design and operation optimisation of a mea-based co ... · design and operation optimisation of a...

Design and Operation Optimisation

of a MEA-Based CO2 Capture Unit

Artur José Rolo de Andrade

Thesis to obtain the Master of Science Degree in

Chemical Engineering

Supervisors:

Prof. Dr. Carla Isabel Costa Pinheiro

Dr. Javier Rodriguez Perez

Examination Committee

Chairperson: Prof. Dr. Sebastião Manuel Tavares da Silva Alves

Supervisor: Prof. Dr. Carla Isabel Costa Pinheiro

Member of the Committee: Prof. Dr. Rui Manuel Gouveia Filipe

November 2014

i

Education is experience and experience is self-reliance.

T. H. White

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Acknowledgements

First of all, I would like to show my appreciation to Prof. Dr. Carla Pinheiro, Prof. Dr. Henrique

Matos, and especially to Prof. Dr. Costas Pantelides for presenting the possibility of taking an internship

in Process Systems Enterprise Limited, which was the basis of this thesis.

Also to Prof. Dr. Carla Pinheiro and to Dr. Javier Rodriguez, I would like to express my gratitude

for all the support, guidance and availability every time I needed help.

I would like to thank everyone in the Power & CCS team, with whom was a pleasure working

during these seven months. From this great team, I would like to particularly express my thanks to Mário

Calado for all his “great ideas at the right moment”.

I would also like to thank Dr. Maarten Nauta and Dr. Charles Brand for the initial gPROMS

training. To the remaining members of PSE, particularly the Portuguese and Italian “communities”, I

would like to show my gratitude for creating such an amazing working environment.

To my colleagues and friends from the “North”, Catarina Marques and Rubina Franco, I would

like to show my gratitude for all the great times we shared during this internship.

To my colleagues, friends and housemates during this few months, Mariana Marques and

Renato Wong, my deepest appreciation and gratitude for sharing this experience with me. Without them

it would not have been the same!

To my friends António Carvalho and Hugo Nogueira, which welcomed me in their home during

my first days in London, my deepest appreciation for all the great moments. To my friend Sara Oliveira,

the first person who said to me “Why not Chemical Engineering?”, my thanks for the great suggestion

and everything else.

To Sónia Ferreira, Carolina Silva, Tiago Fonseca, Carolina Oliveira and Francisco Patrocínio,

the people with whom I shared most of my time in the last five years, my thanks for all the friendship,

support and patience in this long road.

Last, but not least, I would like to show my deepest gratitude to my family and friends,

particularly to my parents and my brother for all the care, support and trust in every moment of my life.

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v

Abstract

The present thesis has the objective of analysing the cost reduction obtained through a rigorous

model-based optimisation of a post-combustion CO2 capture plant for carbon capture and storage

applications. Today’s capture technology is mainly based on chemical absorption with alkanolamines.

Even though this is a well-known technology, its application in power plants presents high costs, thus

limiting its implementation.

This way, a full scale capture plant model was developed considering MEA as the solvent. This

model is based on a conventional flowsheet and was implemented in gPROMS®, using the gCCS®

libraries. It comprises an absorption section, in which CO2 is dissolved by reacting with the amine, and

a regeneration section where it is stripped from the solvent. After its validation, the model was optimised

by modifying the design and operation parameters.

A cost estimation model was applied to the plant model, in order to determine capital and

operational expenditures. From the total cost obtained, 69% is due to the steam required in the

regeneration section. As for the equipment cost, the absorber packing is the most relevant fraction.

Considering typical values for the capture rate, CO2 purity and MEA concentration in the solvent

as constraints, the plant’s model optimisation led to a reduction of 15% in the specific total cost. Without

imposing these typical values, the total cost was further reduced. These results clearly show the

potential of model-based optimisation in the reduction of the cost associated with CO2 capture, thus

contributing to its effective implementation in power plants.

Keywords:

Carbon capture and storage, post-combustion capture, chemical absorption, MEA, cost

optimisation, gPROMS

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Resumo

A presente tese tem como objetivo a análise da redução de custos obtida por otimização do

modelo de uma unidade de captura de CO2 por pós-combustão para captura e armazenamento de

carbono. As atuais tecnologias de captura são maioritariamente baseadas em absorção química por

alcanolaminas. Apesar de esta ser uma tecnologia conhecida, a sua aplicação em centrais elétricas

apresenta elevados custos, limitando a sua implementação.

Desta forma, foi desenvolvido o modelo de uma unidade de captura à escala industrial,

utilizando MEA como solvente. Este modelo é baseado num flowsheet convencional e foi implementado

em gPROMS®, recorrendo à biblioteca gCCS®. Neste considerou-se uma zona de absorção, onde o

CO2 é dissolvido por reação com a amina, e uma zona de regeneração, onde este o solvente é

regenerado. Após validação, o modelo foi otimizado por modificação dos parâmetros de design e

operação.

Um modelo de estimativa de custos foi aplicado ao modelo desenvolvido, de forma a

determinar despesas de investimento e operacionais. Do custo total obtido, 69% deve-se ao vapor

requerido pela regeneração. Do custo dos equipamentos, a fração mais relevante é o enchimento do

absorvedor.

Considerando valores típicos de taxa de captura, pureza da corrente de CO2 e concentração

do solvente, a otimização da unidade levou à redução do custo total específico em 15%. Não impondo

estes valores típicos, o custo total foi ainda mais reduzido. Estes resultados mostram o potencial da

otimização com recurso a modelos na redução dos custos de captura de CO2, contribuindo para a sua

implementação efetiva.

Palavras-chave:

Captura e armazenamento de carbono, captura pós-combustão, absorção química, MEA,

otimização de custos, gPROMS

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ix

Contents

1. Introduction ...................................................................................................................................... 1

1.1. Motivation ................................................................................................................................ 1

1.2. State of the Art ......................................................................................................................... 2

1.3. Original Contributions .............................................................................................................. 2

1.4. Dissertation Outline ................................................................................................................. 2

2. Background ..................................................................................................................................... 5

2.1. Carbon Capture and Storage .................................................................................................. 5

2.2. Carbon Capture Technologies ................................................................................................. 5

2.2.1. Post-Combustion Capture ............................................................................................. 6

2.2.2. Pre-Combustion Capture ............................................................................................... 7

2.2.3. Oxy Combustion ............................................................................................................ 8

2.3. Processes Based on Chemical Absorption ............................................................................. 9

2.3.1. Primary and secondary amine based processes ........................................................ 10

2.3.2. Tertiary amine based processes ................................................................................. 12

2.3.3. Ammonia based processes ......................................................................................... 15

2.3.4. Amino acid salts based processes .............................................................................. 16

2.3.5. Hot potassium carbonate based processes ................................................................ 17

2.4. Processes Based on Physical Solvents ................................................................................ 18

2.4.1. Available technology.................................................................................................... 19

3. Materials and Methods .................................................................................................................. 21

3.1. gPROMS® ModelBuilder ........................................................................................................ 21

3.2. gCCS® Capture Library .......................................................................................................... 21

3.2.1. Chemical Absorber (A) ................................................................................................ 21

3.2.2. Chemical Stripper (ST) ................................................................................................ 22

3.2.3. Condenser (C) ............................................................................................................. 22

3.2.4. Flow Multiplier (FM) ..................................................................................................... 23

3.2.5. Heat Exchanger (HX) .................................................................................................. 23

3.2.6. Heat Exchanger Process/Utility (HXU) ........................................................................ 23

3.2.7. Junction (M) ................................................................................................................. 23

3.2.8. Process Sink (S) .......................................................................................................... 24

x

3.2.9. Process Source (SR) ................................................................................................... 24

3.2.10. Pump Simple (P) ......................................................................................................... 24

3.2.11. Stream Converter Absorber/Stripper (SC) .................................................................. 24

3.2.12. Reboiler (R) ................................................................................................................. 25

3.2.13. Recycle Breaker (RB) .................................................................................................. 25

3.2.14. Utility Sink (SU) ........................................................................................................... 25

3.2.15. Utility Source (SRU) .................................................................................................... 25

3.3. Physical Properties Package – gSAFT .................................................................................. 26

4. Models Validation .......................................................................................................................... 27

4.1. Flowsheet A – Absorber Model Validation............................................................................. 27

4.2. Flowsheet B – MEA Capture Plant Model Validation ............................................................ 32

5. MEA Full Scale Capture Plant Model ............................................................................................ 37

5.1. Base Case ............................................................................................................................. 37

5.2. Cost Estimation Model ........................................................................................................... 39

5.2.1. CAPEX Estimation....................................................................................................... 39

5.2.2. OPEX Estimation ......................................................................................................... 41

5.3. Cost Estimation Results for the Base Case........................................................................... 42

6. Optimisation Problem Formulation ................................................................................................ 45

6.1. Objective Function ................................................................................................................. 45

6.2. Decision Variables ................................................................................................................. 45

6.3. Constraints ............................................................................................................................. 46

7. Optimisation Results ..................................................................................................................... 49

7.1. Base Case Optimisation with Standard Constraints .............................................................. 49

7.1.1. Specific Total Cost Minimisation ................................................................................. 49

7.1.2. Effect of the Initial Guesses in the Optimisation Results ............................................. 55

7.1.3. Effect of the Number of Absorption Trains .................................................................. 58

7.1.4. Specific Heat Requirement Minimisation..................................................................... 59

7.2. Effect of the Process Constraints .......................................................................................... 61

7.2.1. Effect of the Capture Rate ........................................................................................... 62

7.2.2. Effect of the CO2 Purity ............................................................................................... 66

7.2.3. Effect of the MEA Concentration ................................................................................. 68

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7.2.4. Specific Total Cost Minimisation with Inequality Constraints ...................................... 70

8. Conclusions and Future Work ....................................................................................................... 73

8.1. Conclusions ........................................................................................................................... 73

8.2. Future Work ........................................................................................................................... 75

9. Bibliography ................................................................................................................................... 77

Appendices ......................................................................................................................................... 83

A1. Cost Estimation Model Description ........................................................................................ 83

A1.1. Shell mass estimation ........................................................................................................ 83

A1.2. Heat exchanger area estimation ........................................................................................ 83

A1.3. Reboiler area estimation .................................................................................................... 84

A1.4. Condenser area estimation ................................................................................................ 84

A1.5. Pump volumetric flow rate and power estimation .............................................................. 84

A2. Base Case and Optimisations Detailed Results .................................................................... 87

xii

List of Figures

Figure 2.1 – Existing technologies for CO2 separation and capture [16]. -------------------------------------- 6

Figure 2.2 – Schematic of a coal-fired power plant with carbon capture [17]. -------------------------------- 6

Figure 2.3 – Schematic of an IGCC power plant with carbon capture [17]. ----------------------------------- 8

Figure 2.4 – Conventional flowsheet for a PCC plant [18]. -------------------------------------------------------- 9

Figure 2.5 – Split-flow configuration of the Fluor’s Econamine FG PlusSM process [30]. ----------------- 12

Figure 2.6 – Praxair’s Amine process flowsheet [35]. ------------------------------------------------------------- 14

Figure 2.7 – Shell’s Cansolv CO2 Capture System flowsheet [38]. -------------------------------------------- 14

Figure 2.8 – Alstom’s Chilled Ammonia process flowsheet [40]. ------------------------------------------------ 16

Figure 2.9 - CO2 bulk removal capacity for different solvents [49]. --------------------------------------------- 18

Figure 2.10 – UOP SelexolTM process flowsheet for CO2 and H2S co-capture [50]. ----------------------- 18

Figure 2.11 – Lurgi Rectisol® process flowsheet for CO2 and H2S selective capture [49]. --------------- 19

Figure 3.1 – Chemical absorber/stripper icon used in the gCCS® capture library. ------------------------- 21

Figure 3.2 – Parameters required for Onda correlation (on the left) and for Billet & Schultes correlation

(on the right). ---------------------------------------------------------------------------------------------------- 22

Figure 3.3 – Condenser icon used in the gCCS® capture library. ---------------------------------------------- 22

Figure 3.4 – Flow multiplier icon used in the gCCS® capture library. ------------------------------------------ 23

Figure 3.5 – Heat exchanger icon used in the gCCS® capture library. ---------------------------------------- 23

Figure 3.6 – Junction icon used in the gCCS® capture library. -------------------------------------------------- 23

Figure 3.7 – Process sink icon used in the gCCS® capture library. -------------------------------------------- 24

Figure 3.8 – Process source icon used in the gCCS® capture library. ---------------------------------------- 24

Figure 3.9 – Pump simple icon used in the gCCS® capture library. -------------------------------------------- 24

Figure 3.10 – Stream converter absorber/stripper icon used in the gCCS® capture library. ------------- 24

Figure 3.11 – Reboiler icon used in the gCCS® capture library. ------------------------------------------------ 25

Figure 3.12 – Recycle breaker icon used in the gCCS® capture library. -------------------------------------- 25

Figure 3.13 – Utility sink icon used in the gCCS® capture library. ---------------------------------------------- 25

Figure 3.14 – Utility source icon used in the gCCS® capture library. ------------------------------------------ 25

Figure 4.1 – Flowsheet A used for validation (a models use Billet & Schultes correlation and b models

use Onda correlation). ---------------------------------------------------------------------------------------- 28

Figure 4.2 – Parity diagram of the absorbed amount of CO2 (using Billet & Schultes correlation). ---- 30

Figure 4.3 – Deviation between experimental and simulated absorbed CO2 with the considered lean

solvent loading. ------------------------------------------------------------------------------------------------- 31

Figure 4.4 – Simulated and experimental temperature profiles for run 10 (lean loading of 0.284

molCO2/molMEA). ------------------------------------------------------------------------------------------------- 32


molCO2/molMEA). ------------------------------------------------------------------------------------------------- 32


molCO2/molMEA). ------------------------------------------------------------------------------------------------- 32

Figure 4.7 – Flowsheet B used for validation. ----------------------------------------------------------------------- 34

xiii

Figure 5.1 – MEA capture plant flowsheet as seen in gPROMS® ModelBuilder. --------------------------- 38

Figure 5.2 – Total cost distribution in the base case (Total = 43.15 €/tCO2). ---------------------------------- 42

Figure 5.3 – Distribution of main equipment costs for the base case. ----------------------------------------- 43

Figure 5.4 – OPEX distribution in the base case (Total = 64.56 M€/year). ----------------------------------- 43

Figure 5.5 – Distribution of variable production costs for the base case (Total = 61.4 M€/year). ------- 43

Figure 5.6 – Distribution of the amine losses in base case (Total = 12.5 kt/year). ------------------------- 44

Figure 5.7 – Distribution of the utilities costs for the base case (Total = 56.4 M€/year). ------------------ 44

Figure 6.1 – Ratio between vapour velocity and vapour flooding velocity across the absorber (on the

left) and the stripper (on the right), for the base case. ------------------------------------------------ 47

Figure 7.1 – Total cost distribution for the specific total cost minimisation with standard constraints (Total

= 36.69 €/tCO2). ------------------------------------------------------------------------------------------------- 51

Figure 7.2 – Distribution of main equipment costs for the specific total cost minimisation with standard

constraints. ------------------------------------------------------------------------------------------------------ 51

Figure 7.3 – CO2 molar flux to the liquid phase across the absorber (top of the absorber equivalent to

0), before and after the specific total cost minimisation with standard constraints. ------------ 52

Figure 7.4 – Representative temperature profiles of the lean-rich heat exchanger in the base case (on

the left) and after the total cost minimisation (on the right). ----------------------------------------- 52

Figure 7.5 – Representative temperature profiles of the lean solvent cooler in the base case (on the left)

and after the total cost minimisation (on the right). ---------------------------------------------------- 53

Figure 7.6 – Axial temperature profiles in the stripping columns for the gas phase (on the left) and the

liquid phase (on the right), before and after the specific total cost minimisation with standard

constraints. ------------------------------------------------------------------------------------------------------ 53

Figure 7.7 – CO2 and H2O molar fluxes from the gas phase to the liquid phase across the stripper (top

of the stripper equivalent to volume 0), before and after the specific total cost minimisation

with standard constraints. ------------------------------------------------------------------------------------ 54

Figure 7.8 – Axial temperature profile of the vapour (on the left) and liquid (on the right) phases in the

absorber for the optimised cases with initial lean loadings of 0.1, 0.2 and 0.3 molCO2/molMEA.

--------------------------------------------------------------------------------------------------------------------- 57

Figure 7.9 – Variation of the optimal absorber diameter with the number absorption trains. ------------ 58

Figure 7.10 – Variation of the optimal specific total cost (on the right) and specific CAPEX (on the right)

with the number of absorption trains. --------------------------------------------------------------------- 59

Figure 7.11 – Total cost distribution in the specific heat consumption minimisation (Total = 88.98 €/tCO2).

--------------------------------------------------------------------------------------------------------------------- 60

Figure 7.12 – Distribution of main equipment costs for the specific heat consumption minimisation. - 61

Figure 7.13 – OPEX distribution in the specific heat consumption minimisation (Total = 69.34 M€/year).

--------------------------------------------------------------------------------------------------------------------- 61

Figure 7.14 – Variation of the optimal specific total cost with the imposed capture rate. ----------------- 62

Figure 7.15 – Variation of the optimal lean solvent flow rate (on the right) and loading (on the left) with

the imposed capture rate. ------------------------------------------------------------------------------------ 63

xiv

Figure 7.16 – Axial temperature profile of the vapour phase (on the left) and liquid phase (on the right)

in the absorber for the optimised cases with capture rates (CR) of 70, 80, 90 and 99%. ---- 64

Figure 7.17 – Axial temperature profile of the vapour phase (on the left) and liquid phase (on the right)

in the stripper for the optimised cases with capture rates (CR) of 70, 80, 90 and 99%. ------ 64

Figure 7.18 – CO2 molar flux from the gas to the liquid phase across the stripper, for the optimised

cases with capture rates (CR) of 70, 80, 90 and 99%. ----------------------------------------------- 65

Figure 7.19 – sCAPEX percentage variation with the imposed capture rate (90% capture rate

considered 100%). --------------------------------------------------------------------------------------------- 66

Figure 7.20 – Variation of the optimal specific total cost with the imposed CO2 purity. ------------------- 66

Figure 7.21 – Variation of the optimal condenser temperature with the imposed CO2 stream purity. - 67

Figure 7.22 – H2O molar flux from the gas to the liquid phase across the stripper, for the optimised

cases with CO2 purities (CP) of 75, 85, 95 and 99%. ------------------------------------------------- 67

Figure 7.23 – Variation of the optimal specific total cost with the imposed MEA mass fraction in the CO2

free lean solvent. ----------------------------------------------------------------------------------------------- 69

Figure 7.24 – Variation of the optimal lean solvent flow rate with the imposed MEA mass fraction in the

CO2 free lean solvent. ---------------------------------------------------------------------------------------- 69

Figure 7.25 – Optimal specific total cost obtained through its minimisation with and without standard

constraints, and value in the base case. ----------------------------------------------------------------- 71

xv

List of Tables

Table 2.1 – Typical composition (volumetric fraction) of flue gas from coal and natural gas fired power

plants [18]. -------------------------------------------------------------------------------------------------------- 7

Table 2.2 – Commercially available processes using primary or secondary amine-based solvents [3,

28, 29]. ----------------------------------------------------------------------------------------------------------- 11

Table 2.3 – Available processes using tertiary amine-based solvents [35, 36]. ----------------------------- 13

Table 2.4 – Processes in development using ammonia-based solvents [40]. ------------------------------- 15

Table 2.5 – Available processes using hot potassium carbonate-based solvents [47, 48, 46]. --------- 17

Table 2.6 – Commercially available processes using physical solvents [50, 51, 49]. ---------------------- 19

Table 4.1 – Characteristics of the flue gas and lean solvent used by Tobiesen et al. [10]. -------------- 27

Table 4.2 - Characteristic data and constants for Sulzer Mellapak 250YTM [54, 57]. --------------------- 28

Table 4.3 – Experimental rich loading and simulation results using both Billet & Schultes correlation and

Onda correlation. ----------------------------------------------------------------------------------------------- 29

Table 4.4 – Experimental absorbed amount of CO2 and simulation results using both Billet & Schultes

correlation and Onda correlation. -------------------------------------------------------------------------- 30

Table 4.5 – Pilot plant design parameters [11]. --------------------------------------------------------------------- 33

Table 4.6 – Process specifications for examples 1 and 2 [11]. -------------------------------------------------- 33

Table 4.7 – Flue gas composition in each example. --------------------------------------------------------------- 33

Table 4.8 – Experimental and Simulation results for the process key parameters and respective

variation. --------------------------------------------------------------------------------------------------------- 35

Table 5.1 – Flue gas conditions considered in the MEA capture plant model. ------------------------------ 37

Table 5.2 – Design parameters and operating conditions considered in the original capture plant model.

--------------------------------------------------------------------------------------------------------------------- 37

Table 5.3 – Results obtained from the simulation of the original and base cases. ------------------------- 39

Table 5.4 – Type, construction/reference material, sizing variable and cost correlation parameters for

the main capture plant equipment [58]. Cost correlation parameters in a USD basis referred

to January 2010 (CEPCI2010=532.9).-------------------------------------------------------------------- 40

Table 5.5 – Typical installation factors for the estimation of project installed capital cost [58]. --------- 41

Table 5.6 – Other expenses required for the estimation of the total investment required [58]. ---------- 41

Table 5.7 – Utilities and solvents costs (CEPCI1998=389.5, CEPCI2004=444.2 [61]) [62]. ------------- 42

Table 6.1 – Decision variables, with respective initial value, lower bound and upper bound. ----------- 46

Table 6.2 – Equality constrained variables, with respective constrained value. ---------------------------- 46

Table 6.3 – Inequality constrained variables, with respective upper and lower bounds. ------------------ 46

Table 6.4 – Additional inequality constrained variables, with respective upper and lower bounds, used

for the minimisation of the specific heat consumption. ----------------------------------------------- 47

Table 7.1 – Detailed results from the specific total cost minimisation with standard constraints, and

comparison with the base case (Table A2.1).----------------------------------------------------------- 50

Table 7.2 – Modified initial guesses, used for comparison with the initial optimisation. ------------------- 55

xvi

Table 7.3 – Decision variables and key parameter resulting from the specific total cost minimisation,

starting from a lean loading of 0.1 molCO2/molMEA and comparison with the optimal results

previously obtained. ------------------------------------------------------------------------------------------- 56

Table 7.4 – Decision variables and key parameter resulting from the specific total cost minimisation,

starting from a lean loading of 0.3 molCO2/molMEA and comparison with the optimal results

previously obtained. ------------------------------------------------------------------------------------------- 56

Table 7.5 – Lagrange multipliers obtained for the equality constraints in the minimisation of the specific

total cost with standard constraints. ----------------------------------------------------------------------- 62

Table 7.6 – Additional inequality constrained variables, with respective upper and lower bounds, used

in the specific total cost minimisation without equality constraints. -------------------------------- 70

Table A1.1 – Minimum practical wall thickness [58]. --------------------------------------------------------------- 83

Table A1.2 – Typical maximum allowable stresses for stainless steel 304 [58]. ---------------------------- 83

Table A1.3 – Typical shaft efficiencies for centrifugal pumps [59]. --------------------------------------------- 85

Table A1.4 – Typical driver efficiencies for electrical motors [54]. ---------------------------------------------- 85

Table A2.1 – Detailed results from the application of the cost estimation model in the base case. ---- 87

Table A2.2 – Detailed results from the specific total cost minimisation with 1 absorber and standard

constraints, and comparison with the initial specific total cost minimisation results (Table 7.1).

--------------------------------------------------------------------------------------------------------------------- 88



--------------------------------------------------------------------------------------------------------------------- 89



--------------------------------------------------------------------------------------------------------------------- 90

Table A2.5 – Detailed results from the specific heat requirement minimisation with standard constraints,

and comparison with the base case. ---------------------------------------------------------------------- 91

Table A2.6 – Detailed results from the specific total cost minimisation with an imposed capture rate of

70%, and comparison with the initial specific total cost minimisation results (Table 7.1). --- 92





Table A2.9 – Detailed results from the specific total cost minimisation with an imposed CO2 purity of






xvii

Table A2.12 – Detailed results from the specific total cost minimisation with an imposed MEA mass

fraction the CO2 free lean solvent of 20%, and comparison with the initial specific total cost

minimisation results (Table 7.1). --------------------------------------------------------------------------- 98

Table A2.13 – Detailed results from the specific total cost minimisation with an imposed MEA mass

fraction the CO2 free lean solvent of 40%, and comparison with the initial specific total cost

minimisation results (Table 7.1). --------------------------------------------------------------------------- 99

Table A2.14 – Detailed results from the specific total cost minimisation with inequality constraints, and

comparison with the initial specific total cost minimisation results (Table 7.1). --------------- 100

xviii

List of Abbreviations

A – Chemical absorber

C – Condenser

CCS – Carbon capture and storage

CS – Carbon steel

CSIRO – Commonwealth Scientific and Industrial Research Organisation

EOR – Enhanced oil recovery

FG – Flue gas

FM – Flow multiplier

HSS – Heat stable salts

HX – Integrated heat exchanger

HXU – Heat exchanger using a utility

IGCC – Integrated gasification combined cycle

ISBL – Inside battery limits

LS – Lean solvent after make-up

M – Junction

MDEA – Methyldiethanolamine

MEA – Monoethanolamine

MSEP – Mean squared error of prediction

NLP – Nonlinear programing

OSBL – Offsite battery limits

P – Pump simple

PCC – Post-combustion capture

PSE – Process systems enterprise limited

R – Reboiler

RB – Recycle breaker

RR’NH – Generic primary/secondary amine

RR’R’’N – Generic tertiary amine

RS – Rich solvent

S – Process sink

SAFT – Statistical Associating Fluid Theory

SC – Stream converter absorber/stripper

SR – Process source

SRU – Utility source

SS-304 – Stainless steel 304

ST – Chemical stripper

SU – Utility sink

TG – Treated gas

USD – United states dollar

VR – Variable Range

xix

List of Symbols

Symbols

Variable Description Unit

a Cost parameter a -

A Area m2

b Cost parameter b -

C Installed capital cost/ISBL investment €

Ce Equipment cost USD/€

Cmaintenance Maintenance cost €

Ctaxes/insurance Taxes and insurance cost €

Cutilities Utilities total cost €

Csolvent Solvent total cost €

Cs/CFl/Ch/CP,0 Packing characteristic constants -

CAPEX Capital expenditures M€/year

CEPCI Chemical Engineering's Plant Cost Index -

CP CO2 purity %

CR CO2 capture rate %

D Diameter m

E Welded joint efficiency -

f Installation factor -

fm Material factor -

F Mass flow rate kg/s

FV Volumetric flow rate m3/s

L Height m

Mshell Shell’s mass kg

n Cost parameter n -

N Number of equipment units -

NRuns Number of experimental runs -

OF Objective function €/tCO2

OF2 Secondary objective function GJ/tCO2

OPEX Operational expenditure M€/year

P Pressure bar

PP Power required by each train of pumps kW

PS Power required by each pump W

Q Heat duty W

S Maximum allowable stress bar

Sv Sizing variable -

sCAPEX Specific CAPEX €/tCO2

sOPEX Specific OPEX €/tCO2

t Thickness mm

T Temperature K

U Overall heat transfer coefficient W/(m2.K)

v Value -

w Mass fraction g/g

WP Specific work J/kg

x Molar fraction Mol/mol

Δ Difference -

ΔP Pressure drop bar

η Efficiency -

ρ Mass density kg/m3

xx

Subscripts

Variable Description

0 Source column

1 Destination column

1998/2004/2010/2013 Reference year

bottom Extra bottom section of the column

c Civil

Cond Condenser

driver Pump driver

er Equipment erection

el Electrical

f Line

HX Heat exchanger

i Component

ic Instrumentation and control

l Lagging and paint

lm Logarithmic mean

liq Liquid phase

min Minimum value

Packing Packing section of the column

Pump Pump

Reb Reboiler

s Structures and buildings

shaft Pump shaft

steam Steam stream

top Extra top section of the column

utility Utility stream

vap Vapour phase

Superscripts

Variable Description

dry Dry basis fraction

exp Experimental value

in Inlet stream

m/construction Construction material

out Outlet stream

P Units in parallel

reference Reference material

S Stream

sim Simulated value

Unit Unitary variable

1

1. Introduction

CO2 is a naturally-occurring gas with a major influence in the reflection of solar radiation back

to Earth, keeping the planet’s surface temperature in levels suitable for the existence of life. However

due to human invention and industrialisation, its concentration in the atmosphere has greatly increased,

leading to a rapid rise in the planet’s temperature. According to the 2013 IEA report [1], between the

years of 2001 and 2011, the worldwide CO2 emissions from fossil fuel combustion increased by 31%,

reaching a value of 31 billion tonnes per year. From this value, approximately 42% are due to the

electricity and heat production sector.

The use of renewable energy sources is an option to mitigate CO2 emissions. However,

considering the 2013 IEA report on energy statistics [2], fossil fuels (coal, oil and natural gas) still

represent around 81% of the world’s primary energy supply.

According to the Global CCS Institute, carbon capture and storage has the potential to

significantly reduce the CO2 emissions to the atmosphere from power plants and chemical industries,

such as ammonia, hydrogen, steel and cement production, as well as in fuels preparation and natural

gas processing.

The 2005 IPCC Special report on CSS [3], states that, for a power plant, the capture cost is the

main component of the overall CCS costs, mainly due to the process energy requirements. For a power

plant, this implies an extra consumption of steam and electricity, leading to a reduction in net efficiency

and consequently an increase in the electricity costs.

Also according to this report, the improvement of commercially available capture technologies

should allow a reduction of its costs by 20 to 30%, being the option with more potential for the reduction

of CO2 emissions in the near future.

1.1. Motivation

Process Systems Enterprise Limited (PSE) is the world’s leading provider of advanced process

modelling to the process industries. In July 2014, the company has launched the gCCS® modelling

environment, being the first modelling software specifically designed for the modelling of full carbon

capture and storage (CCS) chains, from power generation through capture, compression and transport

to injection [4].

gCCS® is constructed on PSE’s platform for advanced modelling, gPROMS®, allowing the use

of high-fidelity predictive models. Using the gCCS® model libraries, it is possible to simulate each stage

of the CCS chain individually and to analyse the interoperability across different chain components.

Shell’s Peterhead CCS project will be the first commercial application of gCCS® in the United Kingdom,

where it will be used to provide insight into the transient behaviour of the amine-based capture unit, and

its effect on operations when integrated in the full CCS chain [5].

The gCCS® capture library contains models for the simulation of both chemical and physical

solvent-based pre- and post-combustion capture units. For rigorous physical properties estimation,

these models use gSAFT, a thermodynamic property package created by PSE, which is based on the

Statistical Associating Fluid Theory (SAFT) developed at Imperial College London [4].

2

With this in mind, the present thesis has the objective of applying gPROMS® optimisation

features to a conventional CO2 capture plant model, built using the gCCS® capture library, in order to

minimise the costs associated with the unit’s design and operation.

1.2. State of the Art

The current status off the CCS technology is well described in the 2005 IPCC Special report on

CSS [3], where not only CO2 capture processes based on absorption are addressed, but also more

recent technologies.

For well-known solvents, such as monoethanolamine (MEA), recent developments focus on the

processes improvement, in order to reduce the capture economic penalty. Abu-Zahra et al. [6], evaluate

the effect of specific parameters in the energy consumption. Ahn et al. [7] test different flowsheet

configurations in order to reduce the process heat requirement. In [8], Damartzis et al. optimise several

flowsheet configurations, based on the minimisation of not only the heat requirement, but of a set of the

key process parameters. Even though most of today’s studies are based on MEA, there are some

articles, such as [9] by Urech et al., in which the performance of different solvents is evaluated.

All of the examples mentioned above resort to process modelling to test new process

configurations or proceed to their optimisation. For the validation of these models, experimental data

are required. In the case of MEA-based capture plants, these data can be found in Tobiensen et al.

[10], and Notz et al. [11].

1.3. Original Contributions

As mentioned in the motivation section, the present thesis intends to further contribute to the

CO2 capture cost reduction. To that end, PSE’s gCC® libraries were used to assemble a capture plant

model based on the MEA solvent. These models were validated by comparison with experimental data

from Tobiensen et al. [10], and Notz et al. [11] articles.

Starting from a case study developed by PSE, the design and operating conditions of a MEA-

based capture were optimised using gPROMS®. For that, a model for the estimation of the CAPEX and

the OPEX associated with the capture plant was developed. The optimisation studies focused on the

minimisation of the total cost (CAPEX + OPEX) per tonne of captured CO2, initially imposing a capture

rate, CO2 purity and solvent concentration, and ultimately evaluating the effect of varying these

constraints.

1.4. Dissertation Outline

The present thesis is organised in the following way: Chapter 2 presents a literature review on

the technologies employed in CO2 capture, namely the solvents currently applied or being developed,

and processes examples. Chapter 3 features a brief description of gPROMS® ModelBuilder, the

required models from gCCS® capture library and the used physical properties package (gSAFT).

Chapter 4 describes the validation of flowsheets built using these models against experimental data

found in the literature. In Chapter 5, the capture plant model used as full scale base case is described,

as well as the model used for costs estimation and its application to the base case. Chapter 6 presents

the description of the considered optimisation problem, in terms of objective function, decision variables

3

and process constraints. In Chapter 7, the results from the base case optimisation are presented and

analysed, containing a study on the effect of the initial guesses, the number of absorption trains and a

comparison between the specific total cost minimisation and the specific heat requirement minimisation.

Chapter 7 also features an analysis on the effect on the optimal total cost of varying the initially imposed

process constraints. Finally, Chapter 8 contains the conclusions drawn from this thesis and future work

suggestions.

4


5

2. Background

A literature review was conducted in order to understand the basic concepts associated with

the CCS chain, and more specifically the existing technologies to capture CO2 from fossil fuelled power

plants. Amongst these technologies, solvent-based processes were further analysed.

2.1. Carbon Capture and Storage

The CCS chain comprises several technologies involved in removal of carbon dioxide from a

gaseous stream, its transportation and final sequestration in site away from the atmosphere, where it

will be stored for a long period. This can be applied to the CO2 obtained in the fossil fuel burning in a

power plant, in the preparation of natural gas, or in other chemical industries [3].

The capture stage features the processes required for CO2 separation from a stream

comprising a mixture of gases. After this, the CO2 stream is compressed in order to reduce its volume

fraction up to 0.2%. The transportation is mainly carried through pipelines, or alternatively through

shipping or road tanking. Considering the elevated amount of CO2 annually emitted (mentioned in

section 1.1), the available storage locations are geological formations or the deep ocean. The required

injection technology is similar to the one employed in the oil and gas industries, and can be associated

with enhanced oil recovery (EOR), thus constituting a possible revenue source. Besides storage, in the

past years the captured CO2 has been considered for the production of other chemical, mineral fixation

and the production of bio fuels through the use of micro-algae [3, 12].

Even though these technologies have been applied in several other industries, due to the

considerable costs associated with the application of the capture process to such high flow rates, its

application to the flue gas obtained in a large-scale power plant fuelled by coal or natural gas is currently

on a demonstration phase, [13]. Nevertheless, in April 2014, the Boundary Dam Power Station

(Canada) started a CCS project for the capture of 1 MtCO2/year, using amine-based post-combustion

technology, with the captured CO2 being used for EOR. The Kemper County IGCC power plant (United

States) will start operating in 2015. It will capture 3.5 MtCO2/year (65% capture rate), also destined to

EOR applications [14]. Besides these two examples, until 2018, there are planned the start-up of 4

more capture units in the United States, with capacities reaching the 3.5 MtCO2/year and are destined

to EOR. In Europe, there are 2 capture units, from which the Don Valley (United Kingdom) intends the

capture of 4.5 MtCO2/year for geological storage [15]. All the mentioned capture projects are set to be

applied in coal-fuelled power plants. As for gas-fuelled capture units, the first capture unit is set to be

operational in 2017 in Peterhead (United Kingdom), using post-combustion technologies to capture 1

MtCO2/year [14].

2.2. Carbon Capture Technologies

Most capture processes are based on gas scrubbing processes developed over 60 years ago

for the production of town gas. Since then, this technology has been improved in order to be applied in

other industries, and more recently to carbon capture [3]. Besides the gas scrubbing process, based on

the CO2 absorption in a suitable solvent, there are several other technologies being developed in order

6

to reduce the energy penalty associated with carbon capture. The main technologies considered are

shown in Figure 2.1.

Figure 2.1 – Existing technologies for CO2 separation and capture [16].

For power plants, the technology employed in the capture process depends on the

characteristics of the gas being treated. Therefore, it will vary with the fuel and technology used for

energy production.

2.2.1. Post-Combustion Capture

Post-combustion capture (PCC) is applied in power plants based on the combustion of a fossil

fuel (coal, natural gas or oil), where a capture unit is used for the removal of the CO2 present in the flue

gas. A schematic of a coal-fired power plant is presented in Figure 2.2. In this case, the fuel is burnt

with air for steam generation, which is then used for the production of electricity in a steam turbine.

Figure 2.2 – Schematic of a coal-fired power plant with carbon capture [17].

The flue gas produced during the fuel combustion is mainly composed of nitrogen, carbon

dioxide, water and oxygen, and also of reduced amounts of SOx, NOx and ash, which are removed

7

through electrostatic precipitation and desulphurization before the capture process. The concentration

of each species will depend on the burnt fuel and its characteristics. Table 2.1 presents the typical

composition of flue gases obtained in coal and natural gas fuelled power plants [18].

Table 2.1 – Typical composition (volumetric fraction) of flue gas from coal and natural gas fired power plants [18].

Gas Constituent Composition (vol%)

Coal Natural Gas

N2 70-75 73-76

CO2 10-15 4-5

H2O 8-15 8-10

O2 3-4 12-15

Trace Gases <1 <1

As shown in Figure 2.1, CO2 capture can be achieved through several technologies.

Nevertheless, according to the Global CCS Institute report on post-combustion capture from 2012, [18],

the technologies currently tested on slip streams of pilot plants are universally based on absorption with

aqueous solvents.

Absorption processes allow CO2 capture rates between 80 and 90%, and purity as high as

99.9% (in volume) for the recovered CO2 [3]. Since the flue gas being treated has a considerably low

CO2 content and is at atmospheric pressure, the CO2 partial pressure is reduced. Therefore, the chosen

solvent has to be able to ensure acceptable loadings and kinetics in the referred conditions. Due to this,

chemical absorption poses a better option in PCC processes, when compared with physical solvents.

The solvent choice also requires the consideration of other parameters such as its volatility and

propensity to degrade [3].

The inclusion of a PCC unit in a coal fuelled power plant leads to an increase of 29% in the

energy input to achieve the same output, while for a natural gas fuelled power plant this increase is of

16% [13].

2.2.2. Pre-Combustion Capture

Pre-combustion capture technology is usually applied to power plants based on the gasification

of a fossil fuel, more specifically integrated gasification combined cycle (IGCC) plants. The gasification

process is achieved through partial oxidation in the presence of oxygen and produces synthesis gas

(syngas). This mixture, mainly composed of hydrogen and carbon monoxide, enters a water gas shift

reactor, where CO is converted to CO2. The obtained gas is typically at a total pressure that ranges

from 20 to 70 bar and has a CO2 content between 15% and 60%. After being purified, the hydrogen rich

gas is burnt in a gas turbine, producing electricity [3], as shown in Figure 2.3.

8

Figure 2.3 – Schematic of an IGCC power plant with carbon capture [17].

The CO2 removal technology used in pre-combustion capture is similar to the one used for

natural gas purification and reforming, being based on the acid gases (CO2 and H2S) removal through

absorption processes. Since these processes are conducted at elevated pressure and/or high CO2

content, the absorption can be carried out not only with chemical solvents, but also with physical

solvents [19].

The possibility of using physical solvents with acceptable loadings poses an advantage over

chemical solvents, since they can be regenerated through pressure reduction, instead of thermal

stripping, thus reducing the energy penalty. Nevertheless, chemical solvents with high loading and

reduced energy requirements can be used for lower CO2 partial pressures (bellow 15 bar) [3].

For an IGCC plant, the inclusion of a pre-combustion capture unit will increase the energy

penalty in around 21% for a 90% CO2 capture rate [13]. However, despite the lower energy penalty, the

capital cost of an IGCC power plant is considerably higher, not being a widely used technology in the

power industry.

2.2.3. Oxy Combustion

Oxy combustion plants are based on fuel burning under the presence of just oxygen, obtained

from the air cryogenic separation. The absence of nitrogen leads to the production of a flue gas, with a

CO2 content between 70% and 90% (dry basis), which is mostly (around 80%) recycled to the boiler for

temperature control.

Depending on the local regulations, the non-recycled flue gas can be dehydrated and directly

compressed and stored, without any emissions. The presence of impurities such as N2, O2 and Ar may

forbid the direct storage of the flue gas. In that case, the flue gas is partly condensed at a temperature

of about -50℃. The liquefied CO2 may be further purified in a distillation process, before being flashed

and sent to storage. The non-condensed gases still have a CO2 content up to 35%, which can be

removed through absorption or a membrane process, before being vented [20].

9

2.3. Processes Based on Chemical Absorption

As mentioned in the previous sections, the chemical absorption technology for CO2 separation

is widely used in the chemical industry. Post-combustion capture is mainly based on this technology,

as well as pre-combustion capture at lower CO2 partial pressures.

These processes are based on the CO2 chemical dissolution in an alkaline solvent, through its

selective reaction with one or several of the solvent components, usually amines. This dissolution is

conducted in a packed column (absorber) at a temperature usually between 40 and 60 °C. The solvent

loaded with CO2 (usually called rich solvent) is pumped to the regeneration section. Before entering the

regenerator, the rich solvent is heated through an integrated heat exchange with the regenerated

solvent (also called lean solvent). The regeneration process typically occurs through stripping at a

temperature between 100 and 140 °C and above atmospheric pressure. The heat demanded by this

process is provided by a reboiler, which constitutes the main energy requirement of the capture unit.

The lean solvent is pumped back to the absorber after being regenerated. Since the solvent is subject

to losses during the process, a make-up is required before re-entering the absorber. Some of these

losses are due to the solvent’s reaction with oxygen or other impurities, which produce compounds,

typically called heat stable salts (HSS) that are not regenerable in the stripper. These compounds are

removed through a reclaiming process, applied to a split stream of the lean solvent, which consists in

the application of a strong base, such as potassium hydroxide, at elevated temperatures. Besides this

treatment, an activated carbon filter is usually used before the solvent’s make-up [3].

A conventional flowsheet for a PCC plant is shown in Figure 2.4. There are several

modifications that can be applied to this flowsheet in order to reduce the energy requirements and to

cope with the solvent’s properties.

Figure 2.4 – Conventional flowsheet for a PCC plant [18].

The chemical solvent most commonly used for carbon capture is an aqueous solution of the

primary amine monoethanolamine (MEA). Besides primary amines, secondary, tertiary and hindered

amines are also considered for carbon capture, as well as aqueous solutions of ammonium salts,

amino-acid salts or potassium carbonate. In the following sections, it is presented, for each kind of

chemical solvent, the mechanisms involved in the CO2 dissolution and the processes that are presently

available and/or under development.

10

2.3.1. Primary and secondary amine based processes

Alkanolamines are organic compounds that include an amine and a hydroxyl groups. When

compared with other amine compounds, the alkanolamines are typically preferred for CO2 dissolution,

since the hydroxyl group confers a reduced vapour pressure, an adequate basicity for the contact with

acid gases and an elevated solubility in water, as well as an elevated dielectric constant, which prevents

a liquid phase separation or salts precipitation [21].

Primary and secondary alkanolamines (simply named amines), differ from tertiary ones due to

the presence of at least one hydrogen atom in the amine group, which increases the molecule reactivity.

This way, primary and secondary amines react strongly with CO2 and at elevated reaction rates.

To cope with the elevated reactivity of primary or secondary amines, they can be modified so

that the carbon atom bounded to the amine group is secondary or tertiary, thus sterically hindering the

amine and reducing its reactivity. Examples of these amines are 2-amino-2-methyl-1-propanol and 2-

piperidineethanol [22].

2.3.1.1. Reaction with CO2

Primary or secondary amines (generally represented by 𝑅𝑅′𝑁𝐻) react with CO2 producing a

compound called carbamate (𝑅𝑅′𝑁𝐶𝑂𝑂−). This occurs through the formation of an intermediary

zwitterion (𝑅𝑅′𝑁𝐻+𝐶𝑂𝑂−), which corresponds to the mechanism’s slow step [23]. This mechanism can

be represented by equations (2.1) and (2.2).

𝐶𝑂2 + 𝑅𝑅′𝑁𝐻 ⇆ 𝑅𝑅′𝑁𝐻+𝐶𝑂𝑂− (2.1)

𝑅𝑅′𝑁𝐻+𝐶𝑂𝑂− + 𝑅𝑅′𝑁𝐻 ⇆ 𝑅𝑅′𝑁𝐶𝑂𝑂− + 𝑅𝑅′𝑁𝐻2+ (2.2)

Besides these reactions, CO2 is also converted into bicarbonate through the reaction described

in equation (2.3). Nevertheless, for loadings below 0.5 molCO2/molamine, the bicarbonate formation can

be considered insignificant when compared with the carbamate formation [24].

𝐶𝑂2 + 𝑅𝑅′𝑁𝐻 + 𝐻2𝑂 ⇆ 𝐻𝐶𝑂3− + 𝑅𝑅′𝑁𝐻2

+ (2.3)

In the case of hindered amines, these molecules form a carbamate of low stability due to the

large group connected to the amine group. This unstable carbamate is hydrolysed (equation (2.4)),

originating a bicarbonate ion, [22].

𝑅𝑅′𝑁𝐶𝑂𝑂− + 𝐻2𝑂 ⇆ 𝐻𝐶𝑂3− + 𝑅𝑅′𝑁𝐻 (2.4)

Combining the equations (2.1), (2.2) and (2.4), it can be observed that for hindered amines the

global reaction is equivalent to the reaction described by equation (2.3), being the reaction stoichiometry

of 1:1 between CO2 and the amine. In the case of un-hindered amines, the global stoichiometry is 1:2

between CO2 and the amine. This difference in the global stoichiometry leads to a maximum loading of

0.5 molCO2/molamine for unhindered amines, while hindered amines present a maximum loading of 1

molCO2/molamine [22].

2.3.1.2. Available processes

From the amines used as solvents for carbon capture, monoethanolamine (MEA) is the most

reactive, and therefore, the one used in most of the commercially available processes. The elevated

reactivity allows acceptable reaction rates at low CO2 partial pressures, which makes MEA the most

common choice in post-combustion capture [13].

11

Since higher amine concentrations lead to a reduction of the equipment size and the process

thermal and pumping requirements (the solvent flow rate is reduced), it is important to keep it as

elevated as possible. Nevertheless, due to its reactivity, MEA requires a considerably elevated heat of

regeneration and is highly prone to oxidative and thermal degradation, which limits the solvent

concentration. The application of oxidation inhibitors allows the increase of MEA concentration to the

typical value of 30% in weight [25]. Besides this, the solvent concentration is also limited by its tendency

to slip along the treated gas [13].

Amine-based processes are also conditioned by temperature, due to possibility of the

carbamate polymerization. In the case of MEA, this thermal degradation is negligible if the regeneration

temperature is kept below 110 to 120 °C [26].

For a coal-fuelled power plant, with a CO2 capture rate of 90% and a MEA concentration of 30

wt%, the energy required in the regeneration process is about 3.7 GJ/tCO2 [27].

Table 2.2 presents some of the commercially available processes for carbon capture, which

employ MEA or proprietary solvents containing a blend of sterically hindered amines. Most of them are

already used in other industries and pilot plants that received a side stream from a power plant flue gas.

Therefore, they just require an adequate scale-up to be applied in a full scale power plant.

Table 2.2 – Commercially available processes using primary or secondary amine-based solvents [3, 28, 29].

Process Solvent characteristics Process characteristics Other applications

ABB Lummus Cress

15 to 20 wt% of MEA. Conventional process. Soda ash production

Fluor’s Econamine FG

PlusSM

30 wt% of MEA;

Proprietary oxidation inhibitor.

Split flow configuration;

Absorber’s intercooling;

Lean vapour compression.

Beverages and urea production

MHI’s KM-CDR

KS1TM (blend of sterically hindered amines).

Lean vapour compression;

Reboiler’s condensate used for stripping.

Urea production

As can be seen in Table 2.2, the Fluor’s Econamine FG Plus process uses a solvent with a

MEA concentration of 30 wt% through the use of a proprietary inhibitor. According to [18], it is claimed

that Fluor’s Econamine FG PlusSM is able to reduce the steam consumption in 30%, when compared to

a conventional process using MEA. This technology is used in 25 capture units worldwide, working on

a portion of flue gas obtained from natural gas burning.

In order to improve the capture efficiency, the Fluor’s Econamine FG PlusSM process uses

several modifications to the conventional flowsheet. The absorber’s intercooling is based on the removal

of a fraction of the liquid circulating in the absorber, which is cooled to a selected temperature. This

temperature reduction improves the physical absorption of CO2 and leads the chemical absorption

equilibrium to the formation of products, increasing the capture rate and decreasing the solvent’s

circulation rate. According to Ahn et al. [7], this allows a reboiler heat duty reduction of about 3%,

comparing to an amine-based conventional process.

A lean vapour compression configuration is based on the lean solvent flashing at low pressure

and temperature. The obtained vapour is compressed and returns to the stripping column acting as a

stripping agent, thus reducing the steam consumption in the reboiler. The temperature reduction in the

lean solvent also leads to reduction in the cooling. According to [7], this leads to a reduction of 22% in

12

the reboiler heat duty. On the other hand, according to [28], this would also lead to an increase in the

cost of the equipment and in the electricity consumption.

The split flow-configuration is based on the splitting of the rich solvent into two streams to which

are applied different regeneration processes. This can occur through the rich solvent feeding at different

points of the stripping column (called double section column in [8] and [7]) or using separate

regeneration equipment, which can be two stripping columns (called multi-feed stripper in [8]) or a

stripping column and a flash drum (shown in [30]). In all of these configurations, one of the rich solvent

streams undergoes a less intensive stripping process, at lower temperature or pressure, thus reducing

the reboiler heat requirement, and originating a semi-lean solvent. In the case of only one regeneration

column is used, the semi-lean solvent is a liquid stream removed from a mid-point of the stripper. The

obtained semi-lean solvent is then fed to a mid-point of the absorption column, enhancing the average

driving force in the column and reducing the temperature (called multi-feed absorber in [8]). An example

of the combination of a flash drum and a stripping column in the Fluor’s Econamine FG PlusSM process

is presented in the patent US 7,337,967 B2 [30], and its flowsheet is presented in Figure 2.5.

Figure 2.5 – Split-flow configuration of the Fluor’s Econamine FG PlusSM process [30].

MHI’s KM-CDR process applies a proprietary solvent composed by sterically hindered amines

in order to reduce the heat requirements and the solvent degradation. According to [18], it is claimed

that this process is able to reduce the steam consumption in 32%, compared to a conventional MEA-

based process. This process is commercially available for post-combustion in natural gas fuelled power

plants, while is in a demonstration phase for coal-fired plants [29].

2.3.2. Tertiary amine based processes

Unlike primary or secondary amines, tertiary amines do not possess hydrogen atoms bounded

to the amine group. Therefore, the carbamate formation mechanism is not possible, which leads to

considerably slower capture kinetics. On the other hand, these solvents allow higher CO2 loadings in

the solvent and require less heat in the regeneration process [3].

13

The reduced capture rates are usually compensated with the addition of a more reactive amine,

such as triethylenetetramine [31] or piperazine [32]. Likewise, tertiary amines are also used as inhibitors

in order to reduce the high reactivity of primary and secondary amines [33, 34].


As mentioned, tertiary amines (𝑅𝑅′𝑅′′𝑁) are not able to react directly with CO2 in order to form

a zwitterion. As opposed to primary and secondary amines, the amine base-catalytic effect on the CO2

conversion into HCO3- is the relevant one, being described by equations (2.5) and (2.6), which are

equivalent to the reaction in equation (2.3) [22].

𝐶𝑂2 + 𝑂𝐻− ⇆ 𝐻𝐶𝑂3− (2.5)

𝑅𝑅′𝑅′′𝑁 + 𝐻2𝑂 ⇆ 𝑅𝑅′𝑅′′𝑁𝐻+ + 𝑂𝐻− (2.6)

Considering this mechanism, the limiting step is the CO2 hydrolysis. Hence, tertiary amines

present a considerable reduced reaction rate when compared with primary or secondary amines. Since

the global stoichiometry is 1:1 between CO2 and the amine, the maximum loading is1 molCO2/molamine.

2.3.2.2. Processes in development

The possibility of achieving higher loadings and the lower heat of reaction make tertiary amines

a suitable choice not only for PCC, but also for pre-combustion capture. Most of these processes are

based methyldiethanolamine (MDEA), [3]. Two examples of processes using tertiary amine-based

solvents are shown in Table 2.3.

Table 2.3 – Available processes using tertiary amine-based solvents [35, 36].

Process Solvent characteristics Process characteristics

Praxair’s Amine 20 to 40 wt% of MDEA;

10 to 20 wt% of MEA. Oxygen tolerant process.

Shell’s Cansolv CO2 Capture

System

Customizable: o DC-103: low energy consumption; o DC-103B: improved kinetics.

Integrated separation of SO2 and CO2;

Absorber intercooling;

Lean vapour compression.

According to [35], pilot tests were being conducted in order to improve Praxair’s technology.

This process uses a maximum amine concentration of 50 wt%, blending a tertiary amine with a primary

amine. To cope with the oxidation problems associated with such an elevated concentration, the

process flowsheet is modified in order to include a vacuum flash separation in the rich solvent stream,

releasing the dissolved oxygen (Figure 2.6).

14

Figure 2.6 – Praxair’s Amine process flowsheet [35].

Shell’s Cansolv CO2 Capture System relies on a solvent that can be customized in order to

improve the capture kinetics or to reduce the energy consumption. According to [37], this solvent

comprises 25 to 40 wt% of a tertiary amine (MDEA, triethanolamine or N,N’-di-(2-

hydroxyethyl)piperazine), 3 to 6 wt% of piperazine and 5 to 25 wt% of N-(2-hydroxyethyl)piperazine.

The process flowsheet is shown in Figure 2.7, and is the one currently applied at the Boundary Dam

Power Station, the first commercial-scale PCC system applied at a coal-fired power plant in the world

[38, 39].

Figure 2.7 – Shell’s Cansolv CO2 Capture System flowsheet [38].

Besides the flowsheet presented in the figure above, Shell’s Cansolv process can integrate

both CO2 and SO2 capture in a single absorption equipment, using an appropriate solvent in two

different sections. It also uses the SO2 regeneration overhead stream as a heat source for the CO2

regeneration [36].

15

2.3.3. Ammonia based processes

Absorption with aqueous ammonia is widely used in the gas industry for gases sweetening, and

poses several advantages for carbon capture. When compared with conventional amine solvents,

ammonia is readily available and has a reduced cost. It is also more resistant to degradation, requires

a lower heat of regeneration and allows higher solvent loadings [40]. On the other hand, ammonia

solvents present slower kinetics than MEA and considerably higher volatility, which leads to a higher

solvent loss. To cope with this loss, the absorption is usually conducted at lower temperature (from 0

to 10 °C) and the regeneration is carried at higher pressure (from 2 to 136 bar) [13].


Carbon dioxide absorption in aqueous ammonia solution is based on the equilibrium between

ammonium carbonate ((𝑁𝐻4)2𝐶𝑂3) and ammonium bicarbonate (𝑁𝐻4𝐻𝐶𝑂3), which is described by

equations (2.7) to (2.11), [40].

𝑁𝐻3 + 𝐻2𝑂 ⇆ 𝑁𝐻4+ + 𝑂𝐻− (2.7)

𝐶𝑂2 + 2𝐻2𝑂 ⇆ 𝐻𝐶𝑂3− + 𝐻3𝑂+ (2.8)

𝐻𝐶𝑂3− + 𝐻2𝑂 ⇆ 𝐶𝑂3

2− + 𝐻3𝑂+ (2.9)

𝑁𝐻4+ + 𝐻𝐶𝑂3

− ⇆ 𝑁𝐻4𝐻𝐶𝑂3 (2.10)

2𝑁𝐻4+ + 𝐶𝑂3

2− ⇆ (𝑁𝐻4)2𝐶𝑂3 (2.11)

Considering the CO2 loading, the rich solvent will be mainly composed of ammonium

bicarbonate, while the lean solvent will be mainly composed of ammonium carbonate. Due to the

absorption low temperature, these species tend to precipitate, and the solvent can be obtained as a

slurry [40].


One of the major concerns about the ammonia-based processes is the solvent slip due to its

vaporization. This loss can be reduced with an appropriate water washing system or by reducing either

the ammonia concentration in the solvent or the absorption temperature. Nevertheless, the reduction

of concentration, leads to a decrease of the solvent’s capacity, while the temperature reduction causes

a decrease in the reaction rate [40].

Table 2.4 presents processes based on ammonia solvents, which are already operating in pilot

plants and ready to proceed towards commercialization [40].

Table 2.4 – Processes in development using ammonia-based solvents [40].


Alstom’s Chilled Ammonia

28 wt% of NH3.

Flue gas cooled to approximately 0 °C;

Stripping at a pressure from 20 to 40 bar;

Washing water regenerated in a stripper.

CSIRO Process 6 wt% of NH3. Flue gas cooled to approximately 10 °C;

Stripping at a pressure from 3 to 8.5 bar.

KIER Process 13 wt% of NH3. Flue gas cooled to approximately 25 °C;

Stripping at 6.5 bar.

RIST Process Less than 10 wt% of NH3.

Flue gas cooled to approximately 40 °C;

Stripping at 1 bar and 80 °C;

Washing water regenerated in a stripper.

16

From the processes mentioned in the previous table, Alstom’s Chilled Ammonia is the one

closest to commercialization. In this case the solvent is used in the form of a slurry and the absorption

is conducted at a temperature between 0 and 10 °C. As can be seen in Figure 2.8, the liquid effluent of

the washing water process is stripped in order to obtain a gas stream with the recovered ammonia,

which is later fed to the main stripper. The reduced absorption temperature associated with the washing

water process allows a reduction in the solvent loss, contributing to improve the process economic

feasibility. Nevertheless, the need to cool the flue gas to such a low temperature may render the process

unfeasible unless a source of appropriate cold water is available [40].

Figure 2.8 – Alstom’s Chilled Ammonia process flowsheet [40].

2.3.4. Amino acid salts based processes

Another class of chemical solvents currently being tested to capture carbon dioxide from flue

gases is aqueous solutions of amino acid salts. Unlike amines and ammonia, amino acids have a

significantly low vapour pressure, leading to lower solvent losses through vaporization. They are also

less toxic and more resistant to degradation caused by the contact with gases such as oxygen [41].


Similarly to amines, amino acids are able to form carbamates, when the amine group is primary

or secondary (equation (2.12)). For amino acids with hindered amine groups, the species predominantly

formed is the bicarbonate ion (equation (2.13)), [42].

𝐶𝑂2 + 2( 𝐾+ 𝐶𝑂𝑂−𝑅𝑅′𝑁𝐻) ⇄ (𝐾+)2 𝐶𝑂𝑂−𝑅𝑅′𝑁𝐶𝑂𝑂− + 𝐶𝑂𝑂−𝑅𝑅′𝑁𝐻2+ (2.12)

𝐶𝑂2 + 𝐾+ 𝐶𝑂𝑂−𝑅𝑅′𝑁𝐻 + 𝐻2𝑂 ⇄ 𝐻𝐶𝑂3− + 𝐾+𝐶𝑂𝑂−𝑅𝑅′𝑁𝐻2

+ (2.13)

Besides the carbamate species, amino acids with primary or secondary amine groups also form

zwitterions with reduced solubility, which tend to precipitate.


Amino acids such as glycine, alanine, proline, and taurine have been used as promoters for

gas purifying processes, and are now being considered for CO2 removal from flue gases. Siemens has

17

developed the Postcap process based on the bicarbonate formation, using a proprietary solvent in a

conventional flowsheet. According to [43], this process is suited for capture in IGCC plants and has

been tested in pilot plants, being ready for a full plant scale up.

2.3.5. Hot potassium carbonate based processes

Hot aqueous solutions of potassium carbonate have been used for the removal of carbon

dioxide from gaseous streams since 1954. Compared to conventional amine solvents, this solvent

allows the absorption to occur at higher temperature, thus reducing the heat requirement in the

regeneration process. Besides this, potassium carbonate is less expensive, shows reduced toxicity and

less propensity to degradation. Nevertheless, hot potassium carbonate solvents present a considerably

reduced reaction rate, which renders its unpromoted use unfeasible for PCC [44]. In the same way as

tertiary amine-based processes, promoters, such as piperazine, diethanolamine, arsenic trioxide and

boric acid, are blended with potassium carbonate in order to improve the process kinetics [45]. On the

other hand, since this is a process suited for absorption at high CO2 partial pressure, it presents an

option to IGCC, showing a lower capital cost, when compared with processes based on physical

solvents [45].


As in tertiary amines processes, potassium carbonate does not react directly with CO2. Instead,

the carbonate ions increase the solvents alkalinity, thus improving the CO2 conversion to bicarbonate

ions, as can be seen through equations (2.15) and (2.16) [45].

𝐾2𝐶𝑂3 ⇄ 2𝐾+ + 𝐶𝑂32− (2.14)

𝐶𝑂2 + 𝑂𝐻− ⇄ 𝐻𝐶𝑂3− (2.15)

𝐶𝑂32− + 𝐻2𝑂 ⇄ 𝐻𝐶𝑂3

− + 𝑂𝐻− (2.16)


The application of hot potassium carbonate solvents to flue gases is an under development

technology, only tested in a pilot scale. Nevertheless, there are several commercial processes applied

to other industries, such as the ammonia production [46]. Two of these processes are presented in

Table 2.5.

Table 2.5 – Available processes using hot potassium carbonate-based solvents [47, 48, 46].


UOP’s Benfield

Hot K2CO3;

Corrosion inhibitor;

ACT-1™ activator.

Different flowsheets available.

Giammarco-Vetrocoke’s Low

Energy

Hot K2CO3;

Glycine;

Secondary Amine.

Split-stream configuration;

High and low pressure strippers;

Semi-lean and lean streams obtained in the high pressure stripper used as stripping agents at the low pressure stripper;

Semi-lean and lean solvent obtained at the low pressure stripper fed at different points of the absorber.

18

2.4. Processes Based on Physical Solvents

Physical absorption for acid gas removal, such as H2S and CO2, is a well-known technology

applied in the oil, gas and chemical industries. In this processes, the CO2 partial pressure is high enough

to obtain an acceptable solvent loading using a physical solvent. This can be observed for the solvent

Rectisol® in Figure 2.9, where at higher partial pressures the physical solvent offers higher loadings

then the chemical ones.

Figure 2.9 - CO2 bulk removal capacity for different solvents [49].

Besides the CO2 partial pressure, the H2S content in the syngas is also considerable, due to

the presence of of sulphur compounds in the fuel. This acid gas can be removed with CO2, if no

restrictions are imposed concerning the gas transportation, or in a similar absorption process before

the CO2 capture. Nevertheless, when H2S is selectively captured, a thermal regeneration is required

and the tail gas obtained is then sent to a sulphur recovery unit [3]. Figure 2.10 and Figure 2.11 show

examples of flowsheets for combined and selective capture, respectively.

Figure 2.10 – UOP SelexolTM process flowsheet for CO2 and H2S co-capture [50].

19

Figure 2.11 – Lurgi Rectisol® process flowsheet for CO2 and H2S selective capture [49].

2.4.1. Available technology

The main processes using physical absorption for acid gas removal are SelexolTM, Rectisol®,

Fluor SolventSM and Purisol®. Each of these solvents presents different specifications, such as

selectivity towards H2S and CO2 or temperature of operation. Table 2.6 shows the main characteristics

of these processes.

Table 2.6 – Commercially available processes using physical solvents [50, 51, 49].

Process Solvent Chemical

Designation Solvent Characteristics Process characteristics

UOP SelexolTM

Dimethylether of polyethylene glycol.

Absorbs CO2, H2S and mercaptans;

Becomes less efficient at low temperatures.

Selective capture of H2S;

CO2 solvent regeneration achieved through flashing;

Doesn’t require reclaiming, purging or water washing.

Lurgi Rectisol®

Methanol.

High volatility;

Solubility towards CO2 is the highest of physical solvents.

Absorption occurs at a temperature between -62 and -40 °C;

Requires water washing;

Allows a selective capture of H2S;

CO2 solvent regeneration achieved through flashing;

Shift reaction occurs after desulphurization.

Fluor SolventSM

Propylene Carbonate.

Reduced vapour pressure;

Low selectivity towards H2S;

Decomposes at elevated temperatures,

Low solvent capacity.

Selective capture of H2S is not recommended, due to thermal degradation;

Solvent regeneration achieved through a series of flashes from high pressure to vacuum.

Lurgi Purisol®

N-methyl-2-pyrolidone.

High vapour pressure;

Solubility towards H2S is the highest of physical solvents.

Operation from -15 °C to room

temperature;

Requires water washing.

20


21

3. Materials and Methods

3.1. gPROMS® ModelBuilder

Throughout the present thesis, gPROMS® ModelBuilder 3.7.1 is the simulation platform used

for flowsheet model simulation and optimisation. This advanced modelling tool can be used for the

development, validation, execution and deployment of models, using fast and robust numerical solvers.

Starting from an existing model library, such as the gCCS® library, it is possible to assemble a

flowsheet, through a drag and drop system, in the topology tab of a model entity. This way, a model is

built, being composed by the models retrieved from the library and the respective connections.

Nevertheless, in the gPROMS language tab of the same model, it is possible to include any other

auxiliary equations and custom sub models. Through the inclusion of the created model on a process

entity, it is possible its simulation.

Another feature of gPROMS® ModelBuilder is the optimisation tool, which can be used to

optimise the steady state and/or dynamic behaviour of a continuous flowsheet, considering both design

and operation properties. The optimisation procedure acts on a specified process, and requires the

indication of the unassigned variables acting as objective function or process constraints and the

assigned variables, which work as decision/control variables.

Since the models considered in this thesis are non-linear, the optimisation problem constitutes

a nonlinear programming problem (NLP). gPROMS® ModelBuilder 3.7.1 uses the SRQPD solver in the

solution of NLP problems, with an “Improved Estimation Based” convergence criterion [52].

3.2. gCCS® Capture Library

The gCCS® libraries contain steady-state and dynamic models for all the major operations along

the CCS chain. These high-fidelity models are based on rigorous physical properties and can be

implemented in the gPROMS® ModelBuilder environment. The capture library includes the different

models required for the simulation of a chemical-based post-combustion capture unit. In the following

sections these models are briefly described.

3.2.1. Chemical Absorber (A)

Figure 3.1 – Chemical absorber/stripper icon used in the gCCS® capture library.

One dimensional column model based on the gPROMS Advanced Model Library for Gas–Liquid

Contactors (AML:GLC). This rate-based model is based on the two-film theory, according to which the

liquid and gas bulk phases transfer heat and mass across liquid and gas films, separated by an

infinitesimally thin interface. It is considered chemical equilibrium in both bulk and films phases, as well

22

as, at the interface, where it is also assumed phase equilibrium. The heat and mass transfer in the two

films are modelled according to Fick’s law and the gSAFT thermodynamic model (see sections 3.3) is

used for the prediction of physical properties of each phase.

For the pressure drop correlation the model can apply the dry bed factor or Billet & Schultes

correlations. For the calculation of mass transfer coefficients, this model considers Onda or Billet &

Schultes correlations (described in references [53] and [54], respectively), for which the required

specifications are presented in Figure 3.2. Besides the specifications presented, the absorber model

also requires the indication of the column’s height and diameter, the position of the temperature sensor

and the number of active columns.

Figure 3.2 – Parameters required for Onda correlation (on the left) and for Billet & Schultes correlation (on the right).

3.2.2. Chemical Stripper (ST)

The chemical stripper model is similar to the chemical absorber model except by the change in

the thermodynamic model for physical properties prediction. Even though gSAFT is still used, it is

modified in order to be more suited for the typical stripping operating conditions.

3.2.3. Condenser (C)

Figure 3.3 – Condenser icon used in the gCCS® capture library.

This model is used for the simulation of a dynamic partial condenser, considering the use of a

utility such as water. It is assumed equilibrium between liquid and vapour phases, perfect mixture and

that the cooling utility inlet and outlet is exclusively liquid.

In this model, it is required the specification of the utility stream pressure drop and temperature

increment, and, if working in calibration mode, the operating temperature. The volume and diameter of

the vessel have to be also specified, not being relevant if operating in a steady state flowsheet.

23

3.2.4. Flow Multiplier (FM)

Figure 3.4 – Flow multiplier icon used in the gCCS® capture library.

Model applied for the multiplication of a process stream’s flow rate. It requires the specification

of the value by which the flow rate will be multiplied.

3.2.5. Heat Exchanger (HX)

Figure 3.5 – Heat exchanger icon used in the gCCS® capture library.

Steady state model that simulates the heat exchange between two process streams, which may

present a co-current or counter-current configuration. It is assumed a fixed heat transfer coefficient and

pressure drop, which must be specified. No heat losses to the exterior or fouling are also assumed.

In calibration mode, the outlet temperature of either the cold or the hot stream must be specified,

or alternatively the temperature approach in one of the heat exchanger sides. For operational mode,

the heat exchange area has to be specified.

3.2.6. Heat Exchanger Process/Utility (HXU)

This model is similar to the heat exchanger model, being one of the contacting streams a utility.

It requires the specification of the heat exchange applied to the process stream, if it is being cooled of

heated.

In operational mode, besides the outlet temperature for the process stream, the temperature

difference for the utility stream must also be specified, if its flow rate is set as calculated.

3.2.7. Junction (M)

Figure 3.6 – Junction icon used in the gCCS® capture library.

Steady state model applied to the mixing and/or splitting of a variable number of streams. In

this model, it is assumed no heat losses or pressure drop, ideal mixing and equal temperature, pressure

and composition for the outlet streams.

The outlet pressures may be equalised or set to be equal to the minimum inlet pressure. The

outlet flow rates may be set to be calculated by the downstream units, or calculated by specifying all

but one split fraction/percentage. The system phase also has to be specified for initialisation purposes.

24

3.2.8. Process Sink (S)

Figure 3.7 – Process sink icon used in the gCCS® capture library.

Model applied for the definition of a process stream leaving the flowsheet. It may be used for

the calculation of a cumulative flow rate, in dynamic operation. Both the pressure and the flow rate may

be calculated by upstream units or specified.

3.2.9. Process Source (SR)

Figure 3.8 – Process source icon used in the gCCS® capture library.

This model provides the introduction of a material flow into the flowsheet. It is assumed to have

infinite capacity, being able to calculate the cumulative solvent consumption in dynamic operation. The

property estimation method is defined by the solvent choice and the section of operation, that is,

absorption or stripping.

As inputs, this model requires the specification of the temperature, composition and phase. The

flow rate and the pressure can be chosen as specified or calculated by a downstream unit.

3.2.10. Pump Simple (P)

Figure 3.9 – Pump simple icon used in the gCCS® capture library.

Isothermal and isenthalpic steady state model, used for the specification of a stream flow rate

(manual mode) or its inlet/outlet pressures (advanced mode). The flow rate specification can be either

done manually (through a value input) or set through the models control signal port, using a controller

model. Due to the model’s simplicity, it can be used for the simulation of the pressure increase or

decrease of both liquid and gas streams.

3.2.11. Stream Converter Absorber/Stripper (SC)

Figure 3.10 – Stream converter absorber/stripper icon used in the gCCS® capture library.

This model is required for the transition between physical properties package used in the

absorber and the stripper models, due to the different operating conditions. It requires the specification

of the conversion direction and the outlet stream phase (liquid or gas).

25

3.2.12. Reboiler (R)

Figure 3.11 – Reboiler icon used in the gCCS® capture library.

Dynamic model with energy and mass holdup for simulation of a boiling MEA solution. In this

model, it is assumed equilibrium between liquid and vapour phase, perfect mixing and the total

condensation of the steam used as utility.

This model requires the specification of the steam pressure drop, initial phase, volume and

diameter. In calibration mode the operating temperature must also be specified. For operational mode,

it is only required a temperature initial guess.

If the dynamic mode is activated, the reboiler pressure must be specified, as well the initial

conditions. If heat losses are activated, the overall heat transfer coefficients must be specified for both

liquid and vapour phases, as well as the ambient temperature.

3.2.13. Recycle Breaker (RB)

Figure 3.12 – Recycle breaker icon used in the gCCS® capture library.

This is a model without physical meaning, being used for the initialisation of a recycle loop.

Considering the initial state as open, the inlet or outlet pressure and/or flow rate must be specified, as

well as the temperature, composition and phase. The final state can be specified as open, acting as a

source/sink, closed, and thus connecting the inlet and outlet streams, or make-up.

For the make-up final state, it is possible to calculate the amount of a specified solvent required

to fulfil the respective mass fraction and total flow rate. For that, it is required to indicate the component

not-closed and the solvents with make-up. In the present thesis, these are H2O and MEA respectively.

3.2.14. Utility Sink (SU)

Figure 3.13 – Utility sink icon used in the gCCS® capture library.

Similar to the process sink, this model is used to define a utility stream leaving the flowsheet.

3.2.15. Utility Source (SRU)

Figure 3.14 – Utility source icon used in the gCCS® capture library.

Model applied to the introduction of a water-based utility flow in the flowsheet. As in the source

capture model, the temperature and phase must be specified, while the flow rate and pressure can be

indicated or calculated by a downstream unit.

26

3.3. Physical Properties Package – gSAFT

gSAFT is a physical properties package commercialised by PSE, using the SAFT technology

developed at the Imperial College London. This technology uses the Statistical Associating Fluid Theory

(SAFT) equation of state, an advanced molecular thermodynamic method, based in physically-realistic

models of molecules and their interactions with other molecules. In these models it is taken into account

the shape, size and specific interactions between the existing molecules in a mixture, which are

considered as chains of spherical segments. This is particularly relevant when considering non-

spherical molecules with strong directional interactions, such as the hydrogen bounds in a MEA-based

solvent.

The current implementation of gSAFT is based on the equation of state SAFT-VR, according to

which the attraction/repulsion between segments or molecules is modelled using the “square-well”

potential energy function. This function is characterised by the diameter of the segment, the range of

dispersion interactions and its strength. The physical basis of these parameters allows its systematic

use to describe similar molecules or predict physical properties in a wide range of conditions [55].

For carbon capture, gSAFT presents a modelling alternative to phase and chemical equilibrium

in the CO2-MEA-H2O system. Using the SAFT equation of state the chemical bound between CO2 and

MEA can be incorporated as a short-range association, being included in the molecular model. This

way, the involved reactions are treated implicitly, thus greatly reducing the complexity of the model,

thus increasing its robustness. A more detailed description of the SAFT equation of state and its

application to the CO2-MEA-H2O system can be found in [56].

27

4. Models Validation

The validation of two capture unit models was achieved through the comparison of experimental

data publically available with the simulation results. For that purpose, the experimental data presented

in the papers by Tobiesen et al. [10], (Flowsheet A) and by Notz et al. [11], (Flowsheet B) were used.

4.1. Flowsheet A – Absorber Model Validation

Tobiesen et al. [10] present the experimental data obtained from twenty non-equal runs using

a pilot scale absorber (packing height of 4.36 m and internal diameter of 0.15 m). Considering the inlet

conditions shown in Table 4.1, and assuming that the flue gas is water saturated and only composed

of water, CO2 and N2, the conditions of the inlet streams were calculated and used for the process

simulation.

Table 4.1 – Characteristics of the flue gas and lean solvent used by Tobiesen et al. [10].

Run

Flue Gas Lean Solvent

Fv (m3/h)

𝒙𝑪𝑶𝟐𝒅𝒓𝒚

(%)

T

(°C) P

(bar) Fv

(L/min) Loading

(molCO2/molMEA) Concentration (kmolMEA/m3)

ρ (kg/m3) T

(°C)

1 150 1.65 40 1.0294 4 0.218 4.91 1060 39

2 150 1.57 41 1.0299 4 0.22 4.91 1056 40

3 151 1.56 40 1.0299 4 0.215 4.91 1061 39

4 151 1.57 41 1.0299 4 0.217 4.91 1058 40

5 150 2.04 51 1.0038 4 0.216 4.91 1059 54

6 150 2.41 50 1.0217 4 0.183 4.93 1056 54

7 150 3.03 49 1.018 6 0.284 4.63 1069 52

8 148 2.41 48 1.0204 6 0.241 4.74 1062 50

9 152 3.19 61 1.0147 6 0.233 4.71 1059 62

10 151 2.81 48 1.0117 3 0.217 4.97 1058 54

11 151 2.16 48 1.0124 3 0.219 4.93 1059 53

12 151 2.96 46 1.0183 3 0.307 4.99 1080 51

13 151 6.65 60 1.0197 6 0.297 5.05 1080 64

14 153 4.34 48 1.0305 3 0.37 5.06 1092 51

15 143 12.12 59 1.0338 9 0.357 5.1 1090 62

16 151 9.44 61 1.0396 6.2 0.402 5.1 1100 60

17 144 15.33 66 1.0306 9 0.409 5.09 1102 68

18 151 12.50 62 1.038 8 0.346 5.13 1090 69

19 142 8.35 64 1.0211 9 0.347 5.17 1088 66

20 142 4.54 64 1.0214 9 0.292 5.22 1084 64

In the twenty runs, it was used Sulzer’s structured packing Melapack 250YTM. Since the

absorber model can use either Onda or Billet & Schultes correlations (described in [53] and [54],

respectively), there are several parameters that depend on the packing characteristics, which are

required (Figure 3.2). For Sulzer Mellapak 250YTM, the relevant parameters are shown in Table 4.2.

28

Table 4.2 - Characteristic data and constants for Sulzer Mellapak 250YTM [54, 57].

Size 250Y

Specific area (m2/m3) 250

Void fraction 0.970

Cs 3.157

CFl 2.464

Ch 0.554

CP,0 0.292

Dry bed packing factor 66

For each run, both Billet & Schultes correlation and Onda correlation were applied, in order to

verify which one presents a smaller deviation, when compared with the experimental results. Therefore,

it was considered the flowsheet presented in Figure 4.1, where using the same sources, simulations

were conducted using Billet & Schultes correlation (a models) and Onda correlation (b models). In this

flowsheet besides the absorber, source and sink models, a pump model without any specification is

needed before the absorber liquid inlet (P-101a/b) in order to provide the required pressure for the liquid

phase in the absorber model.

Figure 4.1 – Flowsheet A used for validation (a models use Billet & Schultes correlation and b models use Onda

correlation).

Considering the results obtained, the validation of the absorber model was based on the

comparison of the rich solvent loading and the absorbed amount of CO2, being these variables

calculated using the molar fractions (𝑥𝑖𝑠), mass fractions (𝑤𝑖

𝑠) and mass flow rates (𝐹𝑠) of the rich solvent

(RS), flue gas (FG) and treated (TG) gas streams, through equations (4.1) and (4.2).

𝑅𝑖𝑐ℎ 𝐿𝑜𝑎𝑑𝑖𝑛𝑔 (𝑚𝑜𝑙𝐶𝑂2𝑚𝑜𝑙𝑀𝐸𝐴⁄ ) =

𝑥𝐶𝑂2𝑅𝑆

𝑥𝑀𝐸𝐴𝑅𝑆 (4.1)

𝐴𝑏𝑠𝑜𝑟𝑏𝑒𝑑 𝐶𝑂2 (𝑘𝑔 ℎ⁄ ) = 𝑤𝐶𝑂2𝐹𝐺 . 𝐹𝐹𝐺 − 𝑤𝐶𝑂2

𝑇𝐺 . 𝐹𝑇𝐺 (4.2)

The simulation results and experimental data are presented in Table 4.3 and Table 4.4. The

deviation between them, calculated through equation (4.3).

𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 (%) =𝑣𝑠𝑖𝑚 − 𝑣𝑒𝑥𝑝

𝑣𝑒𝑥𝑝× 100 (4.3)

29

Table 4.3 – Experimental rich loading and simulation results using both Billet & Schultes correlation and Onda

correlation.

Run

Rich Loading (molCO2/molMEA)

Experimental Simulated

(Billet & Schultes Correlation) Deviation

(%) Simulated

(Onda Correlation) Deviation

(%)

1 0.284 0.295 4 0.255 -10

2 0.275 0.292 6 0.255 -7

3 0.272 0.288 6 0.250 -8

4 0.276 0.290 5 0.252 -9

5 0.274 0.294 7 0.255 -7

6 0.267 0.280 5 0.232 -13

7 0.345 0.364 6 0.328 -5

8 0.296 0.309 4 0.277 -6

9 0.299 0.309 3 0.274 -9

10 0.333 0.359 8 0.286 -14

11 0.309 0.331 7 0.272 -12

12 0.401 0.415 4 0.371 -8

13 0.39 0.401 3 0.366 -6

14 0.443 0.445 1 0.430 -3

15 0.435 0.438 1 0.428 -2

16 0.447 0.451 1 0.444 -1

17 0.451 0.449 -0.4 0.447 -1

18 0.429 0.425 -1 0.414 -3

19 0.4 0.408 2 0.393 -2

20 0.339 0.345 2 0.323 -5

As can be observed from the table above, the deviations verified for the rich solvent loading are

considerably lower than the ones verified for the absorbed amount of CO2. Therefore, it is possible to

conclude that the rich solvent loading is less sensible to errors, being the absorbed amount of CO2 a

better measure of the models accuracy. Comparing the values obtained using the different correlations,

simulation results show that, when the Onda correlation is used, the absorbed CO2 tends to be under-

calculated, while Billet & Schultes correlation usually predicts values above the experimental ones. It

can also be observed that Onda correlation leads to deviations between -44% and -10%, while Billet &

Schultes correlation leads to lower deviations, between -13% and 26%. Furthermore, calculating the

mean squared error of prediction (MSEP in equation (4.4)), for the absorbed amount of CO2, it has the

value of 0.45, when Billet & Schultes correlation is applied, and 2.44, when Onda correlation is applied.

𝑀𝑆𝐸𝑃 =∑ (𝑣𝑖

𝑠𝑖𝑚 − 𝑣𝑖𝑒𝑥𝑝

)2𝑁𝑟𝑢𝑛𝑠

𝑖=1

𝑁𝑟𝑢𝑛𝑠

(4.4)

Similar conclusions are retrieved from the rich loading analysis, where the MSEP is 0.0002 and

0.11, using Billet & Schultes and Onda correlations, respectively. Therefore, Billet & Schultes correlation

poses as a more reliable method for the calculation of the mass transfer coefficients in the absorber

model. Nevertheless, a maximum variation of 26% is deemed acceptable, considering that this is

predictive model, i.e. obtained without any fitting to the experimental data.

30

Table 4.4 – Experimental absorbed amount of CO2 and simulation results using both Billet & Schultes correlation and

Onda correlation.

Run

Absorbed CO2 (kg/h)

Experimental Simulated

(Billet & Schultes Correlation) Deviation

(%) Simulated

(Onda Correlation) Deviation

(%)

1 3.27 3.94 21 1.91 -42

2 2.93 3.69 26 1.78 -39

3 2.98 3.73 25 1.80 -40

4 3.04 3.71 22 1.80 -41

5 3.24 3.95 22 1.97 -39

6 4.43 4.96 12 2.48 -44

7 4.66 5.76 24 3.13 -33

8 4.22 4.99 18 2.65 -37

9 5.01 5.46 9 2.94 -41

10 4.66 5.46 17 2.64 -43

11 3.64 4.28 18 2.04 -44

12 3.79 4.19 11 2.47 -35

13 7.43 8.06 8 5.33 -28

14 3.02 2.96 -2 2.34 -23

15 9.25 9.50 3 8.35 -10

16 3.88 3.92 1 3.37 -13

17 4.94 4.62 -6 4.39 -11

18 10.7 9.30 -13 8.06 -25

19 7.05 7.26 3 5.44 -23

20 5.75 6.30 10 3.74 -35

The relation between the values of absorbed CO2 obtained through simulation and the

experimental ones is shown in Figure 4.2.

Figure 4.2 – Parity diagram of the absorbed amount of CO2 (using Billet & Schultes correlation).

Through Figure 4.3, it is observable that for higher lean loadings there is a reduction in the

deviation obtained. This trend is explained by the fact that at lower lean loadings there is a higher driving

force for the CO2 in the liquid film. This higher driving force lead to a maximisation of the errors

associated with the correlation used for calculating mass transfer coefficients, thus increasing the error

in the predicted value for the CO2 flux across the liquid film and, consequently, in the calculated

absorbed amount of CO2. On the other hand, for higher loading the system is closer to equilibrium,

0

2

4

6

8

10

0 2 4 6 8 10

Sim

ula

ted

Ab

sorb

ed

CO

2

(kg/

h)

Experimental Absorbed CO2 (kg/h)

31

hence the driving force is lower, which leads to a reduced deviation in the predicted flow of absorbed

CO2.

Figure 4.3 – Deviation between experimental and simulated absorbed CO2 with the considered lean solvent loading.

Besides the deviations due to the mass transfer coefficients calculation method, it should also

be considered the uncertainty associated with the experimental measures, which Tobiensen et al. refer

to be of ±2%. It should also be taken into account that the calculation of the water content in the flue

gas was conducted assuming that the flue gas was saturated in water. According to Tobiensen et al.,

this can lead to an error of 2% in the calculation of the CO2 molar fraction in the flue gas.

Besides the rich solvent loading and the absorbed amount of CO2, Tobiensen et al. provide

temperature profiles for the absorber in four of the runs. The comparison between experimental and

simulated temperature profiles is shown in Figure 4.4 to Figure 4.6, for runs 10, 12 and 15 (Table 4.3

and Table 4.4), thus representing the different ranges of lean loadings considered. It should be noted

that the temperature probes inside the column don’t allow to determine which phase, liquid or gas, is

being measured.

From the temperature profiles it is possible to observe in the three runs a maximum close to

the column’s top (approximately at 3 m). This show that most of the absorption occurs at the top of

column where the driving force is more elevated (solvent has a lower CO2 content). Since it is

considered resistance to heat and mass transfer, this maximum is located not at the precise column’s

top, but slightly below. This increase is in good agreement with the experimental data, for all the three

cases.

Comparing the temperature values, once again for a higher lean loading (run 15) it was possible

to obtain a more accurate simulation. From the analysed runs, the maximum deviation is observed in

run 12, with a variation of approximately 3.5 °C (6%), which is deemed acceptable. According to

Tobiensen et al., these profiles are affected by the water content in the gas feed, which may have

caused the verified deviation.

-14

-4

6

16

26

0.15 0.2 0.25 0.3 0.35 0.4 0.45

De

viat

ion

in A

bso

rbe

d

CO

2(%

)

Lean Loading (molCO2/molMEA)

32

Figure 4.4 – Simulated and experimental temperature profiles for run 10 (lean loading of 0.284 molCO2/molMEA).



4.2. Flowsheet B – MEA Capture Plant Model Validation

Notz et al. article [11] presents the mass balances obtained from a capture pilot plant for 2

examples of CO2 concentrations in the flue gas. In these experiments, it is used a MEA-based solvent

with a mass fraction of approximately 30%. The pilot plant’s configuration is based on a conventional

flowsheet, except for the use of water washing columns on the top of both the absorber and the stripper.

The pilot plant design parameters and process specifications for both examples are presented

in Table 4.5 and Table 4.6, respectively.

0

1

2

3

4

45 47 49 51 53 55 57 59 61 63

He

igh

t (m

)

T (°C)

Tvap, sim

Tliq, sim

Tvap,exp

Tliq, exp

0

1

2

3

4

45 47 49 51 53 55 57 59 61 63

He

igh

t (m

)

T (°C)

Tvap, sim

Tliq, sim

Tvap,exp

Tliq, exp

0

1

2

3

4

55 57 59 61 63 65 67 69 71 73 75

He

igh

t (m

)

T (°C)

Tvap, sim

Tliq, sim

Tvap,exp

Tliq, exp

33

Table 4.5 – Pilot plant design parameters [11].

Equipment Packing Packing Height (m) Diameter (m)

Absorber Structured packing: Sulzer Mellapak

250.YTM

4.20

0.12 Desorber 2.52

Washing sections 0.42

Table 4.6 – Process specifications for examples 1 and 2 [11].

Process variables Example 1 Example 2

Flue gas mass flow rate (kg/h) 72 72.4

Flue gas temperature (°C) 48.0 48.2

Flue gas pressure (bar) 1004.5 1009.7

Flue gas CO2 partial pressure (mbar) 54.6 109.4

Solvent mass flow rate (kg/h) 200.1 200.0

Solvent inlet temperature (°C) 40.0 40.2

MEA mass fraction in the CO2 free solvent (g/g) 0.288 0.303

Desorber pressure (bar) 1.9991 1.9995

Desorber inlet temperature (°C) 112.85 108.56

Evaporator heat requirement (kW) 6.48 6.76

Absorber washing water mass flow rate (kg/h) 30.9 55.1

Absorber washing water make-up (kg/h) 1.9 1.2

Absorber washing water temperature (°C) 43.9 37.4

Stripper washing water mass flow rate (kg/h) 1.62 1.14

The flue gas composition presented by Notz et al. was calculated considering the CO2 partial

pressure in the flue gas, the air average composition, assuming saturation in water. Since the available

models in the gCCS® libraries using MEA as a solvent don’t consider the presence of oxygen in the

system, its mass fraction in the flue gas was added to the nitrogen mass fraction. Therefore, the flue

gas and lean solvent starting composition considered in both examples is presented in Table 4.7.

Table 4.7 – Flue gas composition in each example.

Stream Component Mass fraction

Example 1 Example 2

Flue Gas

CO2 0.085 0.165

H2O 0.071 0.069

N2 (N2+O2) 0.844 0.766

Lean Solvent

CO2 0.052 0.063

H2O 0.673 0.653

MEA 0.275 0.284

Considering the plant configuration used by Notz et al. and the data presented in Table 4.5 to

Table 4.7, the examples 1 and 2 were simulated, using the flowsheet shown in Figure 4.7. In this

flowsheet, the washing water processes (A-202 and A-203) were simulated using absorber models.

Recycle-breaker models (RB-201 to RB-204) were required in order to initialise the cyclic operations,

particularly RB-204, which is also used to simulate the solvent’s make-up. The stream converters

models (SC-201 to SC-204) were also necessary in order to change the thermodynamic properties

prediction model from the one suited for the absorber process, to one more proper to the stripper

operating conditions. As in the flowsheet used for Validation A, the pump models P-201, P-202, P203

and P-205 were used to change the liquid stream pressure to the one required by the column’s gas

inlet. On the other hand, the pump model P-204 is used to set the pressure in the stripping column.

34

Finally, in the four contactor models it was used Billet & Schultes correlation for the calculation of mass

transfer coefficients, as it was considered more accurate than Onda correlation in Validation A.

It should be noted that, since the evaporator heat duty is one of the process specifications and

the reboiler input is the equilibrium temperature, this parameter was modified in order to meet the

specified duty.

Figure 4.7 – Flowsheet B used for validation.

In order to validate the capture plant model, the key parameters being considered were the CO2

capture rate, the specific heat requirement, the lean solvent loading and the rich solvent loading They

are calculated through equations (4.5) to (4.8), respectively. As mentioned in section 2.3, the specific

heat consumption is a key indicator of the process cost, and considers the heat consumed in the reboiler

(QReb).

35

𝐶𝑎𝑝𝑡𝑢𝑟𝑒 𝑅𝑎𝑡𝑒 (%) = (1 −𝑤𝐶𝑂2

𝑇𝐺 . 𝐹𝑇𝐺

𝑤𝐶𝑂2

𝐹𝐺 . 𝐹𝐹𝐺) × 100 (4.5)

𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐 ℎ𝑒𝑎𝑡 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡 (𝐺𝐽 𝑡𝑜𝑛𝐶𝑂2⁄ ) =

𝑄𝑅𝑒𝑏

𝐴𝑏𝑠𝑜𝑟𝑏𝑒𝑑 𝐶𝑂2

(4.6)

𝐿𝑒𝑎𝑛 𝐿𝑜𝑎𝑑𝑖𝑛𝑔 (𝑚𝑜𝑙𝐶𝑂2𝑚𝑜𝑙𝑀𝐸𝐴⁄ ) =

𝑥𝐶𝑂2𝐿𝑆

𝑥𝑀𝐸𝐴𝐿𝑆 (4.7)

𝑅𝑖𝑐ℎ 𝐿𝑜𝑎𝑑𝑖𝑛𝑔 (𝑚𝑜𝑙𝐶𝑂2𝑚𝑜𝑙𝑀𝐸𝐴⁄ ) =

𝑥𝐶𝑂2𝑅𝑆

𝑥𝑀𝐸𝐴𝑅𝑆 (4.8)

The key parameters obtained from the capture plant simulation are shown in Table 4.8, as well

as, the experimental values obtained by Notz et al. and the respective deviation, calculated through

equation (4.3).

Table 4.8 – Experimental and Simulation results for the process key parameters and respective variation.

Example 1 Example 2

Key parameter Experimental Simulated Deviation

(%) Experimental Simulated

Deviation (%)

CO2 capture rate (%)

75.91 70.14 -8 51.32 38.71 -25

Specific heat requirement

(GJ/tCO2) 5.01 5.43 8 3.98 5.27 32

Lean loading (molCO2/molMEA)

0.265 0.320 21 0.308 0.351 14

Rich loading (molCO2/molMEA)

0.386 0.430 11 0.464 0.464 -0.1

As it can be observed, the used model tends to under predict the amount of captured CO2 in

both examples. Since the reboiler temperature was modified in order to meet the specified heat duty,

both the CO2 capture rate and the specific heat requirement obtained through simulation can be used

to define an expected deviation between the model prediction and what is achieved in the real plant,

for a given heat duty, that is, for a given steam consumption.

From example 2, where simulated and experimental rich loadings are quite similar, it is possible

to conclude that the heat input in the reboiler is not enough to achieve the intended lean loading, since

it is 14% higher. Considering that in example 1 there is a lower lean loading, according to the previous

section conclusions, there should be a considerable over-prediction in the CO2 capture rate. However,

since the reboiler heat duty isn’t enough for the required amount of CO2, the capture rate is decreased,

leading to a minimisation of the variation caused by the overestimation in the absorption section. Since

the lean loading is higher in example 2, the variation associated with the absorption in the section is

reduced. Therefore the variation associated with the heat duty estimation prevails and there is a higher

difference between the experimental and the simulation results.

Nevertheless, it should be taken into account the approximations considered, which are quite

similar to the ones applied in validation A, and may contribute to the verified deviations.

Based on these results it is possible to conclude that, for a full capture plant model with a given

heat consumption, the capture rate tends to be under-predicted. This deviation is higher for higher lean

loadings, and can achieve at least 25%. In the same way, for a given a capture rate the specific heat

consumption tends to be overestimated.

36


37

5. MEA Full Scale Capture Plant Model

Under the assumption that the validation results presented in the previous section are still valid

for a full scale plant, a full size capture plant model was built. An approximate cost estimation model

was also developed, in order to estimate both the investment required and operational costs, which

were later optimised.

5.1. Base Case

The original flowsheet used for the simulation of a MEA based full scale capture plant was

based on a case study developed by PSE. The flue gas considered is characteristic of a natural gas

fuelled power plant emitting approximately 2 million tonnes of CO2 per year, and its conditions are

shown in Table 5.1.

Table 5.1 – Flue gas conditions considered in the MEA capture plant model.

Mass flow rate (kg/s) 1214.81

Mass fractions (g/g)

CO2 0.058

H2O 0.065

N2 0.877

Temperature (°C) 140.75

Pressure (bar) 1.013

Starting from these conditions, and before the capture process itself, the flue gas is saturated

in water at 40.95 °C, being used for that purpose process water at 25 °C. Then it enters the capture

process, whose flowsheet is defined by the design parameters and operating conditions shown in the

table below.

Table 5.2 – Design parameters and operating conditions considered in the original capture plant model.

Parameter Value

Absorber

Diameter (m) 20

Height (m) 11.89

Packing Sulzer Mellapak 250YTM

Stripper

Diameter (m) 8.5

Height (m) 10

Packing Sulzer Mellapak 250YTM

Lean-rich heat exchanger Cold stream outlet temperature (°C) 89.65

Lean solvent cooler Process stream outlet temperature (°C) 70.75

Reboiler Temperature (°C) 117.84

Reboiler pump Pressure (bar) 1.79

Condenser Temperature (°C) 40

Lean solvent Flow rate (kg/s) 736.02

MEA mass fraction (g/g) 0.300

The referred conditions were applied in a conventional PCC flowsheet, which topology is shown

in Figure 5.1. Besides the models already considered in section 4.2, this flowsheet includes a flash

model (F-301) used for the simulation of saturation process, in which the temperature is imposed by

the PID controller model (PID-301) that calculates the required flow rate of quenching water (P-301).

In order to keep the absorber pressure above atmospheric, the pump model P-302 acts as a gas blower,

in order to increase the flue gas pressure to 1.1 bar. In the columns’ models the Billet & Schultes

correlation was used for the calculation of the mass transfer coefficients and pressure drop. Since the

38

considered packing is the one used in the previous sections, its characteristic parameters are the ones

shown in Table 4.2.

Figure 5.1 – MEA capture plant flowsheet as seen in gPROMS® ModelBuilder.

In this model were also added several flow multiplier models (FM-301 to FM-310) at the inlets

and outlets of the absorber, regeneration section and reboiler to simulate the existence of equipment

working in parallel. For the estimation of the required number of absorption and stripping trains technical

restrictions found through PSE internal reports were considered. Due to construction limitations, the

absorber diameter should have a maximum value of 15 m or 20 m if in-site construction is possible.

Regarding the stripping section, it is considered a limit total reboiler duty of 200 MW per stripping train,

with a maximum of 4 reboilers per column (each with a duty inferior to 50 MW). Besides this, it must

also be considered that the vapour velocity in both columns should be between 70 and 80% of the

vapour flooding velocity, [54], being this velocities ratio an output of the columns model.

In order to obtain a case with a standard capture rate of 90% and a MEA mass fraction of 30%

in the CO2 free lean solvent, the lean-solvent flow rate and the respective MEA mass fraction were

39

adjusted. The values obtained for these variables, as well as, the main simulation results obtained for

the original and corrected cases are shown in Table 5.3.

Table 5.3 – Results obtained from the simulation of the original and base cases.

Parameter Original case Base case

Capture rate (%) 48 89.9

CO2 purity (vol%) 95.8 95.8

MEA mass fraction in the CO2 free lean solvent (wt%) 31.8 30.0

Lean solvent flow rate (kg/s) 736.02 1450.14

MEA mass fraction after make-up (g/g) 0.300 0.285

Number of absorption trains 2 2

Number of striping trains 1 2

Number of reboilers per stripping train 4 4

Specific heat consumption (GJ/tCO2) 5.53 5.66

Lean loading (molCO2/molMEA) 0.260 0.249

Rich loading (molCO2/molMEA) 0.472 0.463

The flowsheet obtained after correction for the standard capture conditions was later used as

comparison with the optimisation results, being designated as the base case.

5.2. Cost Estimation Model

To estimate the costs associated with the design and operation of a capture plant a cost

estimation model was implemented. For that purpose, it was considered the procedure described in

reference [58]. Considering that the objective of the present economic model is the optimisation of a

carbon capture plant, only the cost fractions that depend on the plant design parameters and operation

conditions were considered, in order to simplify the optimisation process.

Using this costing model, it is possible to obtain the capture plant annualized investment, or

CAPEX, and the annual operating cost, or OPEX, both in a euro bases referred to the year 2013. In the

case of the investment annualisation, it was considered a linear amortization over a period of 10 year

(equation (5.1)).

𝐶𝐴𝑃𝐸𝑋 =𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡

10 𝑦𝑒𝑎𝑟𝑠 (5.1)

Both CAPEX and OPEX were also expressed as functions of the captured amount of CO2,

designated specific CAPEX (sCAPEX) and specific OPEX (sOPEX), respectively (equations (5.2) and

(5.3)).

𝑠𝐶𝐴𝑃𝐸𝑋(€ 𝑡𝑜𝑛𝐶𝑂2⁄ ) =

𝐶𝐴𝑃𝐸𝑋 (€ 𝑦𝑒𝑎𝑟⁄ )

𝐴𝑏𝑠𝑜𝑟𝑏𝑒𝑑 𝐶𝑂2 (𝑡𝑜𝑛𝐶𝑂2𝑦𝑒𝑎𝑟⁄ )

(5.2)

𝑠𝑂𝑃𝐸𝑋(€ 𝑡𝑜𝑛𝐶𝑂2⁄ ) =

𝑂𝑃𝐸𝑋 (€ 𝑦𝑒𝑎𝑟⁄ )

𝐴𝑏𝑠𝑜𝑟𝑏𝑒𝑑 𝐶𝑂2 (𝑡𝑜𝑛𝐶𝑂2𝑦𝑒𝑎𝑟⁄ )

(5.3)

5.2.1. CAPEX Estimation

For an approximate calculation of the investment required, it was used the factorial method,

which is based on the cost of the major process equipment. The equipment considered is presented in

Table 5.4, as well as the respective type and construction material. Considering that amine solvents

are corrosive, stainless steel 304 (SS-304) is required in all the equipment that contacts directly with

40

the capture solvent. Since the pump driver does not contact with the solvent, it is assumed that it’s

made of carbon steel (CS).

Table 5.4 – Type, construction/reference material, sizing variable and cost correlation parameters for the main capture

plant equipment [58]. Cost correlation parameters in a USD basis referred to January 2010 (CEPCI2010=532.9).

Equipment Model Type Construction

material/ Reference material

S a b n

Pressure vessels

A-301 Vertical SS-304/CS

Shell mass (kg)

17 400 79 0.85 S-301

Packing A-301 Structured

packing SS-304/CS

Volume (m3)

0 7 600 1 S-301

Heat Exchangers

HX-301 Plate and frame

SS-304/CS Area (m2)

128 000 89 000 0.5 HXU-301

C-301 U-tube shell

and tube SS-304/CS

Area (m2)

28 000 54 1.2

R-301 U-tube kettle SS-304/CS Area (m2)

29 000 400 0.9

Pumps P-305 Single stage

centrifugal SS-304/CS

Flow (L/s)

8 000 240 0.9 P-304

Pump drivers

P-305 Explosion proof motor

CS/CS Power (kW)

-1 100 2 100 0.6 P-304

The table above also shows the parameters a, b and n, which are used in the cost correlations

for the calculation of the purchased equipment cost (𝐶𝑒). These correlations have the generic form

shown in equation (5.4) and are applied to a specific size parameter (Sv), for which the calculation

procedure is presented in Appendix A1.

𝐶𝑒 = 𝑎 + 𝑏. 𝑆𝑣𝑛 (5.4)

Considering that for most of the used correlations the construction material is different from the

reference material, a correction factor (𝑓𝑚) is required in order to take into account an additional cost

(equation (5.5)). These material factors are relative to carbon steel (𝑓𝑚𝑐𝑠 = 1) and specific for each

material. In the case of stainless steel 304, the material factor has the value of 1.3 [58].

𝐶𝑒𝑚 = 𝐶𝑒 .

𝑓𝑚𝑐𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛

𝑓𝑚𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒

(5.5)

It should be noted that the obtained equipment costs are in U.S. dollars (USD) referred to

January 2010 USD Therefore, in order to correct this cost to euros (€) referred to the year of 2013, it is

required to apply the Chemical Engineering's Plant Cost Index referred to 2013 (CEPCI2013 = 567.6,

[59]) and the exchange rate between euros and USD (1 € = 1.3547 USD [60]).

𝐶𝑒,2013𝑚 (€) = 𝐶𝑒,2010

𝑚 (𝑈𝑆𝐷).𝐶𝐸𝑃𝐶𝐼2013

𝐶𝐸𝑃𝐶𝐼2010

.1 €

1.3547 𝑈𝑆𝐷 (5.6)

To account for the installation cost of each equipment unit, the factors shown in Table 5.5 were

considered, which are relative to the processing of fluids.

41

Table 5.5 – Typical installation factors for the estimation of project installed capital cost [58].

Item Value

Equipment erection (𝑓𝑒𝑟) 0.3

Piping (𝑓𝑝) 0.8

Instrumentation and control (𝑓𝑖𝑐) 0.3

Electrical (𝑓𝑒𝑙) 0.2

Civil (𝑓𝑐) 0.3

Structures and buildings (𝑓𝑠) 0.2

Lagging and paint (𝑓𝑙) 0.1

Using equation (5.7), it is possible to calculate the installed capital cost (𝐶), or ISBL investment.

It should be considered that the columns packing and spare pumps do not require installation costs.

For the calculation of number of spare pumps, it was considered the existence of a spare pump for each

set of pumps working in parallel.

𝐶 = ∑ [𝐶𝑒,𝑖,2013𝑚 (1 + 𝑓𝑝 +

𝑓𝑒𝑟 + 𝑓𝑒𝑙 + 𝑓𝑖𝑐 + 𝑓𝑐 + 𝑓𝑠 + 𝑓𝑙

𝑓𝑚,𝑖𝑐𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛

)]

𝑖=𝑀

𝑖=1

+ 𝐶𝑒,𝑝𝑎𝑐𝑘𝑖𝑛𝑔,2013𝑚 + 𝐶𝑒,𝑠𝑝𝑎𝑟𝑒𝑠,2013

𝑚 (5.7)

Table 5.6 show other expenses to be considered in the estimation of the total investment

required.

Table 5.6 – Other expenses required for the estimation of the total investment required [58].

Offsite investment (% of the ISBL investment) 30

Design & engineering (% of ISBL+OSBL investment) 30

Maintenance (% of ISBL+OSBL investment) 10

Working capital (% of total fixed cost) 10

The total fixed capital can be estimated based on the installed capital cost, considering the

offsite (OSBL) investment and design & engineering and maintenance expenses, thus obtaining the

total fixed cost. The working capital can also be estimated as 10% of this cost, allowing the estimation

of the total investment required (equation (5.8)).

𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 = 𝐶(1 + 0.30)(1 + 0.30 + 0.10)(1 + 0.10) (5.8)

5.2.2. OPEX Estimation

For the calculation of the operational expenses, fixed and variable production costs were

considered. Among the fixed production costs, maintenance costs, and taxes and insurance were

considered, which can be approximated to 3% and 1% of ISBL investment, respectively. For the variable

production costs the annual consumption of utilities (steam, cooling water and electricity) and solvent

were considered. This way, the OPEX is estimated using equation (5.9).

𝑂𝑃𝐸𝑋 = 𝐶𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 + 𝐶𝑡𝑎𝑥𝑒𝑠/𝑖𝑛𝑠𝑢𝑟𝑎𝑛𝑐𝑒 + 𝐶𝑢𝑡𝑖𝑙𝑖𝑡𝑖𝑒𝑠 + 𝐶𝑠𝑜𝑙𝑣𝑒𝑛𝑡 (5.9)

For the annualization of solvent and utilities consumption it was considered an operating year

of 340 days. The unitary costs considered for each utility are presented in Table 5.7, as well as the MEA

cost per kg.

42

Table 5.7 – Utilities and solvents costs (CEPCI1998=389.5, CEPCI2004=444.2 [61]) [62].

Steam cost (USD2004/t) 12

Cooling water cost (USD2004/t) 0.01

Electricity cost (USD2004/kWh) 0.054

MEA cost (USD1998/kg) 1.54

For the calculation of steam consumption, low pressure steam (saturated at 3 bar) was

considered. The annual consumption is based on the heat required in the process reboilers. The cooling

water requirements are given by the utility consumption of lean solvent coolers and condensers. In

these models, it was considered that the used water is cooled in a refrigeration tower, and therefore the

utility inlet and outlet temperatures are 29 and 49 °C, respectively [63]. For the estimation of the

electricity consumption, it was considered the power required in both lean solvent and rich solvent pump

drivers.

The solvent (MEA) consumption is estimated based on the required make-up and amine

degradation. According to Zahra [64], the washing sections in a capture plant are able to reduce the

MEA concentration to 1 ppm. Therefore it was considered that the MEA concentration in the treated

flue gas is reduced to this value, and the recovered MEA used as part of the required make-up. Also

in reference [64], it is referred that MEA has a degradation rate of 1.5 kgMEA/tCO2.

5.3. Cost Estimation Results for the Base Case

The inclusion of the cost estimation model in the capture plant model required the addiction of

new equations in order to connect the correspondent variables in each model. The results are shown

in Table A2.1 (Appendix A2). In this table it is possible to observe a CAPEX of 15.99 M€/year and an

OPEX of 64.56 M€/year, which correspond to a total cost of 80.55 M€ per year, equivalent to 43.15 €

per tonne of captured CO2.

Figure 5.2 – Total cost distribution in the base case (Total = 43.15 €/tCO2).

As can be observed in Figure 5.2, the CAPEX only represents 20% of the total capture cost.

Figure 5.3 shows the distribution of the considered equipment costs (not considering installation

factors), in which the CAPEX is based.

20%

80%

CAPEXOPEX

43

Figure 5.3 – Distribution of main equipment costs for the base case.

The columns packing represent the major fraction of the equipment costs, from which 87% are

due to absorber’s packing. Next, there is the heat transfer equipment cost, from which the reboilers

represent 80%.

Figure 5.4 – OPEX distribution in the base case (Total = 64.56 M€/year).

From the total operation expenses, the fixed production costs only represent 5% (Figure 5.4).

This is a result of the considerable difference between OPEX and CAPEX, since the fixed production

costs are approximated to a percentage of the ISBL investment.

Figure 5.5 – Distribution of variable production costs for the base case (Total = 61.4 M€/year).

In Figure 5.5 the distribution of utilities and solvent costs inside the variable production costs is

represented. As mentioned in the previous section, the solvent consumption results from the solvent

loss due to degradation and volatilization in the stripper and the absorber, considering that the solvent

5%9% 2%

84%

Columns Shell

Heat Transfer Equipment

Pumps and Drivers

Packing

95%

5%

Variable Production Costs

Fixed Production Costs

92%

8%

Total Utilities Cost

Solvent Cost

44

in the treated gas is partly recovered. Figure 5.6 features the distribution of MEA losses in the process,

without considering a washing section after the absorber.

Figure 5.6 – Distribution of the amine losses in base case (Total = 12.5 kt/year).

In the absence of water washing, the amine loss in the absorber represents 77% of a total loss

of 12.5 kt/year. Taking into account this loss, it is clear that a washing section is required after the

absorber, allowing the reduction of the MEA loss to 3.03 kt/year. This means a significant reduction in

the solvent expenses from 31.8 M€/year to 5 M€/year.

Nevertheless, Figure 5.5 shows that the major contribution to variable operating costs is due to

utilities consumption. The distribution of these expenses is shown in Figure 5.7.

Figure 5.7 – Distribution of the utilities costs for the base case (Total = 56.4 M€/year).

The steam consumption in the process reboilers strongly surpasses the electricity and water

consumption. In fact, the steam cost corresponds to 69% of the capture plant total cost

(CAPEX+OPEX), being the major process expense, as it is stated in literature (see section 2.3.1.2).

Considering the presented results, it is possible to conclude that CAPEX is mainly dependent

on the size of each absorber (height and diameter). It is also possible to see that the OPEX (and the

total cost, considering the relevance of the OPEX over the CAPEX) is mainly affected by the steam

consumed in the regeneration reboilers, which is directly related to the heat required. Therefore, it is

expected that the optimisation of the capture plant flowsheet will mainly rely on changing the absorbers’

size and the steam spent in the reboiler.

0%

77%

23%

Stripper

Absorber

Degradation

1%

98%

1%

Electricity

Steam

Cooled Water

45

6. Optimisation Problem Formulation

The optimisation of the capture process flowsheet was performed using the optimisation

functionalities of gPROMS® ModelBuilder 3.7.1. This functionality allows the minimisation of the

objective function, by modifying decision (or control) variables, considering the defined constraints.

In order to simplify the optimisation problem, several considerations were taken. The number

of absorption and stripping trains were not included in the optimisation problem, being changed

manually in the model for each case.

Since the pressure in the stripping section is set in the pump model (P-305) in Pascal, the

variation of this variable would be conditioned by the reduced effect on the objective function, being

considered a scale problem. Therefore, this pressure is instead explicitly defined in the flowsheet in bar

units, to improve its effect on the objective function during optimization.

According to Zahra [64], for a given lean solvent flow rate, capture rate and lean loading, the

optimal steam consumption is obtained for the combination of the highest possible stripping pressure

and reboiler temperature. Therefore, before proceeding to the process optimisation, the base flowsheet

was modified in order to start from the highest allowable temperature, which is 120 °C, as mentioned in

section 2.3.1. This way, this variable was not included in the optimisation problem. This assumption is

confirmed in the last optimisation (section 7.2.4), where the reboiler temperature was used as a decision

variable, which in fact converged to the imposed maximum value.

6.1. Objective Function

The objective function is defined by the process variable being minimised or maximised. For

the optimisation of the capture plant with the cost estimation model included, the objective function

should lead to the minimisation of both the specific CAPEX and specific OPEX. Therefore the objective

function (OF) is defined as the sum of these two costs (equation (6.1)).

𝑂𝐹 = 𝑠𝐶𝐴𝑃𝐸𝑋 + 𝑠𝑂𝑃𝐸𝑋 (6.1)

Considering that the steam spent in the reboilers is the major cost of the process, an alternative

objective function (OF2) can be defined as the specific heat requirement (defined in equation (4.6)),

since it is directly related with the specific steam cost. Nevertheless, this objective function was only

used as comparison in section 7.1.4.

𝑂𝐹2 = 𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐 ℎ𝑒𝑎𝑡 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡 (6.2)

6.2. Decision Variables

The decision variables define which of the capture flowsheet variables are used to minimise the

objective function. In this case, the variables that define the equipment design and the process

operating conditions were chosen. It is also necessary to specify the initial value of the variable and a

lower and upper bound. Nevertheless, the range considered shouldn’t be excessive in order to facilitate

the optimization convergence. The decision variables, initial values and bounds, are show in Table 6.1.

46

Table 6.1 – Decision variables, with respective initial value, lower bound and upper bound.

Decision Variable Initial Value Lower Bound Upper Bound

Absorber section diameter (m) 20 10 20

Absorber section height (m) 11.89 5 30

Condenser temperature (K) 313.15 275 350

Lean-rich heat exchanger cold outlet temperature (K) 362.8 308.15 390

Lean solvent cooler process outlet temperature (K) 313.9 303.15 360

Make-up recycle-breaker source flow rate (kg/s) 1156.64 500 2500

Make-up recycle-breaker source MEA mass fraction (g/g) 0.2876 0.25 0.3

Reboiler pressure (bar) 1.851 1.7 3.0

Stripper section diameter (m) 8.5 5 20

Stripper section height (m) 10 5 40

From the variables listed in the table above, the parameters referred to the make-up recycle-

breaker model will define the flow rate and MEA concentration of the lean solvent. The MEA mass

fraction in the lean solvent must be a decision variable, in order to allow the lean loading to vary and

keep the MEA mass faction in the CO2 free lean solvent constant.

As referred in the previous section, the reboiler temperature is not included in this list of

variables, since a value is already imposed in order to simplify the optimisation process.

6.3. Constraints

The process constraints are used to keep the standard conditions initially imposed, such as the

capture rate, the MEA mass fraction in the CO2 free solvent and the minimum CO2 purity in the product

stream, and to keep technical specifications, such as the columns flooding. These constraints are

divided in equality and inequality constraints, and are listed in Table 6.2 and Table 6.3, respectively.

Table 6.2 – Equality constrained variables, with respective constrained value.

Constrained variable Imposed value

Capture rate (g/g) 0.90

CO2 molar fraction in the CO2 stream (mol/mol) 0.95

MEA mass fraction in the CO2 free lean solvent (g/g) 0.30

Table 6.3 – Inequality constrained variables, with respective upper and lower bounds.

Constrained variable Lower bound Upper Bound

Vapour velocity/vapour flooding velocity in the top of the absorber 10-10 0.7

Vapour velocity/vapour flooding velocity in the bottom of the stripper 10-10 0.7

Process stream ΔT in the lean solvent cooler (°C) 10-10 1010

All the listed variables are, except for the MEA mass fraction in the CO2 free lean solvent, which

is calculated using equation (6.3). The process stream temperature difference (ΔT) in the lean solvent

cooler is required, since in some of the optimisation cases it tended to achieve unfesible values below

0. Therefore this variable was included in the flowsheet model and a very small positive number was

defined as a lower bound (10-10), while a very large number was fixed as upper bound (1010).

𝑀𝐸𝐴 𝑚𝑎𝑠𝑠 𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑖𝑛 𝑡ℎ𝑒 𝐶𝑂2 𝑓𝑟𝑒𝑒 𝑙𝑒𝑎𝑛 𝑠𝑜𝑙𝑣𝑒𝑛𝑡 =𝑤𝑀𝐸𝐴

𝐿𝑆

𝑤𝑀𝐸𝐴𝐿𝑆 + 𝑤𝐻2𝑂

𝐿𝑆 + 𝑤𝑁2

𝐿𝑆 (6.3)

47

The ratio between vapour operating velocity and vapour flooding velocity should be kept below

70% to 80%, [54]. Since the vapour velocity varies across the column, the variation of this ratio across

the column’s height was taken into account for the base case (Figure 6.1).

Figure 6.1 – Ratio between vapour velocity and vapour flooding velocity across the absorber (on the left) and the

stripper (on the right), for the base case.

For the considered conditions, in the absorber the maximum ratio is obtained near the columns

top (Height = 0 m), presenting a small variation. Therefore the bound of 70% was imposed at the

column’s top. On the other hand, for the stripper the maximum ratio is verified at the columns bottom

(Height = 10 m). So the maximum value was imposed ate column’s bottom. In both cases the minimum

value was defined as a very small positive value (10-10).

As mentioned in section 6.1, the specific heat consumption may also be considered as an

objective function to be minimised. When this is the case, additional inequality constraints (listed in

Table 6.4) must be considered, in order to ensure the optimisation convergence.

Table 6.4 – Additional inequality constrained variables, with respective upper and lower bounds,

used for the minimisation of the specific heat consumption.


Absorber’s top pressure (bar) 1.01325 1010

Stripper’s top pressure (bar) 1.01325 1010

Temperature difference in lean-rich heat exchanger inlet (K) 5 1010

Temperature difference in lean-rich heat exchanger outlet (K) 5 1010

Temperature difference in lean solvent cooler inlet (K) 5 1010

Temperature difference in lean solvent cooler outlet (K) 5 1010

Taking into account the high values obtained for the optimal heights of the stripper and the

absorber in section 7.1.4, it was required to impose a minimal pressure at the top of both columns

(considering that the operating pressure is specified at the bottom), in order to ensure the operation at

least at atmospheric conditions. As for the minimum temperature difference in the inlets and outlets of

the heat exchangers, this is required in order to ensure the optimisation convergence, since it is no

longer conditioned by the respective cost.

0.53

0.54

0.55

0.56

0.57

0.58

0 2 4 6 8 10 12

vap

ou

r ve

loci

ty/

vap

ou

r fl

oo

din

g ve

loci

ty

Height from the top of the absorber (m)

0.2

0.4

0.6

0 5 10

Vap

ou

r ve

loci

ty/

vap

ou

r fl

oo

din

g ve

loci

ty

Height from the top of the stripper (m)

48


49

7. Optimisation Results

After solving the optimisation problem applied to the base case, a minimisation of the total cost

per tonne of captured CO2 was achieved. As mentioned before, for the base case optimisation a capture

rate of 90%, a MEA mass fraction in the CO2 free lean solvent of 30% and a minimum CO2 purity of 95

vol% were imposed. To evaluate the effect of each of these constraints in the total cost of the CO2

capture, they were changed, and the new optimal costs were compared with the standard ones (Section

7.2).

The main results of these optimisations are shown in Appendix A2, and commented in the

following sections.

7.1. Base Case Optimisation with Standard Constraints

The base case was optimised considering the decision variables and constraints mentioned

before, in order to minimise the total cost per tonne of captured CO2. Based on this procedure, the effect

of modifying the optimisation initial values was studied, namely the initial lean loading, and the effect of

having a different number of absorption trains.

Besides the minimisation of the total cost, another case was conducted with the minimisation

of the specific heat requirement with the objective of verifying if the minimum total cost leads to a

minimum heat consumption.

7.1.1. Specific Total Cost Minimisation

The results obtained from this optimisation process are presented in Table 7.1, where it also

can be observed its comparison with the base case. As it is shown, the main variations occur in the

absorber size (height and diameter are reduced by 29% and 12%, respectively), in the stripper size

(diameter is reduced by 15% and height is increased 69%) and in the lean solvent flow rate (reduced

by 18%).

It can be observed a reduction in the CAPEX of 21% and in the OPEX of 13%, which lead to a

specific total cost of 36.69 € per tonne of captured CO2 (Figure 7.1), which means a reduction of 15%.

As expected, there is a considerable variation in the specific heat requirement, which shows an optimal

value of 4.87 GJ/tCO2, with the steam consumption still representing 98% of the utilities requirements

and 69% of the total annual cost. Besides the reduction in the steam consumption, for the OPEX

reduction also contributes the decrease in the cooling water, electricity and solvent consumption, as

well as the other costs which are estimated based on the ISBL investment.

50

Table 7.1 – Detailed results from the specific total cost minimisation with standard constraints, and comparison with

the base case (Table A2.1).

Decision Variables Variation

Absorber (A-301) Diameter (m) 17.61 -12%

Height (m) 8.49 -29%

Stripper (ST-301) Diameter (m) 7.26 -15%

Height (m) 16.90 69%

Lean-Rich Heat Exchanger (HX-301) Outlet temperature of the rich solvent (K) 381.07 5%

Lean Solvent Cooler (HXU-301) Outlet temperature of the lean solvent (K) 313.87 -0.01%

Reboiler (R-301) Temperature (K) 393.15 1%

Pressure (bar) 1.85 3%

Condenser (C-301) Temperature (K) 317.01 1%

Lean Solvent (RB-303) Flow Rate (kg/s) 1 194.94 -18%

MEA mas fraction (g/g) 0.288 1%

Investment (k€) Variation

Equipment Cost

Absorber Shell (2 units) 1 796 -25%

Absorber Packing (2 units) 24 708 -45%

Stripper Shell (2 units) 1 257 18%

Stripper Packing (2 units) 8 354 23%

Lean-Rich Heat Exchanger (10 units) 765 203%

Lean Solvent Cooler (6 units) 436 25%

Reboiler (2x4 units) 4 310 1%

Condenser (2x2 units) 869 78%

Rich Solvent Pump (10 units) 262 -17%

Rich Solvent Pump Driver (10 units) 191 -5%

Lean Solvent Pump (10 units) 265 -18%

Lean Solvent Pump Driver (10 units) 129 -26%

ISBL Investment 62 823 -21%

Total Fixed Investment 125 772 -21%

Production Cost (k€/Year) Variation


Electricity 358 -12%

Steam 47 533 -14%

Cooled Water 587 -23%

Solvent (MEA) 4 983 -0.6%

Total 53 462 -13%

Fixed Production Costs 2 513 -21%

Total Production Cost 55 975 -13%

Total Cost (M€/year) Variation

CAPEX 12.58 -21%

OPEX 55.97 -13%

Total 68.55 -15%

Key Parameters Variation

CO2 Capture Rate (%) 90 0.1%

CO2 Purity (vol%) 95 -0.8%

MEA mass fraction in the CO2 free lean solvent (wt%) 30 -0.01%

Vapour velocity/vapour flooding velocity in the top of the absorber 0.70 25%

Vapour velocity/vapour flooding velocity in the bottom of the stripper 0.70 22%

Process stream ΔT in the lean solvent cooler (K) 13.94 -57%

Lean Loading (molCO2/molMEA) 0.200 -20%

Rich Loading (molCO2/molMEA) 0.458 -1%

CO2 recovery rate in the regeneration section (%) 56 22%

Specific Energy Requirement (GJ/tCO2) 4.87 -14%

sCAPEX (€/tCO2) 6.73 -21%

sOPEX (€/tCO2) 29.95 -13%

Specific Total Cost (€/tCO2) 36.69 -15%

51

Figure 7.1 – Total cost distribution for the specific total cost minimisation with standard constraints (Total = 36.69

€/tCO2).

For the CAPEX, the main reduction is associated with reduction of the volume of packing

required in the absorber, reducing the percentage associated with column’s packing in total equipment

cost to 77%, as can be observed in Figure 7.2. From this figure, it is also possible to conclude that the

heat exchanger equipment have more relevance in the total cost, mainly due to the increase in the cost

of the lean-rich heat exchanger.

Figure 7.2 – Distribution of main equipment costs for the specific total cost minimisation with standard constraints.

The reduction in the absorber size leads to a reduction of the packing volume, as well as the

shell’s mass, which are the main fractions of the equipment cost. This reduction is possible due to the

reduction in the lean loading (reduced to 0.200 molCO2/molMEA) and the approach to the flooding velocity

in the absorber, which now reaches 70% in the top of the column. According to [54], the approximation

to 70-80% of the flooding velocity improves the mass transfer efficiency (through increasing the

interfacial area). This improvement in efficiency associated with the reduction in the lean loading can

be observed in Figure 7.3, where the CO2 molar flux from the gas to the liquid phase is highly increased

through the optimisation, even considering the lower packing volume in the absorber and solvent flow

rate.

18%

82%

CAPEX

OPEX

7%

15%

1%

77%

Columns Shell


Pumps and Drivers

Packing

52

Figure 7.3 – CO2 molar flux to the liquid phase across the absorber (top of the absorber equivalent to 0), before and

after the specific total cost minimisation with standard constraints.

The reduction in the lean solvent flow rate also reduces the size (and therefore the cost) of

pumps and respective drivers, and reduces the respective electricity consumption in 12%.

The increase of 5% in the rich solvent temperature at the lean-rich heat exchanger outlet

reduces the temperature difference across the stripper. Figure 7.4 shows the representative

temperature profile in the heat exchanger before and after optimisation, in which is possible to see a

reduction in the driving force for the heat transfer (ΔTlm). Both, the increase in the heat transferred and

the reduction in ΔTlm lead to an increase of the required area, which will require six additional heat

exchangers, increasing the cost in 203%.

Figure 7.4 – Representative temperature profiles of the lean-rich heat exchanger in the base case (on the left) and after

the total cost minimisation (on the right).

According to Zahra [64], the reduction in the absorber’s temperature should reduce the energy

required in regeneration, considering that the absorption process tends to be favoured at lower

temperatures, due to its exothermic nature. Nevertheless, the optimisation process does not change

considerably the outlet temperature of the lean solvent cooler (variation of -0.01%). Figure 7.5 features

the cooler temperature profile before and after optimisation.

0

0.003

0.006

0.009

0 1000 2000 3000 4000

CO

2m

ola

r fl

ux

(km

ol.

m-2

.s-1

)

Volume of packing from the absorber top (m3)

Base

Optimal

280

300

320

340

360

380

400

420

0 0.5 1

Tem

pe

ratu

re (

K)

Relative position to the cold stream inlet

Cold Stream Hot Stream

280

300

320

340

360

380

400

420

0 0.5 1

Tem

pe

ratu

re (

K)

Relative position to the cold stream inlet

Cold Stream Hot Stream

53

Figure 7.5 – Representative temperature profiles of the lean solvent cooler in the base case (on the left) and after the

total cost minimisation (on the right).

Since the temperature of the cold stream entering the stripper is increased, the process stream

entering cooler has lower temperature, as can be seen in the temperature profiles above. Even though

the exchanged heat is reduced (from 183 MW to 78 MW), the driving force for heat transfer (ΔTlm) is

decreased, which ultimately leads to an increase in the required area (from 2138 m2 to 2691 m2), and

therefore to an increase in number of the required coolers and their cost. Furthermore, decreasing the

cooler outlet temperature would increase the required area, as well as the area of the lean-rich heat

exchanger. This way, it is possible to conclude that a possible increase in the absorber’s efficiency, due

to the lean solvent temperature reduction, does not compensate the additional required costs.

In the stripper, the diameter is reduced in 15%, meeting the maximum vapour velocity allowed,

and the height is increased in 69%, leading to an increase in the column’s volume, thus increasing the

shell and packing costs. Considering that the liquid inlet temperature in the stripper is higher, as well as

the reboiler temperature, the temperature in the columns is kept at higher values, as it shown in the

temperature profiles shown in Figure 7.6.

Figure 7.6 – Axial temperature profiles in the stripping columns for the gas phase (on the left) and the liquid phase (on

the right), before and after the specific total cost minimisation with standard constraints.

Based on the new temperature profile, the CO2 transfer from the liquid solvent to the gas phase

is expected to be improved. This can be further understood considering the CO2 flux profile across the

column, presented in Figure 7.7, where, in the non-optimised case, there is a re-absorption in the top

280

300

320

340

360

0 0.5 1

Tem

pe

ratu

re (

K)

Relative position to the utility stream inletUtility Stream Process Stream

280

300

320

340

360

0 0.5 1

Tem

pe

ratu

re (

K)

Relative position to the utility stream inlet

Utility Stream Process Stream

360

365

370

375

380

385

390

395

0 0.5 1

Tem

pe

ratu

re (

K)

Relative position from the stripper top

Optimal Base

360

365

370

375

380

385

390

395

0 0.5 1

Tem

pe

ratu

re (

K)

Relative position from the stripper top

Optimal Base

54

of the stripping column, due to the solvent’s lower temperature when entering the column. On the other

hand, after optimisation the solvent temperature entering the stripping column is higher than the vapour

stream exiting in the same point. This leads to an increase in the CO2 flux to the vapour phase in the

strippers’ top, thus increasing the columns’ efficiency. The efficiency is also enhanced by the diameter

reduction and height increase, leading to an increase in the CO2 recovery rate from 46% to 56%, without

leading to an increase in the heat consumption.

Figure 7.7 – CO2 and H2O molar fluxes from the gas phase to the liquid phase across the stripper (top of the stripper

equivalent to volume 0), before and after the specific total cost minimisation with standard constraints.

Figure 7.7 also shows the water flux to the liquid phase. It can be observed that, in the optimised

case, the water flux to the liquid phase tends to be lower. In fact, less water is transferred from the

stripping gas to the liquid phase, and therefore the vapour exiting the column is richer in water (36%

compared to 19% in the base case).

The condenser’s temperature increase of 1% (Table 7.1) can be related to the allowed reduction

in the CO2 purity in the product stream, which changes from 95.8% to the bound of 95%, and to the

increase of 4% in reboiler pressure. This change in condenser’s temperature associated with the higher

temperature in stripper vapour outlet, increases the temperature difference of process stream in the

condenser from 51 K to 61 K. Considering that the vapour outlet of the stripping column is richer in

water, the flow rate of the condenser feed is increased. Both this factors contribute to the increase of

the heat exchanged in each stripping train condensers from 19 MW to 45 MW, which lead to an increase

in the condenser area, and consequently of the equipment cost of 78%. Furthermore, the cooling water

consumption in this equipment is also increased from 468 to 1134 kg/s. Nevertheless, the total water

consumption is reduced by 23%, due to the reduction verified in the lean solvent cooler.

According to Zahra [64], the heat required in the regeneration process can be divided into: heat

required to increase the reboiler inlet temperature (sensible heat); heat required to partly vaporize this

inlet (latent heat) and energy required to desorb the CO2 from the amine solution (heat of absorption).

With the decrease of the lean loading, it can be observed a decrease in the solvent flow rate, which

contributes to the reduction of the flow rate of the reboiler feed, thus reducing the sensible heat

requirement (despite the increase of 1% in the reboiler temperature). Due to the increased efficiency of

the stripping column, the CO2 flow rate in the vapour exiting each reboiler in each stripping train is

-0.015

-0.01

-0.005

0

0.005

0.01

0 200 400 600

CO

2m

ola

r fl

ux

(km

ol.

m-2

.s-1

)

Volume of packing from the stripper top (m3)

Base Optimal

-0.02

0

0.02

0.04

0.06

0 200 400 600H

2O m

ola

r fl

ux

(km

ol.

m-2

.s-1

)

Volume of packing from the stripper top (m3)

Base Optimal

55

reduced from 3.38 kg/s to 2.04 kg/s, thus reducing the heat required for desorption. The different inlet

composition, pressure and temperature of the reboiler lead to a change from 33.2 MW to 31.7 MW in

the heat spent in vaporization in each reboiler. These three factors lead to a reduction in the total heat

duty of the eight reboilers from 359 MW to 310 MW, which is equivalent to a specific heat requirement

of 4.87 GJ/tCO2.

In section 2.3.1.2 it is mentioned that the typical specific heat requirement for a MEA capture

plant is 3.7 GJ/tCO2, which is considerably lower than the obtained value. Nevertheless, it must be taken

into account that the optimised flowsheet is a conventional one, which can be further optimised by

applying modifications such as those mentioned in section 2.3.1.2. Furthermore, considering the

conclusions from flowsheet B validation, the models applied tend overestimate the specific heat

consumption.

7.1.2. Effect of the Initial Guesses in the Optimisation Results

Considering that in the formulated optimisation problem there are ten decision variables (Table

6.1) and only three equality constraints (Table 6.2), it can be expected that multiple solutions can lead

to the same optimal solution or even to a close value. Since the optimisation procedure that was used

do not ensure a global optimisation, the initial guesses of the non-linear optimisation problem may

influence the results obtained, and even determine the convergence or not of the problem. Since the

lean solvent CO2 loading is a main influence in the capture model, all the optimizations performed

started from a lean loading of 0.2 molCO2/molMEA. In order to evaluate the influence of this parameter in

the process convergence, the optimisation described in the previous section was conducted starting

from a lean loading of 0.1 and 0.3 molCO2/molMEA. For that the lean solvent flow rate and MEA

composition were changed, as well as the reboiler pressure (considering a temperature of 393.15 K in

the reboiler), being the new values shown in Table 7.2.

Table 7.2 – Modified initial guesses, used for comparison with the initial optimisation.

Lean loading (molCO2/molMEA) 0.1 0.3

Reboiler Temperature (K) 393.15 393.15

Pressure (bar) 1.77 2.05

Lean Solvent Flow Rate (kg/s) 814.29 2311.40

MEA mas fraction (g/g) 0.294 0.285

The results obtained for the decision variables, as well as the key parameters for the

optimisation problem starting from the new initial guesses are shown in Table 7.3 and Table 7.4. As

can be observed in the first table the temperature difference across the lean solvent cooler is an active

constraint, tending to a null value.

56

Table 7.3 – Decision variables and key parameter resulting from the specific total cost minimisation, starting from a

lean loading of 0.1 molCO2/molMEA and comparison with the optimal results previously obtained.


Absorber Diameter (m) 17.71 0.6%

Height (m) 8.70 2%

Stripper Diameter (m) 7.22 -0.5%

Height (m) 16.97 0.4%

Lean Rich Heat Exchanger Temperature of the rich solvent (K) 383.62 0.7%

Lean Solvent Cooler Temperature of the lean solvent (K) 324.58 3%

Reboiler Temperature (K) 393.15 0%

Pressure (bar) 1.85 -0.2%

Condenser Temperature (K) 317.00 -0.004%

Lean Solvent Flow Rate (kg/s) 1205.49 0.9%

MEA mas fraction (g/g) 0.288 -0.02%


CO2 Capture Rate (%) 90 -0.01%

CO2 Purity (vol%) 95 -0.009%

MEA mass fraction CO2 free (wt%) 30 -0.0007%

Specific Energy Consumption (GJ/tCO2) 4.82 -0.9%

Lean Loading (molCO2/molMEA) 0.201 0.4%

Rich Loading (molCO2/molMEA) 0.457 -0.1%

Vapour velocity/vapour flooding velocity in the top of the absorber 0.70 0.3%

Vapour velocity/vapour flooding velocity in the bottom of the stripper 0.70 0.4%


sCAPEX (€/tCO2) 6.83 1%

sOPEX (€/tCO2) 29.60 -1%

Specific Total Cost (€/tCO2) 36.40 -0.8%

Table 7.4 – Decision variables and key parameter resulting from the specific total cost minimisation, starting from a

lean loading of 0.3 molCO2/molMEA and comparison with the optimal results previously obtained.


Absorber Diameter (m) 17.52 -0.5%

Height (m) 8.31 -2%

Stripper Diameter (m) 7.23 -0.4%

Height (m) 16.31 -3%

Lean Rich Heat Exchanger Temperature of the rich solvent (K) 382.80 0.5%

Lean Solvent Cooler Temperature of the lean solvent (K) 307.79 -2%

Reboiler Temperature (K) 393.15 0%

Pressure (bar) 1.86 0.3%

Condenser Temperature (K) 317.12 0.04%

Lean Solvent Flow Rate (kg/s) 1204.72 0.8%



CO2 Capture Rate (%) 0.90 -0.03%

CO2 Purity (vol%) 0.95 -0.02%

MEA mass fraction CO2 free (wt%) 0.30 0.003%

Specific Energy Consumption (GJ/tCO2) 4.86 -0.2%

Lean Loading (molCO2/molMEA) 0.202 1%




Process stream ΔT in the lean solvent cooler (K) 18.05 29%

sCAPEX (€/tCO2) 6.70 -0.4%

sOPEX (€/tCO2) 30.03 0.2%

Specific Total Cost (€/tCO2) 36.71 0.06%

57

Comparing the results obtained for the two optimisations with the initial optimisation, it is

possible to observe minor variations in most of the decision variables (the reboiler temperature is

imposed in 393.15 K, thus the variation is null). This results in the variation of the value achieved for

the objective function between 36.43 and 36.73 €/tCO2, not changing more than 1% when compared

with initial optimisation result.

There are considerable variations in the absorber height, which are compensated by a slight

increase in the lean loading, in the first case, or in the lean solvent flow rate, in the second case. In the

same way, for the starting initial loading of 0.3 the stripper height is 3% lower, which can be related to

the increase of 1% in the lean loading.

One of the main differences observed is in the temperature of the lean solvent after the lean

solvent cooler, which changes between 307.79 and 324.58 K. Figure 7.8 shows the temperature profiles

across the absorber for both liquid and vapour phases, obtained from the three cases with three initial

lean loadings.

Figure 7.8 – Axial temperature profile of the vapour (on the left) and liquid (on the right) phases in the absorber for the

optimised cases with initial lean loadings of 0.1, 0.2 and 0.3 molCO2/molMEA.

From the profiles presented above, it is possible to conclude that, despite the different

temperature of the lean solvent, the temperature profile of both liquid and vapour phases across the

absorber tend to be similar in the three scenarios. Considering that the flue gas temperature isn’t

changed, since it was not included in the optimisation problem, and that the captured amount of CO2 is

approximately the same, the temperature profiles tend to be similar, thus showing the reduced

relevance of the lean solvent temperature for the considered conditions.

Considering this results, the need for a lean solvent cooler before the absorption section seems

to be questionable, suggesting that the advantage of increasing the absorber efficiency is surpassed

by the cooling related costs. The removal of these costs in both equipment and utilities costs lead to a

decrease of simply 0.01% in the specific total cost.

Since the cooler related costs show a small influence in the capture cost, and at least one of

the optimisation scenarios in section 7.2 requires the lean solvent cooling, it was decided not to remove

314

319

324

329

334

0 0.5 1

Tem

ep

ratu

re (

K)

Relative position to the absorber top

Initial Lean Loading = 0.1Initial Lean Loading = 0.2Initial Lean Loading = 0.3

308

318

328

338

0 0.5 1

Tem

pe

ratu

re (

K)


Initial Lean Loading = 0.1Initial Lean Loading = 0.2Initial Lean Loading = 0.3

58

the lean solvent cooler from the flowsheet, nor from the cost estimation models, considering that this is

an equipment present in any conventional capture plant, that can increase the capture plant flexibility.

7.1.3. Effect of the Number of Absorption Trains

As mentioned in Section 5.1, the number of absorption trains was chosen considering the

construction limitation regarding the column’s diameter and the vapour flooding velocity. This process

variable was not considered in the optimisation problem, in order to simplify it, but it can be changed in

the base case model, and therefore optimised for each value.

Since the absorber diameter is a control variable of the optimisation problem, it was evaluated

the possibility of having only one absorption train and the consequences of having more than two (up

to four). It should be noted that in order to converge, the optimisation of the capture plant with only one

absorption train required the increase of the diameter upper bound to 30 m, thus suggesting that the

optimal value is above 20 m.

The results of these optimisations are shown in Table A2.1, Table A2.2 and Table A2.3 (in

Appendix A2), and allow to verify that the control variable with largest variation occurs in the absorber

diameter (Figure 7.9). As expected the optimal diameter for one absorption train is still above 20 m,

making this option unfeasible.

Figure 7.9 – Variation of the optimal absorber diameter with the number absorption trains.

Since one of the active constraints of the optimisation results is the approximation of the

absorber’s vapour velocity to the flooding velocity, it is expected that, by reducing the vapour flow rate

in one column, the optimal diameter is decreased. This is further justified by the fact that the vapour

flood velocity is approximately the same in the four cases, due to the ratio between the liquid and vapour

flow rates being approximately the same [54]. In Figure 7.9, it can also be observed that the optimal

absorber diameter tends to present a smaller variation for higher numbers of absorption trains.

Comparing the obtained cost results, it can be observed that the total cost and the CAPEX

show a similar trend, as can be observed in Figure 7.10. Therefore, it is possible to conclude that the

variation in the total cost is mainly due to the variation in the CAPEX, that is, in the equipment cost.

From the CAPEX, it is observed that the major variation occurs in the cost of the absorption shells

(Table A2.2 to Table A2.4), which is directly related to the absorber diameter.

0.0

10.0

20.0

30.0

1 2 3 4

Ab

sorb

er

dia

me

ter

(m)

Number of absorption trains

59

Figure 7.10 – Variation of the optimal specific total cost (on the right) and specific CAPEX (on the right) with the

number of absorption trains.

It should also be noted that the packing cost in the absorbers does not change considerably in

each optimisation, since it is proportional to the volume of packing in each absorber, and therefore to

the total volume of absorption packing. Even though the total volume doesn’t change considerably, from

one to four absorption trains it can be observed a reduction in it from 4 144 m3 to 4 065 m3, thus

suggesting an increase in the absorber’s efficiency with the reduction of its size.

Considering that for a single absorption train the absorber diameter is technically unfeasible,

the total cost varies approximately 0.8% between two and four absorption trains, thus showing the

reduced effect of the absorber’s shell cost, and consequently of the number of absorption trains, in the

total cost. Considering that for two absorption trains the absorber diameter is still 15 m, the use of three

absorption trains may be a better option, since the total cost difference is simply of 0.4%. Therefore,

the choice between two and three absorption trains would be a trade-off between the possibility of

constructing the columns in-site, and the cost of increasing the space required for the absorption

section. This would require a more accurate cost estimation, and therefore it was considered that 2

absorption trains would be a feasible solution, still being considered in the following optimisation studies.

7.1.4. Specific Heat Requirement Minimisation

The steam consumption is directly proportional to the heat required in the regeneration

reboilers, and represents 69% of the capture plant annual cost. The minimisation of the specific total

cost allowed a 14% reduction in specific heat requirement, from 5.66 GJ/tCO2 to 4.87 GJ/tCO2. However,

this value is still considerably higher than the one used as benchmark (3.7 GJ/tCO2).

In order to evaluate if a greater reduction in this parameter is possible, and its effect in the

capture total cost, the described optimisation procedure was conducted with a different objective

function (OF2 in equation (6.2)). It was also necessary to use the additional constraints shown in Table

6.4, as well as an increase in the absorber and stripper upper bounds to 60 and 70 m, respectively.

The results of this optimisation are presented in Table A2.5, showing that, when compared with

the results obtained for the total cost minimisation, a further reduction of 8% in the specific heat

requirement (to 4.46 GJ/tCO2) is possible. Nevertheless, this reduction is achieved through a drastic

increase in the absorber and stripper sizes, which increases the sCAPEX by 674%, hence increasing

the specific total cost in 142%, to the value of 88.98 € per tonne of captured CO2. This leads to an

increase in the CAPEX relevance in the total cost to 59%, as can be observed in Figure 7.11.

36.2

36.4

36.6

36.8

37.0

37.2

1 2 3 4

Spe

cifi

c to

tal c

ost

(€

/tC

O2)


6.2

6.4

6.6

6.8

7.0

7.2

1 2 3 4

sCA

PEX

(€

/tC

O2)


60

Figure 7.11 – Total cost distribution in the specific heat consumption minimisation (Total = 88.98 €/tCO2).

In the analysis of the reboiler related costs, it must be taken into account that the total heat

consumed is reduced from 310 MW to 285 MW, does reducing the number of required reboilers per

stripping train to 3 (maximum heat duty per reboiler of 50 MW, section 5.1), which contributes to a total

reboilers cost that is 11% lower.

The reduction in the specific heat requirement follows the same trend as in the total cost

minimisation, being the total reboiler inlet flow, the amount of CO2 regenerated and the total flow rate

of the vapour stream further reduced. These reductions are possible due to the increase in the stripping

columns height in 337%, and the reduction of the lean solvent flow rate in 13%. Once again the column’s

size is increased in order to maximize the stripping action inside the column. The stripping columns

optimal height results from a trade-off between reducing the amount of CO2 entering the reboilers and

the packing and shell costs increase. Since these costs are no longer being considered in the present

optimisation, the strippers’ height and diameter increase until the effect on the reboilers’ heat duty is no

longer significant.

Considering that the solvent flow rate and the lean loading are both reduced the absorber’s

height has to be increased in order to meet the desired capture rate, leading to an increase of 274%.

Both the increase in absorber and stripper sizes contribute to the increase of the respective shell and

packing costs, leading to an increase of the packing’s relevance (from 78% to 95%) in the cost of the

main equipment, as shown in Figure 7.12. Nevertheless, it should be noted that the obtained columns’

height (Table A2.5) may be technically unfeasible, thus requiring a more specific analysis of the

construction constraints.

59%

41% CAPEX

OPEX

61

Figure 7.12 – Distribution of main equipment costs for the specific heat consumption minimisation.

The reduction in the flow rate contributes to the reduction in the pumps cost and in the

respective electricity consumption. However, due to the increase in the absorber and stripper’s height,

the head required in each pump is increased, which ultimately leads to an increase of 130% in the

electricity, surpassing the reduction caused by the flow rate reduction. Nevertheless, the reduction in

the steam consumption, still lead to a reduction of 7% in variable production costs.

Despite these reductions, the OPEX increases in 24%, since the fixed production costs are

estimated based on the ISBL investment. This way, the variable production costs represent 72% of the

OPEX, as shown in Figure 7.13.

Figure 7.13 – OPEX distribution in the specific heat consumption minimisation (Total = 69.34 M€/year).

Based on this analysis, it is possible to conclude that the minimal specific heat requirement

(4.46 GJ/tCO2) doesn’t correspond to a minimal capture cost. This is achieved at the cost of increasing

the absorbers and strippers’ sizes, in a way that their cost surpasses the cost reduction achieved by

lowering the consumed steam.

7.2. Effect of the Process Constraints

The capture rate of 90%, the CO2 purity in the product stream of 95 vol% and the MEA mass

fraction in the CO2 free lean solvent of 30% are standard requirements usually imposed for a capture

plant. Therefore, in the formulation of the optimisation problem they were defined as equality constraints

(Table 6.2).

The optimisation feature of gPROMS® Model Builder 3.7.1 provides an output which includes

the Lagrange multiplier of active constraints. This parameter is defined as the derivative of the objective

4% 1%0%

95%

Columns Shell


Pumps and Drivers

Packing

72%

28% Variable ProductionCosts

Fixed ProductionCosts

62

function with the constrained variable, and allows the estimation the value of the objective function for

a small relaxation of the active constraint bound. For the minimisation of the specific total cost with

standard constraints, the Lagrange multipliers of the equality constraints are shown in Table 7.5.

Table 7.5 – Lagrange multipliers obtained for the equality constraints in the minimisation of the specific total cost with

standard constraints.

Equality constraints Value Lagrange Multiplier

Capture rate (%) 90 5.82

CO2 Purity (vol%) 95 4.82

MEA mass fraction CO2 free (wt%) 30 -17.20

From the table above, it is possible to verify that a decrease in the capture rate or in the purity

of the product stream or an increase in the MEA concentration would result in a reduction of the capture

cost. Considering this, the same optimisation problem was solved, with exception for the value imposed

on each of these constraints, in order to evaluate its effect on the capture plant specific total cost.

7.2.1. Effect of the Capture Rate

The effect of the capture rate in the specific total cost was tested for the values of 70%, 80%

and 99%. The results obtained after optimisation are shown in Table A2.6, Table A2.7 and Table A2.8,

being compared with those obtained from the initial optimisation.

Considering that the capture rate has a major effect in the reboilers total heat duty, the number

of required stripping trains and reboilers in each train change in each optimisation. This way, for a

capture rate of 70 and 80% 2 stripping trains containing 3 reboilers each are required, while for a capture

rate of 99, 3 stripping trains are required, also associated with 3 reboilers each.

Figure 7.14 captures of the optimal specific total cost for the four capture rates considered. It is

possible to observe that for capture rates below 90% there is a reduced cost variation, which at 70% is

only 2% lower than the initial optimisation scenario. On the other hand, by increasing the capture rate

from 90 to 99% there is an increase in the optimal cost of 28%.

Figure 7.14 – Variation of the optimal specific total cost with the imposed capture rate.

Based on the optimal values obtained, it is possible to demonstrate that the increase in the

capture rate is achieved through the increase in the absorber’s height and in the lean solvent flow rate,

which is accompanied by a small decrease in the optimal lean loading. This trend can be observed in

Figure 7.15, for capture rates between 70% and 90%.

20

30

40

50

70 80 90 99

Spe

cifi

c to

tal c

ost

(€

/tC

O2)

Capture rate (%)

63

Figure 7.15 – Variation of the optimal lean solvent flow rate (on the right) and loading (on the left) with the imposed

capture rate.

On the other hand, the increase of the capture rate to 99% leads to the lean solvent flow rate

reduction, when compared with the 90% case, through the reduction of the optimal lean loading from

0.200 to 0.111 molCO2/molMEA. With the approximation to the physical limit, the costs of increasing the

solvent flow rate would surpass the costs of keeping a low CO2 recovery rate, that is, a higher lean

loading.

Looking into the lean solvent temperature at the cooler’s outlet (Table A2.6 to Table A2.8), it is

possible to see that with the increasing capture rate this temperature is decreased. In the cases of a

capture rate of 70 and 80% the temperature difference across the coolers tends to the imposed limit of

0, once again showing that this equipment might not be required.

Considering that the higher the capture rate the more heat is released in the absorber, for lower

capture rates the temperature in the absorber tends to be lower and solvent cooling loses relevance,

since the flue gas temperature is constant. On the other hand, for higher capture rates, such as 99%,

the temperature in the absorber tends to increase. Taking into account that the optimal flow rate in this

case is 20% lower than in the 90% capture rate case, this heating effect is enhanced, as can be verified

by looking to the absorber temperature profiles shown in Figure 7.16. Since the absorption efficiency

tends to decrease at high temperatures, for high capture rates the lean solvent cooling gains relevance.

Therefore, the lean solvent coolers’ outlet temperature shows a decrease with the increase of the

capture rate.

600

800

1000

1200

70 80 90 99

Lean

so

lve

nt

flo

w r

ate

(kg

/s)

Capture rate (%)

0

0.05

0.1

0.15

0.2

0.25

70 80 90 99

Lean

load

ing

(mo

l MEA

/mo

l CO

2)

Capture rate (%)

64

Figure 7.16 – Axial temperature profile of the vapour phase (on the left) and liquid phase (on the right) in the absorber

for the optimised cases with capture rates (CR) of 70, 80, 90 and 99%.

A reduction in the optimal rich solvent temperature at the lean-rich heat exchanger outlet is also

observed. The reduction is associated with a major change in the strippers’ operating conditions. This

can be observed in Figure 7.17, that shows the optimal temperature profiles in both liquid and vapour

phases for each optimisation.

Figure 7.17 – Axial temperature profile of the vapour phase (on the left) and liquid phase (on the right) in the stripper

for the optimised cases with capture rates (CR) of 70, 80, 90 and 99%.

Despite the lower inlet temperature of the rich solvent, the bulk temperature in both phases is

kept at a higher value across the column. This can be explained by the reduction of 20% in the solvent

flow rate, associated with an increase of 45% in the vapour flow rate exiting the reboiler (at the same

temperature), which reduces the temperature variation in the vapour phase.

Figure 7.17 also shows that for the 99% capture rate case, the temperature difference between

the strippers’ liquid inlet and vapour outlet changes. While for lower capture rates the liquid inlet optimal

temperature is higher than the vapour outlet temperature, in the 99% capture rate case, the liquid

310

315

320

325

330

335

0 0.5 1

Tem

ep

ratu

re (

K)


CR = 70% CR = 80%CR = 90% CR = 99%

300

305

310

315

320

325

330

335

0 0.5 1

Tem

ep

ratu

re (

K)


CR = 70% CR = 80%CR = 90% CR = 99%

370

375

380

385

390

395

0 0.5 1

Tem

ep

ratu

re (

K)

Relative position to the stripper top

CR = 70% CR = 80%CR = 90% CR = 99%

330

345

360

375

390

405

0 0.5 1

Tem

ep

ratu

re (

K)


CR = 70% CR = 80%CR = 90% CR = 99%

65

stream enters the column at a lower temperature. This way, instead of an initial increase in the CO2 flux

from the liquid to the vapour phase, there is an increase of the flux in the opposite direction, as seen in

Figure 7.18. In this case, the reduced temperature of the liquid phase leads to the partial dissolution of

the vapour phase, thus leading to an initial CO2 absorption into the liquid phase. Nevertheless, due to

this transfer to the liquid phase, this phase’s temperature is increased leading to a major increase in

the CO2 flux to the vapour phase.

Figure 7.18 – CO2 molar flux from the gas to the liquid phase across the stripper, for the optimised cases with capture

rates (CR) of 70, 80, 90 and 99%.

Considering this, in the 99% capture rate case, the temperature profile in the stripper is defined

by the reboiler temperature and the heat released in the initial absorption and is not particularly affected

by the rich solvent temperature, which can be decreased to the optimal value of 346.52 K.

Nevertheless, the CO2 recovered in the stripping section at a capture rate of 99% is increased

from 56% (optimal value at 90% capture rate) to 76%. This is due to an increasing in the columns’

height and in the vapour flow rate exiting the reboilers and consequently in the steam consumption,

thus greatly increasing the sOPEX.

Besides the steam consumption, the electricity annual consumption also tends to increase with

the increasing capture rate, either due to the flow rate increase or to the increase in the column’s height.

The cooling water consumption is also increased, since the flow rate entering the condenser is higher

for higher capture rates. In the case of 99% capture the cooling consumption is further increased, due

to the reduction in the lean solvent temperature after the coolers.

Observing the variation in the sCAPEX for lower capture rates (Figure 7.19), it is possible to

conclude that its value does not change considerably, suggesting an increase, which leads to a minor

increase in the optimal specific total cost between the capture rates of 80 and 70%. This way, it is

possible to conclude that for lower capture rates the reduction in the captured amount of CO2 may

surpass the reduction in the total cost, thus increasing the cost per tonne of captured CO2.

-0.01

-0.008

-0.006

-0.004

-0.002

0

0.002

0 0.2 0.4 0.6 0.8 1

CO

2m

ola

r fl

ux

(km

ol.

m-2

.s-1

)


CR = 70% CR = 80% CR = 90% CR = 99%

66

Figure 7.19 – sCAPEX percentage variation with the imposed capture rate (90% capture rate considered 100%).

7.2.2. Effect of the CO2 Purity

Another parameter usually imposed in the CO2 capture plant is the purity of the CO2 stream.

This is usually conditioned by the requirements of the compression and storage processes, and, as

mentioned in section 2.2.1, can be has high as 99.9% (in volume).

The effect of this parameter in the final capture cost was evaluated through the base case

optimisation, modifying the CO2 purity constraint to the values of 75, 85 and 99%. It should be noted

that in the case of a CO2 purity of 99% it was required the use of chilled water in the condenser, since

the optimal temperature is below 303.15 K. For that, it was assumed as inlet and outlet temperature

283.15 and 298.15 K, respectively. The cost of this utility was considered 0.08 USD2004/t, [62].

The optimisations results are present in Table A2.9, Table A2.10 and Table A2.11. In Figure

7.20, it is shown the variation of the specific total cost in each optimised scenario. Similarly to what was

demonstrated with the variation of the imposed capture rate, when the imposed CO2 purity is increased

to a value closer to the physical limit, the specific capture cost tends to increase considerably, with an

observed variation of 5% between purities of 95% and 99%. On the other hand, for lower purities, the

variation of the total cost is below 1%, being observed a slight increase in the specific total cost with the

decrease of the imposed CO2 purity, which suggests that an optimal value between 75% and 95% can

be achieved.

Figure 7.20 – Variation of the optimal specific total cost with the imposed CO2 purity.

Looking into the decision variables for each optimal scenario (Table A2.9 to Table A2.11), it is

possible to observe that the main variation occurs in the condenser temperature, as can be observed

95

96

97

98

99

100

70 80 90sC

AP

EX v

aria

tio

n (

%)

Capture rate (%)

36

36

37

37

38

38

39

75 85 95 99

Spe

cifi

c to

tal c

ost

(€

/tC

O2)

CO2 Purity (%)

67

in Figure 7.21. This is an expected variation since the CO2 stream purity is defined by the amount of

water removed through condensation, which is directly related with the equilibrium temperature.

Figure 7.21 – Variation of the optimal condenser temperature with the imposed CO2 stream purity.

Associated with the reduction of the condenser temperature there is an increase in the

respective cost, as well as in the cooling water consumption. This increase in the cost of the consumed

water is even higher when a purity of 99% is imposed, since chilled is required and it is considered 8

times more expensive than cooled water. In fact, this leads to an increase of 435% in cost of the

consumed water, which now represents 6% of the utilities expenses and is the major cause of the cost

increase seen when the CO2 purity is increased to 99%.

Besides the variation in the condenser temperature, the optimal lean solvent flow rate also

tends to decrease with the increasing CO2 purity, being compensated with an increase in the absorber

height and a reduction in the lean loading. The optimal rich solvent temperature after the lean-rich heat

exchanger is also decreased, thus reducing the temperature of the stripper’s liquid inlet stream. This

reduction can be further understood by observing Figure 7.22, where the water flux to the liquid phase

across the stripping column is presented.

Figure 7.22 – H2O molar flux from the gas to the liquid phase across the stripper, for the optimised cases with CO2

purities (CP) of 75, 85, 95 and 99%.

From the profiles shown in the above figure, it is possible to conclude that the reduction in the

rich solvent temperature after the integrated heat exchanger tends to reduce the amount of water

volatilised in the stripper’s top due to the liquid and vapour phases’ temperature difference. So, it is

possible to reduce the amount of water entering the condenser, thus minimising its cost increase and

the water consumption.

250

290

330

370

75 85 95 99

Tem

ep

ratu

re (

K)

CO2 Purity (%)

-0.01

0

0.01

0.02

0.03

0 0.2 0.4 0.6 0.8 1

H2O

mo

lar

flu

x (k

mo

l.m

-2.s

-1)


CP = 75% CP = 85% CP = 95% CP = 99%

68

The increase in the amount of water in the liquid phase of the stripper would cause an increase

in the steam consumption. Nevertheless the reduction on the optimal lean solvent flow rate tends to

minimise this effect, being the variation in the steam consumption quite reduced.

Nevertheless, the variation in rich solvent temperature leads to an increase in the lean-rich heat

exchanger and the lean solvent coolers cost with the increase of the CO2 purity. This way, the increase

in the heat exchanger’s and in the condenser’s costs is the major cause to the CAPEX increase.

The OPEX variation between the CO2 purities of 95% and 99% is mainly due to the use of

refrigerated water. Nonetheless, for lower purities, it can be seen that optimal OPEX tends to increase.

A lower purity in the CO2 stream means an increase in the MEA losses in the regeneration section,

which were not considered to be recovered. This way, the solvent cost gains relevance in the capture

cost for lower purities, in opposition to the effect of reducing the water consumption. This results in a

minor increase in the OPEX (0.5%), and consequently in the total cost (0.2%), between the optimal

cases with CO2 purities of 85% and 75%.

7.2.3. Effect of the MEA Concentration

As referred in section 2.3.1, the MEA concentration in the capture solvent is limited by its

corrosive potential and by its proclivity to slip in the absorption process. The corrosion inhibitors applied

nowadays allow the use of MEA concentrations as high as 30 wt%, and therefore this was the value

initially imposed in the optimisation problem. Understanding the economic benefits of increasing this

concentration may contribute to understanding if the development of new and more effective

degradation inhibitors is worth the associated costs.

This way, the equality constraint concerning the MEA mass fraction in the CO2 free lean solvent

was changed to the value of 40 wt%. In the same way, to evaluate the effects of reducing the MEA

concentration this variable was changed to the value of 20 wt%. The optimal results obtained for each

of these simulations are featured in Table A2.12 and Table A2.13. Once again, it should be noted that,

due to total heat consumption reduction, in the case of a MEA concentration of 40 wt% the number of

reboilers per stripping train is 3, instead of 4.

In Figure 7.23, it is possible to observe the reduction of the specific total cost with increasing

concentration of MEA in the solvent. In fact, the increase of the MEA mass fraction in the CO2 free lean

solvent to 40% allows the reduction of capture cost in 4%. On the other hand, the reduction of this

variable to 20% causes an increase of 6% in the total cost.

69

Figure 7.23 – Variation of the optimal specific total cost with the imposed MEA mass fraction in the CO2 free lean

solvent.

Comparing the decision variables optimal values, it is possible to conclude that the lean solvent

flow rate is the one with the main variation, besides the MEA mass fraction itself. Figure 7.24 shows

this reduction of flow rate with the increasing solvent concentration.

Figure 7.24 – Variation of the optimal lean solvent flow rate with the imposed MEA mass fraction in the CO2 free lean

solvent.

Associated with the solvent flow rate reduction, it is also verified a reduction in the diameter of

both the absorber and the stripper, in order to meet 70% of the vapour flooding velocity, and a reduction

in both the pump related costs and in the electricity consumption. On the other hand, for a higher MEA

concentration, the columns’ optimal height is increased, thus increasing their related costs in both the

20% and 40% optimal cases.

The reduction in the flow rate reduces the heat transferred in the lean-rich-heat exchangers.

When comparing the optimal cases with MEA mass fractions of 20% and 30%, the rich solvent

temperature after these heat exchangers is approximately the same, which leads to the decrease of

104% in this equipment cost (being required less 10 units), with the increasing amine concentration.

However, due to the same decrease in the flow rate, the heat released in the absorber leads to an

increase in the rich solvent temperature, which affects the heat exchange driving force in the lean-rich

heat exchanger and may lead to an increase of the heat exchange area. This is verified when comparing

the optimal cases with MEA mass fractions of 30% and 40%, in which the exchanged heat is in fact

reduced, but the area is increased, leading to an increase this equipment cost.

The mentioned factors lead to an increase of 8% in the optimal specific CAPEX when the MEA

mass fraction in the CO2 free lean solvent is reduced from 30% to 20%. This trend is not seen when

34

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20 30 40

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800

1100

1400

1700

20 30 40

Lean

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lve

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70

this variable is increased from 30% to 40%, in which the specific CAPEX does not change significantly

(variation of 0.02%).

The optimal lean loading also tends to increase at higher MEA concentrations (which is

associated with an increase in the absorber’s height). This increase, associated with the reduction in

the optimal reboiler pressure lead to a reduction in its heat duty, allows a decrease in the specific heat

consumption to 4.62 GJ/tCO2, when the MEA mass fraction in the CO2 free lean solvent is 40%.

The variation in the steam consumption, leads to the reduction in the OPEX with the increasing

of the MEA concentration in the solvent, and consequently in the total cost. Nevertheless, it should be

noted that due to the decrease in the CAPEX variation, the total cost reduction shows a higher impact

from the MEA mass fraction of 20% to 30%, than from 30% to 40%, being the OPEX variation

approximately the same.

From the different optimisations performed by varying the process constraints, the increase of

the MEA mass fraction in the CO2 free lean solvent to 40% allowed to obtain the lowest specific heat

consumption, and therefore the lowest specific total cost. Considering the Lagrange multipliers initially

obtained (Table 7.5), this was an expected outcome.

Nevertheless there are some considerations that have to be taken into account, when

considering higher amine concentrations. According to Delfort et al. [65], there are inhibitors that allow

the reduction of the amine degradation through oxidation, even at concentrations as high as 40%.

Nevertheless, these inhibitors will have an impact on the solvent cost, which should be considered. On

the other hand, due to the increasing concentration of MEA in the solvent, the amount of slipped amine

in the absorption section is increased. When comparing the optimal scenarios with 30% and 40% of

MEA in the CO2 free lean solvent, this increase is from 0.17 to 0.39 kg/s. This would affect the design

and the operation of the washing section that follows the absorber, which would increase the costs

associated with washing section, thus reducing, or even nullifying, the economic advantage of using a

higher amine concentration of amine.

7.2.4. Specific Total Cost Minimisation with Inequality Constraints

The variation of the process constraints shows that, by reducing the capture rate and the CO2

or by increasing the MEA mass fraction in the lean solvent, it is possible to reduce the total capture

costs, as it was expected through the analysis of the Lagrange multipliers (Table 7.5). In order to obtain

an optimal process in the terms of both capital and operational costs per tonne of captured CO2, the

original equality constraints (Table 6.2) were replaced by the inequality constraints shown in Table 7.6.

Table 7.6 – Additional inequality constrained variables, with respective upper and lower bounds, used in the specific

total cost minimisation without equality constraints.


Capture rate (g/g) 0.70 0.90

CO2 molar fraction in the CO2 stream (mol/mol) 0.75 0.95

MEA mass fraction in the CO2 free lean solvent (g/g) 0.2 0.4

In order to prove the assumption that led to the reboiler temperature modification to the

maximum value of 393.15 K before proceeding to the optimisation procedure itself, this variable was

also included as a decision variable. To that end, the lower and upper bounds of 385.15 and 393.15 K

71

were set. The obtained optimisation results are shown in Table A2.14, and prove that the reboiler

temperature indeed reached the upper bound, demonstrating that the highest temperature leads to the

reduction of the plant cost.

From the optimal results it is possible to conclude that, as expected, the minimum cost per

tonne of captured CO2 is achieved with the highest MEA concentration, with the respective variable

hitting the upper bound. On the other hand, it can be observed that both the capture rate and the CO2

purity do not reach the lower bound, showing optimal values of 75% and 88%, respectively. In these

conditions, the achieved specific heat requirement reaches the value of 4.60 GJ/tCO2, mainly due to a

reduction in the lean solvent flow rate and an increase in the lean loading.

Comparing these optimal results with those obtained from the constrained optimisation, there

is a general reduction in the equipment cost, mainly due the columns’ size and solvent flow rate

reduction, leading to a specific CAPEX 6% lower. Related to these reductions there is also a decrease

in the utilities consumption, as well as in the solvent degradation, leading to a reduction of 5% in the

specific OPEX. Figure 7.25 features the specific total cost in the base case and in the optimal cases

obtained with and without standard conditions, thus showing that without imposing the standard

constraints it is possible to reduce the specific total cost in 20% when comparing with the base case.

Figure 7.25 – Optimal specific total cost obtained through its minimisation with and without standard constraints, and

value in the base case.

As referred in the previous section, the use of a higher amine concentration requires a broader

study concerning the oxidation inhibitors requirements and respective cost and the effect of having a

larger washing section, due to the increase in the solvent slip.

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Base Optimal withstandard constraints

Optimal withoutstandard constraints

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73

8. Conclusions and Future Work

8.1. Conclusions

One of the main focuses of the CCS-related investigation in the literature is the development

and improvement of the CO2 capture technology, in order to reduce the process cost. For a post-

combustion capture plant, the most common used capture technology is CO2 absorption by an amine-

based solvent, typically MEA, due to the low CO2 partial pressures in the flue gas. This technology is

currently applied in other industrial areas, and one of the main sources of expense is the energy

consumption in the solvent regeneration process. However, due to the high amount of CO2 emitted by

a power plant (in the order of millions of tonnes of CO2) the process scale-up leads to a considerable

increase in the electricity cost, thus limiting its application to a full scale plant.

The gCCS® capture library allowed the development of a capture plant model in the gPROMS®

ModelBuilder environment. In order to validate these models, the simulation results were compared

with experimental data found in the literature. From Tobiensen et al. [10], it was possible to conclude

that between Onda and Billet & Schultes correlations, the first one is more accurate in the prediction of

mass transfer coefficients in the absorption process. Using this correlation, deviations in the range of -

13% to 26% were obtained when comparing the simulated and experimental flow of captured CO2,

which are deemed acceptable when considering that the used models do not consider any fitting to the

experimental data. It was also verified that this deviation tends to increase with the decrease of the

solvent lean loading. From the experimental data found in Notz et al., it was observed that for a complete

pilot plant capture plant model with a specified heat input, the CO2 capture rate tends to be under

estimated, with deviations that can reach -25%, leading to an over prediction of the specific heat

requirement up to 32%.

For a full scale capture plant optimisation, it was considered as base case a PSE’s case study,

which lean solvent flowrate and MEA mass fraction were modified in order to meet a 90% capture rate

and a MEA fraction in the CO2 free lean solvent of 30%. It was also developed an approximate cost

estimation model, for the calculation of the capture plant CAPEX and OPEX. The application of this

model to the base case, allowed the estimation of a specific total cost of 43.15 €/tCO2, from which the

OPEX represents 80% and is associated to a specific heat requirement of 5.66 GJ/tCO2. The cost

estimation allowed to conclude that the most expensive equipment, and therefore, the fraction with

more influence in the CAPEX is the absorber’s packing (73% of the equipment total cost). For the

OPEX, the main expense is related with the reboilers’ steam consumption, which in fact represents 69%

of the annual total cost.

From the base case specific total cost minimisation, considering a capture rate of 90%, a CO2

purity of 95 vol% and a MEA fraction in the CO2 free lean solvent of 30%, it was possible to reduce the

total cost by 15%. Based on these results it is possible to conclude that a reduction in the absorber

diameter (in order to meet the flooding limit) and in the lean loading increase the absorber efficiency,

allowing the same capture rate in a smaller absorber and with a lower solvent flow rate. The stripper

efficiency is also increased by its diameter reduction and increase in the column’s height and the rich

74

solvent inlet temperature, thus allowing a lower lean loading with lower steam consumption (4.87

GJ/tCO2).

The effect of changing the lean loading before the optimisation procedure (by varying the lean

solvent flow rate and reboiler pressure) in the optimal specific total cost was tested, being observed a

variation of less than 1%. From these tests it was also possible to conclude that the lean solvent

temperature after the cooler is not a relevant parameter in the considered conditions. Furthermore, it

was concluded that in these cases the lean solvent cooler would not be required. Nevertheless, since

the condenser’s associated costs only represent 0.01% of the total cost, it was not removed from the

flowsheet model.

Starting from the same initial guesses the effect of increasing the number of absorption trains

was evaluated. This way, it was possible to conclude that the decision variable mainly affected is the

absorber diameter. Considering only one absorber would be technically unfeasible due to the high

optimal absorber diameter, being the use of two absorption trains conditioned by the possibility of using

absorber with a diameter above 15 m. Nevertheless, increasing the number from 1 to 4 only increased

the specific total cost in 0.4%, since the total volume of packing tends to be the same.

In order to conclude if the minimum total cost corresponds to the minimum steam consumption,

the optimisation procedure was conducted for the minimisation of the specific heat consumption. This

led to a further reduction of the specific heat requirement to 4.46 GJ/tCO2, but at the cost of greatly

increasing the columns’ height, thus leading to an increase in the total cost (143%).

For the evaluation of the effect of the imposed capture rate, CO2 purity and MEA concentration

on the specific total cost, the optimisation procedure was conducted by changing these constraints.

Regarding the capture rate it was observed that between 70% and 90%, the variation is achieved

through an increase in the lean solvent flow rate and in the absorber and stripper height, associated

with a slight decrease in the lean loading. This leads to an increase of 2% in the optimal total cost in

this capture rate range. On the other hand, between the capture rates of 90% and 99%, there is 28%

increase of the specific total cost, due to a major decrease in the optimal lean solvent flow rate and lean

loading, accompanied by a further increase in the columns’ height. In the case of a 99% capture it was

also concluded that due to the heat released in the absorption process and the reduced solvent flow

rate, the lean solvent cooler gains relevance. In the same case, the rich solvent temperature entering

the stripper is decreased, since the vapour flow rate is increased and the solvent flow rate is decreased,

being the temperature across the columns kept at higher values.

The tested variation of the imposed CO2 purity showed similar results to the capture rate

variation, but at a lower scale. Between the purities of 75% and 95%, there is an increase of only 0.2%

in the specific total cost. The condenser temperature is related to this variation, since it defines the

amount of water condensed, and therefore the purity of the final stream. This way, with the increasing

purity the cooling water consumption and the condenser costs tend to increase. On the other hand, for

a lower CO2 purity the amount of lost solvent in the regeneration process tends to increase, thus leading

to a minimum in the sOPEX. When the purity is increased to 99%, the specific cost increases by 4%,

associated with a major variation in the OPEX. This was due to the further reduction in the condenser

temperature that required the use of chilled water, which was considered eight times more expensive,

75

leading to an increase of 435% in the cooling/refrigerating water annual costs. Nevertheless, it was

verified that this parameter did not have a considerable influence in the specific heat consumption, thus

the reduced effect on the total cost.

The MEA concentration was varied by the modification of the imposed MEA mass fraction in

the CO2 free lean solvent to 20% and 40%. The variation in the specific total cost was more significant

than in the previous cases (except when approaching the physical limits), showing a reduction by 5%,

when changing this constraint from 20% to 30%, and by 4%, when changing it from 30% to 40%. These

variations are mainly due to the possibility of reducing the solvent flow rate, with the increasing

concentration, associated whit an increase in the optimal lean loading. Both factors contribute to the

reduction of the specific heat requirement, allowing the total cost reduction. Nevertheless, it should be

considered that there are implications of using a more concentrate solvent, which were not considered,

such as, the size of the water washing sections or the costs associated with the required oxidation

inhibitors cost.

At last, the same optimisation procedure was conducted, with only inequality constraints. This

led to the optimal capture rate of 75%, CO2 purity of 88% and MEA mass fraction in the CO2 free lean

solvent of 40%, which is the variable upper bound. This allowed a specific total cost reduction to 34.70

€/tCO2 (5% less when compared with the initial optimal results), associated with the specific heat

requirement of 4.60 GJ/tCO2.

The analysis presented shows that the total cost is greatly affected by the trade-off between

the solvent’s flow rate, the absorber height and the lean solvent’s loading. Increasing the MEA

concentration in the solvent has a positive effect in all this variables. This is also verified for the capture

rate in a limited range of values. It was also observed that at moderate capture rates, increasing both

the stripper’s height and the rich solvent temperature at its inlet also tend to reduce the steam

consumption, and therefore the total cost. The interconnection between these variables shows the

relevance of a model-based full plant optimisation for the reduction of the capture cost, which is an

important step for the effective CCS implementation in real power plants.

8.2. Future Work

The optimisation of a conventional flowsheet configuration using a typical solvent is simply the

first step in the reduction of a capture plant costs. As mentioned in section 2.3, today’s technology

already considers several modifications on the capture flowsheet which are considered more economic.

In the same section, are also mentioned other solvents, with considerable advantages, that are now

being tested for implementation in the CCS process. The construction of flowsheet models based on

these more economic configurations and solvents, and their optimisation will help to further reducing

the capture costs.

Nevertheless, for a conventional flowsheet as the one considered in the present thesis, further

work can also be applied. It should be considered that the flue gas preparation (including its saturation),

the washing sections and the CO2 compression were not optimised. The inclusion of these sections in

the flowsheet to be optimised, may lead to more accurate results.

76

One of the conclusions of the present thesis is that the use of a more concentrated solvent

would reduce the capture costs. As mentioned before, this would require larger washing sections and

more efficient corrosion inhibitors. It should also be considered that in the cost estimation model it was

considered a generic degradation rate for MEA, which may no longer be applicable. Taking this into

account, a further understanding of the use of more concentrated solvents should be considered.

Finally, the presented cost estimation model follows the factorial method, and is based on the

approximate calculation of the required sizing variables. This way, the use of a more precise model for

the process costing would provide more accurate results.

77

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Engineering Research and Design, vol. 77, no. 6, pp. 498-504, 1999.

81

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82


83

Appendices

A1. Cost Estimation Model Description

As mentioned in Section 5.2, the cost estimation model uses cost correlations found in the

literature. Their use requires size variables, which are not usually an output of the existing models. This

way, these variables calculation procedure is described in this appendix.

A1.1. Shell mass estimation

For the estimation of the absorber and stripper shell mass it was considered the density of

stainless steel 304 (ρss-304 = 8000 kg/m3, [58]). The calculation of the wall thickness (t) considered both

the minimum thickness (tmin) for a given diameter (Table A1.1) and the equation specified by the ASME

BPV Code [58]. From these two values, the highest one is considered (Equation (A1.1)).

Table A1.1 – Minimum practical wall thickness [58].

Vessel Diameter (m) Minimum Thickness (mm)

1 5

1 to 2 7

2 to 2.5 9

2.5 to 3.0 10

3.0 to 3.5 12

𝑡(𝑚𝑚) = max (𝑡𝑚𝑖𝑛 ,𝑃. 𝐷

2. 𝑆. 𝐸 − 1.2. 𝑃) (A1.1)

For the calculation of the wall thickness, it is required the maximum pressure in the vessel (P),

retrieved from the column model, its diameter (D), the maximum allowable stress (S) and the welded

joint efficiency (E), which is assumed to be 1. The maximum allowable stress depends on the vessel

maximum temperature, which is also obtained from the column model, and was estimated by the linear

interpolation of the values shown in Table A1.2.

Table A1.2 – Typical maximum allowable stresses for stainless steel 304 [58].

Temperature (°F) S (ksi = 1000 psi)

100 20.0

200 15.0

Using the wall thickness, the column’s diameter and packing height (LPacking), the shell mass

(Mshell) is calculated by equation (A1.2), where it is also considered an extra length of 3 meters in both

the column’s top and bottom (Ltop and LBottom).

𝑀𝑆ℎ𝑒𝑙𝑙 = 𝜋. 𝑡. (𝐷 + 𝑡)(𝐿𝑃𝑎𝑐𝑘𝑖𝑛𝑔 + 𝐿𝑡𝑜𝑝 + 𝐿𝑏𝑜𝑡𝑡𝑜𝑚). 𝜌𝑠𝑠−304 (A1.2)

A1.2. Heat exchanger area estimation

The lean-rich heat exchanger and the lean solvent cooler were assumed to be of the plate and

frame kind due to their high efficiency when processing liquid streams. The heat exchange area (AHX)

is an output of both heat exchangers models, requiring the indication of a heat transfer coefficient. Since

the contacting solutions are mainly composed of water, it was considered an overall heat transfer

coefficient of 5000 W/(m2.K) [58]. For the estimation of the number of units required the obtained area

84

was divided by cost correlation upper bound of applicability (500 m2), as seen in equation (A1.3). The

area for each heat exchanger used for the cost calculation (𝐴𝐻𝑋𝑈𝑛𝑖𝑡) is then calculated based on the

number of units, using equation (A1.4).

𝑁𝐻𝑋 = int (𝐴𝐻𝑋 (𝑚2)

500) + 1 (A1.3)

𝐴𝐻𝑋𝑈𝑛𝑖𝑡 =

𝐴𝐻𝑋

𝑁𝐻𝑋

(A1.4)

A1.3. Reboiler area estimation

The reboiler area is not an output of the respective model and therefore was estimated using

equation (A1.5). For that, it was assumed a typical overall heat transfer coefficient (UReb) of 1140

W/(m2.K), [63], as well as the heat exchanged in the reboiler (QReb), the steam temperature (TSteam) and

the reboiler temperature (TReb), which are model’s outputs.

𝐴𝑅𝑒𝑏 =𝑄𝑅𝑒𝑏

𝑈𝑅𝑒𝑏 . (𝑇𝑆𝑡𝑒𝑎𝑚 − 𝑇𝑅𝑒𝑏) (A1.5)

A1.4. Condenser area estimation

The condenser was assumed as being of the tube and shell type. Its area (ACond) is calculated

using equation (A1.6). In here, it is assumed the typical overall heat transfer coefficient (UCond) of 850

W/(m2.K), [63]. Besides this, it is also required the heat exchanged in the condenser (QCond), the cooling

water inlet and outlet temperature (𝑇𝑈𝑡𝑖𝑙𝑖𝑡𝑦𝐼𝑛 and 𝑇𝑈𝑡𝑖𝑙𝑖𝑡𝑦

𝑂𝑢𝑡 ) and the condenser inlet and outlet temperature

(𝑇𝐶𝑜𝑛𝑑𝐼𝑛 and 𝑇𝐶𝑜𝑛𝑑

𝑂𝑢𝑡 ), which are model’s outputs.

𝐴𝐶𝑜𝑛𝑑 =𝑄𝐶𝑜𝑛𝑑

𝑈𝐶𝑜𝑛𝑑 .((𝑇𝐶𝑜𝑛𝑑

𝑂𝑢𝑡 − 𝑇𝑈𝑡𝑖𝑙𝑖𝑡𝑦𝐼𝑛 ) − (𝑇𝐶𝑜𝑛𝑑

𝐼𝑛 − 𝑇𝑈𝑡𝑖𝑙𝑖𝑡𝑦𝑂𝑢𝑡 ))

ln ((𝑇𝐶𝑜𝑛𝑑

𝑂𝑢𝑡 − 𝑇𝑈𝑡𝑖𝑙𝑖𝑡𝑦𝐼𝑛 )

(𝑇𝐶𝑜𝑛𝑑𝐼𝑛 − 𝑇𝑈𝑡𝑖𝑙𝑖𝑡𝑦

𝑂𝑢𝑡 ))

(A1.6)

As in the heat exchangers unit area estimation, for the condenser it was used the cost

correlation upper bound (1000 m2), according to equations (A1.7) and (A1.8).

𝑁𝐶𝑜𝑛𝑑 = int (𝐴𝐶𝑜𝑛𝑑 (𝑚2)

1000) + 1 (A1.7)

𝐴𝐶𝑜𝑛𝑑𝑈𝑛𝑖𝑡 =

𝐴𝐶𝑜𝑛𝑑

𝑁𝐶𝑜𝑛𝑑

(A1.8)

A1.5. Pump volumetric flow rate and power estimation

The utilised pump models were modified in order to retrieve the density of the passing fluid (ρfl),

through which the volumetric flow rate (Fv) was calculated. The number of pumps working in parallel

(𝑁𝑃𝑢𝑚𝑝𝑃 ) is based on this value and considers the cost correlation upper bound (0.126 m3/s), according

to equation (A1.9). The unit volumetric flow rate (𝐹𝑉𝑈𝑛𝑖𝑡) is then calculated based on this value (equation

(A1.10)).

𝑁𝑃𝑢𝑚𝑝𝑃 = int (

𝐹𝑉 (𝑚3 𝑠⁄ )

0.126) + 1 (A1.9)

85

𝐹𝑉𝑈𝑛𝑖𝑡 =

𝐹𝑉

𝑁𝑃𝑢𝑚𝑝𝑃 (A1.10)

The specific work done by the fluid passing in each train of pumps working in parallel (WP) was

estimated using Bernoulli’s equation (equation (A1.11)). For that, it was considered that the solvent

would exit the source column (0) at half of the bottom’s extra length (LBottom) and enter the destination

column (1) at half of the top’s extra length (LTop). It was also assumed a pressure drop of 0.62 bar in

each heat exchanger (∆𝑃𝐻𝑥) and a line pressure drop (∆𝑃𝑓) of 0.5 bar [63]. Besides these, it is required

the pressure at the bottom of the source column (𝑃𝐵𝑜𝑡𝑡𝑜𝑚,0) and at the top of the destination column

(𝑃𝑇𝑜𝑝,1) and the gravitational acceleration (g = 9.8 m/s2).

𝑊𝑃 = 𝑔. ((𝐿𝐵𝑜𝑡𝑡𝑜𝑚 + 𝐿𝑃𝑎𝑐𝑘𝑖𝑛𝑔,1 + 0.5. 𝐿𝑇𝑜𝑝) − 0.5. 𝐿𝐵𝑜𝑡𝑡𝑜𝑚) +

+(𝑃𝑇𝑜𝑝,1 − 𝑃𝐵𝑜𝑡𝑡𝑜𝑚,0) + (∆𝑃𝐻𝑥 + ∆𝑃𝑓)

𝜌𝑠

(A1.11)

The power required by each train of pumps (PP) is calculated using the stream mass flow rate

(F) and the shaft efficiency (𝜂𝑠ℎ𝑎𝑓𝑡), through equation (A1.12). The shaft efficiency is calculated by the

linear interpolation of values presented in Table A1.3.

𝑃𝑃 = 𝑊𝑃 .𝐹

𝑁𝑃𝑢𝑚𝑝𝑃 . 𝜂𝑠ℎ𝑎𝑓𝑡 (A1.12)

Table A1.3 – Typical shaft efficiencies for centrifugal pumps [63].

Shaft Efficiency (%) FV (m3/min)

70 1.89

80 37.8

Based on the obtained power, it was calculated the number of pumps working in series in each

train (𝑁𝑃𝑢𝑚𝑝), considering the driver cost correlation upper bound (2500 kW). The unitary power required

for each pump (𝑃𝑆) is then calculated using equation (A1.13) and (A1.14). In here it is also used the

driver efficiency (𝜂𝑑𝑟𝑖𝑣𝑒𝑟), which is obtained by interpolating the values in Table A1.4.

𝑁𝑃𝑢𝑚𝑝 = int (𝑃𝑃 (𝑘𝑊)

2500) + 1 (A1.13)

𝑃𝑆 =𝑃𝑃

𝑁𝑃𝑢𝑚𝑝𝑆 . 𝜂𝑑𝑟𝑖𝑣𝑒𝑟 (A1.14)

Table A1.4 – Typical driver efficiencies for electrical motors [58].

Driver Efficiency (%) Size (kW)

85 15

90 75

86


87

A2. Base Case and Optimisations Detailed Results

Table A2.1 – Detailed results from the application of the cost estimation model in the base case.

Decision Variables

Absorber (A-301) Diameter (m) 20

Height (m) 11.89

Stripper (ST-301) Diameter (m) 8.5

Height (m) 10

Lean-Rich Heat Exchanger (HX-301) Outlet temperature of the rich solvent (K) 362.80

Lean Solvent Cooler (HXU-301) Outlet temperature of the lean solvent (K) 313.90

Reboiler (R-301) Temperature (K) 390.99

Pressure (bar) 1.79

Condenser (C-301) Temperature (K) 313.15

Lean Solvent (RB-303) Flow Rate (kg/s) 1 450.14

MEA mas fraction (g/g) 0.285

Investment (k€)

Equipment Cost

Absorber Shell (2 units) 2 385

Absorber Packing (2 units) 44 640

Stripper Shell (2 units) 1 064

Stripper Packing (2 units) 6 781

Lean-Rich Heat Exchanger (4 units) 252

Lean Solvent Cooler (5 units) 347

Reboiler (2x4 units) 4 266

Condenser (2x1 units) 488

Rich Solvent Pump (12 units) 317

Rich Solvent Pump Driver (12 units) 202

Lean Solvent Pump (12 units) 322

Lean Solvent Pump Driver (12 units) 175

ISBL Investment 79 873

Total Fixed Investment 159 905

Production Cost (k€/Year)


Electricity 408

Steam 55 177

Cooled Water 767

Solvent (MEA) 5 013

Total 61 365

Fixed Production Costs 3 195

Total Production Cost 64 560

Total Cost (M€/year)

CAPEX 15.99

OPEX 64.56

Total 80.55

Key Parameters

CO2 Capture Rate (%) 90

CO2 Purity (vol%) 96

MEA mass fraction in the CO2 free lean solvent (wt%) 30

Vapour velocity/vapour flooding velocity in the top of the absorber 0.56

Vapour velocity/vapour flooding velocity in the bottom of the stripper 0.57

Process stream ΔT in the lean solvent cooler (K) 32.56

Lean Loading (molCO2/molMEA) 0.249

Rich Loading (molCO2/molMEA) 0.463

CO2 recovery rate in the regeneration section (%) 46

Specific Energy Requirement (GJ/tCO2) 5.66

sCAPEX (€/tCO2) 8.57

sOPEX (€/tCO2) 34.59

Specific Total Cost (€/tCO2) 43.15

88

Table A2.2 – Detailed results from the specific total cost minimisation with 1 absorber and standard constraints, and

comparison with the initial specific total cost minimisation results (Table 7.1).


Absorber (A-301) Diameter (m) 24.84 41%

Height (m) 8.55 0.7%

Stripper (ST-301) Diameter (m) 7.24 -0.2%

Height (m) 16.20 -4%

Lean-Rich Heat Exchanger (HX-301) Outlet temperature of the rich solvent (K) 381.24 0.04%




Condenser (C-301) Temperature (K) 317.10 0.03%

Lean Solvent (RB-303) Flow Rate (kg/s) 1 198.15 0.3%



Equipment Cost

Absorber Shell (1 unit) 1 220 -32%

Absorber Packing (1 unit) 24 763 0.2%

Stripper Shell (2 units) 1 223 -3%

Stripper Packing (2 units) 7 980 -4%



Reboiler (2x4 units) 4 312 0.04%

Condenser (2x2 units) 873 0.4%

Rich Solvent Pump (10 units) 263 0.2%

Rich Solvent Pump Driver (10 units) 189 -0.9%

Lean Solvent Pump (10 units) 266 0.2%

Lean Solvent Pump Driver (10 units) 129 0.3%





Electricity 355 -0.8%

Steam 47 557 0.05%

Cooled Water 593 0.9%

Solvent (MEA) 4 992 0.2%

Total 53 497 0.1%


Total Production Cost 55 930 -0.08%


CAPEX 12.18 -3%

OPEX 55.93 -0.08%

Total 68.11 -0.6%




MEA mass fraction in the CO2 free lean solvent (wt%) 30 0.00%





Rich Loading (molCO2/molMEA) 0.458 0.05%

CO2 recovery rate in the regeneration section (%) 56 -0.3%

Specific Energy Requirement (GJ/tCO2) 4.87 0.08%

sCAPEX (€/tCO2) 6.52 -3%

sOPEX (€/tCO2) 29.94 -0.05%


89





Height (m) 8.41 -0.9%


Height (m) 17.25 2%






Lean Solvent (RB-303) Flow Rate (kg/s) 1 192.99 -0.2%

MEA mas fraction (g/g) 0.288 0.01%


Equipment Cost

Absorber Shell (3 units) 2 265 26%

Absorber Packing (3 units) 24 496 -0.9%



Lean-Rich Heat Exchanger (10 units) 764 -0.09%

Lean Solvent Cooler (6 units) 437 0.5%

Reboiler (2x4 units) 4 281 -0.7%

Condenser (2x2 units) 856 -1%

Rich Solvent Pump (10 units) 262 -0.1%

Rich Solvent Pump Driver (10 units) 192 0.5%

Lean Solvent Pump (10 units) 265 -0.09%

Lean Solvent Pump Driver (10 units) 128 -0.3%

ISBL Investment 64 053 2%

Total Fixed Investment 128 233 2%



Electricity 360 0.4%

Steam 47 162 -0.8%

Cooled Water 582 -0.9%

Solvent (MEA) 4 949 -0.7%

Total 53 053 -0.8%

Fixed Production Costs 2 562 2%



CAPEX 12.82 2%

OPEX 55.61 -0.6%

Total 68.44 -0.2%



CO2 Purity (vol%) 95 -0.002%

MEA mass fraction in the CO2 free lean solvent (wt%) 30 0.04%

Vapour velocity/vapour flooding velocity in the top of the absorber 0.70 -0.1%

Vapour velocity/vapour flooding velocity in the bottom of the stripper 0.69 -0.7%

Process stream ΔT in the lean solvent cooler (K) 13.93 -0.09%




Specific Energy Requirement (GJ/tCO2) 4.86 -0.19%

sCAPEX (€/tCO2) 6.90 3%

sOPEX (€/tCO2) 29.94 -0.05%


90





Height (m) 8.35 -2%


Height (m) 17.29 2%






Lean Solvent (RB-303) Flow Rate (kg/s) 1 192.70 -0.2%



Equipment Cost





Lean-Rich Heat Exchanger (10 units) 770 0.7%

Lean Solvent Cooler (6 units) 437 0.4%


Condenser (2x2 units) 875 0.6%

Rich Solvent Pump (10 units) 262 -0.1%


Lean Solvent Pump (10 units) 265 -0.1%







Steam 47 567 0.07%

Cooled Water 590 0.5%

Solvent (MEA) 4 985 0.05%

Total 53 502 0.07%


Total Production Cost 56 109 0.2%


CAPEX 13.05 4%

OPEX 56.11 0.2%

Total 69.16 0.9%





Vapour velocity/vapour flooding velocity in the top of the absorber 0.70 -0.09%


Process stream ΔT in the lean solvent cooler (K) 14.03 0.6%

Lean Loading (molCO2/molMEA) 0.199 -0.5%


CO2 recovery rate in the regeneration section (%) 56 0.3%


sCAPEX (€/tCO2) 6.99 4%

sOPEX (€/tCO2) 30.03 0.2%


91

Table A2.5 – Detailed results from the specific heat requirement minimisation with standard constraints, and

comparison with the base case.



Height (m) 44.41 423%

Stripper (ST-301) Diameter (m) 19.89 174%

Height (m) 73.84 337%

Lean-Rich Heat Exchanger (HX-301) Outlet temperature of the rich solvent (K) 380.21 -0.2%

Lean Solvent Cooler (HXU-301) Outlet temperature of the lean solvent (K) 327.12 4%


Pressure (bar) 1.82 -2%

Condenser (C-301) Temperature (K) 316.98 -0.01%




Equipment Cost


Absorber Packing (2 units) 14 4925 487%



Lean-Rich Heat Exchanger (10 units) 656 -14%

Lean Solvent Cooler (6 units) 109 -75%

Reboiler (2x4 units) 3 857 -11%



Rich Solvent Pump Driver (10 units) 292 53%


Lean Solvent Pump Driver (10 units) 205 59%





Electricity 824 130%

Steam 43 831 -8%


Solvent (MEA) 4 770 -4%

Total 49 786 -7%


Total Production Cost 69 336 24%


CAPEX 97.85 678%

OPEX 69.34 24%

Total 167.19 144%


CO2 Capture Rate (%) 0.90 -0.01%

CO2 Purity (vol%) 0.95 -0.02%

MEA mass fraction in the CO2 free lean solvent (wt%) 0.30 -0.0001%

Vapour velocity/vapour flooding velocity in the top of the absorber 0.63 -10%

Vapour velocity/vapour flooding velocity in the bottom of the stripper 0.09 -88%


Absorber’s top pressure (bar) 1.02 -6%

Stripper’s top pressure (bar) 1.82 -0.8%

Temperature difference in lean-rich heat exchanger inlet (K) 10 3%

Temperature difference in lean-rich heat exchanger outlet (K) 13 7%

Temperature difference in lean solvent cooler inlet (K) 5 -11%

Temperature difference in lean solvent cooler outlet (K) 24 124%


Rich Loading (molCO2/molMEA) 0.473 3%

CO2 recovery rate in the regeneration section (%) 63% 11%


sCAPEX (€/tCO2) 52.08 674%

sOPEX (€/tCO2) 36.90 23%

Specific Total Cost (€/tCO2) 88.98 143%

92

Table A2.6 – Detailed results from the specific total cost minimisation with an imposed capture rate of 70%, and




Height (m) 6.37 -25%


Height (m) 15.52 -8%






Lean Solvent (RB-303) Flow Rate (kg/s) 972.93 -19%



Equipment Cost


















Steam 36 613 -23%



Total 40 972 -23%




CAPEX 9.58 -24%

OPEX 42.88 -23%

Total 52.45 -23%


CO2 Capture Rate (%) 70 -22%








CO2 recovery rate in the regeneration section (%) 54 -4%


sCAPEX (€/tCO2) 6.57 -2%

sOPEX (€/tCO2) 29.45 -2%


93




Absorber (A-301) Diameter (m) 17.48 -0.7%

Height (m) 7.17 -16%


Height (m) 15.61 -8%









Equipment Cost





Lean-Rich Heat Exchanger (13 units) 1 023 34%













Steam 41 935 -12%



Total 46 933 -12%




CAPEX 10.84 -14%

OPEX 49.10 -12%

Total 59.94 -13%











Specific Energy Requirement (GJ/tCO2) 4.82 -0.9

sCAPEX (€/tCO2) 6.51 -3%

sOPEX (€/tCO2) 29.50 -2%


94





Height (m) 14.39 69%


Height (m) 30.27 79%

Lean-Rich Heat Exchanger (HX-301) Outlet temperature of the rich solvent (K) 346.52 -9%

Lean Solvent Cooler (HXU-301) Outlet temperature of the lean solvent (K) 303.38 -3%







Equipment Cost

















Electricity 387 8%

Steam 65 857 39%

Cooled Water 1 080 84%

Solvent (MEA) 5 724 15%

Total 73 047 37%




CAPEX 19.80 57%

OPEX 77.00 38%

Total 96.81 41%


CO2 Capture Rate (%) 99 10%









Specific Energy Requirement (GJ/tCO2) 6.09 25%

sCAPEX (€/tCO2) 9.58 42%

sOPEX (€/tCO2) 37.23 24%


95

Table A2.9 – Detailed results from the specific total cost minimisation with an imposed CO2 purity of 75%, and




Height (m) 8.27 -3%


Height (m) 16.66 -1%






Lean Solvent (RB-303) Flow Rate (kg/s) 1 210.01 1%



Equipment Cost


















Steam 47 330 -0.4%



Total 53 772 0.6%


Total Production Cost 56 206 0.4%


CAPEX 12.18 -3%

OPEX 56.21 0.4%

Total 68.39 -0.2%



CO2 Purity (vol%) 75 -21%





Lean Loading (molCO2/molMEA) 0.202 1% Rich Loading (molCO2/molMEA) 0.456 -0.2%

CO2 recovery rate in the regeneration section (%) 56 -1% Specific Energy Requirement (GJ/tCO2) 4.85 -0.4%

sCAPEX (€/tCO2) 6.52 -3% sOPEX (€/tCO2) 30.10 0.5%


96





Height (m) 8.43 -1%


Height (m) 16.78 -1%






Lean Solvent (RB-303) Flow Rate (kg/s) 1 199.37 0.4%



Equipment Cost

Absorber Shell (2 units) 1 787 -0.5%


Stripper Shell (2 units) 1 249 -0.7%















Steam 47 376 -0.3%



Total 53 487 0.05%




CAPEX 12.33 -2%

OPEX 55.95 -0.04%

Total 68.28 -0.4%










CO2 recovery rate in the regeneration section (%) 56 -0.3%

Specific Energy Requirement (GJ/tCO2) 4.85 -0.3%

sCAPEX (€/tCO2) 6.60 -2%

sOPEX (€/tCO2) 29.95 -0.01%


97




Absorber (A-301) Diameter (m) 17.74 0.8%

Height (m) 8.91 5%

Stripper (ST-301) Diameter (m) 7.27 0.1%

Height (m) 18.98 12%

Lean-Rich Heat Exchanger (HX-301) Outlet temperature of the rich solvent (K) 378.43 -0.7%




Condenser (C-301) Temperature (K) 288.28 -9%




Equipment Cost

















Electricity 362 1%

Steam 47 840 0.6%

Cooled Water 124 435%

Refrigerated Water 3 018


Total 56 113 5%




CAPEX 12.93 3%

OPEX 58.70 5%

Total 71.63 4%



CO2 Purity (vol%) 99 4%









sCAPEX (€/tCO2) 6.89 2%

sOPEX (€/tCO2) 31.26 4%


98

Table A2.12 – Detailed results from the specific total cost minimisation with an imposed MEA mass fraction in the CO2

free lean solvent of 20%, and comparison with the initial specific total cost minimisation results (Table 7.1).



Height (m) 8.22 -3%

Stripper (ST-301) Diameter (m) 7.63 5%

Height (m) 16.71 -1%

Lean-Rich Heat Exchanger (HX-301) Outlet temperature of the rich solvent (K) 384.70 1%



Pressure (bar) 1.93 4%


Lean Solvent (RB-303) Flow Rate (kg/s) 1 608.78 35%

MEA mas fraction (g/g) 0.195 -32%


Equipment Cost

Absorber Shell (2 units) 1823 1%




Lean-Rich Heat Exchanger (20 units) 1 560 104%



Condenser (2x3 units) 1 048 21%

Rich Solvent Pump (13 units) 347 32%


Lean Solvent Pump (13 units) 354 33%

Lean Solvent Pump Driver (13 units) 166 29%





Electricity 477 33%

Steam 50 545 6%



Total 56 304 5%




CAPEX 13.65 9%

OPEX 59.03 5%

Total 72.69 6%



CO2 Purity (vol%) 95 -0.003%

MEA mass fraction in the CO2 free lean solvent (wt%) 20 -33%





Rich Loading (molCO2/molMEA) 0.462 1%


Specific Energy Requirement (GJ/tCO2) 5.16 6%

sCAPEX (€/tCO2) 7.28 8%

sOPEX (€/tCO2) 31.49 5%


99

Table A2.13 – Detailed results from the specific total cost minimisation with an imposed MEA mass fraction in the CO2

free lean solvent of 40%, and comparison with the initial specific total cost minimisation results (Table 7.1).



Height (m) 9.39 11%


Height (m) 18.27 8%









Equipment Cost




Stripper Packing (2 units) 8 399 0.5%









ISBL Investment 63 093 0.4%

Total Fixed Investment 126 312 0.4%




Steam 45 288 -5%



Total 50 950 -5%

Fixed Production Costs 2 524 0.4%



CAPEX 12.63 0.4%

OPEX 53.47 -4%

Total 66.10 -4%




MEA mass fraction in the CO2 free lean solvent (wt%) 40 33%








sCAPEX (€/tCO2) 6.73 0.02%

sOPEX (€/tCO2) 28.50 -5%


100

Table A2.14 – Detailed results from the specific total cost minimisation with inequality constraints, and comparison

with the initial specific total cost minimisation results (Table 7.1).



Height (m) 7.22 -15%


Height (m) 16.51 -2%



Reboiler (R-301) Temperature (K) 393.15 -0.00004%






Equipment Cost


















Steam 37 568 -21%



Total 42 357 -21%




CAPEX 9.89 -21%

OPEX 44.33 -21%

Total 54.22 -21%




MEA mass fraction in the CO2 free lean solvent (wt%) 40 33%

Vapour velocity/vapour flooding velocity in the top of the absorber 0.70 0%

Vapour velocity/vapour flooding velocity in the bottom of the stripper 0.70 0%






sCAPEX (€/tCO2) 6.33 -6%

sOPEX (€/tCO2) 28.37 -5%


design and operation optimisation of a mea-based co ... · design and operation optimisation of a...

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