acid gas extraction for disposal

Acid Gas Extraction for Disposal

and Related Topics

Scrivener Publishing

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Publishers at Scrivener

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Phillip Carmical ([email protected])

Acid Gas Extraction for

Disposal and Related Topics

Edited by

Ying Wu

Sphere Technology Connection

Calgary, Canada

John J. Carroll

Gas Liquids Engineering

Calgary, Canada

and

Weiyao Zhu

University of Science and Technology Beijing

Beijing, China

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v

Contents

Preface xv

1 Rate-Base Simulations of Absorption Processes; Fata Morgana or Panacea? 1

P.J.G. Huttenhuis and G.F. Versteeg1.1 Introduction 11.2 Procede Process Simulator (PPS) 21.3 Mass Transfer Fundamentals 31.4 CO

2 Capture Case 8

1.5 Conclusions and Recommendations 15References 16

2 Modelling in Acid Gas Removal Processes 17

Alan E. Mather2.1 Introduction 172.2 Vapour-Liquid Equilibria 182.3 Modelling 21

2.3.1 Empirical Models 222.3.2 Activity Coeffi cient Models 222.3.3 Two (and more) Solvent Models 232.3.4 Single Solvent Models 242.3.5 Equation of State Models 24

2.4 Conclusions 25References 26

3 Th ermodynamic Approach of CO2 Capture, Combination of

Experimental Study and Modeling 29

Karine Ballerat-Busserolles, Alexander R. Lowe,

Yohann Coulier, and J.-Y. Coxam3.1 Introduction 303.2 Th ermodynamic Model 31

vi Contents

3.3 Carbon Dioxide Absorption in Aqueous Solutions of Alkanolamines 32

3.4 Conclusion 35References 36

4 Employing Simulation Soft ware for Optimized Carbon Capture Process 39

Wafa Said-Ibrahim, Irina Rumyantseva, and Manya Garg4.1 Introduction 404.2 Acid Gas Cleaning – Process and Business Goals 404.3 Modeling Gas Treating in Aspen HYSYS® 42

4.3.1 Inbuilt Th ermodynamics 434.3.2 Rate-Based Distillation in Aspen HYSYS 44

4.4 Conclusion 45References 46

5 Expectations from Simulation 47

R. Scott Alvis, Nathan A. Hatcher, and Ralph H. Weiland5.1 Introduction 485.2 Realism 48

5.2.1 Conclusion 1 495.2.2 Conclusion 2 505.2.3 Conclusion 3 505.2.4 Conclusion 4 51

5.3 Reliability of Simulation Data: What’s Data and What’s Not 525.3.1 Conclusion 5 545.3.2 Conclusion 6 545.3.3 Conclusion 7 555.3.4 Conclusion 8 55

5.4 Case Studies 565.4.1 Hellenic Petroleum Refi nery Revamp 565.4.2 Treating a Refi nery Fuel Gas 585.4.3 Carbon Dioxide Removal in an LNG Unit 605.4.4 Tail Gas Treating 65

5.5 Concluding Remarks 67References 67

Contents vii

6 Calorimetry in Aqueous Solutions of Demixing Amines for Processes in CO

2 Capture 69

Karine Ballerat-Busserolles, Alexander R. Lowe,

Yohann Coulier, and J.-Y. Coxam6.1 Introduction 706.2 Chemicals 726.3 Liquid-Liquid Phase Equilibrium 736.4 Mixing Enthalpies of {Water-Amine} and

{Water-Amine-CO2} 75

6.4.1 Excess Enthalpies 776.4.2 Enthalpies of Solution 78

6.5 Acknowledgements 79References 79

7 Speciation in Liquid-Liquid Phase-Separating Solutions of Aqueous Amines for Carbon Capture Applications by Raman Spectroscopy 81

O. Fandiño, M. Yacyshyn, J.S. Cox, and P.R. Tremaine7.1 Introduction 817.2 Experimental 84

7.2.1 Materials 847.2.2 Sample Preparation 847.2.3 Raman Spectroscopic Measurements 857.2.4 Methodology Validation 867.2.5 Laser Selection Optimization 86

7.3 Results and Discussion 877.3.1 Ammonium Carbamate System 877.3.2 Methylpiperidine Band Identifi cation 887.3.3 (N-methylpiperidine + Water + CO

2) System 89

7.3.4 (2-methylpiperidine + Water + CO2) System 90

7.3.5 (4-methylpiperidine + Water + CO2) System 91

7.4 Conclusions 917.5 Acknowledgements 92References 93

8 A Simple Model for the Calculation of Electrolyte Mixture Viscosities 95

Marco A. Satyro and Harvey W. Yarranton8.1 Introduction 958.2 Th e Expanded Fluid Viscosity Model 98

viii Contents

8.3 Results and Discussion 998.3.1 EF Model for Salts Neglecting Dissociation 1008.3.2 EF Model for Ionic Species 102


9 Phase Equilibria Investigations of Acid Gas Hydrates: Experiments and Modelling 107

Zachary T. Ward, Robert A. Marriott, and Carolyn A. Koh9.1 Introduction 1079.2 Experimental Methods 1089.3 Results and Discussion 1109.4 Conclusions 1129.5 Acknowledgements 112References 112

10 Th ermophysical Properties, Hydrate and Phase Behaviour Modelling in Acid Gas-Rich Systems 115

Antonin Chapoy, Rod Burgass, Bahman Tohidi,

Martha Hajiw, and Christophe Coquelet10.1 Introduction 11610.2 Experimental Setups and Procedures 117

10.2.1 Saturation and Dew Pressure Measurements and Procedures 117

10.2.2 Hydrate Dissociation Measurements and Procedures 119

10.2.3 Water Content Measurements and Procedures 120 10.2.4 Viscosity and Density Measurements and

Procedures 120 10.2.5 Frost Point Measurements and Procedures 120 10.2.6 Materials 121

10.3 Th ermodynamic and Viscosity Modelling 122 10.3.1 Fluid and Hydrate Phase Equilibria Model 122

10.4 Results and Discussions 12810.5 Conclusions 13610.6 Acknowledgements 136References 136

Contents ix

11 “Self-Preservation” of Methane Hydrate in Pure Water and (Water + Diesel Oil + Surfactant) Dispersed Systems 141

Xinyang Zeng, Changyu Sun, Guangjin Chen,

Fenghe Zhou, and Qidong Ran11.1 Introduction 14211.2 Experiments 142

11.2.1 Material 142 11.2.2 Apparatus 143 11.2.3 Experimental Procedure 146

11.3 Results and Discussion 146 11.3.1 Self-Preservation Eff ect without Surfactant

in Low Water Cut Oil-Water Systems 146 11.3.2 Self-Preservation Eff ect without Surfactant

in High Water Cut Oil-Water Systems 148 11.3.3 Th e Eff ect of Diff erent Surfactants on

Self-Preservation Eff ect in Diff erent Water Cut Oil-Water Systems 149

11.4 Conclusions 15111.5 Acknowledgement 151References 151

12 Th e Development of Integrated Multiphase Flash Systems 153

Carl Landra, Yau-Kun Li, and Marco A. Satyro12.1 Introduction 15412.2 Algorithmic Challenges 15512.3 Physical-Chemical Challenges 15612.4 Why Solids? 15612.5 Equation of State Modifi cations 15712.6 Complex Liquid-Liquid Phase Behaviour 16012.7 Hydrate Calculations 16212.7 Conclusions and Future Work 165References 167

13 Reliable PVT Calculations – Can Cubics Do It? 169

Herbert Loria, Glen Hay, Carl Landra, and

Marco A. Satyro13.1 Introduction 16913.2 Two Parameter Equations of State 17113.3 Two Parameter Cubic Equations of State Using Volume

Translation 17213.4 Th ree Parameter Cubic Equations of State 175

x Contents

13.5 Four Parameter Cubic Equations of State 177 13.6 Conclusions and Recommendations 177 References 180

14 Vapor-Liquid Equilibria Predictions of Carbon Dioxide + Hydrogen Sulfi de Mixtures using the CPA, SRK, PR, SAFT, and PC-SAFT Equations of State 183

M. Naveed Khan, Pramod Warrier, Cor J. Peters,

and Carolyn A. Koh 14.1 Introduction 184 14.2 Results and Discussion 185 14.3 Conclusions 188 14.4 Acknowledgements 188 References 188

15 Capacity Control Considerations for Acid Gas Injection Systems 191

James Maddocks 15.1 Introduction 191 15.2 Requirement for Capacity Control 192 15.3 Acid Gas Injection Systems 196 15.4 Compressor Design Considerations 197 15.5 Capacity Control in Reciprocating AGI Compressors 199 15.6 Capacity Control in Reciprocating Compressor/PD

Pump Combinations 213 15.7 Capacity Control in Reciprocating

Compressor/Centrifugal Pump Combinations 215 15.8 Capacity Control When Using Screw Compressors 215 15.9 Capacity Control When Using Centrifugal

Compression 21815.10 System Stability 21915.11 Summary 220Reference 220

16 Review and Testing of Radial Simulations of Plume Expansion and Confi rmation of Acid Gas Containment Associated with Acid Gas Injection in an Underpressured Clastic Carbonate Reservoir 221

Alberto A. Gutierrez and James C. Hunter 16.1 Introduction 222

Contents xi

16.2 Site Subsurface Geology 223 16.2.1 General Stratigraphy and Structure 224 16.2.2 Geology Observed in AGI #1 and AGI #2 227

16.3 Well Designs, Drilling and Completions 227 16.3.1 AGI #1 228 16.3.2 AGI #2 231

16.4 Reservoir Testing and Modeling 232 16.4.1 AGI #1 233 16.4.2 Linam AGI #2 233 16.4.3 Comparison of Reservoir between Wells 234 16.4.4 Initial Radial Model and Plume Prediction 234 16.4.5 Confi rmation of Plume Migration Model and

Integrity of Caprock 236 16.5 Injection History and AGI #1 Responses 236 16.6 Discussion and Conclusions 238References 241

17 Th ree-Dimensional Reservoir Simulation of Acid Gas Injection in Complex Geology – Process and Practice 243

Liaqat Ali and Russell E. Bentley 17.1 Introduction 244 17.2 Step by Step Approach to a Reservoir Simulation

Study for Acid Gas Injection 245 17.3 Seismic Data and Interpretation 245 17.4 Geological Studies 246 17.5 Petrophysical Studies 246 17.6 Reservoir Engineering Analysis 247 17.7 Static Modeling 247 17.8 Reservoir Simulation 248 17.9 Case History 24917.10 Injection Interval Structure and Modeling 24917.11 Petrophysical Modeling and Development of

Static Model 25017.12 Injection Zone Characterization 25117.13 Reservoir Simulation 25317.14 Summary and Conclusions 256References 257

xii Contents

18 Production Forecasting of Fractured Wells in Shale Gas Reservoirs with Discontinuous Micro-Fractures 259

Qi Qian, Weiyao Zhu, and Jia Deng18.1 Introduction 26018.2 Multi-Scale Flow in Shale Gas Reservoir 261

18.2.1 Multi-scale Nonlinear Seepage Flow Model of Shale Gas Reservoir 261

18.2.2 Adsorption – Desorption Model of Shale Gas Reservoir 263

18.3 Physical Model and Solution of Fractured Well of Shale Gas Reservoir 264

18.3.1 Th e Dual Porosity Spherical Model with Micro-Fractures Surface Layer 264

18.3.2 Th e Establishment and Solvement of Seepage Mathematical Model 266

18.4 Analysis of Infl uencing Factors of Sensitive Parameters 27318.5 Conclusions 27718.6 Acknowledgements 278References 278

19 Study on the Multi-Scale Nonlinear Seepage Flow Th eory of Shale Gas Reservoir 281

Weiyao Zhu, Jia Deng, and Qi Qian19.1 Introduction 28219.2 Multi-Scale Flowstate Analyses of the Shale

Gas Reservoirs 28319.3 Multi-Scale Nonlinear Seepage Flow Model in Shale Gas

Reservoir 285 19.3.1 Nonlinear Seepage Flow Model in

Nano-Micro Pores 285 19.3.2 Multi-Scale Seepage Model Considering of

Diff usion, Slippage 288 19.3.3 Darcy Flow in Micro Fractures and Fractured

Fractures 28919.4 Transient Flow Model of Composite Fracture

Network System 29119.5 Production Forecasting 29419.6 Conclusions 29819.7 Acknowledgements 299References 299

Contents xiii

20 CO2 EOR and Sequestration Technologies in PetroChina 301

Yongle Hu, Xuefei Wang, and Mingqiang Hao20.1 Introduction 30220.2 Important Progress in Th eory and Technology 302

20.2.1 Th e Miscible Phase Behaviour of Oil-CO2 System 302

20.2.2 CO2 Flooding Reservoir Engineering Technology 304

20.2.3 Separated Layer CO2 Flooding, Wellbore

Anti-Corrosion and High Effi ciency Lift Technology 306

20.2.4 Long Distance Pipeline Transportation and Injection Technology 306

20.2.5 Produced Fluid Treatment for CO2 Flooding

and Cycling Gas Injection Technology 306 20.2.6 CO

2 Flooding Reservoir Monitoring,

Performance Analysis Technology 307 20.2.7 Potential Evaluation for CO

2 Flooding and Storage 308

20.3 Progress of Pilot Area 311 20.3.1 Block Hei59 312 20.3.2 Block Hei79 313


21 Study on the Microscopic Residual Oil of CO2 Flooding

for Extra-High Water-Cut Reservois 319

Zengmin Lun, Rui Wang, Chengyuan Lv, Shuxia Zhao,

Dongjiang Lang, and Dong Zhang21.1 Introduction 31921.2 Overview of CO

2 EOR Mechanisms for Extra High

Water Cut Reservoirs 32021.3 Experimental Microscopic Residual Oil Distribution

of CO2 Flooding for Extra High Water Cut Reservoirs 321

21.3.1 NMR Th eory 321 21.3.2 In situ NMR Test for Water Flooding and CO

2

Flooding 32221.4 Displacement Characteristics of CO

2 Flooding

and Improve Oil Recovery Method for Post CO

2 Flooding 325

21.4.1 CO2 Displacement Characteristics for Extra

High Water Cut Reservoirs 325 21.4.2 Improved Oil Recovery for Post CO

2 Flooding 326


xiv Contents

22 Monitoring of Carbon Dioxide Geological Utilization and Storage in China: A Review 331

Qi Li, Ranran Song, Xuehao Liu, Guizhen Liu, and

Yankun Sun22.1 Introduction 33222.2 Status of CCUS in China 33222.3 Monitoring of CCUS 336

22.3.1 Monitoring Technology at Home and Abroad 336 22.3.2 U-tube Sampling System 341 22.3.3 Monitoring Technologies in China’s

CCUS Projects 34122.4 Monitoring Technology of China’s Typical CCUS Projects 343

22.4.1 Shenhua CCS Demonstration Project 343 22.4.2 Shengli CO

2-EOR Project 345

22.5 Environmental Governance and Monitoring Trends in China 345

22.6 Conclusion 35122.7 Acknowledgements 352References 352

23 Separation of Methane from Biogas by Absorption-Adsorption Hybrid Method 359

Yong Pan, Zhe Zhang, Xiong-Shi Tong, Hai Li,

Xiao-Hui Wang, Bei Liu,Chang-Yu Sun, Lan-Ying Yang,

and Guang-Jin Chen23.1 Introduction 35923.2 Experiments 361

23.2.1 Experimental Apparatus 361 23.2.2 Materials 362 23.2.3 Synthesis and Activation of ZIF-67 363 23.2.4 Gas-Slurry Equilibrium Experiments 363 23.2.5 Data Processing 364 23.2.6 Breakthrough Experiment 366

23.3 Results and Discussions 367 23.3.1 Adsorbent Characterization 367 23.3.2 Ab-Adsorption Isothermal 368 23.3.3 Breakthrough Experiment 370


Index 377

Preface

Th e fi ft h in the series of Symposia on the injection of gases for disposal and enhanced recovery was held in Banff , Canada, in May 2015. Th is volume contains select papers that were presented at the Symposium. In addition, some papers were backups and they too are included here.

Th e keynote presentation, and Chapter 1 in this book, was on the mod-elling of processes for removing CO

2 from gas streams. Th is is followed by

several chapters on acid gas removal technology, including data and cor-relation. Th is includes several interesting papers on hydrates.

Th e fi nal chapters discuss the reservoir aspects of gas injection. Included in these sections are papers on acid gas injection and CO

2 for enhanced oil

recovery.

YW, JJC, & WZ

May 20 15

xv

1

Ying Wu, John J. Carroll and Weiyao Zhu (eds.) Acid Gas Extraction for Disposal and Related Topics,

(1–16) © 2016 Scrivener Publishing LLC

1

Rate-Base Simulations of Absorption Processes; Fata Morgana or Panacea?

P.J.G. Huttenhuis and G.F. Versteeg

Procede Gas Treating BV, Enschede, Th e Netherlands

AbstractTh e design and simulation of separation processes have been traditionally han-

dled using the concept of ideal stages and effi ciencies. Th e growing importance of

chemically based separation processes, such as the use of alkanolamines for gas

processing and now carbon capture underline the importance of proper modeling

of coupled mass transfer and chemical kinetics in multiphase systems.

In the present study it will be demonstrated by means of various (real-life) cases

that rate-based simulation can be a beautiful tool to improve on the process per-

formance and develop new insights in gas-liquid processes accompanied by com-

plex chemical reactions. But also in this rate-based approach the user should fully

understand the mechanisms behind the phenomena occurring. Otherwise, this

approach can lead to erroneous results.

1.1 Introduction

Th e design of absorption processes based on complex aqueous chemical reactions such as CO

2-capture, selective H

2S-removal as well as rate lim-

ited physical separations like LNG pre-treatment are neither simple nor straightforward. Reaction kinetics, mass transfer rates and thermodynam-ics are coupled and their eff ects must be taken into account simultaneously. Th e development of sound simulation models is dependent on algorithms,

2 Acid Gas Extraction for Disposal and Related Topics

which take into account the aforementioned phenomena in a rigorous and consistent manner.

How the mass transfer parameters collectively aff ect the results, is an important part of the training required by a process engineer to become profi cient in using this type of technology.

In this paper a high pressure CO2 capture case is simulated with a rate

based simulator. Th e impact of the several mass transfer parameters on the absorption performance is presented and it is shown that knowledge of these parameters is required to obtain reliable and correct results from the simulator.

1.2 Procede Process Simulator (PPS)

Th e simulations described in this paper have been carried out with the Procede Process Simulator. Procede Process Simulations developed a new fl owsheeting tool, Procede Process Simulator (PPS), specifi cally designed for steady-state simulations of acid gas treating processes [1]. Th e pro-cess models include all features relevant for the design, optimization, and analysis of acid gas treating processes, like selective H

2S removal, post

combustion CO2 capture or CO

2 removal with a physical solvent. Th e

simulator consists of a user-friendly graphical user interface and a pow-erful numerical solver that handles the rigorous simultaneous solution of thermodynamics, kinetics and mass transfer equations (this combina-tion usually called a “rate-based” model). PPS also supports the main unit operations relevant for gas treating plants like absorbers, strippers, fl ash drums, heaters, pumps, compressors, mixers and splitters as well, as novel unit operations designed to make the process engineer’s work more pro-ductive such as automatic ways to calculate water and solvent makeup. PPS has been extensively validated and used for several carbon capture projects [2–4]. A thorough and systematic comparison between the equilibrium based and rate based modeling approaches using the absorption of CO

2

from fl ue gas produced by a coal-fi red power plant into an aqueous MEA solution as a benchmark was presented in [5].

Th e Procede Process Simulator includes an extensive, carefully evalu-ated database of thermodynamic model parameters, binary interaction parameters, kinetics constants, chemical equilibrium constants, diff usivi-ties and other required physical properties. Th e physical property model parameters were optimized to accurately predict the vapour-liquid equi-libria (VLE), thermodynamic and physical properties, and the kinetically enhanced mass transfer behavior of acid gases in amine-based capturing

Rate-base Simulations of Absorption Processes 3

processes. Several models for hydrodynamics and mass transfer such as the Higbie penetration model [6] are available.

Th e thermodynamic model combines consistent liquid activity co effi cient models derived from a Gibbs excess function with the neces-sary modifi cations to handle ions in aqueous solutions with a cubic equa-tion of state for the gas phase. For the convenient prediction of column performance, the program also includes an extensive database of various tray types as well as a large collection of both random and structured pack-ing data. Several mass transfer (k

G, k

L and a) and hydrodynamic models

were implemented that benefi t from accurate physical property models for density, viscosity, surface tension, diff usivity and thermal conductivity spe-cifi cally selected and validated for acid gas treating applications.

Th is attention to detail allowed for the construction of a simulator able to describe complete acid gas treating processes, including complex processes with multiple (mixed or hybrid) solvent loops. Th is simulator provides signifi cant understanding of the performance of potential new solvents, current operations and an environment to better understand cur-rent operations.

1.3 Mass Transfer Fundamentals

Most important part of the Procede Process Simulator is the mass transfer module. In this module the mass transfer from gas phase to liquid phase and vice versa is calculated.

In the example described below gaseous component A (=CO2) is trans-

ported to the liquid phase (B), were the reaction takes place.

A + B → P (1.1)

Th e reaction rate can be calculated from the reaction rate constant k1,1

and the concentration A and B in the liquid phase:

rA

= k1,1

CAC

B (1.2)

where: ri = reaction rate of component i

k1,1

= the kinetic rate constant of the reaction between A and B C

i = concentration of component i

A commonly used fundamental mass transfer model to describe this absorption process quantitatively is the stagnant fi lm model. In this stag-nant fi lm model the fl uid (in this case both gas and liquid phase) are divided in two diff erent zones: a stagnant fi lm of thickness δ (gas and liq-uid) near the interface and a well-mixed bulk (gas and liquid) behind it, in which no concentration gradients occur. A schematic representation of


Solubility

Mass transfer

Mass transfer

&

kinetics

CG

CL

CL,i

CG,i

Driving force

Liquid

Gas

m = CL,i/CG,i

G L

kG = DG

G

kL = DL

L

Figure 1.1 Driving force for a gas – liquid process according to the fi lm model.

the absorption process according the stagnant fi lm model is presented in Figure 1.1.

In Figure 1.1 the parameters (according the fi lm model) for the driv-ing force in a countercurrent gas-liquid system with and without chemical reaction are shown:

Gas and liquid resistances are determined by the diff usion coeffi cients and the fi lm thickness in both phases. In the fi lm model it is assumed that equi-librium exists at the gas-liquid interface. For an acid gas – solvent system, where a chemical reaction takes place in the liquid, mass transfer in the liq-uid may be enhanced by the chemical reaction as can be seen in Figure 1.1.

Depending on the values of the stated variables in the reaction rate equations, several limiting conditions can be identifi ed. If one assumes a negligible gas phase resistance (high k

G; in most CO

2 capture absorption

processes kG is not limiting) the following absorption rate for component

A (=CO2) can be developed:

rA

= mAk

LaEC

A,G (1.3)

where: rA = absorption rate of component A [mol. s–1.m–3 reactor]

mA = physical solubility of component A in the solvent, -

kL = liquid side mass transfer coeffi cient, m.s–1

a = eff ective gas-liquid area, m2.m–3 reactor E = chemical enhancement factor, - C

A,G = concentration of component A in gas phase, mol.m–3


E is the enhancement factor, which is the ratio of the fl ux with reaction and the fl ux without reaction at identical driving forces. For non-reactive systems the enhancement factor is by defi nition equal to one. To calculate the CO

2

fl ux, the chemical enhancement should be determined and for this calcula-tion the defi nition of the Hatta number (Ha) is introduced. Th e dimension-less number Hatta number compares the maximum chemical conversion in the mass transfer fi lm to the maximum diff usion fl ux through the fi lm. For the example described above, the Hatta number is defi ned as follows:

1,1 B Aa

L

k C DH

k

(1.4)

where: k1,1

= the reaction rate constant; C

B = concentration of reactant (=B) in the liquid phase;

DA = diff usion coeffi cient of component A in the solvent;

kL = liquid side mass transfer coeffi cient.

Dependent of the value of the Hatta number the several reaction regimes can be identifi ed. For CO

2 capture at low pressure in general the pseudo fi rst

order regime can be identifi ed (Ha >> 2) and in this case the Enhancement factor (E) is equal to the Hatta number. In this case the absorption rate can be calculated as follows:

, , 1,1 ,A A L A G A L A G B A A Gr m k aEC m k aHaC mAa k C D C (1.5)

So when thermodynamic (m), kinetic (k1,1

) and mass transfer informa-tion (a) and physical properties (D) are available the absorption rate of CO

2

into the liquid phase can be determined. Under these conditions, the mass transfer of CO

2 is independent of the liquid side mass transfer coeffi cient k

L.

In this case the reaction between CO2 and the solvent takes place at the

gas liquid interface and in the bulk of the liquid no CO2 is present any-

more; i.e. it is converted to ionic species completely.In PPS the Higbie penetration model is used to calculate the mass transfer

instead of the above described fi lm model. In contrast to the above described fi lm model the Higbie Penetration Model can be used for a wide range of conditions, the entire range of Hatta numbers, (semi-) batch reactors, mul-tiple complex reactions and equilibrium reactions, components with diff er-ent diff usion coeffi cients and also for systems with more than one gas phase component. However, the principles as discussed above are identical.

For rate based modelling of absorbers and regenerators the contactor is discretized into a series of mass transfer units as shown in Figure 1.2. In counter-current operation the input of each transfer unit is the liquid


from above and the vapour from below the unit. Th e output is the liquid to the unit below and the vapour to the unit above. Th e resulting num-ber of transfer units (NTU) and the physical appearance (e.g. sieve trays, random packing, etc.) of these units are completely diff erent depending on the way the model is constructed. Nevertheless the model is completely general in the sense that it captures all the essential phenomena happen-ing in reality – thermodynamic driving forces, eff ective areas and rates for mass transfer, chemical kinetics and limited residence time.

In rate based modelling the gas and liquid phases are separated by an interface, the gas and liquid phases have diff erent temperatures and the mass and heat transfer rates between the two phases are determined by the driving force between the two phases, the contact area, and the mass and heat transfer coeffi cients. Th e amount of mass transfer area is determined by the desired quality of the separation. Th e mole fractions of the gas (y) and liquid (x) phase are calculated by integration of the diff erential mass balance equations (1) and (2) across the height of the column (h).

ii e

dxL J a Vdh

(1.6)

ii e

dyG J a Vdh

(1.7)

where L is the total mole fl ow of the liquid phase and G is the total mole fl ow of the gas phase, i is the component index. V is the total volume of the segment. Th e eff ective interfacial area for mass transfer (a

e) depends

on the packing type or other mass transfer area present in the contactor such as the specifi c area for mass transfer used to model tray columns or

Vapor to

Vapor from

Liquid from

Liquid to

Mass & heat transfer

VLE at interface

Vapor bulk

well mixed

Vapor

film

(diffusion)

Liquid

film or

element

(diffusion)

Liquid bulk

well mixed

(reactive)

stage above

stage below

stage above

stage below

Figure 1.2 General mass transfer model for vapours and liquids.


bubble interfacial area present in a bubble tower. Th e mass fl ux (J) in moles /(area * time) is calculated based on the driving force. If the driving force is defi ned as the concentration diff erence between the gas and liquid phase the fl ux is expressed as in Eqn 1.8.

,

L ii ov i G i

i

xJ k y

m

(1.8)

where m is the distribution coeffi cient based on the ratio of liquid and gas concentrations. If the integration of this set of equations is done numerically the height of one transfer unit depends on the numerical discretization used for integration. In the case of a packed column, with negligible axial dispersion, the NTU is set at a value that results in plug fl ow. In case of trays, with the assumption that at each tray the liquid and gas phase are ideally mixed, the NTU can be set equal to the number of trays. Th is results in less plug fl ow due to axial dispersion. It should be noticed that in this way the axial dispersion is described by ideally mixed contactors in series.

In case of chemical absorption and the driving force is concentration based, the overall mass transfer coeffi cient k

ov is a function of the mass

transfer coeffi cient of the gas phase (kG) and liquid phase (k

L), the distribu-

tion coeffi cient based on concentrations (m). E is the enhancement factor as discussed before.

, , ,

1 1 1

ov i G i i L i ik k m k E

(1.9)

Details related to the construction of empirically determined mass trans-fer parameters are important since the interactions between their diff er-ent governing equations and equation parameters are not always intuitive. For example, in physical separation processes only the product of mass transfer coeffi cient and specifi c interfacial area for the gas and liquid mass transfer is required (k

Ga

e and k

La

e), because this product determines the

absorption rate. For chemically reactive, mass transfer limited separation processes the individual values of mass transfer coeffi cients and specifi c mass transfer areas (k

G, k

L, a

e) are required for the gas and liquid phases.

A signifi cant amount of experimental studies related to predict these mass transfer parameters in absorption columns have been carried out. From these studies several empirical or semi-empirical correlations are derived by regression of the correlations with the experimental (pilot) data or cor-relations are derived from theoretical hydraulic models. In general overall


or volumetric mass transfer coeffi cients are determined from these experi-ments; however, a distinction between mass transfer coeffi cient (k

L and k

G)

and eff ective interfacial area (ae) is basically not possible.

1.4 CO2 Capture Case

A high pressure (60 bar) CO2 capture plant was simulated based on real plant

data and the process fl ow scheme of the simulated plant is presented below:In Figure 1.3 a fl ow scheme of a standard CO

2 capture plant is presented

containing an absorber and desorber, fl ash vessel and various heat exchang-ers and solvent circulation pumps. Th e CO

2 is removed with an activated

MDEA solution, i.e. a commonly used solvent containing MDEA and piperazine. Th e absorber is equipped with 20 valve trays. Geometric details of the valves, like weir height and tray spacing have been incorporated in the simulation. Th e in-house developed correlations have been used to cal-culate the various mass transfer parameters (k

G, k

L and a

e). Th e gas stream

is a hydrocarbon stream containing mainly methane and 3.0 vol. % CO2.

With the default simulation the following mass transfer parameters were calculated using the default correlations implemented in the simulator:

• kG = 2.6.10–3 m.s–1

• kL = 2.6.10–4 m.s–1

• ae = 38 m2.m–3

NG outlet

4

33

1

NG Inlet

12 Flash gas

PCV-1

20

9 3

87

E-101

PCV-2

21

5

13

C-102

6

CO2

15

18

14

V-101

E-102

C-101

P-101

16

10

Formulator-1

Figure 1.3 Process fl ow sheet of the simulated CO2 capture plant.


Note that the mass transfer parameters are calculated for every tray, so the above presented data are average values over the whole column.

With these settings a CO2 capture of 75 % was calculated with the simu-

lator. In reality a slightly higher (few percent) capture was measured and by the execution of a sensitivity study with the three mass transfer param-eters, it was studied how this capture can be infl uenced. As described above the physical and chemical properties of the solvent-gas mixture are rigor-ous implemented into PPS and the most “diffi cult” parameters to predict are the mass transfer parameters k

G, k

L and a.

In Figure 1.4 the infl uence of the eff ective interfacial area (ae) on the

calculated CO2 outlet concentration is presented. Th e area has been varied

between values of 10% and 500 % of the original number dervied from the default correlation (= 38 m2.m–3).

From Figure 1.4 it can be concluded that the CO2 capture rate is very

dependent on the value of the eff ective interfacial area. Especially, a reduc-tion of area does have a drastic eff ect on the overall CO

2 capture. Th e

reason for this large eff ect is that the CO2 capture is more or less linear

dependent on the CO2 absorption in the liquid phase, so lowering the area

will result in lower absorption. When the eff ective area calculated with the default correlation was increased with 22% to 46 m2.m–3, the CO

2 concen-

tration predicted by the simulator was inline with the capture measured in the fi eld.

An increment in area does result in increased CO2 capture, however, the

eff ect is less pronounced as for reduced eff ective area. Especially at very

0.0

0.4

0.8

1.2

1.6

2.0

2.4

2.8

0 50 100

Interfacial area [m2.m–3]

CO

2 o

utl

et

con

c. [

Vo

l. %

]

150 200

CO2 concentration

measured in the field

Figure 1.4 Infl uence of eff ective interfacial area on the calculated CO2 outlet

concentration.


high eff ective areas (> 100 m2.m–3), a further increase in area does not result in the same increase in CO

2 capture. Th e reason for this lower impact of

area on the capture is, that at these high capture rates, the driving force for mass transfer, i.e. the concentration diff erence between gas and (corrected) liquid phase is decreasing with increasing CO

2 capture. In Figure 1.5 the

gas phase concentration and (corrected) liquid phase concentration is pre-sented as function of tray number for the default case (a

e = 38 m2.m–3). Th e

corrected liquid concentration is the gas phase concentration which is in equilibrium with the liquid phase. Th e diff erence between these two lines is the driving force for mass transfer.

From Figure 1.5 it be seen that the gas phase concentration is reduced from around 3 mol% (in the top) to around 0.7 mol% in the bottom of the absorber. It can also be concluded that the driving force is lower in the middle of the column. Th is can be explained when the temperature profi le in the column is studied in more detail. In Figure 1.6 this liquid tempera-ture in the absorber is presented for three diff erent interfacial areas and it can be seen that in the middle of the column the temperature is increased to more than 80 °C (for a

e = 38 m2.m–3). At this high temperature the equi-

librium partial pressure CO2 is much higher than at lower temperature,

i.e. the capacity of the solvent for CO2 capture is decreased. Due to this

reduced driving force, the CO2 mass transfer from gas to liquid phase will

be reduced.When the eff ective area is decreased with a factor 5 to 7.6 m2.m–3 a sig-

nifi cant lower CO2 capture is established (refer to Figure 1.4). When the

0

0.5

1

1.5

2

2.5

3

3.5

0 5 10 15 20 25

CO

2 c

on

cen

tra

tio

n [

mo

l%]

Tray [–]

Figure 1.5 Gas phase concentration (green triangles) and (corrected) liquid

concentration of CO2 (blue dots) as function of tray number for the interfacial area of

38 m2.m–3.


area is increased with a factor 5 to 190 m2.m–3, the CO2 capture is increased,

however, the increment is signifi cant lower than expected. Th e reason for this limited increment can be explained when the driving force between gas and liquid phase is studied for this simulation (Figure 1.7).

From Figure 1.7 it can be concluded that almost no driving force for mass transfer is available in approximately 50 % of the column, i.e. between tray 8 and 15. Due to the high CO

2 capture the temperature

is increased in the absorber (Figure 1.6) to approximately 85 °C and at

60

65

70

75

80

85

90

0 5 10 15 20

Liq

uid

te

mp

era

ture

[C

]

Tray [–]

ae=190

ae=38

ae=7.6

Figure 1.6 Temperature profi le in the absorber for three diff erent eff ective areas (ae = 7.6,

38 and 190 m2.m–3; default value multiplied by factor of 0.2, 1 and 5).

0

0.5

1

1.5

2

2.5

3

0 5 10 15 20

CO

2 c

on

cen

tra

tio

n [

mo

l%]

Tray [–]

Figure 1.7 Gasphase concentration (green triangles) and (corrected) liquid concentration of

CO2 (blue dots) as function of tray number for the interfacial area of 190 m2.m–3 (factor = 5).


this high temperature no absorption can take place anymore, due to the high equilibrium CO

2 pressure. From this fi gure it can be concluded that

the addition of more trays (or more interfacial area) will not result in more CO

2 capture. Th e overall CO

2 capture can be increased by applying

inter stage cooling in the middle of the column or increase the solvent circulation rate.

In Figure 1.8 the infl uence of the liquid side mass transfer coeffi cient on the calculated CO

2 outlet concentration is presented graphically.

From Figure 1.8 it can be concluded that both for low and for high values of the liquid side mass transfer coeffi cient, the impact on the CO

2

capture is much lower than for the interfacial area. Th e reason for this relatively low infl uence is that the reaction does take place in the pseudo fi rst order regime. As discussed in the former chapter, when the reaction is fast compared to mass transfer, the absorption rate is not infl uenced by the value of the liquid side mass transfer coeffi cient. In Figure 1.9 the calculated chemical enhancement in the absorber is calculated for three diff erent values for the liquid side mass transfer coeffi cient (k

L = 5.2.10–5,

2.6.10–4 and 1.3.10–3 m.s–1, i.e. the default value is multiplied with respec-tively a factor 0.2, 1 and 5).

From this Figure 1.9 can be seen that for most of the conditions the enhancement >> 1 and for this conditions the absorption rate is not depen-dent on k

L. For the lower values of k

L in the bottom of the column, the

chemical enhancement is approaching the value 1 and in this case, the CO2

capture becomes dependent on the value of kL.

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.5 1.0 1.5

CO

2 o

utl

et

con

c. [

vo

l. %

]

Liquid side mass transfer coefficient

[KL*10–3 m.s–1]

Figure 1.8 Infl uence of liquid side mass transfer coeffi cient on the calculated CO2 outlet

concentration.

acid gas extraction for disposal

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