA-409, NETZSCH Geratebau GmbH, Germany).

Mathematical model development

For the analyses of the TG-DSC data, a mathematical

model was developed. In the isoconversional method, the

rate of decomposition of a material is given by:

dadt

¼ kf að Þ ð1Þ

where

a ¼ mo � mtð Þ= mo � m1ð Þ ð2Þ

Using Arrhenius temperature dependence of k, Eq. (1) is

written as

dadt

¼ A exp � E

RT

� �f að Þ ð3Þ

Introducing the heating rate, b, considering the general

form of conversion function, f að Þ ¼ 1 � að Þn, and assum-

ing first-order reaction kinetics, i.e., n = 1, we obtain

Eq. (4)

dadT

¼ A

bexp � E

RT

� �1 � að Þ ð4Þ

Now, if Eq. (4) is integrated for the initial conditions,

a = 0, at T = T0, and after mathematical manipulations,

we obtain Eq. (5):

G að Þ ¼Za

0

da= 1 � að Þ ¼ ART2=bE 1 � 2RT/E½ � exp � E

RT

� �

ð5Þ

Rearranging Eq. (5), it is known that the quantity, 2RT/

E, is negligible compared with unity and hence can be

ignored [35]; then, we obtain Eq. (6)

G að Þ ¼ ART2=bE� �

exp �E=RTð Þ ð6Þ

Kinetic parameters calculation

Kinetic parameters of biomass pyrolysis reaction are

essential to optimize the process to obtain products via

thermal degradation. Non-isothermal thermogravimetric

analyses are the simplest and popular methods to under-

stand thermodynamics properties of biomass because these

methods seem to be promising alternatives to find mean-

ingful activation energies without knowing the kinetic

model of the reaction mechanism. The kinetic and ther-

modynamic parameters of the sample were calculated

using the Kissinger–Akahira–Sunose (KAS) and Flynn–

Wall–Ozawa (FWO) methods [36–38] using isoconver-

sional standard equations, as described below.

KAS method

Upon rearranging and taking logarithm of both sides of

Eq. (6), we obtained

lnbT2

� �¼ ln AR=EG að Þð Þ � E=RT ð7Þ

Now, the left-hand side ln bT2

� �� �of Eq. (7) was plotted

on the y-axis, and the inverse of pyrolysis temperature was

plotted on the x-axis to calculate the kinetic parameters

from the value of slope and intercept.

FWO method

Integrating Eq. (4) with the initial conditions, a = 0, at

T = T0, and introducing Doyle’s approximation [39], and

after some mathematical manipulations, we obtained

Eq. (8), the final form used by FWO procedure:

ln bð Þ ¼ ln AE=RG að Þð Þ�E=RT ð8Þ

The ln(b) of Eq. (8) was plotted on the y-axis, and the

inverse of pyrolysis temperature was plotted on the x-axis,

Thermogravimetric analyses revealed the bioenergy potential of Eulaliopsis binata

123

for selected a value to calculate kinetic parameters from

the value of the slope and intercept.

At each point of conversion rate value of a was calcu-

lated using Excel with conversion rate points from 0.1 to

0.9 for calculation of A (s-1) with conversion rate points

plotted between ln(b) and ln bT2

� �versus 1/T produced a

straight line with slopes of equations, which were used to

calculate activation energy. The graph between pyrolysis

temperature and mass loss percentage was drawn that

showed us the mass loss mechanism in response to

increasing temperature.

Thermodynamic parameters of activation including pre-

exponential factor (A) in Arrhenius equation, as well as

enthalpy (DH), free Gibbs energy (DG) and the changes of

entropy (DS), were also calculated using following equa-

tions [39, 40]. These parameters are associated with energy

changes related to ongoing chemical reactions. To be more

specific, the instant of transforming reactants to products,

thermodynamic parameters of the reaction were deter-

mined, Eqs. (9)–(12).

A ¼ b � E exp E=RTmð Þ½ �= RT2m

� �ð9Þ

DH ¼ E � RT ð10ÞDG ¼ E þ RTm ln KBTm=hAð Þ ð11ÞDS ¼ DH � DG=Tm ð12Þ

where KB, Boltzmann constant (1.381 9 10-23 J K-1);

Tm, DTG peak temperature (K); R, universal gas constant

(8.314 J K-1 mol-1).

Table 1 Proximate analyses of the Babui grass

Analyses Value/%

Moisture 7.33 ± 0.15

Volatile solids 82.62 ± 0.19

Alkali 5.72 ± 0.21

Fixed carbon 4.33 ± 0.26

High heating value/MJ kg-1 15.10 ± 1.39 (MAE)

MAE mean absolute error

Table 2 Comparison of proximate and high heating values of Babui grass with previously studied bioenergy crops

Sr. no. Energy crops Proximate analyses (%) High heating values/MJ kg-1 References

Volatiles Moisture Ash

1 Agave spp. 78.10 6.44 7.40 16.35 [51]

2 Babui grass (E. binata) 82.62 7.33 5.72 15.10 This study

3 Giant reed (Arundo donax L.) 68.4 8.2 5.0 17.2 [41]

4 Miscanthus (Miscanthus spp.) 78.8 10.0 2.7 17.80 [41]

5 Reed canary grass (Phalaris arundinacea L.) 74–81 7.2 5.4 16.30 [52]

6 Sweet sorghum (Sorghum bicolor L.) stem 89.85 5.98 2.8–5.0 20–25 [53, 54]

7 Virginia mallow (Sida hermaphrodita) 71.47 8.7 2.63 15.03 [55]

8 Willow (Salix spp.) 73.16 1.51 4.74 16.69 [55]

270 370 470 570 670 770 870 970 1070 1170 12700

–5

–10

–15

–20

–25

–30

–35

–40

–45

–50

Temperature/K

DT

G/%

min

–1

Heating rate/K min–1

10

30

50

Fig. 1 DTG curves of sample

versus pyrolysis temperature at

different heating rates

M. S. Ahmad et al.

123

On the one hand, the thermodynamics parameters are

activation thermodynamics and were calculated from the

following similar equation except for the temperature

dependence of the rate constant (k), which was calculated

using the following equation:

DG�=RT ¼ ln TkB=khð Þ ð13aÞDG� ¼ DH� � TDS� ð13bÞ

where k is rate constant and DG* is the free energy barrier,

the enthalpy (DH*) and entropy of activation (DS*) of the

reaction complex thermodynamics parameters of complex

activation.

Results and discussion

Biomass yield and proximate analyses of Babui grass

The fresh biomass from each plot was shown to be as

3.00 ± 0.15 kg m-2, which reflects that 28,500–31,500 kg

of fresh biomass can be produced from one hectare of the

salt-affected soil under study. The comparison of soil

properties of normal and salt-affected soil used in this

study is shown in ‘‘Experimental’’ section, while proximate

values are shown in Table 1. It is interesting to note that

Babui grass showed higher volatile content (82.62%) and

lower ash content (5.72%), when subjected to combustion.

The Babui grass volatile matter is higher when compared to

established bioenergy crops such as Arduno donax and

Miscanthus gigantus, which contains 68.4 and 78.8%

volatile matter, respectively [41]. The calculated HHV of

Babui grass was shown to be 15.10 MJ kg-1. A compar-

ison of the proximate analyses of the Babui and its HHV

value with different energy crops is shown in Table 2, and

it is clear that Babui grass has considerable potential for

bioenergy production.

Table 3 Characteristic temperatures associated with pyrolysis

Heating rate/K min-1 Temperature/K

T1 T2 T3 T4 T5

10 408 523 568 598 648

30 418 553 573 535 658

50 433 558 578 613 673

Table 4 Mass loss during different stages of decomposition

Stages Heating rate/K min-1

10 30 50

Stage-I, WL (%) 6.50 6.64 6.69

Stage-II, WL (%)

Zone-I 12.34 19.14 19.34

Zone-II 26.84 25.32 26.00

Stage-III, WL (%) 59.77 60.88 61.71

Final residues (%) at 1073–1273 K 27.32–25.71 26.02–24.80 26.72–25.73

270 370 470 570 670 770 870 970 1070 1170 1270

0

–2

2

4

6

8

10

12

14

16

18

Temperature/K

Hea

t flo

w/m

W

Heating rate/K min–1

10

30

50

Fig. 2 DSC curves of the Babui

grass at different heating rates

Thermogravimetric analyses revealed the bioenergy potential of Eulaliopsis binata

123

Analyses of TG and DTG curves

The mass loss curves indicate the physical and chemical

changes occurring during the thermal conversion of bio-

mass into products [21, 42]. Thermogravimetric analysis is

often a measure percent mass loss of a sample as a function

of pyrolysis temperature. The mass loss percent of Babui

grass was studied at heating rates of 10, 30 and 50 K min-1

(Fig. 1). The analyses have shown that heating rate does

not influence the mass loss percent. The curves have the

typical appearance of degradation of lignocellulosic bio-

mass just like the DTG curves obtained for the pyrolysis of

cardoon leaves, switch grass, red pepper waste, rice husk

and elephant grass [21, 25, 40, 43].

The mass loss can be divided into three stages (Tables 3, 4)

along with a long tail. The first stage ranged from room

temperature to 423 K, where 6.50–6.69% of the total mass

was lost (Table 4), which corresponds to the water retained in

the biomass. Biomass with water content lower than 10% is

considered feasible for combustion; it reflects that Babui grass

biomass is suitable for combustion [25]. At the second stage,

which ranged from 523 to 638 K, a considerable mass loss

was observed. The mass loss percentage was found to be 10%

up to 523 K and reached to 58% at 638 K, indicating 50%

mass loss with 115 degrees increase in temperature. Drastic

degradation at this temperature may be attributed to the

degradation of hemicellulose and cellulose content of the

sample because these components along with pectin are

degraded within the temperature range of 493–588 K [40].

The third stage mainly ranged from 653 to 873 K followed by

the long tail, which mainly corresponds to the lignin degra-

dation because the long tail of the biomass pyrolysis reflects

the lignin degradation and char formation [25]. Most of the

mass loss (72%) was observed up to 873 K when compared to

the maximum mass loss (76%) which was achieved at a

1323 K, indicating that no appreciable mass conversion

reactions took place at temperatures higher than 873 K. So,

the suitable temperature for the thermal conversion of biomass

0.0012 0.0014 0.0016 0.0018 0.002 0.0022

0.0012 0.0014 0.0016 0.0018 0.002 0.00222.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

–8

–8.5

–9

–9.5

–10

–10.5

–11

–11.5

–1/K–1

InIn

( /T

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Conversion ( )

Conversion ( )

(a)

(b)

α

α

T

–1/K–1T

ββ

2 )

Fig. 3 Linear fit plots for

determining activation energy

of the sample, where ln(b/T2)

and ln(b) were plotted against

inverse of pyrolysis temperature

(K-1) using KAS (a) and FWO

(b) methods, respectively

M. S. Ahmad et al.

123

may be from 523 to 873 K, which may vary according to the

products required. Biochar yield of 28.5% was observed at

923 K, which is higher when compared to the biochar yields

of rice straw (23.68%) and rice bran (25.17%) even at 973 K

[40]. This finding reflects the suitability of Babui grass bio-

mass for biochar production. Interestingly, the Babui grass

showed higher volatile content (76%) when compared to

volatile content of elephant grass (65%) and rice husk (52%),

which makes the Babui grass better biomass for pyrolysis,

when compared to the elephant grass and rice husk [25, 43]. In

DTG curves, the overall similar pattern was observed at all

heating rates, except that the curve at heating rate 50 K min-1

showed more clear readings of the thermoconversion reac-

tions. Moreover, curves showed a little shift toward higher

temperatures with the higher heating rates (Table 3). The

increase or shift in temperature is related to rates of heat and

mass transfer taking place because of increasing the heat input

to the system. Under heat and mass transfer conditions, the

time of transfer and size of sample influence the reaction rate

and possibly the mechanism.

DSC and heat flow measurement

The heat flow (mW mg-1) to Babui grass biomass is shown

in Fig. 2. It was observed that the heat flow increased

linearly with pyrolysis temperature at all heating rates. At

initial stages, there is an increase in heat flow, followed by

a decreasing heat flow observed at different corresponding

temperatures. It was assumed that the initial parts measured

from room temperature to 933, 1023 and 1163 K are

described by the same reaction mechanism as the mea-

surements taken at 10, 30 and 50 K min-1 heating rates,

respectively. This reaction mechanism was also assumed to

be active during early stages. For all heating rates, a rela-

tively constant increase in the reaction rate was observed to

occur at room temperature and 933, 1023 and 1163 K at

10, 30 and 50 K min-1, respectively, which reflect the

consistency of rate increase with the said temperatures. As

the temperature increased, the reaction rates were observed

to stabilize or even decrease at a higher heating rate. The

heat input curves at the different heating rates indicate the

interactions of the different thermodynamic functions

during the reaction. The decrease in the curves with

increasing temperature signifies reaction cease to occur or

the change of mechanism. It can be observed from exper-

imental data that the reaction stopped due to depletion of

reactants where samples lost 95% (72/76) of its total

110 130 150 170 190 210 230 250

110 130 150 170 190 210 230 250

47

42

37

32

27

22

17

47

42

37

32

27

22

17

Activation energy/kJ mol–1

Activation energy/kJ mol–1

In(A

)/s–

1In

(A)/

s–1

FWO

KAS

In(A) = 0.2019E – 4.598R = 0.99

In(A) = 0.2019E – 4.5943

2

R = 0.992

Fig. 4 Linear fit plots for the compensation effects between the pre-

exponential factors and the activation energy of Babui grass KAS and

FWO methods

Table 5 Conversion points and the corresponding thermodynamic function values (KAS method)

Conversion points, a Ea/kJ mol-1 R2 DH/kJ mol-1 A/s-1 DG/kJ mol-1 DS/J mol-1

0.1 114.80 0.99 109.71 1.12 9 1008 174.17 -105.16

0.2 152.46 0.99 147.36 2.4 9 1011 172.72 -41.38

0.3 202.35 0.99 197.25 5.67 9 1015 171.28 42.37

0.4 163.15 0.98 158.05 2.09 9 1012 172.38 -23.37

0.5 224.85 0.99 219.75 5.21 9 1017 170.74 79.95

0.6 232.61 0.99 227.52 2.47 9 1018 170.57 92.9

0.7 240.51 0.99 235.41 1.2 9 1019 170.40 106.06

0.8 226.22 0.99 221.12 6.86 9 1017 170.71 82.24

0.9 148.87 0.99 143.78 1.16 9 1011 172.84 -47.42

Average 189.54 0.99 184.44 – 171.75 –

Thermogravimetric analyses revealed the bioenergy potential of Eulaliopsis binata

123

weight at 873 K. These findings are in accordance with the

DSC curves observed for the Sargassum thunbergii (a

macroalga), Potamogeton crispus (a freshwater plant), Para

grass and Camel grass studied previously [44–46].

Kinetic analyses and activation of thermodynamic

parameters

For the determination of the kinetic parameters including pre-

exponential factor and activation energy, KAS and FWO

methods were used. The straight-line slopes (Figs. 3, 4) were

used to determine the corresponding activation energies (Ea) of

each point and the pre-exponential factor (A) and thermody-

namic parameters of activation (Tables 5, 6). It was observed

that calculated Ea values varied according to the conversion

points and at higher values of conversion, theEa was shown to

be decreased as conversion increased further (Fig. 5). More-

over, the conversion rate (a) increased with the increase in

pyrolysis temperature (Fig. 6). The average Ea values of the

Babui grass were shown to be 189.54 and 189.60 kJ mol-1, by

KAS and FWO methods, respectively, and showed variation at

different conversion points ranging from 115 to 240 kJ mol-1

which is lower when compared to tobacco waste, i.e.,

118–257 kJ mol-1 [26]. Moreover, average activation energy

of Babui grass (189.54–189.60 kJ mol-1) was shown to be

lower than the average activation energies of cellulose

(191 kJ mol-1), rice husk (221–229 kJ mol-1) and elephant

grass (218–227 kJ mol-1), respectively (Table 7) [25, 47].

However, the range ofEa (115–240 kJ mol-1) was shown to be

higher than switchgrass [43], which reflects that Babui grass

biomass may also be used for co-firing with various biomasses

Table 6 Conversion points and the corresponding thermodynamic function values (FWO method)

Conversion points, a Ea/kJ mol-1 R2 DH/kJ mol-1 A/s-1 DG/kJ mol-1 DS/J mol-1

0.1 116.63 0.99 111.54 1.62 9 1008 174.09 -102.05

0.2 153.54 0.99 148.44 2.98 9 1011 172.69 -39.56

0.3 201.32 0.99 196.23 4.61 9 1015 171.31 40.64

0.4 164.26 0.98 159.16 2.61 9 1012 172.35 -21.51

0.5 223.18 0.99 218.09 3.73 9 1017 170.79 77.16

0.6 230.72 0.99 225.62 1.69 9 1018 170.62 89.74

0.7 238.38 0.99 233.28 7.85 9 1018 170.45 102.50

0.8 224.67 0.99 219.57 5.02 9 1017 170.75 79.64

0.9 153.72 0.99 148.63 3.09 9 1011 172.69 -39.25

Average 189.60 0.99 184.51 – 171.75 –

450 500 550 600 650 700 750 800100

120

140

160

180

200

220

240

Temperature/K

Act

ivat

ion

ener

gy/k

J m

ol–1

Conversion ( )

0.10.20.30.40.50.60.70.80.9

α

Fig. 5 Babui grass activation energy versus pyrolysis temperature

(K)

450 500 550 600 650 700 750 800

Temperature/K

00.10.20.30.40.50.60.70.8

0.91

Con

vers

ion

( )

Heating rate/K min–1

10

30

50

α

Fig. 6 Relationship between the conversion and pyrolysis tempera-

ture (K)

Table 7 Comparison of activation energies of various biomass

Sr.

no.

Biomass Activation energy, Ea/

kJ mol-1References

1 Cellulose 200 [56]

2 Enteromorpha

prolifera

228.1 [2]

3 Eulaliopsis binata 115–240 This study

4 Laminaria japonica 207.7 [24]

5 Pennisetum

purpureum

218–227 [25]

6 Pophyra yezoensis 157.2 [57]

7 Rice husk 221–229 [25]

8 Switch grass 60.9–152.9 [43]

9 Tobacco waste 118–257 [26]

M. S. Ahmad et al.

123

having either very lower or higher Ea values. These findings

make the Babui grass suitable for thermal conversion into

various products and bioenergy.

When the values of Ea were compared with the values of

enthalpies (DH) of activation, it was revealed that there

was a little difference (*5 kJ mol-1) at each conversion

point. This finding is in accordance with the previous

studies [21] which reflects that formation of activated

complex is favored, owing to the lower potential energy

barrier [48]. Moreover, enthalpy of activation represents

the total energy consumed by the biomass during pyrolysis

process, for its conversion to various products, including

gas, oil or biochar [49]. The values of pre-exponential

factors (A) of activation for the Babui grass were ranged

from 1.12 9 108–1.2 9 1019 s-1 to 1.62 9 1008–

7.85 9 1018, as calculated by KAS and FWO methods,

respectively (Tables 5, 6). The lower pre-exponential fac-

tors (\109 s-1) indicate mainly a surface reaction, but if

the reactions are not dependent on surface area, the low

factor may also indicate a closed complex, whereas the

higher A values of activation indicate a simple complex

(C109 s-1) [50]. Moreover, A values of activation ranging

between 1010 and 1012 s-1 show that the activated complex

was probably restricted in rotation compared to the initial

reagent [40]. The A values of Babui grass have shown that

it is a complex biomass. Moreover, A values of Babui grass

are different when compared to A values of red paper waste

(3.80 9 100–2.80 9 1012), rice straw (1.70 9 1007–

9.35 9 1012), rice bran (1.00 9 1007 and 1.58 9 1010) and

were shown to be closely ranging within the A values of

switchgrass, 3.70 9 1003–1.65 9 1021 [21, 40, 43]. The

relationship between activation energies, conversion and

pyrolysis temperature of Babui grass is shown in Figs. 5

and 6 and is described in Table 8. The correlation of the

pre-exponential factors and activation energy calculated

from KAS and FWO methods showed an excellent agree-

ment as depicted in Fig. 4. Linear fit plots for the com-

pensation effects between the pre-exponential factors as

function of the activation energy of Babui grass KAS and

FWO methods supported the strong correlation between the

frequency factor and activation energy of reaction.

Changes of entropies of activation for the Babui grass

have both negative (as low as -05.16 J mol-1) and posi-

tive values (as high as 106.06 J mol-1), where negative

values indicate the degree of disorder of products was

lower when compared to the biomass, and positive values

showed otherwise. The occurrence of the negative and

positive values reflects the complexity of the thermal

conversion of biomass into a variety of products, which

may be further characterized in future.

Gibbs free energy (DG) of activation for the Babui grass

was measured to be ranging from 170 to 174 kJ mol-1,

which was higher when compared with the DG values of

the rice straw (164.59 kJ mol-1) and rice bran

(167.17 kJ mol-1) [40]. Moreover, the average DG value

of activation of Babui grass (172 kJ mol-1) was also

higher from the average DG value of red pepper waste

(139.4 kJ mol-1) [21]. These values indicated that Babui

biomass grass may be used as a bioenergy feedstock.

Conclusions

Low-cost biomass can be produced by growing Babui grass

on salt-affected soils. Thermal analyses of the Babui grass

comprised of three stages; first stage is attributed to release

of intracellular water retained through strong bonds up to

423 K, followed by a major biomass loss which is attrib-

uted to cellulose, hemicellulose and pectin degradation

from 493 to 638 K during second stage, and third stage

which ranges from 638 to 873 K followed by a long tail

above 873 K, which is attributed to lignin degradation and

biochar formation. This degradation pattern reveals that

Babui grass biomass can be converted into gaseous prod-

ucts mainly within the temperature range of 493–638 K

because the major pyrolysis reactions occur during this

stage, and can yield 28.5% at or above 923 K. High

volatile content, HHV of the biomass (15.10 MJ mol-1),

Ea (118–240 kJ mol-1) and DG (171–171 kJ mol-1) have

shown that Babui grass biomass can be either directly

Table 8 Relationship between activation energies, conversion and

pyrolysis temperature of Babui grass

Conversion

range, aTemperature

range/K

Reactions Activation

energy, Ea

a B 0.1 298–463 Release of

retained

moisture with

concomitant

degradation of

simpler/

smaller sugar

molecules

Increased from

starting point

to

114 kJ mol-1

0.1 B a B 0.4 463–588 Degradation of

cellulose,

hemicellulose

and pectin

Increased from

114 to

202 kJ mol-1,

and then

decreased from

202 to

163 kJ mol-1

0.4 B a B 0.7 588–628 Degradation of

lignin

Increased from

163 to

240 kJ mol-1

0.7 B a B 1.0 628–800 Residual lignin

decomposition

and formation

of char

Decreased from

240 to

148 kJ mol-1

Thermogravimetric analyses revealed the bioenergy potential of Eulaliopsis binata

123

converted into energy or other products, in a cost and cost-

efficient manner, and its bioenergy potential is comparable

with the established bioenergy crops such as miscanthus

and switchgrass.

Acknowledgements We are thankful to Higher Education Commis-

sion, Pakistan, and International Foundation for Science, Sweden, for

their financial support. The authors would also like to extend their

sincere appreciation to the Deanship of Scientific Research at King

Saud University to support this research through the Research Group

Project Number RGP-048.

References

1. Mehmood MA, Ibrahim M, Rashid U, Nawaz M, Ali S, Hussain

A, Gull M. Biomass production for bioenergy using marginal

lands. Sustain Prod Consum. 2017;9:3–21.

2. Li D, Chen L, Zhao J, Zhang X, Wang Q, Wang H, Ye N.

Evaluation of the pyrolytic and kinetic characteristics of En-

teromorpha prolifera as a source of renewable bio-fuel from the

Yellow Sea of China. Chem Eng Res Des. 2010;88(5):647–52.

3. Skevas T, Swinton SM, Hayden NJ. What type of landowner

would supply marginal land for energy crops? Biomass Bioen-

ergy. 2014;67:252–9.

4. Edrisi SA, Abhilash P. Exploring marginal and degraded lands

for biomass and bioenergy production: an Indian scenario. Renew

Sustain Energy Rev. 2016;54:1537–51.

5. Saqib A, Tabbssum MR, Rashid U, Ibrahim M, Gill SS, Meh-

mood MA. Marine macro algae Ulva: a potential feed-stock for

bio-ethanol and biogas production. Asian J Agri Biol.

2013;1(3):155–63.

6. Lee Y, Park J, Ryu C, Gang KS, Yang W, Park YK, Jung J, Hyun

S. Comparison of biochar properties from biomass residues

produced by slow pyrolysis at 500 C. Bioresour Technol.

2013;148:196–201.

7. Bridgwater AV. Review of fast pyrolysis of biomass and product

upgrading. Biomass Bioenergy. 2012;38:68–94.

8. Isahak WNRW, Hisham MW, Yarmo MA, Hin TY. A review on

bio-oil production from biomass by using pyrolysis method.

Renew Sustain Energy Rev. 2012;16(8):5910–23.

9. Kebelmann K, Hornung A, Karsten U, Griffiths G. Intermediate

pyrolysis and product identification by TGA and Py-GC/MS of

green microalgae and their extracted protein and lipid compo-

nents. Biomass Bioenergy. 2013;49:38–48.

10. Garcia-Perez M, Chaala A, Pakdel H, Kretschmer D, Roy C.

Vacuum pyrolysis of softwood and hardwood biomass: compar-

ison between product yields and bio-oil properties. J Therm Anal

Calorim. 2007;78:104–16.

11. Horne PA, Williams PT. Influence of temperature on the products

from the flash pyrolysis of biomass. Fuel. 1996;75(9):1051–9.

12. Peacocke G, Bridgwater A. Ablative plate pyrolysis of biomass

for liquids. Biomass Bioenergy. 1994;7(1):147–54.

13. Graham R, Bergougnou M, Overend R. Fast pyrolysis of biomass.

J Anal Appl Pyrol. 1984;6(2):95–135.

14. Mortensen PM, Grunwaldt JD, Jensen PA, Knudsen K, Jensen

AD. A review of catalytic upgrading of bio-oil to engine fuels.

Appl Catal A Gen. 2011;407(1):1–19.

15. Ng KS, Sadhukhan J. Process integration and economic analysis

of bio-oil platform for the production of methanol and combined

heat and power. Biomass Bioenergy. 2011;35(3):1153–69.

16. Ng KS, Sadhukhan J. Techno-economic performance analysis of

bio-oil based Fischer–Tropsch and CHP synthesis platform.

Biomass Bioenery. 2011;35(7):3218–34.

17. Sadhukhan J, Ng KS. Economic and European union environ-

mental sustainability criteria assessment of bio-oil-based biofuel

systems: refinery integration cases. Ind Eng Chem Res.

2011;50(11):6794–808.

18. Rogers J, Brammer J. Estimation of the production cost of fast

pyrolysis bio-oil. Biomass Bioenergy. 2012;36:208–17.

19. Patel M, Zhang X, Kumar A. Techno-economic and life cycle

assessment on lignocellulosic biomass thermochemical conver-

sion technologies: a review. Renew Sustain Energy Rev.

2016;53:1486–99.

20. Slopiecka K, Bartocci P, Fantozzi F. Thermogravimetric analysis

and kinetic study of poplar wood pyrolysis. Appl Energy.

2012;97:491–7.

21. Maia AAD, de Morais LC. Kinetic parameters of red pepper

waste as biomass to solid biofuel. Bioresour Technol.

2016;204:157–63.

22. Heo HS, Park HJ, Park YK, Ryu C, Suh DJ, Suh Y-W, Yim J-H,

Kim S-S. Bio-oil production from fast pyrolysis of waste furni-

ture sawdust in a fluidized bed. Bioresour Technol.

2010;101(1):S91–6.

23. Plis A, Kotyczka-Moranska M, Kopczynski M, Łabojko G.

Furniture wood waste as a potential renewable energy source.

J Therm Anal Calorim. 2016;125(3):1357–71.

24. Li D, Chen L, Yi X, Zhang X, Ye N. Pyrolytic characteristics and

kinetics of two brown algae and sodium alginate. Bioresour

Technol. 2010;101(18):7131–6.

25. Braga RM, Melo DM, Aquino FM, Freitas JC, Melo MA, Barros

JM, Fontes MS. Characterization and comparative study of

pyrolysis kinetics of the rice husk and the elephant grass. J Therm

Anal Calorim. 2014;115(2):1915–20.

26. Wu W, Mei Y, Zhang L, Liu R, Cai R. Kinetics and reaction

chemistry of pyrolysis and combustion of tobacco waste. Fuel.

2015;156:71–80.

27. Zhu F, Feng Q, Xu Y, Liu R, Li K. Kinetics of pyrolysis of ramie

fabric wastes from thermogravimetric data. J Therm Anal

Calorim. 2015;119(1):651–7.

28. Tudorachi N, Mustata F. Thermal degradation and evolved gas

analysis of some vegetable oils using TG/FT–IR/MS technique.

J Therm Anal Calorim. 2015;119(1):1703–11.

29. Xu Y, Zhang Y, Zhang G, Guo Y, Zhang J, Li G. Pyrolysis

characteristics and kinetics of two Chinese low-rank coals.

J Therm Anal Calorim. 2015;122(2):975–84.

30. Varma AK, Mondal P. Physicochemical characterization and

kinetic study of pine needle for pyrolysis process. J Therm Anal

Calorim. 2016;124(1):487–97.

31. Zanatta ER, Reinehr TO, Awadallak JA, Kleinubing SJ, dos

Santos JBO, Bariccatti RA, et al. Kinetic studies of thermal

decomposition of sugarcane bagasse and cassava bagasse.

J Therm Anal Calorim. 2016;125(1):437–47.

32. Tang J, Chen K, Huang F, Xu J, Li J. Characterization of the

pretreatment liquor of biomass from the perennial grass, Eu-

laliopsis binata, for the production of dissolving pulp. Bioresour

Technol. 2013;129:548–52.

33. Yao JL, Yang PF, Hu CG, Zhang D, Luo BS. Embryological

evidence of apomixis in Eulaliopsis binata. Acta Bot Sin.

2004;46(1):86–92.34. Nhuchhen DR, Salam PA. Estimation of higher heating value of

biomass from proximate analysis: a new approach. Fuel.

2012;99:55–63.

35. Coats A, Redfern J. Kinetic parameters from thermogravimetric

data. Nature. 1964;201:68–9.

36. Ozawa TA. A new method of analyzing thermogravimetric data.

Bull Chem Soc Jpn. 1965;38:1881–6.

37. Flynn JH, Wall LA. A quick, direct method for the determination

of activation energy from thermogravimetric data. J Polym Sci B.

1966;4(5):323–8.

M. S. Ahmad et al.

123

38. Akahira T, Sunose T. Trans. Joint convention of four electrical

institutes, paper no. 246 (1969). Research report, Chiba Institute

of Technology. Sci. Technol. 1971;16:22–31.

39. Doyle C. Kinetic analysis of thermogravimetric data. J Appl Poly

Sci. 1961;5(15):285–92.

40. Kim YS, Kim YS, Kim SH. Investigation of thermodynamic

parameters in the thermal decomposition of plastic waste–waste

lube oil compounds. Environ Sci Technol. 2010;44(13):5313–7.

41. Xu Y, Chen B. Investigation of thermodynamic parameters in the

pyrolysis conversion of biomass and manure to biochars using

thermogravimetric analysis. Bioresour Technol. 2013;146:

485–93.

42. Jeguirim M, Dorge S, Trouve G. Thermogravimetric analysis and

emission characteristics of two energy crops in air atmosphere:

Arundo donax and Miscanthus giganthus. Bioresour Technol.

2010;101(2):788–93.

43. Ceylan S, Kazan D. Pyrolysis kinetics and thermal characteristics

of microalgae Nannochloropsis oculata and Tetraselmis sp.

Bioresour Technol. 2015;187:1–5.

44. Biney PO, Gyamerah M, Shen J, Menezes B. Kinetics of the

pyrolysis of arundo, sawdust, corn stover and switch grass bio-

mass by thermogravimetric analysis using a multi-stage model.

Bioresour Technol. 2015;179:113–22.

45. Li D, Chen L, Chen S, Zhang X, Chen F, Ye N. Comparative

evaluation of the pyrolytic and kinetic characteristics of a mac-

roalga (Sargassum thunbergii) and a freshwater plant (Pota-

mogeton crispus). Fuel. 2012;96:185–91.

46. Mehmood MA, Ye G, Luo H, Liu C, Malik S, Afzal I, et al.

Pyrolysis and kinetic analyses of Camel grass (Cymbopogon

schoenanthus) for bioenergy. Bioresour Technol. 2017;228:18–24.

47. Ahmad MS, Mehmood MA, Al Ayed OS, Ye G, Luo H, Ibrahim

M, et al. Kinetic analyses and pyrolytic behavior of Para grass

(Urochloa mutica) for its bioenergy potential. Bioresour Technol.

2017;224:708–13.

48. Sanchez-Jimenez PE, Perez-Maqueda LA, Pereon A, Criado JM.

Generalized master plots as a straightforward approach for

determining the kinetic model: the case of cellulose pyrolysis.

Thermochim Acta. 2013;552:54–9.

49. Vlaev L, Georgieva V, Genieva S. Products and kinetics of non-

isothermal decomposition of vanadium (IV) oxide compounds.

J Therm Anal Calorim. 2007;88(3):805–12.

50. Daugaard DE, Brown RC. Enthalpy for pyrolysis for several

types of biomass. Energy Fuels. 2003;17(4):934–9.

51. Linan-Montes A, de la Parra-Arciniega S, Garza-Gonzalez M,

Garcia-Reyes R, Soto-Regalado E, Cerino-Cordova F. Charac-

terization and thermal analysis of agave bagasse and malt spent

grain. J Therm Anal Calorim. 2014;115(1):751–8.

52. Paulrud S, Nilsson C. Briquetting and combustion of spring-

harvested reed canary-grass: effect of fuel composition. Biomass

Bioenergy. 2001;20(1):25–35.

53. Monti A, Di Virgilio N, Venturi G. Mineral composition and ash

content of six major energy crops. Biomass Bioenergy.

2008;32(3):216–23.

54. Yan H-L, Zong Z-M, Li Z-K, Kong J, Zheng Q-X, Li Y, Wei XY.

Sweet sorghum stalk liquefaction in supercritical methanol:

effects of operating conditions on product yields and molecular

composition of soluble fraction. Fuel Process Technol.

2017;155:42–50.

55. Howaniec N, Smolinski A. Steam gasification of energy crops of

high cultivation potential in Poland to hydrogen-rich gas. Int J

Hydrog Energy. 2011;36(3):2038–43.

56. Orfao J, Antunes F, Figueiredo JL. Pyrolysis kinetics of ligno-

cellulosic materials-three independent reactions model. Fuel.

1999;78(3):349–58.

57. Li D, Chen L, Zhang X, Ye N, Xing F. Pyrolytic characteristics

and kinetic studies of three kinds of red algae. Biomass Bioen-

ergy. 2011;35(5):1765–72.

Thermogravimetric analyses revealed the bioenergy potential of Eulaliopsis binata

123

View publication statsView publication stats

Bioresource Technology 228 (2017) 18–24

Contents lists available at ScienceDirect

Bioresource Technology

journal homepage: www.elsevier .com/locate /bior tech

Pyrolysis and kinetic analyses of Camel grass (Cymbopogon schoenanthus)for bioenergy

http://dx.doi.org/10.1016/j.biortech.2016.12.0960960-8524/� 2016 Elsevier Ltd. All rights reserved.

⇑ Corresponding author.E-mail address: sajjad.bioinfo@gmail.com (M.S. Ahmad).

Muhammad Aamer Mehmood a, Guangbin Ye b, Huibo Luo b, Chenguang Liu c, Sana Malik a, Ifrah Afzal a,Jianren Xu c, Muhammad Sajjad Ahmad a,⇑aBioenergy Research Centre, Department of Bioinformatics & Biotechnology, Government College University Faisalabad, Faisalabad 38000, PakistanbCollege of Bioengineering, Sichuan University of Science and Engineering, Zigong 643000, People’s Republic of Chinac State Key Laboratory of Microbial Metabolism, and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China

h i g h l i g h t s

� Camel grass is an abundant and low cost biological resource.� Pyrolysis experiments were performed using TGA-DSC.� Kinetic and thermodynamic parameters were calculated to understand its pyrolysis.� We have shown that Camel grass is suitable for pyrolysis in cost and energy efficient manner.

a r t i c l e i n f o

Article history:Received 29 November 2016Received in revised form 11 December 2016Accepted 22 December 2016Available online 25 December 2016

Keywords:Low cost biomassTGA-DSCPyrolysisBioenergy

a b s t r a c t

The aim of this work was to study the thermal degradation of grass (Cymbopogon schoenanthus) under aninert environment at three heating rates, including 10, 30, and 50 �C min�1 in order to evaluate its bioen-ergy potential. Pyrolysis experiments were performed in a simultaneous Thermogravimetry–DifferentialScanning Calorimetry analyzer. Thermal data were used to analyze kinetic parameters through isoconver-sional models of Flynn-Wall-Ozawa (FWO) and Kissenger-Akahira-Sunose (KSA) methods. The pre-exponential factors values have shown the reaction to follow first order kinetics. Activation energy valueswere shown to be 84–193 and 96–192 kJ mol�1 as calculated by KSA and FWO methods, respectively.Differences between activation energy and enthalpy of reaction values (�5 to 6 kJ mol�1) showed productformation is favorable. The Gibb’s free energy (173–177 kJ mol�1) and High Heating Value(15.00 MJ kg�1) have shown the considerable bioenergy potential of this low-cost biomass.

� 2016 Elsevier Ltd. All rights reserved.

1. Introduction

Depletion of fossil-based liquid and gaseous fuels is puttingpressure to find sustainable and cleaner alternatives for energyproduction. Sun, is the source of cleanest and abundant source ofenergy, which can be either stored into lithium-ion batteries viaphoto-voltaic cells or into plant biomass via photosynthetic fixa-tion. While, photosynthesis offers concomitant fixation of environ-mental carbon, and biomass can store 58–90 times more energywhen compared to Li-ion batteries (Liao et al., 2016). Due to itsabundance, renewability and higher energy density, biomass isconsidered as the most promising alternative-energy source to fuelthe future of mankind (Liao et al., 2016). However, the cost of

production of biomass-based energy is the key concern. Forinstance, the pyrolysis of miscanthus (an established bioenergycrop) costs US$ 12–26 per GJ of energy (Rogers and Brammer,2012) which also depends the nature and size of the plant used.Moreover, cost of production is also influenced by the price of bio-mass and source of hydrogen used in the process (Patel et al.,2016). In this context, the grasses produced on poor soils, withoutany fertilizer input offers low cost biomass without causing anycompetition with the food or land for food, with an additional pos-sibility of turning the non-profit poor-lands into profitable lands(Mehmood et al., 2016).

The energy stored into plant biomass can be utilized in variousways ranging from direct combustion, pyrolysis and biological fer-mentation. Pyrolysis is the thermal conversion of biomass intosolid, liquid or gaseous products under an inert environment.While, different pyrolytic products depend upon the nature of

M.A. Mehmood et al. / Bioresource Technology 228 (2017) 18–24 19

the biomass, particle size distribution and pyrolysis conditions(Graham et al., 1984). Pyrolysis conditions can be optimized toget higher yields of either of the solid (biochar), liquid (bio-oil)or gaseous products from a particular biomass. While, bio-oil canbe either directly used to produce electricity or may be convertedinto fuels for transportation sector (Mortensen et al., 2011). How-ever, pyrolysis process is a blend of several simultaneous heteroge-nous reactions which are critical to understand for any biomass inorder to use it for fuel, energy and chemicals. Thermogravimetric(TGA) and Differential Scanning Calorimetry (DSC) are useful toolsto understand the pyrolysis behavior of heterogeneous reactionsinvolved in this process (Mabuda et al., 2016). Thermal conversionof biomass into products strictly depends the nature and composi-tion of biomass along with pyrolysis process conditions (Slopieckaet al., 2012). So, it is critically important to understand the pyroly-tic behavior of any biomass prior to its utilization for thermal con-version into products (Maia and de Morais, 2016). Previously,several plants or their wastes including sawdust (Heo et al.,2010), elephant grass, rice husk, (Braga et al., 2014), tobacco waste(Wu et al., 2015), red pepper waste (Maia and de Morais, 2016) andPara grass (Ahmad et al., 2016) have been studied to understandtheir pyrolysis behavior.

Camel grass is a perennial grass which is widely adapted to soilspoor in water and nutrients availability and can get 1.5 m height(Hashim et al., 2016) in Northern Africa and Southern Asia. Thegenus Cymbopogon contains almost fifty different species, most ofthem are considered as commercial biological resource due tolow cost, adaptation to poor soils, growth onmunicipal wastewaterand fragrant leaves. While C. schoenanthus traditionally named asCamel grass, is a desert species and grows well on dry stony places(Hashim et al., 2016). Previously, it has shown various medicinalproperties including sedative, digestive, anti-parasitic (Sousaet al., 2005), antispasmodic, diuretic (Elhardallou, 2011; Sabryet al., 2014) antifungal and anti-inflammatory (Norbert et al.,2014). Due to its abundance and low cost, the Camel grass wasselected for the thermal characterization for the very first time.Thermal degradation and kinetics data have shown that Camelgrass biomass has pyrolysis and energy properties comparablewith traditional energy crops including switch grass andmiscanthus.

2. Materials and methods

2.1. Proximate analyses

The Camel grass (CG) biomass was collected from the saltaffected field, washed with freshwater and left under sun to dry.Sun dried biomass was pulverized in a mechanical grinder to passthrough a 120 mesh, producing particle size of almost 125 lm.Proximate analyses including Volatile Matter (VM%), moisture(%), ash content (%) and Fixed Carbon (FC%) were performed adapt-ing the standard protocols as described in ASTM (E872-82 2006,E871-82 2006, and E1755-01 2007) as described previously(Ahmad et al., 2016).

2.2. High Heating Value and elemental composition analyses

High Heating Value (HHV, MJ kg�1), reflects the energy to bereleased from any biomass upon combustion. It is expensive tomeasure the HHV using experimental methods, for this reasonHHV of CG was calculated using the most trusted correlationmodel established to date (Nhuchhen and Salam, 2012). Where,

HHV ¼ 19:2880� 0:2135VMFC

� 1:9584AshVM

þ 0:0234FCAsh

Experiments were performed in triplicates, and average valueswere used to make calculations. The composition of carbon, hydro-gen, oxygen, nitrogen, and sulphur in the CG biomass was deter-mined using an elemental analyzer (Vario EL Cube, Germany)following the manufacturer’s instruction, with Argon (Ar) as carriergas.

2.3. TGA-DSC analyses

Ten mg (10) of the powdered CG biomass was put in aluminacrucibles, which were put into the equipment chamber under heatfrom rising from room temperature to 1000 �C. The experimentwas repeated at three different heating rates using 10, 30 and 50 �-C min�1, under nitrogen flow rate of 100 mL min�1 in a simultane-ous TGA-DSC analyzer (STA-409, NETZSCH-Gerätebau GmbH,Germany).

2.4. Kinetic and thermodynamic parameters calculation

The experimental data generated by TGA-DSC were analyzed tocalculate the kinetic parameters using mathematical model devel-oped previously (Ahmad et al., 2016) through isoconversionalmodels of KAS (Kissenger-Akahira-Sunose) and FWO (Flynn-Wall-Ozawa) (Akahira and Sunose, 1969; Flynn and Wall, 1966;Ozawa, 1965);

KAS method : lnb

T2

� �¼ lnðAR=EGðaÞÞ � E=RT ð1Þ

FWO method : lnðbÞ ¼ lnðAE=RGðaÞÞ � E=RT ð2Þwhere; b is heating rate (�C min�1), A is pre-exponential factor (s�1),R is real gas constant, E is activation energy (kJ mol�1), G is the inte-gral conversion function, a is conversion point, T is pyrolysis tem-perature (K). The LHS of the each Eqs. ((1) and (2)) was plottedagainst the inverse of pyrolysis temperature to calculate kineticparameters from the value of the slope and intercept by KAS andFWO methods, respectively. At each value of conversion rate, con-version point (a) was used to calculate Aðs�1Þ. Conversion rate

points plotted between ln bT2

� �and lnðbÞ against 1/T generated a

straight line with slopes which were used to calculate E. Thermody-namic parameters including pre-exponential factor ðAÞ, enthalpyðDHÞ, Gibb’s free energy ðDGÞ and the changes of entropy ðDSÞ werealso calculated using equations as given below (Kim et al., 2010; Xuand Chen, 2013).

A ¼ ½b � EexpðE=RTmÞ�=ðRT2mÞ ð3Þ

DH ¼ E� RT ð4Þ

DG ¼ Eþ RTmlnðKBTm=hAÞ ð5Þ

DS ¼ DH � DG=Tm ð6Þ

3. Results and discussion

3.1. Physicochemical properties

The oven dried CG biomass was shown to have lower ash con-tent (6.31%) and higher volatile content (82.67%) when subjectedto combustion. The composition of elements including C, H, O, N,and S were shown to be as 44.96, 6.64, 45.08, 0.93 and 0.23%,respectively. Lower alkali and higher volatile contents reflect thatCG biomass contain higher pyrolyzable content. The pyrolyzablecontent of the CG biomass was shown to be higher when comparedto rice husk (65.33%), nut shell (78.67%), olive stone (72.21%),

Table 1Temperature characteristics associated with pyrolysis.

Heating rate (�C min�1) Temperature (�C)

T1 T2 T3 T4 T5

10 175 270 294 331 45430 179 283 309 347 45950 180 290 319 342 469

Table 2Mass loss during different stages of decomposition.

Stages Heating rate (�C min�1)

10 30 50

20 M.A. Mehmood et al. / Bioresource Technology 228 (2017) 18–24

Miscanthus giganthus (78.80%), Arduno donax (68.40%), Urochloamutica (79.45) and Eichhornia crassipes (70.30%) (Jeguirim et al.,2010; Hu et al., 2015; Ahmad et al., 2016; Saldarriaga et al.,2015). These values indicate the suitability of CG biomass forpyrolysis. Whereas, lower nitrogen and sulphur content reflectsCG biomass may produce little amount of toxic gases when sub-jected to combustion. The calculated HHV of CG biomass wasfound as 15.00 MJ kg�1 which is higher when compared to theHHV of rice husk (Saldarriaga et al., 2015) and is found withinthe range of HHVs (15–17 MJ kg�1) of previously studied energycrops including Agave, Giant Reed, Para grass, Reed Canary grass,Sida and Willow (Paulrud and Nilsson, 2001; Jeguirim et al.,2010; Howaniec and Smolinski, 2011; Linan-Montes et al., 2014;Ahmad et al., 2016).

Stage-I, WL (%) 7.04 6.95 8.91Stage-II, WL (%) Zone-I 21.22 22.11 23.48

Zone-II 38.12 36.18 35.15Stage-III, WL (%) 8.81 9.59 8.87Final residues 1000 �C (%) 24.81 25.17 23.59

3.2. Analyses of TG-DTG curves

Series of reactions occur during pyrolysis process, therefore,understanding the fundamentals of pyrolysis reaction chemistryof any biomass is essential to harness its potential for bioenergyand chemicals (Mabuda et al., 2016). The chemical kinetics ofpyrolysis reaction can be elucidated from thermogravimetricanalysis (TGA) data, which is often a measure of loss in mass dueto thermal degradation at a constant heating under inertenvironment. The curves obtained in this context depict thephysico-chemical changes happening during the thermal conver-sion of biomass into products (Ceylan and Kazan, 2015; Maia andde Morais, 2016). The mass loss pattern of CG biomass was almostsame at all heating rates (Fig. 1) except a little shift towards highertemperatures was observed at higher heating rates (30 and50 �C min�1). Consistency in the pattern indicates that heating ratedoes not influence degradation chemistry, so lower heating ratescan be used to optimize the thermal conversion of CG biomass toobtain gas or liquid phase products. Moreover, thermal degrada-tion pattern at all heating rates has shown typical appearance oflignocellulosic biomass, including switch grass, cardoon leaves,rice husk, elephant grass, red pepper waste, and Para grass(Ahmad et al., 2016; Biney et al., 2015; Braga et al., 2014; Maiaand de Morais, 2016; Xu and Chen, 2013).

The loss in mass of CG in response to increasing temperaturecan be subdivided into three stages (Tables 1 and 2) followed bya long tail. The stage-I started from room temperature to 180 �C,where 6.95–8.91% of the total mass was lost, which may be attrib-uted to the escape of water molecules retained in intercellular andintracellular spaces of the biomass. This range is lower than the

0 200 40020

30

40

50

60

70

80

90

100

110

120

Mas

s Los

s (%

)

Temperatu

Fig. 1. TG and DTG curves and percent mass loss of Camel grass versus pyroly

prescribed range of retained moisture i.e. <10%, to be consideredas feasible for pyrolysis (Braga et al., 2014). The stage-II rangedfrom 270 to 319 �C where 59% of the total biomass was lost. Mostof the thermal degradation reaction occurred during stage-II.Volatilization of biomass within this temperature range is oftenattributed to the degradation of cellulose and hemicellulose con-tent of the biomass which is the important pyrolyzable fractionof any biomass. Generally, celluloses and pectin are believed tobe degraded within the temperature range of 220–315 �C (Xuand Chen, 2013). The stage-III mainly ranged from 340 to 470 �Cfollowed by typical long tail of TG-curves, which indicates degra-dation of lignin and charring (Braga et al., 2014). Most of the ther-mal degradation and biomass conversion reactions occurred up to550 �C, where 71.50% of the mass loss was observed, which is the�96% of the total gasifyable content of the CG biomass. The volatilecontent of CG biomass was found to be higher when compared torice husk (52%), elephant grass (65%), and Para grass (72%), (Ahmadet al., 2016; Biney et al., 2015; Braga et al., 2014) indicating that GCbiomass is better for gasification. Hence, the most suitable temper-ature for gasification of CG biomass may be optimized within tem-perature of 270–550 �C depending upon the required products.Char yield of 30.46% was observed up to 550 �C, which was shownto be higher when compared to char yields of rice straw (23.68%)and rice bran (25.17%) at 700 �C (Xu and Chen, 2013). It reflectsthat CG biomass can be used to produce biochar in an energy effi-cient manner. A residual mass of 25.5% was observed up to 1000 �C.

600 800 1000-45

-40

-35

-30

-25

-20

-15

-10

-5

0

DT

G (%

min

-1)

re (°C)

10K 30K 50K

Heating Rate (°C min-1)

sis temperature (�C) at three different heating rates (10, 30, 50 �C min�1).

-2

0

2

4

6

8

10

12

0 100 200 300 400 500 600 700 800 900 1000

Hea

t Flo

w D

SC (m

W m

g-1)

Temperature (°C)

10K 30K 50K

Heating Rate (°C min-1)

Fig. 2. DSC curves of the Camel grass at different heating rates.

Fig. 3. Linear fit plots for determining activation energy of the Camel grass. Where, lnðb=T2Þ and ln lnðbÞwere plotted against inverse of pyrolysis temperature (K�1) using KAS(A) and FWO (B) methods, respectively.

M.A. Mehmood et al. / Bioresource Technology 228 (2017) 18–24 21

22 M.A. Mehmood et al. / Bioresource Technology 228 (2017) 18–24

3.3. Heat flow measurement via DSC analyses

A linear relationship of heat flow (mWmg�1) was observedwith increasing pyrolysis temperature at all heating rates (Fig. 2)at initial stages, which reflects that the reaction was active duringearly stages. However, all heating rates showed little difference inheat flow, peak heat flow (4.6 mWmg�1) observed at 10 �C min�1

was lowest, and peak heat flow (10.47 mWmg�1) was observedhighest at 30 �C min�1 during the whole process. Overall, constantincrease in heat flow was observed from room temperature to 585,655 and 855 �C at 10, 30 and 50 �C min�1, respectively, whichreflect exothermic reaction chemistry. At higher temperatures,reactions were observed to stabilize between temperaturerange of 600–740 �C, 660–750 �C and 850–875 �C at 10, 30 and50 �C min�1 heating rates. Above these temperatures, the curvesshifted towards x-axis at each of the respective heating rate, indi-cating that the reaction either stopped or followed a differentmechanism. These findings are in accordance with the DSC curvesobserved for the Potamogeton crispus (a freshwater plant), Sargas-sum thunbergii (a macroalga) and Para grass (Li et al., 2012;Ahmad et al., 2016).

Table 3Conversion points, Activation energies and corresponding thermodynamic parameters.

a Ea R2 DHkJ mol�1 kJ mol�1

KAS method0.1 96.98 0.99 91.830.2 148.91 0.99 143.760.3 163.45 0.99 158.300.4 178.67 0.99 173.510.5 177.33 0.98 172.170.6 183.42 0.98 178.260.7 192.74 0.99 187.590.8 191.03 0.99 185.880.9 188.54 0.99 183.38

Avg. 169.01 – 163.85

FWO method0.1 84.59 0.99 79.440.2 150.09 0.99 144.930.3 164.30 0.99 159.150.4 179.16 0.99 174.010.5 178.13 0.98 172.970.6 184.07 0.98 178.920.7 193.17 0.99 188.010.8 192.21 0.99 187.060.9 191.42 0.99 186.26

Avg. 168.57 – 163.42

FWOLnA = 0.2015E - 4.9342

R² = 0.998

KASLnA = 0.2011E - 4.8537

R² = 0.999

10

15

20

25

30

35

40

80 100 120 140 160 180 200

LnA

Activation energy (kJ mol-1)

Fig. 4. Linear fit plots for the compensation effects between the pre-exponentialfactors and the activation energy of Camel grass by KAS and FWO methods.

3.4. Kinetic analyses and thermodynamic parameters

Pyrolysis is thermochemical conversion of biomass directly intogaseous and liquid products and/or chemicals. The knowledge onreaction dynamics and kinetic parameters is essential to design agasification process. As mentioned earlier, pyrolysis of a biomassis a heterogeneous reaction, whose reaction dynamics and chemi-cal kinetics are influenced by the activation energy (E) and pre-exponential factor (A) (White et al., 2011). Hence, the straight linesslopes in Figs. 3 and 4 were used to calculate E, A and thermody-namic parameters (Table 3) for the CG biomass pyrolysis usingthe model-free isoconversional methods of KSA and FWO, asdescribed previously (Ahmad et al., 2016). The calculated E wasshown to vary with varying conversion points (Fig. 5) and a linearrelationship was observed between the conversion rate (a) and thepyrolysis temperature (Fig. 6). The E of CG biomass ranged from 84to 193 kJ mol�1 with average values ranging of 169.01 and168.57 kJmol�1 as calculated by KAS and FWO methods, respec-tively. The E-values of CG biomass were found lower when com-pared to tobacco waste (118–257 kJ mol�1) (Wu et al., 2015).Moreover, average E of CG was found to be lower than the averageactivation energies of rice husk (221–229 kJ mol�1), cellulose(191 kJ mol�1), elephant grass (218–227 kJ mol�1) and Para grass(103–233 kJ mol�1) (Braga et al., 2014; Sanchez-Jimenez et al.,2013) and higher than switchgrass (Ahmad et al., 2016; Bineyet al., 2015), which indicates that CG biomass may be used forco-firing with various types of biomass. The E has a physical signif-icance which can be interpreted throughmolecular collision theory(White et al., 2011). Accordingly, there happen random collisionsbetween the molecules and only those colliding molecules caneffectively initiate a reaction which have the required energy(kinetic energy) to cause disruption of old bonds. The effective col-lision may weaken the old bonds temporarily to cause a cleavage,followed by the formation of new bonds, subsequently formingnew molecules. So, lower activation energy of CG biomass reflectsthat it would be an easy biomass for product formation throughpyrolysis. The relationship between E, a and pyrolysis temperaturefor CG biomass is shown in Figs. 5 and 6 and is described in Table 4.

The enthalpy of reaction (DH) is the amount energy exchangedduring a chemical reaction. When, E-values were compared with

A DG DSs�1 kJ mol�1 J mol�1

2.25 � 1006 177.21 �137.718.19 � 1010 175.00 �50.391.51 � 1012 174.52 �26.163.16 � 1013 174.06 �0.882.42 � 1013 174.10 �3.108.15 � 1013 173.92 6.995.23 � 1014 173.67 22.453.72 � 1014 173.71 19.622.26 � 1014 173.78 15.48

– – –

1.77 � 1005 177.91 �158.831.04 � 1011 174.96 �48.431.79 � 1012 174.49 �24.743.49 � 1013 174.04 �0.062.84 � 1013 174.07 �1.789.28 � 1013 173.91 8.085.69 � 1014 173.66 23.154.70 � 1014 173.68 21.574.02 � 1014 173.70 20.26

– – –

80

100

120

140

160

180

200

450 500 550 600 650 700 750 800

Act

ivat

ion

ener

gy (k

J m

ol-1

)

Pyrolysis temperature (K)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Fig. 5. Camel grass Activation Energy v/s Pyrolysis Temperature.

Fig. 6. Relationship between the Conversion and Pyrolysis Temperature (K).

Table 4Relationship among conversion points, activation energies and pyrolysis temperature.

Conversion range (a) Temperature range (�C) Reactions Activation energy E (kJ mol�1)

a 6 0.1 25–205 Inter/Intracellular moisture release and degradation of simple sugar molecules Increased from starting point to 960.1 6 a 6 0.4 205–340 Degradation of celluloses and pectin Increased from 96 to 1780.4 6 a 6 0.7 340–370 Degradation of lignin Increased from 178 to 1920.7 6 a 6 1.0 370–420 Residual lignin decomposition and formation of char Decreased from 192 to 188

M.A. Mehmood et al. / Bioresource Technology 228 (2017) 18–24 23

DH values for CG biomass pyrolysis, it was there is little potentialenergy barrier (�5 kJ mol�1) which reflects that feasibility of thereaction to happen under provided conditions, because lower dif-ference in E and DH reflects the formation of activation complexis being favored (Vlaev et al., 2007). This finding is in accordancewith the previous studies (Ahmad et al., 2016; Maia and deMorais, 2016). Because enthalpy is the energy consumed duringthe thermal conversion of biomass to various products, includinggas, oil or biochar (Daugaard and Brown, 2003), hence its closenesswith the E-values reflects that product formation may be achievedby providing a little amount (�5 kJ mol�1) of additional energy.

The values of A for the CG were ranged from 2.25 � 1006–3.72 � 1014 s�1 to 1.77 � 1005–5.69 � 1014, as calculated by KASand FWO methods, respectively (Table 3). The A values <109 s�1

indicate a surface reaction, whereas the A-valuesP109 s�1 indicate

a simple complex (Turmanova et al., 2008). The A-values increasedwith the increase in conversion rate, but the range is narrow whichindicate the reliability of calculated E-values. Moreover, A-values ofCG biomass are found closely related to the range of A-values cal-culated for red paper waste (3.80 � 100–2.80 � 1012), rice straw(1.70 � 1007–9.35 � 1012), rice bran (1.00 � 1007 and 1.58 � 1010)and Para grass (1.42 � 1007–2.26 � 1019) (Ahmad et al., 2016;Biney et al., 2015; Maia and de Morais, 2016; Xu and Chen,2013). The entropies for the CG biomass have shownmore negative(as low as �158.83 J mol�1) and less positive (as high as22.45 J mol�1). Where, more negative values indicate degree ofdisorder of products is much lower when compared to the biomass.Gibb’s free energy (DG) of the CG was calculated as173–177 kJ mol�1, which is from the DG values of the rice bran(167.17 kJ mol�1), rice straw (164.59 kJ mol�1) (Xu and Chen,

24 M.A. Mehmood et al. / Bioresource Technology 228 (2017) 18–24

2013) and red pepper waste (139.4 kJ mol�1) (Maia and de Morais,2016). The Gibb’s free energy reflect the amount of availableenergy from the biomass, these values have shown that CG bio-mass has a considerable bioenergy potential.

4. Conclusion

Pyrolysis of Camel grass comprised of three stages; stage-I, upto 180 �C, showed release of retained water, stage-II depictedmajor biomass loss due to degradation of celluloses from 270 to319 �C, and stage-III occurred from 340 to 470 indicating lignindegradation and charring. Temperature range of 270–470 �C maybe employed for its gasification. Pyrolysis yielded 30.46% of biocharat 550 �C. The volatile matter (82.67%), HHV (15.00 MJ kg�1), lowerE (84–193 kJ mol�1) and DG (173–177 kJ mol�1) have shown thatCG biomass can be directly converted into energy, gaseous fuelsand chemicals in a cost and energy-efficient manner.

Conflict of interests

It is declared that authors have no competing interests.

Acknowledgements

We are highly thankful to Higher Education Commission,Pakistan and International Foundation for Sciences, Sweden fortheir financial support.

References

Ahmad, M.S., Mehmood, M.A., Omar, S.A.-A., Ye, G., Luo, H., Ibrahim, M., Rashid, U.,Nehdi, I.A., Qadir, G., 2016. Kinetic analyses and pyrolytic behavior of Para grass(Urochloa mutica) for its bioenergy potential. Bioresour. Technol. http://dx.doi.org/10.1016/j.biortech.2016.10.090.

Akahira, T., Sunose, T., 1969. Transactions of Joint Convention of Four ElectricalInstitutes, 246.

Biney, P.O., Gyamerah, M., Shen, J., Menezes, B., 2015. Kinetics of the pyrolysis ofarundo, sawdust, corn stover and switch grass biomass by thermogravimetricanalysis using a multi-stage model. Bioresour. Technol. 179, 113–122.

Braga, R.M., Melo, D.M., Aquino, F.M., Freitas, J.C., Melo, M.A., Barros, J.M., Fontes, M.S., 2014. Characterization and comparative study of pyrolysis kinetics of the ricehusk and the elephant grass. J. Therm. Anal. Calorim. 115, 1915–1920.

Ceylan, S., Kazan, D., 2015. Pyrolysis kinetics and thermal characteristics ofmicroalgae Nannochloropsis oculata and Tetraselmis sp. Bioresour. Technol.187, 1–5.

Daugaard, D.E., Brown, R.C., 2003. Enthalpy for pyrolysis for several types ofbiomass. Energy Fuels 17, 934–939.

Elhardallou, S.B., 2011. Cytotoxicity and biological activity of selected Sudanesemedicinal plants. Res. J. Med. Plants 5, 201–229.

Flynn, J.H., Wall, L.A., 1966. A quick, direct method for the determination ofactivation energy from thermogravimetric data. J. Polym. Sci. B 4, 323–328.

Graham, R., Bergougnou, M., Overend, R., 1984. Fast pyrolysis of biomass. J. Anal.Appl. Pyrol. 6, 95–135.

Hashim, G.M., Almasaudi, S.B., Azhar, E., Al Jaouni, S.K., Harakeh, S., 2016. Biologicalactivity of Cymbopogon schoenanthus essential oil. Saudi J. Biol. Sci. http://dx.doi.org/10.1016/j.sjbs.2016.06.001.

Heo, H.S., Park, H.J., Park, Y.-K., Ryu, C., Suh, D.J., Suh, Y.-W., Yim, J.-H., Kim, S.-S.,2010. Bio-oil production from fast pyrolysis of waste furniture sawdust in afluidized bed. Bioresour. Technol. 101, S91–S96.

Howaniec, N., Smolinski, A., 2011. Steam gasification of energy crops of highcultivation potential in Poland to hydrogen-rich gas. Int. J. Hydrogen Energy 36,2038–2043.

Hu, Z., Ma, X., Li, L., 2015. Optimal conditions for the catalytic and non-catalyticpyrolysis of water hyacinth. Energy Convers. Manage. 94, 337–344.

Jeguirim, M., Dorge, S., Trouve, G., 2010. Thermogravimetric analysis and emissioncharacteristics of two energy crops in air atmosphere: Arundodonax andMiscanthus giganthus. Bioresour. Technol. 101, 788–793.

Kim, Y.S., Kim, Y.S., Kim, S.H., 2010. Investigation of thermodynamic parameters inthe thermal decomposition of plastic waste- waste lube oil compounds.Environ. Sci. Technol. 44, 5313–5317.

Li, D., Chen, L., Chen, S., Zhang, X., Chen, F., Ye, N., 2012. Comparative evaluation ofthe pyrolytic and kinetic characteristics of a macroalga (Sargassum thunbergii)and a freshwater plant (Potamogeton crispus). Fuel 96, 185–191.

Liao, J.C., Mi, L., Pontrelli, S., Luo, S., 2016. Fuelling the future: microbial engineeringfor the production of sustainable biofuels. Nat. Rev. Microbiol. 14, 288–304.

Linan-Montes, A., de la Parra-Arciniega, S., Garza-Gonzalez, M., Garcia-Reyes, R.,Soto-Regalado, E., Cerino-Cordova, F., 2014. Characterization and thermalanalysis of agave bagasse and malt spent grain. J. Therm. Anal. Calorim. 115,751–758.

Mabuda, A., Mamphweli, N., Meyer, E., 2016. Model free kinetic analysis ofbiomass/sorbent blends for gasification purposes. Renewable SustainableEnergy Rev. 53, 1656–1664.

Maia, A.A.D., de Morais, L.C., 2016. Kinetic parameters of red pepper waste asbiomass to solid biofuel. Bioresour. Technol. 204, 157–163.

Mehmood, M.A., Ibrahim, M., Rashid, U., Nawaz, M., Ali, S., Hussain, A., Gull, M.,2016. Biomass production for bioenergy using marginal lands. Sustainable Prod.Consumption. http://dx.doi.org/10.1016/j.spc.2016.08.003.

Mortensen, P.M.I., Grunwaldt, J.-D., Jensen, P.A., Knudsen, K., Jensen, A.D., 2011. Areview of catalytic upgrading of bio-oil to engine fuels. Appl. Catal. A 407, 1–19.

Nhuchhen, D.R., Salam, P.A., 2012. Estimation of higher heating value of biomassfrom proximate analysis: a new approach. Fuel 99, 55–63.

Norbert, G.K.B.J., Seth, N.W., Dodji, K.B., Roger, N.C.H., Guillaume, K.K., Essè, A.K.K.,Isabelle, G.A., 2014. The use of two new formulations of Ocimum Canum Simsand Cymbopogon Schoenanthus L. in the control of Amitermes Evuncifer Silvestri(Termitidae: Termitinae), in Togo. Int. J. Natl. Sci. Res. 2, 195–205.

Ozawa, T., 1965. A new method of analyzing thermogravimetric data. Bull. Chem.Soc. Jpn. 38, 1881–1886.

Patel, M., Zhang, X., Kumar, A., 2016. Techno-economic and life cycle assessment onlignocellulosic biomass thermochemical conversion technologies: a review.Renewable Sustainable Energy Rev. 53, 1486–1499.

Paulrud, S., Nilsson, C., 2001. Briquetting and combustion of spring-harvested reedcanary-grass: effect of fuel composition. Biomass Bioenergy 20, 25–35.

Rogers, J., Brammer, J., 2012. Estimation of the production cost of fast pyrolysis bio-oil. Biomass Bioenergy 36, 208–217.

Sabry, A., El-Zayat, S., El-Said, A., Abdel-Motaal, F., Magraby, T., 2014. Mycofloraassociated with Halfa-bar leaves and stems (Cymbopogon schoenanthus L.Sprenge), in vitro the antimicrobial activity of the plant leaves and stemssecondary metabolites. Int. J. Curr. Microbiol. App. Sci. 3, 874–882.

Saldarriaga, J.F., Aguado, R., Pablos, A., Amutio, M., Olazar, M., Bilbao, J., 2015. Fastcharacterization of biomass fuels by thermogravimetric analysis (TGA). Fuel140, 744–751.

Sanchez-Jimenez, P.E., Perez-Maqueda, L.A., Pereon, A., Criado, J.M., 2013.Generalized master plots as a straightforward approach for determining thekinetic model: the case of cellulose pyrolysis. Thermochim. Acta 552, 54–59.

Slopiecka, K., Bartocci, P., Fantozzi, F., 2012. Thermogravimetric analysis and kineticstudy of poplar wood pyrolysis. Appl. Energy 97, 491–497.

Sousa, E.M.B.D., Camara, A.P.C., Costa, W.A., Costa, A.C.J., Oliveira, H.N.M., Galvao, E.L., Marques, M.M.O., 2005. Evaluation of the extraction process of the essentialoil from Cymbopogon schoenanthus with pressurized carbon dioxide. Braz. Arch.Biol. Technol. 48, 231–236.

Turmanova, S.C., Genieva, S., Dimitrova, A., Vlaev, L., 2008. Non-isothermaldegradation kinetics of filled with rise husk ash polypropene composites.Express Polym. Lett. 2, 133–146.

Vlaev, L., Georgieva, V., Genieva, S., 2007. Products and kinetics of non-isothermaldecomposition of vanadium (IV) oxide compounds. J. Therm. Anal. Calorim. 88,805–812.

White, J.E., Catallo, W.J., Legendre, B.L., 2011. Biomass pyrolysis kinetics: acomparative critical review with relevant agricultural residue case studies. J.Anal. Appl. Pyrol. 91, 1–33.

Wu, W., Mei, Y., Zhang, L., Liu, R., Cai, J., 2015. Kinetics and reaction chemistry ofpyrolysis and combustion of tobacco waste. Fuel 156, 71–80.

Xu, Y., Chen, B., 2013. Investigation of thermodynamic parameters in the pyrolysisconversion of biomass and manure to biochars using thermogravimetricanalysis. Bioresour. Technol. 146, 485–493.

Contents lists available at ScienceDirect

Bioresource Technology

journal homepage: www.elsevier.com/locate/biortech

Pyrolysis, kinetics analysis, thermodynamics parameters and reactionmechanism of Typha latifolia to evaluate its bioenergy potential

Muhammad Sajjad Ahmadb,c, Muhammad Aamer Mehmooda,b,⁎, Syed Taha Haider Taqvic,Ali Elkamelc, Chen-Guang Liud,⁎, Jianren Xud, Sawsan Abdulaziz Rahimuddine, Munazza Gulle

a College of Bioengineering, Sichuan University of Science and Engineering, Zigong 643000, Chinab Bioenergy Research Center, Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad 38000, Pakistanc Chemical Engineering Department, University of Waterloo, Ontario, Canadad State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, Chinae Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia

A R T I C L E I N F O

Keywords:Typha latifoliaThermogravimetric studyReaction mechanismBioenergyLow-cost biomass

A B S T R A C T

This work was focused on understanding the pyrolysis of Typha latifolia. Kinetics, thermodynamics parametersand pyrolysis reaction mechanism were studied using thermogravimetric data. Based on activation energies andconversion points, two regions of pyrolysis were established. Region-I occurred between the conversion rate0.1–0.4 with peak temperatures 538 K, 555 K, 556 K at the heating rates of 10 K min−1, 30 K min−1, and50 K min−1, respectively. Similarly, the Region-II occurred between 0.4 and 0.8 with peak temperatures of606 K, 621 K, 623 K at same heating rates. The best model was diffusion mechanism in Region-I. In Region-II, thereaction order was shown to be 2nd and 3rd. The values of activation energy calculated using FWO and KASmethods (134–204 kJ mol−1) remained same in both regions reflecting that the best reaction mechanism waspredicted. Kinetics and thermodynamic parameters including E, ΔH, ΔS, ΔG shown that T. latifolia biomass is aremarkable feedstock for bioenergy.

1. Introduction

At present, energy requirements of the world are met through fossilfuels. Owing to their non-renewable nature, fossil fuel deposits may becompletely exhausted from Earth in 70 years from now. Alternatively,biomass is believed to be a reliable future energy source along withsolar, wind and hydrothermal. Among all the energy resources world-wide, biomass provides up to 10%, with an annual increase of 2.5%(Edrisi and Abhilash, 2016). In the USA and Brazil, almost 80 percent ofthe fuel comes from renewables, specifically maize and sugarcane,which are not only expensive but have also created a food versus fueldilemma. Alternatively, non-edible plants produced on non-arablelands offer a low-cost alternative without any direct or indirect com-petition with food or land (Ahmad et al., 2017a). Here, the problem isnot the availability of the biomass, but rather the cost-effective andefficient retrieval of the energy stored in the biomass. Several processeshave been developed to retrieve the biomass energy including directcombustion, thermochemical and biological conversion, where pyr-olysis and biological fermentation are the cleanest methods to convertthe biomass into valuable products. However, the latter is a tedious,

expensive and time-consuming process, mainly due to the recalcitrantnature of the biomass. While, the thermal transformation of biomassinto various products including solid, liquid and gasses often under aninert environment is called pyrolysis. Moreover, pyrolysis processleaves almost no waste, and all converted components can be used forone or another purpose ranging from energy (heat, bio-oil) throughagricultural (char) and industrial chemicals (gases). However, thepyrolysis process depends upon various factors including nature of thebiomass, particle size and temperature parameters. Hence, for an effi-cient conversion of any biomass, it is essential to understand its pyr-olysis behavior to design an optimized pyrolytic process.

Thermogravimetric analyses performed under controlled conditionsare practically feasible to understand the pyrolytic behavior and todetermine the optimum pyrolytic conditions of any biomass (Di Blasi,2009; Kow et al., 2014). The thermochemical conversion has dom-inance over biological processes because it has higher efficiency (Zhanget al., 2006). However, a clear understanding of the pyrolytic condi-tions is required for any biomass prior to feeding this biomass to anycommercial thermal plant. Artificial neural networks (ANN) are modelswhich are developed based on the functionality of a human brain in

http://dx.doi.org/10.1016/j.biortech.2017.08.162Received 6 July 2017; Received in revised form 3 August 2017; Accepted 27 August 2017

⁎ Corresponding authors at: Bioenergy Research Center, Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad 38000, Pakistan (M.A.Mehmood).

E-mail addresses: draamer@gcuf.edu.pk (M.A. Mehmood), cg.liu@sjtu.edu.cn (C.-G. Liu).

Bioresource Technology 245 (2017) 491–501

Available online 01 September 20170960-8524/ © 2017 Elsevier Ltd. All rights reserved.

MARK

which neurons transfer data from synapses on the way to axons throughchemicals called neurotransmitters. Similarly, ANNs are composed ofmultiple neuron layers including input layers, hidden layers, and outputlayer to learn the relationship between them for further problem-sol-ving approaches between experimental and predicted data (Kalogirou,2003). Consequently, ANNs have been proven to be a useful tool topredict the reaction chemistry (Conesa et al., 2004; Yıldız et al., 2016)and has been employed on thermal data for estimation from severalstandpoints through training, validation and testing of experimentaldata (Chen et al., 2017; Uzun et al., 2017).

Previously, several grasses including Camel grass (Mehmood et al.,2017), Corn cobs, Miscanthus, wheat straw (Álvarez et al., 2016) andSorghum weeds (Rezende and Richardson, 2017), along with micro-algae (Maurya et al., 2016), red-peppers waste (Maia and de Morais,2016) and rice-husk (Zhang et al., 2016) were studied for their bioe-nergy potential using thermogravimetric analyses. The Napier Grass isalready being used to produce fuel on the commercial thermal plant (Heet al., 2017). The present study was focused on understanding thepyrolytic behavior, reaction chemistry and bioenergy potential of Typhalatifolia via thermogravimetric analyses for the very first time. T. lati-folia occurs across the globe including Asia, Africa, Americas andEurope. It is a perennial grass produced on marginal lands and is afamous wetland species and can be grown in brackish or polluted water,hence is a low-cost biomass resource. While cultivating on polluted orbrackish water, its bioremediatory action can remove pollutants de-veloping a bitter taste making it unsuitable for food or feed. It hashigher energy potential and can produce 25 units more energy whencompared to fossil fuel (Ussiri and Lal, 2017). To date, there is no studyavailable on pyrolytic behavior using Artificial Neural Network Ap-proach of T. latifolia, studying the reaction mechanism of the thermaldegradation. Generally, the reaction order model is considered as asuitable reaction mechanism of the biomass as it confirms its viabilitythroughout the pyrolysis process. High precision thermogravimetricand kinetic analyses of devolatilization process (Trninić et al., 2012) arealso applied in this study.

2. Materials and methods

2.1. Elemental composition and proximate analyses

The biomass of T. latifolia was collected from soil affected by salinitywhich was being irrigated with underground brackish water. Collectedsample was washed under a tap and left to dry in air for several days.Air dried biomass was crushed using a manual crusher and put into anoven for 48 h, and was grounded to get particles of size ranging from150 to 200 µm. Sun dried crushed biomass was subjected to proximateanalyses to determine volatile matter (VM%), ash (%) moisture content(%) using the standard methods as described in ASTM (E872- & 822006, E871-82 2006, E1755-01 2007). The fixed carbon (FC%) wascalculated using the equation: = −FC ash(%) 100 (

+ +content VM moisture). To determine the VM and moisture content,known mass was put in oven-dried at 380 K in triplicate for 16–24 h toget a constant mass. The loss in mass reflected the moisture content.Similarly, known mass from the oven dried sample was put into pre-weighed ceramic crucibles in triplicate and left at 775 K in a Mufflefurnace for 3–4 h to get a constant mass. Where, loss in mass reflectedthe volatile matter (VM) and residual mass reflected the ash content.The composition of organic elements including carbon (C), hydrogen(H), sulphur (S), nitrogen (N) and oxygen (O) in the sample was esti-mated by an elemental analyzer (Vario EL Cube, Germany). Duringanalyses, the argon (Ar) was used as a carrier gas.

The high heating value (HHV) indicates the amount of energy to beevolved from a biomass. However, the experimental procedures to de-termine the HHV (MJ kg−1) are expensive and may give undesiredexperimental faults (Nhuchhen and Salam, 2012). Hence, previouslyseveral correlation models were developed to calculate HHV. Here, the

most appropriate model established to date was used to calculate theHHV as described (Nhuchhen and Salam, 2012).

2.2. TGA-DSC experiment

TGA-DSC analyses were performed using an STA-409, NETZSCH-Gerätebau GmbH, Germany. After calibration (as described in the in-struction manual), almost ten mg (10) of milled biomass (150–200 µmparticle size) was put into the alumina crucibles and constantly heatedfrom ambient temperature to 1275 K. Where, constant heating at therate of 10, 30 and 50 K min−1 was used. An inert environment wasmaintained using the nitrogen gas flow (100 mL min−1) into the reac-tion chamber.

2.3. ANN model development

An artificial neural network (ANN) model was established to governweight loss as output data, using heating rate and temperature as inputvariables. The feed-forward Levenberg-Marquardt back-propagationalgorithm was selected in MATLAB® R2014b for data prediction. Themodel depicts the input, hidden and output layers of the multi-layernetwork. Two neurons were included in the input layer; heating rateand temperature. In contrast, the output layer had a single neuron, thetemperature dependent weight loss. 1,021 data points were used in thisanalysis which were divided among the training (70%), validation(15%) and testing (15%) phases. The epoch number was set to 52 and 6validation checks were applied. The Mean Square Error (MSE) function,as seen in Eq. (1), was used as an error function to evaluate the per-formance of each phase. The network model was optimized based onthe target (t) and output values (o), as expressed in Eq. (2).

=∑ = −

MSEn λ β

1[ ( ) ]i

ni i1

2 (1)

where λi: experimental values; βi: predicted values; n: number of datapoints

= −⎡

⎣⎢

∑ −∑

⎤

⎦⎥R

t oo

1( )

( )i i i

i i

22

2(2)

2.4. Mathematical model development for thermogravimetric analyses

A mathematical model was derived from analyzing the data ob-tained from the TGA-DSC experiments. In isoconversional methods, thedisintegration rate of the sample is depicted as:

=dαdt

kf α( )(3)

where;

= − − ∞α m m m m( )/( )o t o (4)

heremo is the initial mass, mt is change in the mass, and mα is the re-

sidual massUsing k, Eq. (3) was re-written as follows;

= ⎛⎝

− ⎞⎠

dαdt

Aexp ERT

f α( )(5)

where

A is pre-exponential factor (s−1)E is activation energy ( )kJ

molT is temperature in Kelvin (K)R is Universal gas constant ( )8.314 J

K mol.t is the time in sec

M.S. Ahmad et al. Bioresource Technology 245 (2017) 491–501

492

Later, the heating rate =β dTdt and the conversion function,

= −f α α( ) (1 ) were introudced by which following equation was ob-tained;

= ⎛⎝

− ⎞⎠

−dαdT

Aβ

ERT

αexp (1 )(6)

Then, Eq. (6) was integrated for the initial conditions, α= 0, at=T T0, and after some mathematical manipulation, the following

equation was obtained;

∫= − = − ⎛⎝

− ⎞⎠

g α dα α ART βE RT E exp ERT

( ) /(1 ) / [1 2 / ]α

02

(7)

g is the integral conversion function.Eq. (7) was rearranged, as the quantity RT E2 / was negligible when

compared with unity, hence it was ignored (Coats and Redfern, 1964),which resulted in following equation;

= −g α ART βE exp E RT( ) ( / ) ( / )2 (8)

2.5. Calculation of kinetic and thermodynamic parameters

Kinetic parameters of the pyrolysis reaction are vital to under-standing the thermal degradation behavior of the sample under study.Here, these parameters were calculated using the isoconversionalmodels as described by FWO (Flynn-Wall-Ozawa) and KAS (Kissenger-Akahira-Sunose) (Akahira and Sunose, 1969; Flynn and Wall, 1966;Ozawa, 1965). A model-fitting method named Coats-Redfern (Coats andRedfern, 1964) was used to describe the reaction mechanism whichincludes an order of reaction, diffusional, and the contracting geometry(White et al., 2011). Accordingly, activation energy could be de-termined by using f α( ) or g α( ) that was further compared with theresults found from KAS and FWO methods to predict the most accuratereaction mechanism.

The Eq. (8) was rearranged after taking logarithm on both sides toget following equation;

⎛⎝

⎞⎠

= −β

Tln AR Eg α E RTln ( / ( )) / KAS method2 (9)

Moreover, Eq. (6) was subjected to integration using the initialconditions, α= 0, at =T T0. Later, Doyle’s approximation was in-troduced followed by a few mathematical modifications (Doyle, 1961),which gave Eq. (10);

= −β ln AE Rg α E RTln( ) ( / ( )) / FWO method (10)

The Coats and Redfern (CR) Method relies on asymptotic approx-imation → 0RT

E2 , giving following equation;

⎛⎝

⎞⎠

= −g αT

ln AR βE E RTln( )

( / ) / CR Method2 (11)

The left side of each Eqs. (9), (10) and (11) was plotted (y-axis)against the inverse of pyrolysis temperature (x-axis), for selected con-version point (α) to calculate kinetic parameters. The conversion point(α) was used to calculate the pre-exponential factors (A s−1) using theconversion points plotted between ( )ln β

T2 , βln( ) and ( )ln g αT( )2 against 1/

T which gave a straight line. The activation energy values were calcu-lated from the slopes (E). Moreover, the thermodynamic parametersincluding HΔ (enthalpy), GΔ (Gibbs free energy) and SΔ (entropy) werecalculated as described previously (Kim et al., 2010; Xu and Chen,2013).

=A β Eexp E RT RT[ . ( / )]/( )m m2 (12)

= −H E RTΔ (13)

= +G E RT K T hAΔ ln( / )m B m (14)

= −S H G TΔ Δ Δ / m (15)

where:

KB Boltzmann constant × −(1.381 10 J/K)23

h Plank constant × −(6.626 10 Js)34

Tm DTG peak temperature, K

3. Results and discussion

3.1. Physicochemical parameters

The T. latifolia biomass contained C, H, N, S, and O as 44.00%, 6.09,2.45%, 0.34%, and 32.34%, respectively. The sample was shown tocontain 71% volatile matter, 19.5% fixed carbon and 8.8% alkali. Thelower nitrogen (< 2.45%) and sulfur content (< 0.34%) in the sampleindicated that there is a lower risk of emission of toxic gases (NOx, SOx)from its pyrolysis. The range of volatile content (%) in the was shown tobe within the range exhibited by the established bioenergy crops in-cluding Miscanthus gigantus and Arduno donax (Jeguirim et al., 2010).

The HHV indicates the amount of energy available from the biomassupon combustion. The estimated HHV of the sample was shown be18.32 MJ kg−1 which is reasonably higher than the HHVs of severalwell-known energy crops including A. donax (Giant reed), M. gigantus,Phalaris arundinacea (Reed canarygrass), Salix spp. (Willow), Para grassand Camel grass, which had shown the HHVs as 17.2, 17.80, 16.30,15.03 (Howaniec and Smolinski, 2011; Jeguirim et al., 2010; Paulrudand Nilsson, 2001), 15.10 (Ahmad et al., 2017b), and 15.00 MJ kg−1

(Mehmood et al., 2017), respectively. The estimated HHV indicate theremarkable energy potential of the T. latifolia when compared to re-cognized bioenergy crops. However, the HHV of T. latifolia biomass wasshown to be lower when compared to HHV of sweet sorghum, i.e.20–25 MJ kg−1 (Monti et al., 2008; Yan et al., 2016).

3.2. Analyses of TG-DTG curves

Thermogravimetric analyses exhibit the loss in biomass in responseto increasing reaction temperature. Where lost mass is converted intovarious products. The curves obtained during this analysis (TG-DTGcurves) indicate the thermochemical conversion of the subjected bio-mass into solids, liquids and gaseous products (Maia and de Morais,2016). For the sample under study, the curves showed typical trend ofthermal degradation of lignocelluloses when compared to the TG-DTGcurves (Fig. 1) found for Switchgrass, Cardoon leaves, Elephant grass,Camel grass, rice husk, and red pepper waste (Biney et al., 2015; Bragaet al., 2014; Maia and de Morais, 2016; Mehmood et al., 2017; Xu andChen, 2013).

The characteristic temperatures associated with the mass loss duringthermal degradation, are shown in Tables 1 and 2. The rate of thermalconversion of the sample was shown to be increased with the increasedheating rate (Tables 1 and 2). The thermal conversion of the sample wasshown to comprise of three stages with two zones during stage-II. Thefirst stage started from ambient temperature to 485–495 K for allheating rates, with the loss of 7.42–8.32% in mass, which indicates therelease of retained moisture content within intercellular spaces or in-tracellular compartments. The second stage ranged from 485 to 660 Ktaking all heating rates into account, where most of the mass loss oc-curred (i.e. 51%). This stage was shown to contain two zones for allheating rates, where zone-I occurred between 485 and 591 K whilezone-II appeared between 568 and 660 K. The third stage occurs from660 to 1275 K, where almost 17% of the total mass was lost. The bio-mass containing lesser that 10% retained moisture, is considered fea-sible for combustion, which makes this sample suitable for pyrolysisand combustion (Braga et al., 2014). However, thermal transformationshowed the typical pattern of lignocellulosic biomass. Where, most ofthe thermal conversion happened during stage-II, indicating the de-gradation of hemicellulose, cellulose, and pectin where the typicaltemperature for their degradation ranges from 485 K through 660 K (Xu

M.S. Ahmad et al. Bioresource Technology 245 (2017) 491–501

493

and Chen, 2013). The temperature range associated with the third stagefollowed by the long tail reflects lignin degradation and char formation(Braga et al., 2014). Most of the thermal transformation happened up to700 K where 58%-62% loss of the mass happened. Hence, the thermalconversion of the T. latifolia biomass into various products may beoptimized within this temperature range, using lower heating rate in an

energy efficient manner. These values indicated the advantage of usingT. latifolia for pyrolysis and combustion, when compared to the pre-viously studied biomass samples including rice husk, water hyacinth,and elephant grass (Biney et al., 2015; Braga et al., 2014; Huang et al.,2016). Biochar yields of 24.59, 25.45 and 23.67% were observed up to

Table 2Mass loss during different stages of decomposition with increasing temperature.

Stages Temperature Heating rate (K min−1)

10 30 50

Stage-I, WL (%) Tmin-T1 7.45 7.46 8.32Stage-II, WL (%) Region-I T1-T3 19.65 20.58 21.62

Region-II T3-T5 30.83 30.30 30.17Stage-III, WL (%) T5-Tmax 17.48 16.21 16.22Final residues at 1075–1275 K (%) – 24.59 25.45 23.67

Table 1Characteristic temperatures associated with the mass loss.

Heating rate (K min−1) Temperature (K)

Tmin T1 T2 T3 T4 T5 Tmax

10 298 485 538 568 606 644 130030 499 555 584 621 65450 495 556 591 623 660

Fig. 1. TG-DTG curves indicating percent mass loss ofTypha latifolia.

M.S. Ahmad et al. Bioresource Technology 245 (2017) 491–501

494

660 K at three heating rates, which were comparable to the biocharyields obtained from the pyrolysis of straw (23.68%) and bran (25.17%)of rice plant (Xu and Chen, 2013), and lower than Para grass (31.5%)(Ahmad et al., 2017b), and Camel grass (30.46%) (Mehmood et al.,2017). These values indicated the appropriateness of the sample forbiochar production.

3.3. Heat flow during pyrolysis reaction

The DSC curves showed a direct connection between the heatingtemperature and the flow of heat (mW mg−1) because most of thepyrolysis reaction indicated an active reaction mechanism (Fig. 2).However, ending stages showed a decreasing heat flow. The reactionrate was steadily enhanced from ambient temperatures to 589, 754 and

Fig. 3. Linear fit plots to determine the activation energyvalues using KAS and FWO methods.

Fig. 2. DSC curves indicating heat flow across thebiomass.

M.S. Ahmad et al. Bioresource Technology 245 (2017) 491–501

495

799 K at 10, 30 and 50 K min−1, respectively, which reflects exo-thermic reactions. These differences in heat flow may be due to the poorthermal conductivity of the biomass at various heating rates. The DSCcurves started shifting towards x-axes at higher temperatures, whichindicates the reaction to cease due to depletion of reactant (the bio-mass) or the change of reaction mechanism due to changing composi-tion of the residual biomass. Because most of the volatiles were lostabove 700 K, leading towards a different heat flow into the changingcomposition of the residual biomass. These curves exhibited paralleltrend when compared to the DSC curves observed for bamboo leaves(Kow et al., 2016), the Potamogeton crispus and Sargassum thunbergii,where former is a freshwater plant and later is a marine macroalga (Liet al., 2012).

3.4. Kinetics and thermodynamic parameters

Fig. 3 shows the linear fit plots to determine the activation energyvalues using KAS and FWO methods. These slopes and derived equa-tions were used to calculate the conforming values of E and A as shownin Table 3. The average E-values were 184 kJ mol−1 and 182 kJ mol−1

as evaluated by both (KAS and FWO) methods. Moreover, a plot be-tween E-values and conversion points (α) indicated that both methodshave nearly same values at each conversion point. The relationship ofactivation energies, temperature and conversion points is described inTable 4. The observed range of E is lower than E-values(118–257 kJ mol−1) of tobacco plant waste (Wu et al., 2015), rice husk

(221–229 kJ mol−1), cellulose (191 kJ mol−1) and elephant grass(218–227 kJ mol−1) (Braga et al., 2014; Sanchez-Jimenez et al., 2013)and this range was approximately same as Para grass (Ahmad et al.,2017b) and shown to be higher than switchgrass (Biney et al., 2015).This correspondence of E-values of T. latifolia makes it suitable for co-pyrolysis with several other biomass feedstocks.

The difference between the activation energy values and enthalpiesreflects the likelihood of the pyrolysis reaction to occur. Where lowerdifference indicates that product formation would be favorable. A dif-ference of∼5 kJ mol−1 was observed between the E and ΔH values thatindicated that there is little potential energy barrier to achieve theproduct formation, reflecting that product formation would be easier toachieve (Vlaev et al., 2007). Moreover, pre-exponential factors (A-va-lues) explain the reaction chemistry, which is critically important toknow while optimizing the pyrolysis of biomass. While lower A-values(< 109 s−1) show largely a surface reaction. However, if the reaction isnot surface dependent, then lower A-values also designate a closedcomplex. Alternatively, higher A-values (≥109 s−1) show a simplercomplex (Turmanova et al., 2008). For the sample under study, the A-values ranged from 5.53 × 1010 to 3.02 × 1015 s−1 and 7.61 × 1009 to7.93 × 1015 s−1 as obtained from KAS and FWO methods, respectively(Table 3) that indicated the complexity of biomass. Moreover, A-valuesof sample were higher when compared to A-values of red-peppers waste(3.80 × 100 to 2.80 × 1012 s−1), rice straw (1.70 × 1007 to9.35 × 1012 s−1), rice bran (1.00 × 1007 and 1.58 × 1010 s−1) andwere lower when compared to switchgrass (3.70 × 1003 to

Table 4Relationship between conversion (α), pyrolysis temperature (T) and activation energies (E).

Conversion range (α) Temperature range (K) Reactions Activation energy (E)

α ≤ 0.1 273–485 Release of retained water moisture and degradation of small and simple sugarmolecules

Increased from starting point to147 kJ mol−1

0.1 ≤ α ≤ 0.4 485–591 Thermal conversion of cellulose and pectin Increased from 147 to 185 kJ mol−1

0.4 ≤ α ≤ 0.8 591–660 Degradation of hemicellulose and lignin Increased from 185 to 196 kJ mol−1

0.1 ≤ α ≤ 1.0 660–1200 Residual lignin decomposition and formation of char Decreased from 196 to 177 kJ mol−1

Table 3Kinetic and thermodynamics parameters of T. latifolia.

α EakJ mol−1

R2 ΔHkJ mol−1

As−1

ΔGkJ mol−1

ΔSJ mol−1

EakJ mol−1

R2 ΔHkJ mol−1

As−1

ΔGkJ mol−1

ΔSJ mol−1

KAS method FWO method0.1 147.20 0.99 142.04 5.53E+10 175.37 −53.67 134.78 0.99 129.62 7.61E+09 173.18 −70.160.15 173.56 0.98 168.40 1.08E+13 174.51 −9.85 173.31 0.99 168.15 1.71E+13 171.88 −6.010.2 183.84 0.98 178.68 8.34E+13 174.22 7.18 183.32 0.99 178.16 1.25E+14 171.59 10.570.25 190.85 0.98 185.68 3.37E+14 174.02 18.78 190.15 0.99 184.98 4.88E+14 171.41 21.870.3 189.93 0.99 184.76 2.80E+14 174.05 17.25 189.12 0.99 183.96 3.98E+14 171.43 20.170.35 201.89 0.99 196.73 3.02E+15 173.73 37.02 204.18 0.99 199.01 7.93E+15 171.04 45.050.4 185.30 0.99 180.14 1.12E+14 174.18 9.60 188.97 0.99 183.81 3.86E+14 171.44 19.920.45 171.24 0.99 166.07 6.77E+12 174.58 −13.71 172.40 0.98 167.24 1.42E+13 171.91 −7.520.5 201.89 0.99 196.73 3.02E+15 173.73 37.02 200.18 0.99 195.02 3.59E+15 171.14 38.460.55 193.13 0.99 187.96 5.30E+14 173.96 22.54 196.03 0.99 190.87 1.57E+15 171.25 31.600.6 197.73 0.99 192.57 1.32E+15 173.84 30.16 196.55 0.99 191.39 1.74E+15 171.23 32.450.65 186.65 0.99 181.49 1.46E+14 174.14 11.83 187.22 0.99 182.06 2.73E+14 171.49 17.020.7 198.37 0.99 193.21 1.50E+15 173.82 31.21 196.39 0.99 191.23 1.69E+15 171.24 32.190.75 196.05 0.98 190.89 9.48E+14 173.89 27.38 190.75 0.98 185.58 5.50E+14 171.39 22.860.8 153.94 0.99 148.78 2.14E+11 175.13 −42.44 153.00 0.99 147.84 2.95E+11 172.53 −39.760.85 188.69 0.99 183.53 2.19E+14 174.08 15.22 190.75 0.99 185.58 5.50E+14 171.39 22.860.9 177.61 0.99 172.45 2.41E+13 174.40 −3.13 158.36 0.99 153.20 8.62E+11 172.35 −30.84Avg. 184.58 0.99 179.42 – 174.22 – 182.67 0.99 177.51 – 171.64 –

M.S. Ahmad et al. Bioresource Technology 245 (2017) 491–501

496

1.65 × 1021 s−1) (Maia and de Morais, 2016). Gibbs free energy (ΔG)reflects the amount of energy which become available from that bio-mass upon pyrolysis. Here, the ΔG values were shown to be rangingfrom 173 to 175 that are higher when compared with the ΔG values ofthe rice bran (167.17 kJ mol−1), rice straw (164.59 kJ mol−1) andwaste from red-peppers (139.4 kJ mol−1) (Maia and de Morais, 2016;Xu and Chen, 2013). It indicated that the pyrolysis of the T. latifolia willprovide more energy when compared to the rice bran, rice straw andred-peppers waste.

3.5. Reaction mechanism of the pyrolysis

The CR method was devised on thermogravimetric data at threedifferent heating rates to obtained E-values based on various reactionmechanisms (Table 5). Plots between ln( )g α

T( )2 against 1/T were pro-

duced on various reaction mechanism functions as shown in Fig. 4.There were two regions formed based on conversion of the sample onthree different heating rates. Region-I was defined when α ranged from

⩽ ⩽α0.1 0.4 and Region-II was defined when α ranged from⩽ ⩽α0.4 0.8, where the major part of decomposition occurred. If the

average activation energy values obtained from these mechanismfunctions at different heating rates are nearly equal to the energy valuesobtained from KAS and FWO methods, it shows this mechanism func-tion should be the best-fit reaction of that region.

It was shown that in region-I the average E values are different in

different reaction mechanism functions under three different heatingrates. For reaction order model, the average E values of three heatingrates ranged from 5.75 to 95.79 kJ mol−1, whereas in diffusional stagethese values ranged from 177.14 to 192.25 kJ mol−1 which were clo-sest to the values obtained from KAS and FWO methods. It indicatesthat during the reactions displayed in Region-I, the diffusion played akey role (Khawam, 2006). In contraction geometry, the average E-va-lues were too small and ranged from 88 to 91 kJ/mol. Similarly, inRegion-II the average E-values depended upon 2nd and 3rd order re-action model and ranged from 116 to 200 kJ mol−1 which shows thatE-values obtained from KAS and FWO methods are in between thesevalues. Therefore, reaction order mechanism is classified as diffusiontype followed by the 2nd and 3rd order reaction which is proportionalto the concentration, total or residual amount of reactant(s) in a certainreaction (Khawam, 2006).

3.6. Prediction of pyrolytic behavior by ANN model

ANN model was applied to predict and validate the pyrolysis ex-periment at three different heating rates to simulate the predicted re-sults with experimental data to further understand the pyrolytic beha-vior of T. latifolia. First, one hidden layer was created to have a simplerANN model but three different breakdown stages resulted in sharpchanges in data which complicated behavior of the sample. The bestnetwork performance was achieved with two hidden layers. Moreover,error distribution was analyzed at each step to ensure the accuracy of

Table 5Activation Energy values and reaction mechanism based on Coats-Redfern method.

Region Reaction Model g(α) 10 K min−1 30 K min−1 50 K min−1 Average values

EakJ mol−1

R2 EakJ mol−1

R2 EakJ mol−1

R2 EakJ mol−1

R2

I Reaction OrderZero-order (F0) α 5.52 0.99 5.67 0.98 6.05 0.97 5.75 0.98First-order (F1) − −αln(1 ) 82.50 0.97 82.81 0.99 83.46 0.99 82.92 0.98nth-order (Fn) − − −−α[1 (1 ) / 1]1 94.97 0.99 95.21 0.95 97.18 0.96 95.79 0.97

− − −−α[1 (1 ) / 2]2 15.31 0.88 17.23 0.98 18.54 0.96 17.03 0.94

− − −−α[1 (1 ) / 3]3 25.55 0.96 27.67 0.99 29.33 0.98 27.52 0.98

Diffusion1-D α2 174.48 0.99 e 0.99 179.23 0.99 177.14 0.992-D − − +α ln α α(1 ) (1 ) 182.38 0.99 183.21 0.98 185.93 0.99 183.84 0.993-D (Jander) − −α[1 (1 )1/3]2 190.82 0.98 191.83 0.99 194.11 0.98 192.25 0.98

3-D (Ginstling-Brounshtein) − − −α α1 2/3 (1 )2/3 185.19 0.96 186.22 0.97 188.73 0.97 186.71 0.97

Contracting GeometryCont. Area − −α1 (1 )1/2 88.58 0.99 90.01 0.90 87.51 0.98 88.70 0.96Cont. Volume − −α1 (1 )1/3 90.67 0.84 91.41 0.89 93.85 0.88 91.98 0.87

II Reaction OrderZero-order (F0) α 34.69 0.95 35.61 0.96 36.59 0.96 35.63 0.96First-order (F1) − −ln α(1 ) 67.27 0.97 68.34 0.97 70.37 0.98 68.66 0.97nth-order (Fn) − − −−α[1 (1 ) / 1]1 48.61 0.98 50.51 0.98 49.40 0.99 49.51 0.98

− − −−α[1 (1 ) / 2]2 116.59 0.99 115.43 0.99 118.49 0.99 116.84 0.99

− − −−α[1 (1 ) / 3]3 197.37 0.96 200.71 0.98 202.51 0.97 200.20 0.97

Diffusion1-D α2 79.64 0.99 81.45 0.99 83.83 0.98 80.97 0.992-D − − +α ln α α(1 ) (1 ) 97.13 0.94 98.91 0.96 100.40 0.93 98.81 0.943-D (Jander) − −α[1 (1 )1/3]2 120.01 0.81 124.23 0.84 127.82 0.80 124.02 0.823-D (Ginstling-Brounshtein) − − −α α1 2/3 (1 )2/3 104.66 0.99 108.55 0.99 110.17 0.99 107.79 0.99

Contracting GeometryCont. Area − −α1 (1 )1/2 49.26 0.99 51.12 0.89 54.32 0.92 51.57 0.93Cont. Volume − −α1 (1 )1/3 55.68 0.79 58.59 0.76 61.48 0.72 58.58 0.76

M.S. Ahmad et al. Bioresource Technology 245 (2017) 491–501

497

the network. The regression of each step was carried out during theoptimization of the network that showed good correlation betweentargets and the output values, as shown in the Fig. 5. Moreover, thehistogram error distribution diagram appears to be normally distributedfor the major part of the obtained dataset. The R2 value for the model fitat all stages appeared to be very close to 1, signifying a very good fit ofthe model to the experimental data (Fig. 5). Additionally, all these er-rors fall within a relatively narrow range; thus, indicating a good modelfit. Here, 52 iterations were carried out by MATLAB and the best per-formance was observed at the 46th iteration with the minimum MSE(i.e. 0.53478). The obtained best performance results, as generated byMATLAB, are shown in Fig. 6. This finding indicates that ANN may befrequently applied to understand and envisage the pyrolysis of thebiomass.

4. Conclusion

Major pyrolysis products of T. latifolia can be achieved at485–660 K. The E and ΔG values ranged from 182 to 184 kJ mol−1 and171–175 kJ mol−1, respectively. The HHV value (18.32 MJ kg−1) wasshown to be higher than several established energy crops. Moreover,best-fit plots were obtained by comparing experimental data with thepredicted data points obtained from ANN simulation. The reactionmechanism showed the pyrolysis to contain two regions. Region-I( ⩽ ⩽α0.1 0.4) and Region-II ( ⩽ ⩽α0.4 0.8). The best reaction modelin Region-I was diffusion while in Region-II the best model was built onthe 2nd to 3rd order reaction.

Fig. 4. CR Plots of for pyrolysis of T. latifolia. Region-I ⩽ ⩽α(0.1 0.4)and Region-II ⩽ ⩽α(0.4 0.8).

M.S. Ahmad et al. Bioresource Technology 245 (2017) 491–501

498

Fig. 5. Regression of train, validate and testing stepstogether with histogram diagram.

M.S. Ahmad et al. Bioresource Technology 245 (2017) 491–501

499

Acknowledgement

The financial support of Higher Education Commission, Pakistan ishighly appreciated.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in theonline version, at http://dx.doi.org/10.1016/j.biortech.2017.08.162.

References

Khawam, A., F, D.R., 2006. Solid-state kinetic models: basics and mathematical funda-mentals. J. Phys. Chem. B 110, 17315–17328.

Ahmad, M.S., Mehmood, M.A., Ye, G., Al Ayed, O.S., Ibrahim, M., Rashid, U., Luo, H.,Qadir, G., Nehdi, I.A., 2017a. Thermogravimetric analyses revealed the bioenergypotential of Eulaliopsis binate. J. Therm. Anal. Calorim. http://dx.doi.org/10.1007/s10973-017-6398-x.

Ahmad, M.S., Mehmood, M.A., Al Ayed, O.S., Ye, G., Luo, H., Ibrahim, M., Rashid, U.,Nehdi, I.A., Qadir, G., 2017b. Kinetic analyses and pyrolytic behavior of Para grass(Urochloa mutica) for its bioenergy potential. Bioresour. Technol. 224, 708–713.

Akahira, T., Sunose, T., 1969. Transactions of Joint Convention of Four ElectricalInstitutes. 246.

Álvarez, A., Pizarro, C., García, R., Bueno, J.L., Lavín, A.G., 2016. Determination of ki-netic parameters for biomass combustion. Bioresour. Technol. 216, 36–43.

Biney, P.O., Gyamerah, M., Shen, J., Menezes, B., 2015. Kinetics of the pyrolysis of ar-undo, sawdust, corn stover and switch grass biomass by thermogravimetric analysisusing a multi-stage model. Bioresour. Technol. 179, 113–122.

Braga, R.M., Melo, D.M., Aquino, F.M., Freitas, J.C., Melo, M.A., Barros, J.M., Fontes,M.S., 2014. Characterization and comparative study of pyrolysis kinetics of the ricehusk and the elephant grass. J. Therm. Anal. Calorim. 115, 1915–1920.

Chen, J., Liu, J., He, Y., Huang, L., Sun, S., Sun, J., Chang, K., Kuo, J., Huang, S., Ning, X.,2017. Investigation of co-combustion characteristics of sewage sludge and coffeegrounds mixtures using thermogravimetric analysis coupled to artificial neural net-works modeling. Bioresour. Technol. 225, 234–245.

Coats, A., Redfern, J., 1964. Kinetic parameters from thermogravimetric data. Nature201, 68–69.

Conesa, J.A., Caballero, J.A., Reyes-Labarta, J.A., 2004. Artificial neural network formodelling thermal decompositions. J. Anal. Appl. Pyrol. 71, 343–352.

Di Blasi, C., 2009. Combustion and gasification rates of lignocellulosic chars. Prog. EnergyCombust. Sci. 35, 121–140.

Doyle, C., 1961. Kinetic analysis of thermogravimetric data. J. Appl. Polym. Sci. 5,285–292.

Edrisi, S.A., Abhilash, P., 2016. Exploring marginal and degraded lands for biomass andbioenergy production: An Indian scenario. Renewable Sustainable Energy Rev. 54,1537–1551.

Flynn, J.H., Wall, L.A., 1966. A quick, direct method for the determination of activationenergy from thermogravimetric data. J. Polym. Sci., Part B: Polym. Phys. 4, 323–328.

He, C.-R., Kuo, Y.-Y., Li, S.-Y., 2017. Lignocellulosic butanol production from Napier grass

using semi-simultaneous saccharification fermentation. Bioresour. Technol. 231,101–108.

Howaniec, N., Smolinski, A., 2011. Steam gasification of energy crops of high cultivationpotential in Poland to hydrogen-rich gas. Int. J. Hydrogen Energy 36, 2038–2043.

Huang, L., Liu, J., He, Y., Sun, S., Chen, J., Sun, J., Chang, K., Kuo, J., 2016.Thermodynamics and kinetics parameters of co-combustion between sewage sludgeand water hyacinth in CO2/O2 atmosphere as biomass to solid biofuel. Bioresour.Technol. 218, 631–642.

Jeguirim, M., Dorge, S., Trouve, G., 2010. Thermogravimetric analysis and emissioncharacteristics of two energy crops in air atmosphere: Arundo donax and Miscanthusgiganthus. Bioresour. Technol. 101, 788–793.

Kalogirou, S.A., 2003. Artificial intelligence for the modeling and control of combustionprocesses: a review. Prog. Energy Combust. Sci. 29, 515–566.

Kim, Y.S., Kim, Y.S., Kim, S.H., 2010. Investigation of thermodynamic parameters in thethermal decomposition of plastic waste–waste lube oil compounds. Environ. Sci.Technol. 44, 5313–5317.

Kow, K.-W., Yusoff, R., Abul Aziz, A.R., Abdullah, E.C., 2014. Characterisation of bio-silica synthesised from cogon grass (Imperata cylindrica). Powder Technol. 254,206–213.

Kow, K.-W., Yusoff, R., Abul Aziz, A.R., Abdullah, E.C., 2016. Determination of kineticparameters for thermal decomposition of bamboo leaf to extract bio-silica. EnergySources Part A 38, 3249–3254.

Li, D., Chen, L., Chen, S., Zhang, X., Chen, F., Ye, N., 2012. Comparative evaluation of thepyrolytic and kinetic characteristics of a macroalga (Sargassum thunbergii) and afreshwater plant (Potamogeton crispus). Fuel 96, 185–191.

Maia, A.A.D., de Morais, L.C., 2016. Kinetic parameters of red pepper waste as biomass tosolid biofuel. Bioresour. Technol. 204, 157–163.

Maurya, R., Ghosh, T., Saravaia, H., Paliwal, C., Ghosh, A., Mishra, S., 2016. Non-iso-thermal pyrolysis of de-oiled microalgal biomass: kinetics and evolved gas analysis.Bioresour. Technol. 221, 251–261.

Mehmood, M.A., Ye, G., Luo, H., Liu, C., Malik, S., Afzal, I., Xu, J., Ahmad, M.S., 2017.Pyrolysis and kinetic analyses of Camel grass (Cymbopogon schoenanthus) for bioe-nergy. Bioresour. Technol. 228, 18–24.

Monti, A., Di Virgilio, N., Venturi, G., 2008. Mineral composition and ash content of sixmajor energy crops. Biomass Bioenergy 32, 216–223.

Nhuchhen, D.R., Salam, P.A., 2012. Estimation of higher heating value of biomass fromproximate analysis: a new approach. Fuel 99, 55–63.

Ozawa, T., 1965. A new method of analyzing thermogravimetric data. Bull. Chem. Soc.Jpn. 38, 1881–1886.

Paulrud, S., Nilsson, C., 2001. Briquetting and combustion of spring-harvested reedcanary-grass: effect of fuel composition. Biomass Bioenergy 20, 25–35.

Rezende, M.L., Richardson, J.W., 2017. Risk analysis of using sweet sorghum for ethanolproduction in southeastern Brazil. Biomass Bioenergy 97, 100–107.

Sanchez-Jimenez, P.E., Perez-Maqueda, L.A., Pereon, A., Criado, J.M., 2013. Generalizedmaster plots as a straightforward approach for determining the kinetic model: thecase of cellulose pyrolysis. Thermochim. Acta 552, 54–59.

Trninić, M., Wang, L., Várhegyi, G., Grønli, M., Skreiberg, Ø., 2012. Kinetics of corncobpyrolysis. Energy Fuels 26, 2005–2013.

Turmanova, S.C., Genieva, S., Dimitrova, A., Vlaev, L., 2008. Non-isothermal degradationkinetics of filled with rise husk ash polypropene composites. Express Polym. Lett. 2,133–146.

Ussiri, D.A.N., Lal, R., 2017. The Role of Bioenergy in Mitigating Climate Change CarbonSequestration for Climate Change Mitigation and Adaptation. Springer International

Fig. 6. Best performance Mean Square Error (MSE) vs Targets.

M.S. Ahmad et al. Bioresource Technology 245 (2017) 491–501

500

Publishing, Cham, pp. 433–495.Uzun, H., Yıldız, Z., Goldfarb, J.L., Ceylan, S., 2017. Improved prediction of higher

heating value of biomass using an artificial neural network model based on proximateanalysis. Bioresour. Technol. 234, 122–130.

Vlaev, L., Georgieva, V., Genieva, S., 2007. Products and kinetics of non-isothermal de-composition of vanadium (IV) oxide compounds. J. Therm. Anal. Calorim. 88,805–812.

White, J.E., Catallo, W.J., Legendre, B.L., 2011. Biomass pyrolysis kinetics: a comparativecritical review with relevant agricultural residue case studies. J. Anal. Appl. Pyrolysis91, 1–33.

Wu, W., Mei, Y., Zhang, L., Liu, R., Cai, J., 2015. Kinetics and reaction chemistry ofpyrolysis and combustion of tobacco waste. Fuel 156, 71–80.

Xu, Y., Chen, B., 2013. Investigation of thermodynamic parameters in the pyrolysisconversion of biomass and manure to biochars using thermogravimetric analysis.

Bioresour. Technol. 146, 485–493.Yan, H.-L., Zong, Z.-M., Li, Z.-K., Kong, J., Zheng, Q.-X., Li, Y., Wei, X.-Y., 2016. Sweet

sorghum stalk liquefaction in supercritical methanol: Effects of operating conditionson product yields and molecular composition of soluble fraction. Fuel Process.Technol.

Yıldız, Z., Uzun, H., Ceylan, S., Topcu, Y., 2016. Application of artificial neural networksto co-combustion of hazelnut husk–lignite coal blends. Bioresour. Technol. 200,42–47.

Zhang, S., Dong, Q., Zhang, L., Xiong, Y., 2016. Effects of water washing and torrefactionon the pyrolysis behavior and kinetics of rice husk through TGA and Py-GC/MS.Bioresour. Technol. 199, 352–361.

Zhang, X., Xu, M., Sun, R., Sun, L., 2006. Study on biomass pyrolysis kinetics. J. Eng. GasTurbines Power 128, 493–496.

M.S. Ahmad et al. Bioresource Technology 245 (2017) 491–501

501

Seediscussions,stats,andauthorprofilesforthispublicationat:https://www.researchgate.net/publication/322465165

BioenergypotentialofWolffiaarrhizaappraisedthroughpyrolysis,kinetics,thermodynamicsparametersandTG-FTIR....

ArticleinBioresourceTechnology·January2018

DOI:10.1016/j.biortech.2018.01.033

CITATIONS

0

READS

58

9authors,including:

Someoftheauthorsofthispublicationarealsoworkingontheserelatedprojects:

DevelopmentofBioinfo-PortalfortheAnalysisofGenomicandProteomicData.Viewproject

SecondaryinfectionscontrolsinLeukemiacancerpatientsViewproject

MuhammadSajjadAhmad

UniversityofWaterloo

9PUBLICATIONS27CITATIONS

SEEPROFILE

MuhammadAamerMehmood

GovernmentCollegeUniversityFaisalabad

58PUBLICATIONS251CITATIONS

SEEPROFILE

Chen-GuangLiu

ShanghaiJiaoTongUniversity

45PUBLICATIONS337CITATIONS

SEEPROFILE

ChularatSakdaronnarong

MahidolUniversity

33PUBLICATIONS142CITATIONS

SEEPROFILE

AllcontentfollowingthispagewasuploadedbyMuhammadSajjadAhmadon26February2018.

Theuserhasrequestedenhancementofthedownloadedfile.

Contents lists available at ScienceDirect

Bioresource Technology

journal homepage: www.elsevier.com/locate/biortech

Bioenergy potential of Wolffia arrhiza appraised through pyrolysis, kinetics,thermodynamics parameters and TG-FTIR-MS study of the evolved gases

Muhammad Sajjad Ahmadb, Muhammad Aamer Mehmooda,b,⁎, Chen-Guang Liuc,⁎,Abdul Tawabd, Feng-Wu Baic, Chularat Sakdaronnaronge, Jianren Xuc,Sawsan Abdulaziz Rahimuddinf, Munazza Gullf

a College of Bioengineering, Sichuan University of Science and Engineering, Zigong 643000, Chinab Bioenergy Research Center, Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad 38000, Pakistanc State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, ChinadNational Institute for Biotechnology and Genetic Engineering, Faisalabad 38000, Pakistane Department of Chemical Engineering, Mahidol University, Phutthamonthon, Nakhon Pathom 73170 Thailandf Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia

G R A P H I C A L A B S T R A C T

A R T I C L E I N F O

Keywords:Wolffia biomassPyrolysisTG-FTIR-MSBioenergyWaste-to-fuel

A B S T R A C T

This study evaluated the bioenergy potential of Wolffia arrhiza via pyrolysis. The biomass was collected from thepond receiving city wastewater. Oven dried powdered biomass was exposed to thermal degradation at threeheating rates (10, 30 and 50° Cmin−1) using Thermogravimetry–Differential Scanning Calorimetry analyzer inan inert environment. Data obtained were subjected to the isoconversional models of Kissenger-Akahira-Sunose(KSA) and Flynn–Wall–Ozawa (FWO) to elucidate the reaction chemistry. Kinetic parameters including, Ea(136–172 kJmol−1) and Gibb’s free energy (171 kJmol−1) showed the remarkable bioenergy potential of thebiomass. The average enthalpies indicated that the product formation is favored during pyrolysis. Advancedcoupled TG-FTIR-MS analyses showed the evolved gases to contain the compounds containing C]O functionalgroups (aldehydes, ketones), aromatic and aliphatic hydrocarbons as major pyrolytic products. This low-costabundant biomass may be used to produce energy and chemicals in a cost-efficient and environmentally friendlyway.

https://doi.org/10.1016/j.biortech.2018.01.033Received 14 December 2017; Received in revised form 31 December 2017; Accepted 5 January 2018

⁎ Corresponding authors at: College of Bioengineering, Sichuan University of Science and Engineering, Zigong 643000, China (M.A. Mehmood).E-mail addresses: draamer@gcuf.edu.pk (M.A. Mehmood), cg.liu@sjtu.edu.cn (C.-G. Liu).

Bioresource Technology 253 (2018) 297–303

Available online 13 January 20180960-8524/ © 2018 Elsevier Ltd. All rights reserved.

T

1. Introduction

The finite reserves of fossil fuels and their associated environ-mental issues have triggered the researchers to find alternativesources of energy to meet increasing global requirements of energydemands of transport and industry. It is estimated that fossil re-serves will not be able to fulfill the oil and gas requirements afternext 48 to 64 years (Fernandez-Lopez et al., 2016). The Sun is theultimate source of energy providing 120,000 TW of energy to Earth,which is more than enough when compared to the global energyrequirement i.e. 13 TW. Solar energy can be harnessed in two ways,either by storing into photovoltaic cells or photosynthetic fixationinto plant biomass which can be later converted into fuels (Liaoet al., 2016). In principle, both these approaches are sustainableand cleaner but the storage of electricity produced via solar panelsis a big challenge because one kg of biomass can store 27–42 MJ ofenergy, when compared to lithium-ion batteries which can storeonly 1–2 MJ kg−1 of energy (Liao et al., 2016). Although biomass-based fuel production through biological fermentation has severalchallenges including difficult hydrolysis yet, owing to its abundanceand renewable nature, it is the only foreseeable source of energy,chemicals, and liquid fuels in future (Mehmood et al., 2017a).

Among plant biomass, water born plants are a potential feedstockfor biofuel production, because they do not compete with food crops,have higher photosynthetic efficiency and are capable to producehigher oil content when compared to terrestrial crops. Moreover, theyare proven to be helpful in the cleaning of wastewater and preventeutrophication in lakes, water streams and rivers (Duan et al., 2013;Gill et al., 2016). Besides other aquatic plants, duckweed is a tiny, ra-pidly growing plant which is often found floating on the pond surface. Itcan extract undesirable minerals like nitrogen, phosphorus, aluminum,potassium and other heavy metals from polluted water. Furthermore,duckweed has higher growth rates when compared to other plants, ableto double its biomass within two days. Hence, it can be utilized as apotential feedstock for bioenergy production. Interestingly, duckweedscontain valuable nutrients and its biomass can be used for soil re-clamation. Moreover, it has higher production rates and greater pho-tosynthetic activity (Duan et al., 2013). Sun-dried aquatic biomass mayalso be subjected to thermal conversion to suitable compounds in-cluding bio-oils and chemicals.

Pyrolysis is a thermal decomposition process which is usually per-formed in an inert atmosphere to convert the biomass either into high-value industrial products or to obtain energy. This process dependsupon several factors namely temperature, rate of heating, providedpressure, residence time, moisture content of the biomass, particle sizeof the sample, and its composition (Slopiecka et al., 2012). Though anybiomass can be subjected to pyrolysis, however, it is necessary tocomprehend the pyrolytic behavior to harness the full potential throughpyrolysis. Previously, pyrolytic properties of aquatic biomass namelyPotamogeton crispus and Sargassum thunbergii (Li et al., 2012) have beenstudied. These biomasses were shown to have different pyrolysis be-havior which may be attributed to the difference in their biochemicalcompositions. Similarly, Wolffia arrhiza is a circular floating weed,often found in wastewater. Many of Wolffia species are helpful in waterpurification, able to remove heavy metals from effluent (Suppadit,2011), producing nutrients and protein-rich biomass which can be usedto produce various industrial products. Moreover, Wolffia species ac-cumulates the higher amount of starch which makes this weed suitableto produce a variety of biofuels and it can be easily recovered from itsmedium due to small plant size, thus require less input energy ascompared to other aquatic plants which are heavier than Wolffia. Thepresent work was focused on understanding the pyrolysis behavior ofW. arrhiza biomass. The pyrolysis, kinetic, thermodynamic parametersand TG-FTIR-MS study of the evolved gases of pyrolyzed W. arrhizahave demonstrated that it has remarkable bioenergy potential.

2. Materials and methods

2.1. Collection of W. arrhiza biomass and proximate analyses

Biomass of W. arrhiza (will be referred as Wolffia from now on) wascollected fromwastewater pond, accumulating municipal waste, mixed withindustrial wastes, mainly coming from textile industry. The biomass waswashed with tap water to make samples clean. Biomass samples were driedunder direct heat of the sun for 3 days. Furthermore, samples were placed inan oven at 110 °C to release further moisture contents and then particle sizeof 125 µm was obtained through pulverization in plant disintegrator bypassing through the mesh. The physicochemical analysis was done to de-termine the percentage of moisture, ash and volatile matter through stan-dard protocols of ASTM E1755-01 (2007), ASTM E871-82 (2006) andASTM E872-82 (2006) respectively. However, the percentage of fixedcarbon (FC) was obtained by the following formula FC(%)=100− (moisture+ash+volatiles). Moreover, samples were pre-weighed to put in an oven for 50h at 105 °C to estimate the total solid andmoisture contents in the sample. Percentage of fixed carbon contents andvolatile matter were calculated by placing samples in pre-weighed cruciblesin an oven, at 600 °C for 3–4 h, to calculate the difference between beforeand after heating. All experiments were conducted in triplicate and usedaverage values to ensure precision.

2.2. Calculation of high heating value and determining elementalcomposition

The elemental composition of C (carbon), H (hydrogen), N (ni-trogen), S (Sulphur) and O (oxygen) were obtained by using Ar (argon)as a carrier gas in the elemental analyzer (Vario EL Cube, Germany). Allelemental composition was estimated on total dry mass basis.Furthermore, HHV (high heating values) plays an important role toestimate the amount of heat (energy) released from the sample inburning process. However, as an alternative of experimental methods, afew correlation methods have been established for calculating HHV ofthe sample by using pre-calculated proximate values of the sample.Here, the most reliable correlation model established to date was em-ployed to estimate the HHV of biomass (Nhuchhen and Salam, 2012) ofthe Wolffia biomass.

2.3. TGA-DSC experiment

Generally, pyrolysis of biomass can be pictured as;→ + +Biomass Liquids Gases Char , however, different pyrolysis reac-

tion conditions are used for different biomasses because of variations inthe chemical composition of compounds present in a specific biomass.In the present study, the reaction chemistry of biomass was elucidatedusing kinetic analyses using the data obtained from coupledThermogravimetric-Differential Scanning Calorimetry (TG-DSC) ana-lyses. The sample was placed in aluminum crucible (almost 10mg),three heating rates (10, 30 and 50 °Cmin−1) were established to studythe reaction inside the TG-DSC instrument by observing the mass loss.All experiments were carried out under an inert environment bymaintaining a constant flow rate of Nitrogen (100mLmin−1), in thereaction chamber of the equipment (STA-409, NETZSCH-GerätebauGmbH, Germany).

2.4. Kinetics and thermodynamic parameters calculation

To analyze the TGA-DSC data for establishing the kinetic parametersand to develop mathematical models several methods have been used.In the present study, isoconversional models of KAS (Kissenger-Akahira-Sunose) and FWO (Flynn-Wall-Ozawa) were employed for data analyses(Akahira and Sunose, 1969; Flynn and Wall, 1966; Ozawa, 1965) fromthe pyrolysis of Wolffia.

Conversion rate in pyrolysis reaction was calculated as;

M.S. Ahmad et al. Bioresource Technology 253 (2018) 297–303

298

= − − ∞α (m m )/(m m )o t o (1)

where mo refers to initial mass, mt is mass at any point of time underobservation, and m∞ refers to the final residual mass.

The decomposition rate of the biomass could be written as;

=dα kf αdt

( ) (2)

where f(α) is the reaction model, and k is the constant. Using Arrheniustemperature dependence of k, Eq. (2) could be written as

= ⎛⎝

− ⎞⎠

dαdt

A exp ERT

f α( )(3)

where E=activation energy (kJmol−1), A=pre-exponential factor(s−1), R=universal gas constant and T=absolute temperature (K).

By introducing the heating rate, β, and the conversion function,= −f α α( ) (1 ) Eq. (3) was written as;

= ⎛⎝

− ⎞⎠

−dαdT

Aβ

exp ERT

α(1 )(4)

Now, integrated the α=0, at =T T0 as initial conditions in Eq. (4)and after mathematical manipulations, gives

∫= − = − ⎛⎝

− ⎞⎠

G α dα α ART βE RT E exp ERT

( ) /(1 ) / [1 2 / ]α

0

2

(5)

After rearranging Eq. (5), RT E2 / is approximately equal to unitythat can be ignored (Coats and Redfern, 1964), so Eq. (5) becomes

= −G α ART βE exp E RT( ) ( / ) ( / )2 (6)

Following two Kinetic methods were used;

2.4.1. KAS methodKissenger-Akahira-Sunose model was applied to Eq. (6), after re-

arrangement and taking logarithm of both sides of equation, mathe-matical expression became;

⎛⎝

⎞⎠

= −β

Tln AR EG α E RTln ( / ( )) /2 (7)

The left side of the Eq. (7) was put on the y-axis and right-side (1/T)put on the x-axis to plot the graph.

2.4.2. FWO methodFWO method introduced Doyle’s approximation (Doyle, 1961). By

substituting Doyle’s approximation equation and some mathematicalapproximation Eq. (5) became;

⎜ ⎟= ⎛⎝

⎞⎠

−β ln AERG α

ERT

ln( )( ) (8)

The left side of Eq. (8) was plotted against the inverse of pyrolysistemperature to calculate kinetic parameters for any selected α value.Value of conversion rate (α) was used to calculate the value of −A s( )1 atconversion rate (α). In the plot of βln( ) and ( )ln β

T2 on Y-axis and 1/T onX-axis gave straight lines, that were used to find out the Ea for theprogressing values of conversion. Moreover, the thermodynamic para-meters namely activation energy (E), enthalpies (ΔH), Gibb’s free en-ergy (ΔG), and entropies (ΔS) were calculated using following standardequations.

=A β Eexp E RT RT[ . ( / )]/( )m m2

= −H E RTΔ

= +G E RT K T hAΔ ln( / )α m B m

= −S H G TΔ Δ Δ / m

where:

KB =Boltzmann Constant ( × − J K1.381 10 /23 )h =Plank’s Constant × − Js(6.626 10 )34

Tm =Temperature in, K

2.5. Coupled TG-FTIR-MS analysis

2.5.1. FTIR analysisChemical groups and real-time compounds were detected through

coupled TG-FTIR-GCMS analyzer using 5mg biomass. This biomasssample was treated in thermogravimetric analyzer connected with theFTIR-GCMS (PerkinElmer, Model: TGIRGCMS∗/TGA8000∗). The aimwas to identify the mass losses, functional groups and other kineticparameters of sample components of thermally treated biomass (Luoet al., 2017). The range of spectral resolution was selected from 400 to4000 Wavenumber (cm−1), while the data acquisition frequency wasset at 8 s. The temperature of the thermogravimetric analyzer wasramped from 50 °C to 800 °C before FTIR-MS analysis.

2.5.2. GC–MS analysisThe volatile components passed through FTIR, were instantly ana-

lyzed using coupled GC–MS. The analysis was conducted at 70 eV usingpositive electron impact (EI) mode. The injector temperature was150 °C. The thermal programming of the oven was set at an initial 50 °Ctemperature for 3min, followed by a smooth ramping at 10 °Cmin−1 to280 °C. The final holding time at 280 °C was 5min. The temperature ofboth ion source and transfer line was 50 °C. The separation was con-ducted using 30m×250 µm TR-5MS column. To identify the volatilecompounds, the mass spectra, obtained through GC–MS were blastedusing NIST library.

3. Results and discussion

3.1. Physiochemical properties

The elemental composition of the Wolffia biomass showed the35.55% C, 6.36% H, 5.25% N, 1.16% S and 35.87% O based on totaldried mass. The higher amounts of Nitrogen and Sulphur indicate thehigher amount of protein content and removal of more nutrients fromthe wastewater. Proximate analyses of Wolffia have shown that itsbiomass contains moisture content up to 4.76 ± 0.14% which echoesits appropriateness for pyrolysis, while a suitable range of moisturecontent in biomass is designated as< 10%. The volatile matter ofWolffia was shown to be 72.6% which is higher than water plantPotamogeton crispus (60.03%), a macroalgae Ulva pertusa (59.3%) andlower than maize straw (78.0%), Para grass (79%) and Miscanthus gi-gantus 78.8% (Ahmad et al., 2017; Jeguirim et al., 2010; Li et al., 2012;Ye et al., 2010). Moreover, the biomass showed 10.4% of ash content.Calculated HHV value of Wolffia was shown to be as 17.77MJ kg−1

which is comparable when compared with renowned bioenergy cropsincluding Giant reed, Miscanthus, Ulva pertusa, Para grass, and Maizestraw (Jeguirim et al., 2010; Ye et al., 2010; Ahmad et al., 2017).

3.2. Thermal degradation pattern

Pyrolysis experiments were executed at three heating rates, becausedifferent heating rates may be useful to obtain different products fromthe pyrolysis (Ceranic et al., 2016). Thermal behavior of Wolffia hasshown in DTG and TG curves (Fig. 1A) which indicate physiochemicalchanges, taking place during thermal conversion of its biomass intovarious products (Ceylan and Kazan, 2015; Maia and de Morais, 2016).TG curves have shown the characteristic advent of biomass degradationlikewise the DTG curves produced for the pyrolysis of other lig-nocellulosic biomass such as Tecktona grandis, olive mill waste, datepalm waste and non-woody lignocellulose (Balogun et al., 2014;Benavente and Fullana, 2015; Bousdira et al., 2017; Demirbas, 2017).

Mass loss pattern of Wolffia could be divided into three stages

M.S. Ahmad et al. Bioresource Technology 253 (2018) 297–303

299

(Tables 1 and 2) and a long tail. The first stage occurred when tem-perature increased from ambient to T1 (155–170 °C) where a 7.70–10%loss in mass was observed. This stage indicates the loss of cellular andsurface adsorbed water. Whereas, biomass with retained moisture up to10% is considered as valuable for combustion (Braga et al., 2014). Thesecond stage may be referred as devolatilization stage, where most ofthe thermal transformation happened. It ranged from T1 (170 °C) to T5(569 °C). A variety of volatiles would have been released during thisstage, resulting in a drastic mass loss, degradation of cellulose andhemicellulose, along with the formation of the major pyrolytic pro-ducts. This stage could be further subdivided into two degradationzones which could be ascribed to the existence of different stable

thermal components in the Wolffia biomass (Li et al., 2012). The thirdstage is mainly associated with the degradation of lignin componentand formation of char. Temperature ranged along with tail from 570 to800 °C showed approximately 12% mass loss which mainly correspondsto the lignin decomposition. Here lignin mainly contributed towardsbiochar production as a higher content of lignin in biomass, resulting inhigh biochar production and higher thermal stability (Bousdira et al.,2017; Braga et al., 2014). Final residues ranged from 33.1 to 35.9% at1000 °C which indicated the considerably higher amount of char pro-duced, which imitates the fitness of Wolffia for char production.

3.3. Measurement of heat flow to-and-from the Wolffia biomass

The flow of heat to-and-from the Wolffia biomass (mW mg−1) wasshown to increase with the increment in the reaction temperaturewhich can be clearly seen as the curves attained through DSC (Fig. 1B).An increasing heat flow at initial stages followed by a decreasing heatflow indicate the range of associated temperatures at different heatingrates. First small peaks of endothermic and exothermic reactions wereobserved at a temperature below than 200 °C. With the increase intemperature exothermic effect increased. Exothermic effect extendedup to 700 °C, 750 °C, and 780 °C, for the heating rates of 10, 30 and50 °Cmin−1 respectively. These observations are in good agreementwith the heat flow curves previously constructed for the S. thunbergii, P.crispus and Para grass (Ahmad et al., 2017; Li et al., 2012). However,with the further increase in temperature caused a gradual shift in thecurves towards the x-axis indicating the that either the reaction wasstopped due to depletion of reactants or the reaction followed differentmechanism owing to the changing composition of the residual biomass.

3.4. Kinetics and thermodynamic variables

Determining kinetic parameters is critically important to understand andoptimize the process of thermal decomposition of biomass into desiredproducts. So, keeping in view their importance kinetic parameters in-cluding, pre-exponential factors and activation energy were calculated fromthe slopes obtained when conversion points were plotted against the inverseof pyrolysis temperature (Fig. 2). The corresponding Ea (activation energies)and preexponential factors (s−1) at each point were calculated. The Ea va-lues (Table 3) showed fluctuations related to conversion points; it revealedthe complex nature of samples or reactions occur during the pyrolysisprocess. Moreover, Ea values were shown to be decreasing with the in-creasing conversion rates (Fig. 3A). However, the conversion rate (α) dis-played a direct relationship with the reaction temperature (Fig. 3B). Theaverage Ea values of Wolffia were revealed to be ranging from 168.35 to170.37 kJmol−1 which were lower than Elephant grass (218–227 kJmol−1),rice husk (221–229 kJmol−1), Laminaria japonica (173.2–225.7 kJmol−1),Tobacco waste (118–257 kJmol−1), agricultural residues(220–221 kJmol−1), Phragmites australis (291 kJmol−1), Sargassum pallidum(203.5 kJmol−1) (Braga et al., 2014; Li et al., 2010; Wu et al., 2015; Wanget al., 2016; Du et al., 2012; Li et al., 2010) and higher than that of Ulvapertusa (152–147kJmol−1), Camel grass (168–169 kJmol−1), SewageSludge and coffee ground mixture (166–168 kJmol−1), Pepper waste(29–147kJmol−1), Cattle manure (122–124 kJmol−1), Pine(122–169 kJmol−1), and Dunaliella tertiolecta (145.7 kJmol−1), (Ye et al.,2010; Mehmood et al., 2017b; Chen et al., 2017a,b; Maia and de Morais,2016; Wilk et al., 2017; Shuping et al., 2010). These values indicated thatfeasibility of biomass for co-pyrolysis with several other biomass sources.

Enthalpy of reaction is characterized by the amount of heat exchangedbetween the reagent and activated complex during the thermal process (Xuand Chen, 2013). When the values of enthalpies (ΔH) of Wolffia biomasswere compared with values of Eα, a slight difference (∼5 kJmol−1) wasfound at each conversion point (α) which exhibited that the product for-mation is being favored due to lower potential energy (Vlaev et al., 2007).The preexponential values (A) for Wolffia ranged from1.14×1010–4.46×1014 s−1 to 1.04×1010–9.59×1013 s−1, estimated

0

20

40

60

80

100

120

0 200 400 600 800 1000-16

-14

-12

-10

-8

-6

-4

-2

0

Temperature (°C)

Mas

s Los

s (%

)

DT

G (%

min

-1)

10K

30K

50K

Heating Rate (°C min-1)

-2

0

2

4

6

8

10

12

0 200 400 600 800 1000

Hea

t Flo

w D

SC (m

W m

g-1)

Temperature (°C)

10K

30K

50K

Heating Rate (°C min-1)

(A)

(B)

Fig. 1. TG-DTG (A) and DSC (B) curves indicating the percent mass losses in response tothree heating rates and heat flow to-and-from the biomass, respectively.

Table 1Characteristics temperatures associated with the pyrolysis of Wolffia biomass.

Heating rate (°Cmin−1) Temperature (°C)

T1 T2 T3 T4 T5

10 155 271 294 321 55130 159 272 306 336 56050 170 282 309 344 569

Table 2Gradual mass losses during thermal degradation stages.

Stages Heating rate (°C min−1)

10 30 50

Stage-I, WL (%) 7.77 8.18 10.9Stage-II, WL (%) Zone-I 16.88 15.76 15.06

Zone-II 27.13 28.54 28.92Stage-III, WL (%) 12.32 11.81 12.11Final residues at 1000 °C (%) 35.90 35.71 33.01

M.S. Ahmad et al. Bioresource Technology 253 (2018) 297–303

300

by KAS and FWO methods, respectively (Table 3). While, the(A) < 109 s−1 mainly shows the surface reaction whereas (A)≥109 s−1

shows a simpler complex (Turmanova et al., 2008). Furthermore, A-valuesof Wolffia were close to A-values calculated for rice straw (1.70×1007 s−1

to 9.35×1012 s−1), rice bran (1.00×1007 s−1 and 1.58×1010 s−1), Paragrass (1.42×1007 –2.26×1019 s−1) and Camel grass(2.25×1006–5.69×1014 s−1) (Ahmad et al., 2017; Biney et al., 2015;Mehmood et al., 2017b).

Changes of entropies (ΔS) for Wolffia had both negative and positivevalues as well. The minimum negative value of ΔS was −67.37 Jmol−1

respectively while maximum positive value was 24.74 Jmol−1.Negative entropies specify more arrangement in the activated complexwhen compared with biomass while positive values show otherwise (Xuand Chen, 2013). The co-occurrence of both in any pyrolysis reactionproposes that this particular thermal conversion would have been re-latively complex. The Gibb’s free (ΔG) energy exhibits the internalenergy of the system (biomass in this case) displayed during the reac-tion progress. The ΔG values for the pyrolysis of Wolffia were rangedfrom 170 to 172 kJmol−1 which were higher when compared to redpepper waste (139.4 kJmol−1) and rice straw (164.59 kJmol−1) (Maiaand de Morais, 2016; Xu and Chen, 2013). Overall reaction chemistrydepicted that Wolffia biomass consumes lesser amount of external heatinput and improves the stability of the overall process, hence itsthermal conversion would be an energy efficient process.

3.5. TG-FTIR-GCMS analysis

The characteristic absorptions for most of the identified compoundswere ranging from 700 to 1200 cm−1, 1500 to 1900 cm−1 followed by2200 to 2400 cm−1 and 3000 to 3800 cm−1, based on their specific

-12

-11.5

-11

-10.5

-10

-9.5

-9

-8.5

-8

0.0009 0.0014 0.0019 0.0024 0.0029

ln(

/T2 )

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

2

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

0.0011 0.0016 0.0021 0.0026

ln(

)

Inverse of pyrolysis temperature (K-1)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Fig. 2. Linear fit plots to calculate the activation energy of Wolffia: Where, In (β/T2)(KAS Method), and In(β) (FWO method) were plotted against inverse of pyrolysis tem-perature (K−1).

Table 3Kinetics and thermodynamic parameters for the pyrolysis of Wolffia biomass.

α EakJmol−1

R2 ΔHkJmol−1

As−1

ΔGkJmol−1

ΔSJmol−1

KAS method0.1 136.53 0.99 131.47 1.14× 1010 172.06 −66.650.2 166.62 0.99 161.56 5.29× 1012 171.05 −15.590.3 157.47 0.99 152.40 8.20× 1011 171.34 −31.090.4 175.48 0.99 170.41 3.20× 1013 170.79 −0.620.5 157.48 0.98 152.41 8.21× 1011 171.34 −31.080.6 179.44 0.99 174.38 7.17× 1013 170.68 6.080.7 188.45 0.98 183.38 4.46× 1014 170.43 21.270.8 181.50 0.98 176.44 1.09× 1014 170.62 9.560.9 172.21 0.99 167.14 1.65× 1013 170.89 −6.14Avg. 168.35 – 163.29 – – –

FWO method0.1 136.11 0.99 131.05 1.04× 1010 172.08 -67.370.2 166.56 0.99 161.50 5.23× 1012 171.05 −15.690.3 158.46 0.99 153.39 1.00× 1012 171.31 −29.420.4 176.05 0.99 170.98 3.60× 1013 170.77 0.350.5 159.36 0.98 154.30 1.21× 1012 171.28 −27.880.6 180.88 0.99 175.81 9.59× 1013 170.64 8.500.7 190.50 0.99 185.44 6.76× 1014 170.37 24.740.8 185.56 0.99 180.49 2.48× 1014 170.51 16.400.9 179.87 0.99 174.81 7.82× 1013 170.67 6.81Avg. 170.37 – 165.31 – – –

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

350 550 750 950 1150

Cov

ersi

on (

)

Pyrolysis temperature (K)

10K

30K

50K

Heating Rate (oC/min)

(A)

120

130

140

150

160

170

180

190

200

3 5 0 5 5 0 7 5 0 9 5 0 1 1 5 0

Act

ivat

ion

ener

gy (k

J m

ol-1

)

Pyrolysis temperature (K)

0.10.20.30.40.50.60.70.80.9

(B)

Fig. 3. Relationship of activation energy with pyrolysis temperature (A), and relationshipbetween the conversion (a) and the reaction temperature (B).

M.S. Ahmad et al. Bioresource Technology 253 (2018) 297–303

301

kinetic energies associated with their structural and functional groupsmovements. The strong stretching at 600–800 cm−1, shows halides(CeCl). The bending at 675 cm−1 is for ]CeH of an alkene, also ap-peared in stretching at 3000–3100 cm−1 while 805–835 cm−1 is thetypical replication of aromatic compounds. The existence of 1031 cm−1

shows the deformation of cellulosic moieties while the presence ofAldehydes or Ketones at 1550 to 1600 cm−1 and 1600–1628 cm−1,1700–1750 cm−1 indicate the C]O stretching vibrational energiesalong with carboxylic carbonyl groups, also appearing at 2250 to2300 cm−1. The stretching at 3000 cm−1 identifies the aromatic CeH.Similarly, the free stretching, at 3500–3700 CM−1 is noticeable for theOeH functional activity along with amides. The mononuclear hydro-carbons were noticed at 1400–1500 cm−1 skeletal vibration, togetherwith out-of-plane CeH bending vibrations at 670 and 910 cm−1.

Through GC–MS around 27 compounds and their thermally de-composed components obtained from biomass were detected (Table 4).The GC–MS results show the presence of many of the components ob-tained from the Wolffia biomass having high energy toluene and ben-zene ring containing products e.g 3,4,5-Trimethoxybenzylamine as wellas components with functional groups like acetylene e.g Acetamide, N-methyl-N-[4-[2-acetoxymethyl−1-pyrrolidyl]-2-butynyl]- etc. The ca-lorific (Higher Heating) values of toluene, benzene, and acetylene are inthe order of 40.6, 41.8 and 49.9MJ Kg−1 comparable to diesel andgasoline with 44.8 and 47.3MJ Kg−1 respectively. This can be theore-tically extrapolated to have efficient energy-yielding capacity in termsof usage as fuel. Hence, it has been demonstrated that the W. arrhizabiomass can be used to produce bioenergy and chemicals via pyrolysiswithout any direct competition with the food, fodder or arable soil, in acost and environmentally efficient manner.

4. Conclusions

W. arrhiza is adapted to wastewater, offering a freely accessiblebiomass for bioenergy with concomitant nutrient-removal. Its pyrolysiscomprised of three stages. The stage-1 depicted the evaporation of re-tained moisture. Whereas, the stage-2 showed drastic mass loss

associated with the degradation of carbohydrates indicating that tem-perature of 271–600 °C may be used for its thermal conversion. Whilethe stage-3 indicated lignin degradation and charring. The TG-FTIR-MSanalyses verified the production of energy and industrially valuablechemicals. Its thermodynamic properties indicated a promising bioe-nergy potential when compared to terrestrial plants making it a po-tential feedstock in future energy production scenario.

Acknowledgements

Authors are obliged to Higher Education Commision (HEC)Pakistan, International Foundation of Science Sweden and NationalNatural Science Foundation of China (Grant Numbers: 51561145014,21536006, 21406030) for their financial support.

References

Ahmad, M.S., Mehmood, M.A., Al Ayed, O.S., Ye, G., Luo, H., Ibrahim, M., Rashid, U.,Nehdi, I.A., Qadir, G., 2017. Kinetic analyses and pyrolytic behavior of Para grass(Urochloa mutica) for its bioenergy potential. Bioresour. Technol. 224, 708–713.

Akahira, T., Sunose, T. 1969. Transactions of Joint Convention of Four ElectricalInstitutes, 246.

Balogun, A.O., Lasode, O.A., McDonald, A.G., 2014. Devolatilisation kinetics and pyr-olytic analyses of Tectona grandis (teak). Bioresour. Technol. 156, 57–62.

Benavente, V., Fullana, A., 2015. Torrefaction of olive mill waste. Biomass Bioenergy 73,186–194.

Biney, P.O., Gyamerah, M., Shen, J., Menezes, B., 2015. Kinetics of the pyrolysis of ar-undo, sawdust, corn stover and switch grass biomass by thermogravimetric analysisusing a multi-stage model. Bioresour. Technol. 179, 113–122.

Bousdira, K., Bousdira, D., Bekkouche, S.M.E.A., Nouri, L.H., Legrand, J., 2017. Kineticpyrolysis study and classification of date palm biomass. J. Renew. Sustain. Energy 9,013102.

Braga, R.M., Melo, D.M., Aquino, F.M., Freitas, J.C., Melo, M.A., Barros, J.M., Fontes,M.S., 2014. Characterization and comparative study of pyrolysis kinetics of the ricehusk and the elephant grass. J. Therm. Anal. Calorim. 115, 1915–1920.

Ceranic, M., Kosanic, T., Djuranovic, D., Kaludjerovic, Z., Djuric, S., Gojkovic, P.,Bozickovic, R., 2016. Experimental investigation of corn cob pyrolysis. J. Renew.Sustain. Energy 8, 063102.

Ceylan, S., Kazan, D., 2015. Pyrolysis kinetics and thermal characteristics of microalgaeNannochloropsis oculata and Tetraselmis sp. Bioresour. Technol. 187, 1–5.

Chen, G., He, S., Cheng, Z., Guan, Y., Yan, B., Ma, W., Leung, D.Y.C., 2017a. Comparisonof kinetic analysis methods in thermal decomposition of cattle manure by

Table 4List of compounds released upon the pyrolysis of Wolffia biomass.

Sr. No Compound Formula Molar mass CAS No.

1 Toluene C7H8 92 108-88-32 L-cystine C6H12N2O4S2 240 534-22-53 Methyl phenyl sulfide, 4-acetamido-S-[(p-toluenesulfonyl)imino]- C16H18N2O3S2 350 28,075-40-34 2,3,4-Trimethoxy-6-nitrobenzoic acid C10H11NO7 257 61,948-84-35 Gentamicin A C18H36N4O10 469 13,291-74-26 12-Hydroxy-14-methyl-oxa-cyclotetradec-6-en-2-one C14H24O3 240 77,761-61-67 3,4,5-Trimethoxybenzylamine C10H15NO3 197 18,638-99-88 Boranamine, 1-chloro-1-ethyl-N-methyl-N-phenyl- C9H13BClN 181 55,702-64-29 1-Diphenylsilyloxyoctane C20H28OSi 313 99,221-40-610 Cephaloridine C19H17N3O4S2 415 50-59-911 4-Chlordehydromethyltestosterone C20H27ClO2 335 2446-23-312 Cyclobutene, 2-propenylidene- C7H8 92 52,097-85-513 3-Diphenyl(methyl)silyloxypropene C16H18OSi 254 17,933-45-814 Neblininediol 21-acetate C25H32N2O5 441 54,658-05-815 Acetamide, N-methyl-N-[4-(3-hydroxypyrrolidinyl)-2-butynyl]- C11H18N 210 130,403-61-116 Glycine, N-(N-glycyl-L-leucyl)- C10H19N3O4 245 2576-67-217 18-Benzyl-1,4,10,13-tetraoxa-7,16-diazacyclooctadecane-2,6,17-trione C15H24O2 236 1,005,284-62-718 2,2-Dimethyl-4-(2-methyl-1-propenyl)-5-oxo-2,5-dihydro-3-furancarboxylic acid C11H14O4 210 96,484-50-319 Spiculesporic acid C17H28O6 328 469-77-220 Acetamide, N-methyl-N-[4-[2-acetoxymethyl-1-pyrrolidyl]-2-butynyl]- C14H22N2O3 266 132,377-28-721 1,1-Dichloro-2-methyl-3-(4,4-diformyl-1,3-butadien-1-yl)cyclopropane C10H10Cl2O2 233 132,607-16-022 2-(2-Carboxyethyl)-6,6-dimethyl-3-oxocyclohex-1-enecarboxylic acid C12H16O5 240 57,304-91-323 (1E,4E)-2,2,5-Trimethyl-5-cycloheptene-1,4-dione dioxime C10H16N2O2 196 58,870-13-624 2-Trimethylstannyl-5-trichloromethyl-6-chlorobicyclo[2.2.1]heptane C11H18Cl4Sn 411 74,229-37-125 4-(3,3-Dimethyl-but-1-ynyl)-4-hydroxy-2,6,6-trimethylcyclohex-2-enone C15H22O2 234 930,090-10-126 (3aS,6abeta,9bbeta)-3abeta,4,5,6,6a,7,8,9,9a,9b-Decahydro-7beta,9beta-dihydroxy-6alpha,9aalpha-dimethyl-3-

methyleneazuleno[4,5-b]furan-2(3H)-C15H22O4 266 20,555-03-7

27 4-O-[(2S)-3alpha-(Acetylamino)-6-(aminomethyl)-3,4-dihydro-2H-pyran-2alpha-yl]-6-O-[3-deoxy-4-C-methyl-3-(methylamino)-beta-L-arabinopyranosyl]-2-deoxy-D-streptamine

C21H39N5O8 490 55,649-82-6

M.S. Ahmad et al. Bioresource Technology 253 (2018) 297–303

302

thermogravimetric analysis. Bioresour. Technol. 243, 69–77.Chen, J., Liu, J., He, Y., Huang, L., Sun, S., Sun, J., Chang, K., Kuo, J., Huang, S., Ning, X.,

2017b. Investigation of co-combustion characteristics of sewage sludge and coffeegrounds mixtures using thermogravimetric analysis coupled to artificial neural net-works modeling. Bioresour. Technol. 225, 234–245.

Coats, A., Redfern, J., 1964. Kinetic parameters from thermogravimetric data. Nature201, 68–69.

Demirbas, A., 2017. Higher heating values of lignin types from wood and non-woodlignocellulosic biomasses. Energy Sour. A Recov. Util. Environ. Eff. 39, 592–598.

Doyle, C., 1961. Kinetic analysis of thermogravimetric data. J. Appl. Polym. Sci. 5,285–292.

Du, J., Yuan, Y., Si, T., Lian, J., Zhao, H., 2012. Customized optimization of metabolicpathways by combinatorial transcriptional engineering. Nucl. Acids Res. 40 e142e142.

Duan, P., Chang, Z., Xu, Y., Bai, X., Wang, F., Zhang, L., 2013. Hydrothermal processing ofduckweed: effect of reaction conditions on product distribution and composition.Bioresour. Technol. 135, 710–719.

Fernandez-Lopez, M., Pedrosa-Castro, G., Valverde, J., Sanchez-Silva, L., 2016. Kineticanalysis of manure pyrolysis and combustion processes. Waste Manage 58, 230–240.

Flynn, J.H., Wall, L.A., 1966. A quick, direct method for the determination of activationenergy from thermogravimetric data. J. Poly. Sci. B: Poly. Lett. 4, 323–328.

Gill, S.S., Mehmood, M.A., Ahmad, N., Ibrahim, M., Rashid, U., Ali, S., Nehdi, I.A., 2016.Strain selection, growth productivity and biomass characterization of novel micro-algae isolated from fresh and wastewaters of upper Punjab, Pakistan. Front. Life Sci.9, 190–200.

Jeguirim, M., Dorge, S., Trouve, G., 2010. Thermogravimetric analysis and emissioncharacteristics of two energy crops in air atmosphere: Arundo donax and Miscanthusgiganthus. Bioresour. Technol. 101, 788–793.

Li, D., Chen, L., Chen, S., Zhang, X., Chen, F., Ye, N., 2012. Comparative evaluation of thepyrolytic and kinetic characteristics of a macroalga (Sargassum thunbergii) and afreshwater plant (Potamogeton crispus). Fuel 96, 185–191.

Li, D., Chen, L., Yi, X., Zhang, X., Ye, N., 2010. Pyrolytic characteristics and kinetics oftwo brown algae and sodium alginate. Bioresour. Technol. 101, 7131–7136.

Liao, J.C., Mi, L., Pontrelli, S., Luo, S., 2016. Fuelling the future: microbial engineering forthe production of sustainable biofuels. Nat. Rev. Microbiol. 14, 288–304.

Luo, L., Liu, J., Zhang, H., Ma, J., Wang, X., Jiang, X., 2017. TG-MS-FTIR study on pyr-olysis behavior of superfine pulverized coal. J. Anal. Appl. Pyrol. 128, 64–74.

Maia, A.A.D., de Morais, L.C., 2016. Kinetic parameters of red pepper waste as biomass tosolid biofuel. Bioresour. Technol. 204, 157–163.

Mehmood, M.A., Ibrahim, M., Rashid, U., Nawaz, M., Ali, S., Hussain, A., Gull, M., 2017a.Biomass production for bioenergy using marginal lands. Sustain. Prod. Consum. 9,3–21.

Mehmood, M.A., Ye, G., Luo, H., Liu, C., Malik, S., Afzal, I., Xu, J., Ahmad, M.S., 2017b.Pyrolysis and kinetic analyses of Camel grass (Cymbopogon schoenanthus) for bioe-nergy. Bioresour. Technol. 228, 18–24.

Nhuchhen, D.R., Salam, P.A., 2012. Estimation of higher heating value of biomass fromproximate analysis: a new approach. Fuel 99, 55–63.

Ozawa, T., 1965. A new method of analyzing thermogravimetric data. Bull. Chem. Soc.Jpn. 38, 1881–1886.

Shuping, Z., Yulong, W., Mingde, Y., Chun, L., Junmao, T., 2010. Pyrolysis characteristicsand kinetics of the marine microalgae Dunaliella tertiolecta using thermogravimetricanalyzer. Bioresour. Technol. 101, 359–365.

Slopiecka, K., Bartocci, P., Fantozzi, F., 2012. Thermogravimetric analysis and kineticstudy of poplar wood pyrolysis. Appl. Energy 97, 491–497.

Suppadit, T., 2011. Nutrient removal of effluent from quail farm through cultivation ofWolffia arrhiza. Bioresour. Technol. 102, 7388–7392.

Turmanova, S.C., Genieva, S., Dimitrova, A., Vlaev, L., 2008. Non-isothermal degradationkinetics of filled with rise husk ash polypropene composites. Exp. Poly. Lett. 2,133–146.

Vlaev, L., Georgieva, V., Genieva, S., 2007. Products and kinetics of non-isothermal de-composition of vanadium (IV) oxide compounds. J. Therm. Anal. Calorim. 88,805–812.

Wang, X., Hu, M., Hu, W., Chen, Z., Liu, S., Hu, Z., Xiao, B., 2016. Thermogravimetrickinetic study of agricultural residue biomass pyrolysis based on combined kinetics.Bioresour. Technol. 219, 510–520.

Wilk, M., Magdziarz, A., Gajek, M., Zajemska, M., Jayaraman, K., Gokalp, I., 2017.Combustion and kinetic parameters estimation of torrefied pine, acacia andMiscanthus giganteus using experimental and modelling techniques. Bioresour.Technol. 243, 304–314.

Wu, W., Mei, Y., Zhang, L., Liu, R., Cai, J., 2015. Kinetics and reaction chemistry ofpyrolysis and combustion of tobacco waste. Fuel 156, 71–80.

Xu, Y., Chen, B., 2013. Investigation of thermodynamic parameters in the pyrolysisconversion of biomass and manure to biochars using thermogravimetric analysis.Bioresour. Technol. 146, 485–493.

Ye, N., Li, D., Chen, L., Zhang, X., Xu, D., 2010. Comparative studies of the pyrolytic andkinetic characteristics of maize straw and the seaweed Ulva pertusa. PloS One 5,e12641.

M.S. Ahmad et al. Bioresource Technology 253 (2018) 297–303

303

View publication statsView publication stats