systems analysis of advanced power plant carbon capture

Systems Analysis of Advanced Power Plant

Carbon Capture Technologies

Final Report to the

Global Climate and Energy Program (GCEP)

Stanford University

Stanford, California

from

Hari Mantripragada

Haibo Zhai

Edward S. Rubin

John Kitchin

Wenqin You

Karen Kietzke

Carnegie Mellon University

Pittsburgh, Pennsylvania

July 2016

[this page intentionally left blank]

i

Abstract

This project developed a systems analysis modeling capability that relates the multiple design

and performance parameters of fossil fuel electric power generation systems to the process

parameters and material properties that influence the overall performance and cost of carbon

capture technologies. Integrated performance and cost models were formulated for advanced

carbon capture processes employing novel sorbent materials being developed in three other

projects supported by the Stanford Global Climate and Energy Program (GCEP). The capture

process models were then implemented in the Integrated Environmental Control Model (IECM)

framework to assess the performance and cost of a complete power plant with carbon capture

and storage (CCS). These plant-level analyses are intended to assist GCEP in identifying whether

a specific scientific approach for carbon capture has the potential to be a breakthrough when

applied in a full-scale power generation system. The IECM thus provides a common platform for

conducting comparative analyses of emergent capture technology options for different types of

power plants. The results presented in this report include performance and cost metrics for a

process employing ionic liquids for pre-combustion CO2 capture (in an integrated gasification

combined cycle power plant), and processes employing three types of solid sorbents, including

metal organic frameworks (MOFs) and an activated carbon, for post-combustion CO2 capture (in

a pulverized coal combustion power plant). Case study comparisons with power plant systems

employing current commercial technologies for CO2 capture indicate that further improvements

are needed in advanced CO2 capture materials and capture process designs in order to achieve

substantial reductions in capital and operating costs relative to current systems.

ii

Acknowledgments

The authors of this report happily acknowledge the support for this project provided by the

Stanford University Global Climate and Energy Program (GCEP). We also are extremely

grateful for the invaluable interactions and guidance over the course of this project provided by

GCEP-supported researchers at Northwestern University, Stanford University, and the

University of Notre Dame, including Randy Snurr, Fengqi You;, Jen Wilcox, Karson Leperi

Sally Benson, Richard Sassoon; and Joan Brennecke, Mark Stadtherr, Ed Maginn, Bo Hong and

Sam Seo, without whose help this work would not have been possible. The authors alone,

however, remain responsible for the content of this report.

iii

Table of Contents

Abstract ...................................................................................................................................... i

Acknowledgments ...................................................................................................................... ii

Table of Contents ...................................................................................................................... iii

List of Figures ........................................................................................................................... iv

List of Tables ............................................................................................................................ vi

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

2. Background .............................................................................................................................1

2.1 Research Objectives ..........................................................................................................1

2.1 Research Approach ............................................................................................................2

3. Results ....................................................................................................................................3

3.1 Ionic Liquids for Pre-Combustion CO2 Capture at IGCC Power Plants ..............................4

3.1.1 Capture Process Performance Model ...........................................................................4

3.1.2 Engineering-Economic Models ..................................................................................9

3.1.3 Total Power Plant System Analysis ........................................................................... 11

3.1.4 Sensitivity Analysis ................................................................................................. 14

3.1.5 Evaluation of Improved Solvents ............................................................................. 16

3.2 Solid Sorbent-based Processes for Post-combustion CO2 Capture at PC Power Plants .... 18

3.2.1 PSA/VSA Process Performance Model ..................................................................... 19

3.2.2 Engineering-Economic Models ................................................................................. 27

3.2.4 Evaluation of Improved Sorbents and Process ........................................................... 33

3.2.5 Summary of Results for Solid Sorbent-Based Systems ............................................. 36

4. Conclusions ........................................................................................................................... 36

5. References............................................................................................................................. 37

5.1 References for Section 3.1 (Ionic Liquids) ...................................................................... 37

5.2 References for Section 3.2 (Solid Sorbents) .................................................................... 38

Appendix A: Reduced-Order Performance Models for Pre-Combustion CO2 Capture Using

[P2228][ 2-CNpyr] ...................................................................................................................... 39

Appendix B: Direct Capital Cost Estimation for Pre-combustion CO2 Capture Using [P2228][ 2-

CNpyr] ...................................................................................................................................... 42

Appendix C: Solid Sorbent Pressure Swing Adsorption Process Performance Model ................ 45

Appendix D: Solid Sorbent Pressure Swing Adsorption Process Cost Model ............................. 56

iv

List of Figures

Figure 1. Schematic of the IECM software package showing major inputs and outputs. ..............2 Figure 2. Schematics of the IGCC power plant with pre-combustion CO2 capture (top) and the

PC plant with post-combustion capture (bottom) modeled in the IECM. ......................................3 Figure 3. Properties of [P2228][2-CNpyr] as a function of temperature and/or pressure. ................5

Figure 4. Schematic of an IL-based process for pre-combustion CO2 capture. .............................6 Figure 5. Effects of inlet CO2 concentration on process performance. Absorption temperature =

30 oC; operating pressure=30 bar; CO2 lean loading capacity= 0.25; CO2 removal efficiency =

95%. ...........................................................................................................................................7

Figure 6. Effects of absorption temperature on process performance. CO2 concentration = 35%;

operating pressure=30 bar; CO2 lean loading capacity= 0.25; CO2 removal efficiency = 95%......8

Figure 7. Effects of operating pressure on process performance. CO2 concentration = 35%;

absorption temperature = 30oC; CO2 lean loading capacity= 0.25; CO2 removal efficiency =

95%. ...........................................................................................................................................8 Figure 8. Effects of CO2 lean loading capacity on process performance. CO2 concentration =

35%; absorption temperature = 30oC; operating pressure= 30 bar; CO2 removal efficiency =

95%. ...........................................................................................................................................9

Figure 9. Effects of CO2 removal efficiency on process performance. CO2 concentration = 35%;

absorption temperature = 30oC; operating pressure= 30 bar; CO2 lean loading capacity= 0.25. ...9

Figure 10. Direct Capital Cost Distribution of IL-based CO2 Capture System. ........................... 13 Figure 11. Effects of CO2 removal efficiency on plant performance and cost. ............................ 14

Figure 12. Effects of plant size and coal type on plant LCOE and CO2 avoidance cost. ............. 15 Figure 13. Effect of capacity factor and fixed charge factor on plant LCOE and CO2 avoidance

cost. .......................................................................................................................................... 16 Figure 14. Effects of process and project contingencies for the CO2 capture system on total plant

LCOE and CO2 avoidance cost. ................................................................................................. 16 Figure 15. Effects of hypothetical ionic liquid solvents on the performance and cost of an IGCC

power plant with carbon capture and storage . ........................................................................... 17 Figure 16. Schematic of the PSA/VSA post-combustion CO2 capture system for PC power plants

................................................................................................................................................. 19 Figure 17. Schematic of a 2-column PSA process based on the Skarstrom cycle. ....................... 20

Figure 18. Isotherms for ZIF-78 (Leperi et al, 2014), Zeolite 5A (Leperi et al, 2015) and SU-

MAC (To et al, 2015). ............................................................................................................... 20

Figure 19. CO2 product purity using ZIF-78, Zeolite 5A and SU-MAC in a 1-stage PSA/VSA

process. ..................................................................................................................................... 23

Figure 20. CO2 recovery (capture rate) using ZIF-78, Zeolite 5A and SU-MAC in a 1-stage

PSA/VSA process. .................................................................................................................... 23

Figure 21. Specific sorbent required using ZIF-78, Zeolite 5A and SU-MAC in a 1-stage

PSA/VSA process. .................................................................................................................... 24

Figure 22. Specific work required using ZIF-78, Zeolite 5A and SU-MAC in a 1-stage PSA/VSA

process. ..................................................................................................................................... 24


process. ..................................................................................................................................... 25


PSA/VSA process. .................................................................................................................... 25

v

Figure 25. Combination of adsorption and desorption pressures for fixed CO2 capture efficiency

in a 1-stage PSA/VSA system using ZIF-78, Zeolite 5A and SU-MAC. .................................... 26

Figure 26. Combination of adsorption and desorption pressures for fixed CO2 capture efficiency

in a 2-stage PSA/VSA system using ZIF-78, Zeolite 5A and SU-MAC. .................................... 26

Figure 27. Plant LCOE and cost of CO2 avoided for the plants with CCS. Adsorber CO2 capture

efficiency is 90% for 1-stage systems and 80%for 2-stage systems. ........................................... 30

Figure 28. Distribution of direct capital costs for the 1-stage CO2 capture system with ZIF-78 .. 30 Figure 29. Effect of CO2 capture efficiency on net plant efficiency. (ZIF-78 in a 1-stage system).

................................................................................................................................................. 31 Figure 30. Effect of CO2 capture efficiency on the total plant capital cost and plant LCOE. The

CO2 capture process uses ZIF-78 in a 1-stage system. ............................................................... 31 Figure 31. Effect of plant size and coal type on plant LCOE and cost of CO2 avoided. The CO2

capture process uses ZIF-78 in a 1-stage system. ....................................................................... 32 Figure 32. Effect fixed charge factor and capacity factor on plant LCOE and cost of CO2

avoided. The CO2 capture process uses ZIF-78 in a 1-stage system. .......................................... 32 Figure 33. Effect project and process contingencies on plant LCOE and cost of CO2 avoided. The

CO2 capture process uses ZIF-78 in a 1-stage system. ............................................................... 33 Figure 34. Effect of PSA CO2 capture system energy penalty on net plant efficiency and total

plant capital cost. (For this comparison, 1-stage Zeolite 5A models with 90% CO2 capture are

used as basis). ........................................................................................................................... 34

Figure 35. Effect of PSA CO2 capture system energy penalty on plant LCOE and cost of CO2

avoided. (For this comparison, 1-stage Zeolite 5A models with 90% CO2 capture are used as

basis)......................................................................................................................................... 34 Figure 36. Effect of reduction in total capital requirement (TCR) of the PSA CO2 capture system

total plant capital cost and LCOE. (For this comparison, 1-stage Zeolite 5A models with 90%

CO2 capture are used as basis). ................................................................................................. 35

Figure 37. Effect of sorbent replacement rate and cost of sorbent on plant LCOE and cost of CO2

avoided. (For this comparison, 1-stage Zeolite 5A models with 90% CO2 capture are used as

basis)......................................................................................................................................... 35

vi

List of Tables

Table 1. Advanced capture technology options modeled..............................................................3 Table 2. Capital cost components of the ionic liquid-based capture process. .............................. 10

Table 3. Operating and maintenance cost components of the IL process. ................................... 11 Table 4. Technical and economic assumptions and parameters for IGCC Plant with CCS.......... 12

Table 5. Performance and costs of IGCC power plants with and without CCS. .......................... 13 Table 6. Capital cost components of the PSA/VSA CO2 capture process ................................... 27

Table 7. O&M cost components of the PSA/VSA CO2 capture process ..................................... 27 Table 8. Technical and economic assumptions and parameters for PC Plants with CCS ............ 28

Table 9. Performance and costs of PC power plants with and without CCS. For the CCS cases,

the final CO2 product purity is 99.5%, achieved using the CO2 purification unit (CPU). ............ 29

1

1. Introduction

This report summarizes the results of a project initiated in February 2013 to develop a systems

analysis capability for evaluating emergent carbon capture technologies to reduce atmospheric

emissions of CO2 from fossil fuel power plants. Applications of the analysis framework are

focused on three advanced carbon capture concepts under study in other projects supported by

Stanford University’s Global Climate and Energy Project (GCEP).

Section 2 of this report provides additional background and discussion of the research objectives

of this project. Section 3 then presents the project results, including a description of the

analytical models developed to evaluate each of the three advanced capture processes. Section 3

also describes the framework used to evaluate the performance and cost of a complete fossil fuel

power plant employing each of the candidate capture technologies. Results from this plant-level

analysis form the basis for conclusions regarding the potential of each technology to meet the

goals outlined by GCEP for fossil fuel power plants employing advanced carbon capture and

storage (CCS) systems. Section 4 presents a brief summary of the overall project conclusions

Section 5 provides a list of the references used in this work. A series of four appendices provide

further details of the analytical models presented in Section 3.

2. Background

Carbon capture and storage (CCS) technology has gained widespread international interest over

the past decade as a potentially critical component of climate change mitigation strategies. Given

the world’s current heavy reliance on fossil fuels, the ability of CCS to achieve deep reductions

in greenhouse gas emissions—especially carbon dioxide (CO2)—from power generation and

other industrial sources makes it an attractive option for achieving climate change goals.

At the same time, researchers at GCEP and elsewhere note that present-day technology for CO2

capture and separation is costly and energy-intensive, and that its application to new fossil fuel

power plants would raise electricity generation costs by as much as 75–80%. Thus, there is a

strong interest in developing advanced capture processes having lower energy penalties and

lower overall cost than current systems.

2.1 Research Objectives

Given the wide variety of research activities and approaches to advanced carbon capture, there is

also a need for a system-level analysis capability to provide a common ground for evaluating

advanced process concepts for CO2 capture in the context of a complete (integrated) power plant

design. The systems analysis tool sought by GCEP in its Request for Proposals for this project

included the following characteristics:

An excellent scientific basis rooted in the fundamentals;

A model that relates the performance parameters of a power generation system to the

process parameters and material properties associated with the overall performance of

carbon capture technologies, including quantitative metrics that can be specified for CO2

capture and release;

Has the potential to allow a comparative analysis of emergent capture technology options

employing quantitative metrics and performance targets based on a system-level analysis;

2

Supports application of the model in case studies of technologies being developed with

GCEP funds;

Provides a methodology to assist GCEP in identifying whether a specific scientific

approach for carbon capture has the potential to be a breakthrough when applied in a full-

scale power generation system.

The objective of the current project is to develop and demonstrate such a systems-level

framework, and to apply it in case studies of several advanced capture technologies being

developed with GCEP support.

2.1 Research Approach

In other research supported by the U.S. Department of Energy’s National Energy Technology

Laboratory (DOE/NETL) we have developed a systems analysis framework called the IECM (for

Integrated Environmental Control Model), depicted schematically in Figure 1. The IECM

includes complete performance and cost models for a broad array of fossil fuel power plant

configurations that can employ CO2 capture and storage systems as well as other environmental

control technologies. Alternative power plant designs can be configured from a set of component

technologies, each with its own set of performance and cost models. There is also a library of

representative U.S. coals of different rank as well as typical natural gas compositions. Thus, one

can model and evaluate CO2 capture processes comprehensively in different applications.

Figure 1. Schematic of the IECM software package showing major inputs and outputs.

In the current GCEP project we have expanded the IECM framework to explicitly include

performance and cost models of three advanced CO2 capture processes being supported by

GCEP in other projects (see Table 1). The three capture processes modeled include a novel

chemical sorbent (ionic liquids) and two types of solid physical sorbents (metal organic

frameworks and an activated carbon material). The ionic liquid system is proposed for pre-

combustion capture in an integrated gasification combined cycle (IGCC) power plant, while the

solid sorbents are proposed for post-combustion capture in a pulverized coal (PC) power plant.

Power

Plant

Models

Graphical

User

Interface

Plant and

Fuel

Databases

Fuel Properties- Heating Value

- Composition

- Delivered Cost

Plant Design- Conversion Process

- Emission Controls

- Solid Waste Mgmt

- Chemical Inputs

Cost Factors- O&M Costs

- Capital Costs

- Financial Factors

Plant & ProcessPerformance

- Efficiency

- Resource use

EnvironmentalEmissions

- Air, water, land

Plant & ProcessCosts - Capital

- O&M- COE

Power

Plant

Models

Graphical

User

Interface

Plant and

Fuel

Databases


- Composition

- Delivered Cost


- Emission Controls

- Solid Waste Mgmt

- Chemical Inputs


- Capital Costs

- Financial Factors


- Composition

- Delivered Cost


- Emission Controls

- Solid Waste Mgmt

- Chemical Inputs


- Capital Costs

- Financial Factors


- Efficiency

- Resource use


- Air, water, land


- O&M- COE


- Efficiency

- Resource use


- Air, water, land


- O&M- COE

3

Table 1. Advanced capture technology options modeled

Capture Material Proposed Application Research Group

Ionic liquids (IL) Pre-combustion University of Notre Dame

Metal organic frameworks (MOF) Post-combustion Northwestern University

Activated carbon sorbent (AC) Post-combustion Stanford University

For each capture technology, a process performance model is first developed using data provided

by the research group developing each capture material. Because of proprietary considerations,

and because work on the advanced capture materials was still ongoing during the course of this

project, the process performance models developed in this study rely heavily on published data

for materials that are similar (but not identical in all cases) to those under development.

The performance and cost models for each capture technology are then embedded into the IECM

plant-level model for either IGCC or PC power plants (see Figure 2). Those plant-level models

are then employed to quantify key performance and cost metrics in case studies and comparative

analyses of the type sought by GCEP. In the results that follow, the power plants with novel CO2

capture processes are compared to cases using current (conventional) capture systems, namely,

Selexol sorbent for pre-combustion capture and an amine-based solvent for post-combustion

capture. In all cases, the full power plant system includes the pipeline transport of captured CO2

to a geologic formation for permanent CO2 storage.

Figure 2. Schematics of the IGCC power plant with pre-combustion CO2 capture (top) and the PC

plant with post-combustion capture (bottom) modeled in the IECM.

3. Results

This section of the report describes the various performance and cost models developed in this

study for novel capture processes, and their use in simulations of power plant with pre- and post-

combustion CO2 capture and storage. First, the modeling of pre-combustion capture using ionic

liquids is described. Then, results are presented for the post-combustion processes using solid

Gas Turbine

Combined

Cycle Plant

O2

Air

Shift

Reactor CO2

H2Quench

System

H2

H2O

Electricity

Air

SulfurRecovery

Gasifier

Coal

H2O

Air

Separation

Unit

Sulfur

RemovalCO2 Capture

Sorbent/CO2Sorbent

CO2 to

storageCO2

SeparationCO2

Compression

CO2

Sta

ck

To atmosphere

Coal

Air

Steam

Steam

Turbine

Generator

Electricity

Air Pollution

Control Systems

(NOx, PM, SO2)

CO2

CapturePC Boiler

Mostly

N2 Sta

ck

To atmosphere

CO2 to

storageand

SeparationCO2

Compression

CO2

4

sorbents. The section concludes with an assessment of these advanced concepts relative to the

evaluation criteria outlined by GCEP in their original call for proposals.

3.1 Ionic Liquids for Pre-Combustion CO2 Capture at IGCC Power Plants

This section of the report describes the performance and cost models developed for a pre-

combustion capture process employing ionic liquids. The capture process models are then

embedded in the IECM framework to simulate a complete IGCC power plant. Results are

presented for both the stand-alone capture process and the overall power plant.

3.1.1 Capture Process Performance Model

3.1.1.1 Solvent properties

Ionic liquids (ILs) are organic salts that are liquid at ambient conditions. They have low vapor

pressure (hence, low solvent losses) and potentially can absorb CO2 at high temperatures. Both

their chemical and physical properties may be “tailored” by varying their structure or chemical

constitution to decrease the parasitic energy requirements and improve CO2 carrying capacity.

While previous studies have focused on the use of ILs for post-combustion capture, the GCEP

project at the University of Notre Dame (UND) focuses on pre-combustion capture.

The solvent under investigation is [P2228][2-CNpyr], one of tetraalkylphosphonium 2-

cyanopyrrolide ionic liquids (ILs) synthesized by researchers at UND (Seo et al 2015).Such ILs

can react chemically with CO2. The selected solvent was not optimized but was the most well

characterized example available for the chemically absorbing ILs at the time. As shown in Figure

3, the chemical and physical properties of [P2228][2-CNpyr], including solubility, density, heat

capacity and viscosity, were measured under different pressure and/or temperature conditions.

These data were fitted to regression equations in which the CO2 solubility is well described by a

Langmuir-type model (Seo et al 2015). The CO2 solubility at 0.15 bar and 22 oC reaches 0.8

mole ratio and increases nonlinearly at pressures of less than 1 bar. For a given temperature, the

solubility increases approximately linearly with higher pressure, driven mainly by physical

absorption. The solvent density decreases slightly with temperature, whereas the solvent

viscosity decreases significantly. The molecular weight of [P2228][2-CNpyr] is 322.5 g/mole. The

heat of reaction for [P2228][2-CNpyr] with CO2 is -45 J/mole·oK at 22

oC (Seo et al 2015).

3.1.1.2 Absorption-regeneration model

The typical absorption and stripping configuration employed for amine-based CO2 capture

(Figure 4) is adopted for pre-combustion CO2 capture using [P2228][2-CNpyr] (Rao and Rubin

2002). The CO2 absorption is considered as a steady-state vapor-liquid process consisting of a

number of stages. A multi-stage equilibrium model is established to simulate the absorption

process. Equilibrium is assumed to take place between vapor and liquid streams leaving each

stage. The multi-stage process model takes into account the mass balance (M), equilibrium (E),

summation (S), and enthalpy balance (H). The Newton-Raphson simultaneous correction

algorithm is applied to solve the MESH equations and then provide the profiles of liquid (L, xi)

and vapor (V, yi) streams and temperatures across all equilibrium stages (Seader et al 2011).

5

Figure 3. Properties of [P2228][2-CNpyr] as a function of temperature and/or pressure.

Mass balance for each component at each stage (j):

j i j j i j j yi j

jyi j (1)

Equilibrium for each component at each stage (j):

A Langmuir-type absorption model that incorporates both the stoichiometric reaction and

physical uptake is used to describe the equilibrium (Gurkan et al 2010; Seo et al 2015). Total

CO2 uptake on the basis of mole ratio is predicted in terms of CO2 pressure (PC Henry's law

constant ( n , and reaction equilibrium constant ( as (Gurkan et al 2010; Seo et al 2015):

co j nC

nI o

PC j

nj

PC j

nj

jPC jC

jPC j

(2)

Summation based on mole fractions for each stage (j):

yi j

(3)

i j

0.0

0.3

0.6

0.9

1.2

1.5

0 3 6 9 12

CO

2S

olu

bil

ity

(mo

lar

rati

o C

O2/I

L)

CO2 Pressure (bar)

22 C

60 C

80 C

y = -5.58E-04x + 9.69E-01

R² = 9.99E-01

0.900

0.920

0.940

0.960

0.980

1.000

10 20 30 40 50 60 70

So

lven

t D

ensi

ty (

g/c

m3)

Temperature (oC)

y = 1.5778x + 575.56

R² = 1

600

625

650

675

700

725

750

10 20 30 40 50 60 70

So

lven

t H

eat

Cap

acit

y

(J/K

.mo

le)

Temperature (oC)

y = 7.090E+00e-1.154E-02x

R² = 9.961E-01

2.00

4.00

6.00

8.00

10 20 30 40 50 60 70

Ln

(So

lven

t V

isco

sity

, cp

)

Temperature (oC)

6

Enthalpy balance for each stage (j):

j hj jhj j j j j (4)

To size the absorber, the column height is estimated in terms of the overall mass transfer

coefficient, in which the physical mass transfer coefficients of gas and liquid phases are

estimated using empirical mass transfer correlations for randomly packed columns (Onda et al

1968). To account for the effect of chemical reaction on mass transfer, the physical mass transfer

coefficient of the liquid phase is adjusted by an enhancement factor that reflects the reaction

kinetics. The generic Sherwood/Leva/Eckert (SLE) pressure drop correlation for packed columns

is adopted to estimate the gas-phase pressure drop across the absorber (Strigle 1994).

Given that no water is used to dilute the solvent and there are no vapor losses of the IL solvent in

the capture process, a single-stage flash drum in equilibrium is employed for the stripping

process. The steam required for solvent regeneration is extracted from the plant steam cycle and

the stripping pressure is designed to be equal to the absorption pressure. The flash drum size is

determined in terms of empirical gas velocity and liquid surge time designs (Wankat 1988; Silla

2003). Due to chemical absorption, the energy requirements ( ) for solvent regeneration

include the solvent heating and reaction heat.

Figure 4. Schematic of an IL-based process for pre-combustion CO2 capture.

3.1.1.3 Other process components

In addition to the absorber and stripper, a variety of other equipment is installed to support the

capture process (see Figure 4). A heat exchanger may be needed to lower the temperature of

syngas into the absorber where an absorption intercool is installed to remove the reaction heat. A

solvent cooler also is used to lower the temperature of lean solvent into the absorber. A lean/rich

solvent heat exchanger is designed based on a given cold-side temperature approach to recover

heat from the hot lean solvent. The CO2 product out of the stripper is compressed via a multi-

stage compressor to the supercritical condition needed for pipeline transport and geological

storage. The process modeling was conducted for a wide range of operating scenarios to

characterize key input-output response relations. Reduced-order models (ROMs) were developed

based on the response relations and embedded into the IECM. Details of the ROMs are available

in Appendix A.

It turns out that the pressure drop of the gas stream across the absorption column operating at

high pressure (30 bar) is very small. To compare the overall energy penalty under different

Reboiler

CO2 to compressor

Heat

exchanger

Cooler

Stripper

Absorber

Syngas

7

conditions, the total separation work for CO2 capture takes into account both the electric power

and thermal energy use, in which the required steam heat is expressed as equivalent electric

power based on a typical heat-to-electricity efficiency (19.7%).

3.1.1.4 Performance model results

Figures 5 – 9 show the effects on the key performance parameters of inlet CO2 concentration,

absorption temperature, absorption pressure, CO2 lean loading capacity and CO2 removal

efficiency for a given amount of inlet syngas flow rate (28,400 kmole/hr in this example). For the

designated CO2 removal efficiency of 95% (resulting in 90% capture overall), the liquid-to-gas

(L/G) ratio and total separation work increase strongly with inlet CO2 concentration, whereas

they vary moderately with absorption temperature. An increase in the process operating pressure

can decrease the solvent requirement and the compression power for the CO2 product. However,

it also elevates the regeneration temperature which, in turn, increases the thermal energy required

for solvent regeneration. As a result, the total separation work increase slightly with increasing

operating pressure.

Further analysis also shows that an increase in CO2 lean loading capacity can increase the

solvent requirement but lower the regeneration temperature which, in turn, decreases the thermal

energy use for solvent regeneration. The resulting total separation work decreases with

increasing CO2 lean loading capacity. For a design with a given operating pressure, temperature

and CO2 lean loading capacity, both the solvent requirement and the total separation work

noticeably increase with CO2 removal efficiency.

Figure 5. Effects of inlet CO2 concentration on process performance. Absorption temperature = 30 oC; operating pressure=30 bar; CO2 lean loading capacity= 0.25; CO2 removal efficiency = 95%.

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.20 0.25 0.30 0.35 0.40 0.45

Liq

uid

-to

-Gas

Rat

io

(mo

le r

atio

)

Inlet CO2 Concentration (mole fraction)

50

75

100

125

150

0.20 0.25 0.30 0.35 0.40 0.45

To

tal S

epar

atio

n W

ork

(MW

e)

Inlet CO2 Concentration (mole fraction)

8

Figure 6. Effects of absorption temperature on process performance. CO2 concentration = 35%;

operating pressure=30 bar; CO2 lean loading capacity= 0.25; CO2 removal efficiency = 95%.

Figure 7. Effects of operating pressure on process performance. CO2 concentration = 35%;

absorption temperature = 30oC; CO2 lean loading capacity= 0.25; CO2 removal efficiency = 95%.

0.20

0.30

0.40

0.50

0.60

0.70

0.80

25 30 35 40 45

Liq

uid

-to

-Gas

Rat

io

(mo

le r

atio

)

Absorption Temperature (oC)

50

75

100

125

150

25 30 35 40 45

To

tal S

epar

atio

n W

ork

(MW

e)

Absorption Temperature (oC)

0.20

0.30

0.40

0.50

0.60

0.70

0.80

25 30 35 40 45

Liq

uid

-to

-Gas

Rat

io

(mo

le r

atio

)

Operating Pressure (bar)

200

210

220

230

240

250

25 30 35 40 45

Reg

ener

atio

n

Tem

per

atu

re (

oC

)


3000

3100

3200

3300

3400

3500

25 30 35 40 45

Th

erm

al E

ner

gy

Use

(kJ/

kg C

O2)


50

75

100

125

150

25 30 35 40 45

To

tal S

epar

atio

n W

ork

(MW

e)


9

Figure 8. Effects of CO2 lean loading capacity on process performance. CO2 concentration = 35%;

absorption temperature = 30oC; operating pressure= 30 bar; CO2 removal efficiency = 95%.

Figure 9. Effects of CO2 removal efficiency on process performance. CO2 concentration = 35%;

absorption temperature = 30oC; operating pressure= 30 bar; CO2 lean loading capacity= 0.25.

3.1.2 Engineering-Economic Models

The performance models discussed above are linked to engineering-economic models that

estimate the capital cost, annual operating and maintenance (O&M) costs, and total annual

levelized cost of electricity (LCOE) for the IL-based CCS system. This study employs the

costing method and nomenclature of the Electric Power Research Institute’s Technical

Assessment Guide (EPRI 1993; 2009). The total capital requirement (TCR) of an IL-based

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0.15 0.20 0.25 0.30 0.35

Liq

uid

-to

-Gas

Rat

io

(mo

le r

atio

)

CO2 Lean Loading Capacity (mole frac.)

180

200

220

240

260

0.15 0.20 0.25 0.30 0.35

Reg

ener

atio

n

Tem

per

atu

re (

oC

)


2500

3000

3500

4000

0.15 0.20 0.25 0.30 0.35

Th

erm

al E

ner

gy

Use

(kJ/

kg C

O2)


50

75

100

125

150

0.15 0.20 0.25 0.30 0.35T

ota

l S

epar

atio

n W

ork

(MW

e)CO2 Lean Loading Capacity (mole frac.)

0.20

0.30

0.40

0.50

0.60

0.70

0.80

50% 60% 70% 80% 90%

Liq

uid

-to

-Gas

Rat

io

(mo

le r

atio

)

CO2 Removal Efficiency

25

50

75

100

125

50% 60% 70% 80% 90%

To

tal S

epar

atio

n W

ork

(MW

e)

CO2 Removal Efficiency

10

capture system includes the process facilities cost (PFC)—representing the cost of purchasing

and installing all equipment—plus a number of indirect costs such as the general facilities cost,

engineering and home office fees contingency costs and owner’s costs, which are typically

estimated as a percentage of the PFC.

As given in Table 2, the major direct cost components of PFC include the gas stream heat

exchangers, absorbers, solvent circulation pumps, absorption intercoolers, lean solvent coolers,

solvent regenerators, rich and lean solvent heat exchangers, reboilers, solvent reclaimers, solvent

processing, steam extraction, CO2 product heat exchangers, and CO2 product compressors. As

given in Appendix B, the direct costs of different components are scaled based on the major

sizing parameters using the common 6/10th

power law. The Chemical Engineering Plant Cost

Index is used to adjust the capital costs of different components to the same year dollars.

Table 3 summarizes major fixed and variable O&M cost components. Fixed O&M costs include

operating labor, maintenance costs, and administrative and support labor costs, while variable

O&M costs include IL makeup, chemicals, solid waste treatment, power use, and CO2 transport

and storage. Operating labor is estimated in terms of hourly labor rate, personnel per shift, and

number of shifts. Total maintenance cost is estimated empirically as a percentage of total plant

cost, while administrative and support labor is estimated as a percentage of operating plus

maintenance labor. Variable O&M costs are estimated as the product of the quantity used times

the unit price.

Table 2. Capital cost components of the ionic liquid-based capture process.

CO2 Capture Process Area Costs CO2 Capture Plant Costs

Gas stream heat exchangers Process facilities capital

Absorbers General facilities capital

Solvent circulation pumps Engineering. & home office fees

Absorption intercoolers Project contingency cost

Lean solvent coolers Process contingency cost

Solvent regenerators Interest charges

Rich & lean solvent heat exchangers Royalty fees

Reboilers Preproduction (startup) cost

Solvent reclaimers Inventory capital

Solvent processing

Steam extraction

CO2 product heat exchangers

CO2 product compressors

Process Facilities Capital (sum of above) Total Capital Requirement (sum of above)

11

Table 3. Operating and maintenance cost components of the IL process.

Variable Cost Component Fixed Cost Component

Solvent makeup Operating labor

Chemicals Maintenance labor

Waste disposal Maintenance material

Electricity Admin. & support labor

CO2 transport and storage

Total Variable Cost (sum of above) Total O&M Cost (sum of above)

Once the capital and O&M costs are determined, the LCOE of a power plant with or without

CCS is calculated as:

C E TCR C

C rs R C

(5)

where, LCOE is the levelized cost of electricity generation ($/MWh); TCR is the total capital

requirement ($); CF is the capacity factor (%); FCF is the fixed charge factor (fraction/yr); FOM

is fixed O&M costs ($/yr); VOM is the variable non-fuel O&M costs ($/yr); HR is the net plant

heat rate (GJ/MWh); FC is the unit fuel cost ($/GJ); MW is the net power output (MW); and Hrs

is the total annual hours of operation (hrs/yr).

3.1.3 Total Power Plant System Analysis

The performance and cost models outlined above for pre-combustion CO2 capture using

[P2228][2-CNpyr] have been embedded in the IECM. To incorporate CO2 capture at an IGCC

plant, two additional systems are employed, namely, a two-stage water gas shift (WGS) reactor

and the IL-based CO2 capture system with temperature swing. To provide the thermal energy for

solvent regeneration in the CO2 capture system, low-quality steam is extracted from the steam

cycle, which increases the steam cycle heat rate and decreases the net power plant output. The

sygnas temperature out of the low-temperature WGS reactor is already lowered to about 30 oC.

Thus, the absorption temperature and pressure for CO2 capture are designed to be equal to those

of the inlet syngas. Otherwise, as demonstrated in Figures 6 and 7, a lower absorption

temperature would require deployment of additional heat exchangers to further cool down the

inlet gas stream with an increased cooling duty, but have no significant benefit in reducing the

total separation work, while a higher operating pressure would require deployment of additional

gas blowers or compressors for the inlet gas stream, but moderately increase the total separation

work. Any possible pipeline pressure drop is not considered for solvent streams as the system

operating pressure is maintained at a high level up to 30 bar and no relevant data is available.

Given the tradeoff effects of CO2 lean loading capacity on the L/G ratio and total separation

work (as shown in Figure 8), a medium CO2 lean loading capacity is assumed for case studies.

The solvent loss rate is assumed based on previous studies on ionic liquids for post-combustion

CO2 capture (Maginn et al 2013).

The enhanced IECM (v10-beta) was applied for case studies that provide plant-level estimates of

performance and cost for IGCC plants with and without CCS. Table 4 presents the major

technical and economic parameters and assumptions for the IGCC plant with the IL-based

12

capture system. The IECM default settings are adopted for all other IGCC power plant

components.

Table 4. Technical and economic assumptions and parameters for IGCC Plant with CCS.

Parameter Value

Power Plant Design Parameters

Coal type Illinois #6

Gasifier type Shell Gas turbine type GE 7FB

Number of gas turbines 2

Capacity factor (%) 75

Ambient air temperature (oC) 18.9

CO2 control WGS +Ionic Liquid Absorption

CO to CO2 conversion efficiency (%) 95

Ionic Liquid-based CO2 Capture System Number of trains (#) 2

CO2 removal efficiency (%) 95

Absorption temperature (oC) 29.4

Operating pressure (bar) 29.6 CO2 lean loading capacity (mole fraction) 0.25

Solvent loss (kg solv./t CO2) 0.23

Heat-to-electricity efficiency (%) 19.7 CO2 product final pressure (MPa) 13.79

CO2 product compressor efficiency (%) 80

Economic and Financial Parameters

Cost year 2012 Dollar type constant

Fixed charge factor (fraction) 0.113

Coal price ($/t) 42 CO2 capture system alone

Ionic liquid price ($/t) 11,130

Total transport & storage cost ($/t) 10

General facilities capital (% of PFC) 15 Engineering & home office fees (E) (% of PFC) 10

Project contingency (C) (% of PFC) 20

Process contingency (% of (PFC+E+C)) 20 Total maintenance cost (% of TPC) 2.5

Table 5 summarizes the performance and costs of power plants with and without CCS. For the

assumptions and design conditions given in Table 3, the required regeneration temperature has to

reach 211oC, which is significantly higher than that of an amine-based capture system. Such a

high temperature for solvent regeneration leads to a large thermal energy requirement (3151

kJ/kg CO2). As a result of thermal energy and electric power use for CO2 capture, the addition of

IL-based CCS significantly decreases the net power output and overall plant efficiency.

The deployment of CCS increases the plant LCOE by 65% and results in a relatively large

avoidance cost of $85/t CO2. However, the TCR of the IL-based CO2 capture system alone

accounts for only about 10% of the total plant capital cost. Figure 10 further demonstrates the

direct capital cost impact of the capture system alone, in which solvent heat exchangers and

13

reboilers are the two largest cost components. These techno-economic results imply that

lowering the thermal energy penalty for pre-combustion CO2 capture would be helpful to

enhance the viability of the chemical absorption capture system.

Table 5. Performance and costs of IGCC power plants with and without CCS.

Parameter IGCC

w/o CCS

IGCC

w/ IL-CCS

IGCC

w/ Selexol-CCS

Gross power output (MW) 692 565 663 Net power output (MW) 604 481 537

Net plant efficiency (HHV,%) 43.2 30.7 34.3

CO2 emission rate (kg/kWh) 0.713 0.10 0.09 CO2 capture system alone:

Power use (MW) 14.6 55.9

Thermal energy use: (kJ/kg CO2) 3151 0

(Equiv. MW) 101 0

TCR(2012$/kWnet) 533 413

Plant TCR (2012$/kWnet) 3329 5201 4550 Plant LCOE (2012$/MWh) 88.9 147.1 128.4

Added LCOE for CCS (2012$/MWh) 58.2 39.4

Cost of CO2 captured (2012$/t CO2) 54 39 Cost of CO2 avoided (2012$/t CO2) 86 63

Figure 10. Direct Capital Cost Distribution of IL-based CO2 Capture System.

To further evaluate the IL-based capture system, we compare it to a conventional Selexol-based

two-stage system that employs physical absorption for CO2 capture (Chen and Rubin 2009). The

Absorbers, 10%

Absorption

Intercoolers, 5%

Solvent Pumps, 8%

Lean Solvent

Coolers, 2%

Solvent Heat

Exchangers, 34%

Solvent

Regenerators, 6%

Reboilers, 17%

Steam Extractor,

2%

Solvent Reclaimers,

1%

Solvent Processing,

2%

CO2 Product

Coolers, 1% CO2 Product

Compressors, 11%

14

performance and cost of an IGCC plant with Selexol-based CCS are estimated using the IECM

with results also are provided in Table 5. Although the Selexol-based capture system has a much

larger electric power use than the IL-based capture system, it has no thermal energy requirement

for solvent regeneration and is also lower in capital cost. As a result, the IGCC plant with

Selexol-CCS has a higher net plant efficiency and a lower LCOE. As shown in Table 4, the cost

of CO2 avoided with the Selexol-based CCS is $63/t, substantially less than the IL-based CCS.

However, please note that the Selexol-based capture system is a much more mature process,

compared to IL-based CCS.

3.1.4 Sensitivity Analysis

A sensitivity analysis was performed using the enhanced IECM to investigate the effects of

major plant and system parameters on the performance and cost of the IGCC plant with IL-based

CCS. When a parameter is evaluated, other parameters were held at their base values given in

Table 3. The major parameters considered include CO2 removal efficiency, plant size, coal type,

capacity factor, fixed charge factor, and the IL-based capture system's process and project

contingency cost factors.

CO2 removal efficiency is a key design parameter that directly determines the CO2 capture

system's size, energy penalty, capital cost and O&M costs. Figure 11 shows its effects on the

plant performance and cost as well as on the costs of CO2 captured and avoided. An increase in

CO2 removal efficiency from 50% to 95% lowers the net plant efficiency by 4.2 percentage

points and increases the plant LCOE by $27/MWh. In contrast, both the cost of CO2 captured

and CO2 avoided decrease with increasing CO2 removal efficiency and reach a minimum value at

the level of 95%.

Figure 11. Effects of CO2 removal efficiency on plant performance and cost.

30.0

31.0

32.0

33.0

34.0

35.0

36.0

50 60 70 80 90

Net

Pla

nt

Eff

icie

ncy

(%

)

CO2 Removal Efficiency (%)

120

130

140

150

50 60 70 80 90

Pla

nt L

CO

E (

$/M

Wh

)


50

55

60

65

70

50 60 70 80 90

Co

st o

f C

O2

Cap

ture

d

($/t

)


80

90

100

110

120

50 60 70 80 90

Co

st o

f C

O2

Av

oid

ed

($/t

)


15

Plant size also affects the capital cost of a power plant or an environmental control system, while

coal type and quality affects both the power plant performance and cost (Rubin et al 2007). In the

parametric analysis, the number of gas turbines (which come in fixed sizes) is increased from

one to four to model increasing plant sizes. In addition, three coal types are modeled, including

an Illinois #6 bituminous coal, a Wyoming Power River Basin (PRB) sub-bituminous coal, and a

North Dakota (ND) lignite coal. The coal prices in the IECM fuel database are $42, $9.6 and

$16.8 per metric ton, respectively.

As shown in Figure 12, both the plant LCOE and CO2 avoidance cost decrease pronouncedly

with increasing number of gas turbines, with the range corresponding to plant sizes of

approximately 225 to 960 MW (net power output). Among the three coals, the IGCC plant fired

by the ND lignite has the largest LCOE and CO2 avoidance cost, with lower cost for the higher-

quality coals. Although the Illinois #6 coal has higher quality than the PRB coal, their overall

effects on total cost is very similar, mainly because of the cheaper PRB coal price.

Figure 12. Effects of plant size and coal type on plant LCOE and CO2 avoidance cost.

Given that both the IGCC plant and CCS system are still in early stages of commercialization,

there is uncertainty in their operation and financing (Rubin et al 2007). To account for the

effects of both factors, further parametric analysis was conducted for capacity factor and fixed

charge factor. Figure 13 shows the effects of both parameters on the plant LCOE and CO2

avoidance cost. For any given capacity factor, when the fixed charge factor is increased from

0.10 to 0.15 (reflecting greater financial risk) the plant LCOE increases by 28-30% and the cost

of CO2 avoided increases by 25-27%. For a given fixed charge factor, an improvement in plant

utilization over the life of the plant can significantly decrease both the costs as seen in Figure 13.

120

140

160

180

200

220

1 2 3 4

Pla

nt L

CO

E (

$/M

Wh

)

Number of Gas Turbines

Illinois #6

Wyoming PRB

ND Lignite

80

100

120

140

1 2 3 4

Co

st o

f C

O2

Av

oid

ed

($/t

)

Number of Gas Turbines

Illinois #6

Wyoming PRB

ND Lignite

16

Figure 13. Effect of capacity factor and fixed charge factor on plant LCOE and CO2 avoidance cost.

Since IL-based CO2 capture is currently in the early stages of research and development there is

high uncertainty in both the process and project contingency cost factors associated with the

proposed system. To reflect such uncertainty, Figure 14 shows the effects on plant LCOE and

CO2 avoidance cost of the CO2 capture system's process and project contingencies over a range

of 10% to 30%. Over each of these ranges, the plant LCOE and CO2 avoidance cost change only

by about $1–2/MWh and $2–3/t CO2, respectively. These effects are modest because of the fact

that the capture system alone accounts for only about 10% of the total plant capital requirement.

These results imply that lowering the capital cost of the capture system alone would not

significantly improve the economic viability of the overall IGCC plant with CCS.

Figure 14. Effects of process and project contingencies for the CO2 capture system on total plant

LCOE and CO2 avoidance cost.

3.1.5 Evaluation of Improved Solvents

To help guide the development of new materials for CO2 capture, there is a need for outlining

quantitative targets for material properties. Thus, [P2228][2-CNpyr] was selected as a proxy ionic

liquid for “back engineering” to identify desired properties and their potential for improving the

viability of IL-based capture technology.

The performance and cost models described earlier were applied to investigate the role of

potential breakthroughs on IL properties. Because the thermal energy penalty associated with

chemical absorption for CO2 capture is the most important factor affecting the power plant

100

120

140

160

180

200

0.10 0.11 0.12 0.13 0.14 0.15

Pla

nt L

CO

E (

$/M

Wh

)

Fixed Charge Factor (fraction)

65 75 85

Capacity Factor (%)

80

100

120

140

0.10 0.11 0.12 0.13 0.14 0.15

Co

st o

f C

O2

Av

oid

ed

($/t

)

Fixed Charge Factor (fraction)

65 75 85

Capacity Factor (%)

140

142

144

146

148

150

10 20 30

Pla

nt L

CO

E (

$/M

Wh

)

Process Contingency of CO2 Capture (%)

10 20 30

Project Contingency (%)

80

82

84

86

88

90

10 20 30

Co

st o

f C

O2

Av

oid

ed

($/t

)

Process Contingency of CO2 Capture (%)

10 20 30Project Contingency (%)

17

performance and cost, we evaluate hypothetical ILs with improved properties: lower heat

capacity and lower heat of reaction.

Figure 15 shows the effects of hypothetical solvents with improved properties. If the heat

capacity of the hypothetical solvent were 50% lower than that of [P2228][2-CNpyr], the thermal

duty for solvent regeneration would be decreased by 36%. The resulting net plant efficiency

would be elevated from 30.7% to 33.8%. As a result, the cost of CO2 avoided by CCS using the

hypothetical IL solvent would be similar to that for the Selexol-based CCS system.

If in addition the heat of reaction for the hypothetical solvent were 50% lower than that for

[P2228][2-CNpyr], the CO2 avoidance cost would further decrease to $57/t CO2. This would make

the IL-based technology competitive with current Selexol-based technologies for pre-combustion

CO2 capture at IGCC plants.

Figure 15. Effects of hypothetical ionic liquid solvents on the performance and cost of an IGCC

power plant with carbon capture and storage .

0500

100015002000250030003500

Actual IL 50% of Actual

Heat Capacity

50% of Actual

Heat Capacity + 50% of

Actual Reaction Heat

Ste

am U

se f

or

So

lven

t

Reg

en.

(kJ/

kg C

O2)

30313233343536


Heat Capacity

50% of Actual



Net

Pla

nt

Eff

icie

ncy

(HH

V,%

)

Selexol

125

130

135

140

145

150


Heat Capacity

50% of Actual

Heat Capacity + 50% of Actual

Reaction Heat

Pla

nt L

CO

E (

$/M

Wh

)

Selexol50

60

70

80

90


Heat Capacity

50% of Actual



Co

st o

f C

O2

Av

oid

ed

($/t

CO

2)

Selexol

18

3.2 Solid Sorbent-based Processes for Post-combustion CO2 Capture at PC Power Plants

This section of the report focuses on post-combustion CO2 capture using several solid sorbents

representative of the capture materials developed in GCEP projects at Northwestern and Stanford

universities, specifically:

ZIF-78: a metal organic framework (MOF) solid sorbent, suggested by researchers at

Northwestern University (Leperi et al, 2014).

Zeolite 5A: another solid sorbent suggested by Northwestern University (Leperi et al,

2015).

SU-MAC: an activated carbon-based material developed by researchers at Stanford

University (To et al, 2015).

In contrast to conventional chemical solvents, all of these materials are physical sorbents that

capture CO2 via adsorption onto the material surface. Metal organic frameworks (MOFs) are

porous solids consisting of organic-inorganic hybrid networks. Owing to their extraordinary

surface areas and tunable pore surface properties, they have gained attention as a potentially

more attractive method of separating CO2 from gas streams. Similarly, the activated carbon-

based sorbent developed at Stanford University promises to enhance the mass transfer of CO2

from the gas to adsorbed phase in addition to enhancing the kinetics of adsorption and desorption

(regeneration) processes.

The literature on CO2 capture processes indicates that for physical sorbents a process design

employing a pressure swing system to adsorb and desorb CO2 is clearly preferred to the more

energy-intensive temperature swing processes used for sorbents that react chemically with CO2.

Figure 16 shows a schematic of the CO2 capture system as implemented in the IECM. Flue gas

from the wet FGD system enters an SO2 polisher where the SO2 concentration in flue gas is

reduced to 10 ppmv. The flue gas is then compressed to the PSA adsorption pressure, and then

sent through a cooler and condenser (C&C) unit where it is cooled to the PSA adsorption

temperature via cooling water in heat exchangers. As a result, some amount of water vapor in the

flue gas is condensed. The relatively dry flue gas then enters the PSA/VSA CO2 capture process.

The CO2-lean exhaust gas then passes through an expander where work is extracted before the

CO2-depleted flue gas is emitted to the atmosphere. The CO2 product stream is extracted using a

vacuum pump, and is then compressed to atmospheric pressure. It then goes through a CO2

compression and purification unit (CPU) where the CO2 is further purified and compressed to

pipeline transport conditions (typically 99.5% purity).

19

Figure 16. Schematic of the PSA/VSA post-combustion CO2 capture system for PC power plants

The process models developed in this study are described below.

3.2.1 PSA/VSA Process Performance Model

In a pressure swing adsorption (PSA) process, adsorption occurs at high pressure and desorption

takes place at low pressure. In a modification of the PSA process, called vacuum swing

adsorption (VSA), adsorption takes place at atmospheric pressure and desorption takes place

under vacuum conditions. A typical simple PSA cycle is described by the Skarstorm cycle

(Figure 17), consisting of four steps (Grande, 2012):

Pressurization (adsorption) – one end of the reactor is closed and gas is fed through the

other end in order to increase the pressure in the column.

Feed (adsorption) – the closed end is opened and the feed gas flows through the vessel till

the sorbent bed is saturated.

Blowdown (desorption) – the outlet of the bed is closed and gas is released from the inlet

end, decreasing the pressure in the column, thereby causing desorption of gas from the

sorbent.

Purge (desorption) – gas from a second column is sent through the column to remove any

gas that is embedded in the void spaces.

Though the Skarstrom cycle consists of only four steps, additional steps have been used in

different applications in order to increase the efficiency of the process. However, the four-step

Skarstorm forms the basis of most PSA cycles and is used here to model the performance of a

solid sorbent-based PSA/VSA process for post-combustion CO2 capture.

20

Figure 17. Schematic of a 2-column PSA process based on the Skarstrom cycle.

3.2.1.1 Sorbent properties

Langmuir isotherms were used to represent the adsorption characteristics of CO2 and N2 on

different sorbents. For ZIF-78 and Zeolite 5A, Langmuir isotherm parameters were obtained

from literature (Leperi et al, 2014; Leperi et al, 2015). For SU-MAC, experimental isotherm data

was provided to us by researchers at Stanford University for three operating temperatures (To et

al, 2015). We attempted to derive Langmuir isotherm parameters as a generic function of

temperature and pressure for this sorbent. However, because of limited data with high

uncertainty, the results for SU-MAC are shown only for 25oC feed temperature.

Isotherms for ZIF-78, Zeolite 5A and SU-MAC are plotted in Figure 18. The isotherms follow

familiar patterns in that gas loading on the sorbent increases with increasing partial pressure of

the gas to be captured and decreases with increasing temperature. CO2 isotherms for ZIF-78 and

SU-MAC are less steep compared to those of Zeolite 5A. ZIF-78 has higher CO2 loading

compared with that of Zeolite 5A at all pressures and temperatures. The N2 loading on Zeolite

5A is much lower compared to that of ZIF-78. N2 loadings for ZIF-78 do not change appreciably

with temperature. Adsorption of N2 along with CO2 leads to a decrease in the purity of the CO2

product stream. Selectivity is a parameter used to define the selective adsorption of CO2 over N2

on a sorbent. A standard definition of CO2/N2 selectivity is given by Krishna and van Baten

(2012) as the relative loading at typical operating conditions. However, as seen later, the absolute

loading of N2 on the sorbent is more important in determining the purity of the product stream.

Figure 18. Isotherms for ZIF-78 (Leperi et al, 2014), Zeolite 5A (Leperi et al, 2015) and SU-MAC

(To et al, 2015).

21

3.2.1.2 Simplified PSA/VSA model

Several models can be found in the literature for the performance of a PSA process. However,

for an initial analysis, a simplified PSA/VSA process model as described by Maring and Webley

(2013) is used. In this simplified model, only three steps are considered – pressurization, feed

and blowdown. In the pressurization step, pressure in the column is increased from low pressure

(pL) to high pressure in 100 equal increments, assuming that the bed reaches equilibrium at each

step. A similar procedure is followed in the blowdown step in which pressure is decreased from

high pressure to low pressure in 100 equal increments. At each step, mass and energy balance

equations are solved. All equations were coded in MATLAB. Details of the model are not

repeated here.

A few important features of the model are:

The bed is assumed to contain 1 kg of sorbent. Hence all flow rates are calculated per unit

mass of sorbent.

Volume of gas is calculated based on sorbent density and void space.

In running the model, initial conditions are assumed to be the end of the feed step. The

bed is assumed to be saturated with the feed gas at adsorption pressure.

The solution procedure starts with the blowdown step in which the pressure of the bed is

lowered from initial pressure to final pressure in more than 100 equal increments.

Pressure is lowered by removing some of the gas from the column. The gas removed is

calculated assuming equilibrium is reached at each pressure. Temperature changes

because of desorption of gases are also calculated. If the pressure is below atmospheric

pressure, a vacuum pump is used to extract the gas. Work required for the vacuum pump

is also calculated.

The next step is the pressurization step where feed gas is fed into the column, increasing

the column pressure in equal increments. The initial condition of the pressurization step is

the final condition of the blowdown step. A similar procedure for mass and energy

balance is followed as in the blowdown step. Blower work needed to increase the column

pressure is also calculated.

The feed step follows the pressurization step. A perfect breakthrough in the column is

assumed. Mass balance is achieved by equating the end of feed conditions to the

beginning of blowdown step conditions.

The model was run in MATLAB and the results were first validated with the results presented in

Maring and Webley (2013). The results of our model matched those in the paper. This model

was then used for the three sorbents studied for this work: ZIF-78, Zeolite 5A and SU-MAC.

3.2.1.3 Process performance characteristics

The model described in the previous section was applied to evaluate the performance of the three

sorbents in a PSA/VSA process. The model was first run to solve the mass and energy balance

equations over a range of operating conditions. The results of the model were used to develop

reduced order regression models which were then used to conduct design studies and sensitivity

analyses. Specifically, reduced order models were developed for five performance variables –

molar flow rates of CO2 and N2 products, molar flow rate of flue gas in the feed and

pressurization steps, and the vacuum work needed during the blowdown step. The independent

22

variables are operating or design conditions such as the mole fraction of CO2 in the feed,

temperature of feed gas, and the adsorption and desorption pressures.

Details of the regression analyses are given in Appendix C. These models were used to calculate

key performance metrics needed to evaluate a solid sorbent process for CO2 capture, namely:

CO2 product recovery. This is the amount of CO2 removed from the flue gas stream by

the CO2 capture process—a key process design parameter. A typical CO2 capture process

is expected to remove 90% of CO2 from the flue gas.

CO2 product purity. This is the volume fraction of CO2 in the product stream. CO2 purity

should be high (>95%) for pipeline transport.

Specific sorbent requirement. This is the amount of sorbent needed to capture a unit mass

of CO2 for specified operating conditions (process temperature, pressure, etc.)

Specific work requirement. This is the amount of energy required to operate the blower,

vacuum pump and CO2 compressor per unit mass of CO2 captured. It is desirable to

minimize the amount of work required by the CO2 capture process.

The reduced order models described above were exercised for each solid sorbent to quantify the

above performance metrics as a function of key process design parameters. These design

parameters included two capture system configurations: a 1-stage system, as depicted earlier in

Figure 17, as well as a more complex 2-stage system in which the product from the first stage is

used as feed to the second stage in order to improve the purity of CO2 product stream.

3.2.1.4 Results for the 1-stage PSA/VSA performance model

First, a 1-stage PSA/VSA performance model was used to study the applicability of different

sorbents for post-combustion CO2 capture. The performance metrics for each sorbents were

quantified at different adsorption and desorption pressures, inlet flue gas temperatures, and inlet

CO2 concentrations.

CO2 product purity

Figure 19 shows the CO2 product purity for 1-stage systems using ZIF-78, Zeolite 5A and SU-

MAC. CO2 purity is affected by all operating conditions. From the figures it is clear that in

general CO2 product purity is low under most operating conditions. Desorption pressure has to

be very low and inlet CO2 concentration high in order to increase purity.

The effect of temperature and adsorption pressure on purity is different for different sorbents.

Among the three sorbents, Zeolite 5A gives the highest purity, followed by SU-MAC and ZIF-

78. In practical applications to a power plant, the inlet CO2 concentration is fixed by the type of

coal used, as well as the pollution control equipment upstream of the CO2 capture process. Flue

gas temperature can be lowered in pre-treatment units but cooling of the flue gas is limited by the

size and cost of cooling equipment. Hence, for the design of a CO2 capture process, adsorption

and desorption pressures are the design parameters that can most easily be varied to achieve

target product purity. However, the data in Figure 18 indicate that CO2 purity levels of 95% or

more are not achievable in a 1-stage system for the sorbents and operating conditions shown.

23


process.

CO2 recovery (capture rate)

Figure 20 shows the CO2 recovery for 1-stage systems using the three sorbents. Unlike purity,

high CO2 recovery can be achieved by changing the operating conditions for all three sorbents.

Zeolite 5A and SU-MAC give higher recovery compared to ZIF-78. In general, low desorption

pressure, low feed temperature, high inlet CO2 concentration and high adsorption pressure are

needed to improve CO2 product recovery. As noted earlier, only the adsorption and desorption

pressures are easily adjustable among the operating variables.


PSA/VSA process.

Specific sorbent requirement

Figure 21 shows the specific sorbent requirement for the three sorbents. As desorption pressure

decreases, the amount of sorbent required also decreases. However, the quantity of sorbent

required per unit of CO2 captured is much larger for ZIF-78 and Zeolite 5A compared to SU-

MAC. This affects both the capital and O&M costs of the system (depending on the sorbent

attrition and replacement rates).

24

Figure 21. Specific sorbent required using ZIF-78, Zeolite 5A and SU-MAC in a 1-stage PSA/VSA

process.

Specific work

The specific work attributed to the PSA process (excluding the main CO2 compressor) consists

of the energy required for the inlet flue gas blower, vacuum pump, and CO2 product compression

from desorption pressure to atmospheric pressure, minus the work recovered in the flue gas

expander. Figure 22 shows the specific work results for the three sorbents. Lower desorption

pressure and higher adsorption pressure lead to higher specific work requirements. Higher inlet

CO2 concentration leads to lower specific work requirements. Among the three sorbents, Zeolite

5A has the lowest specific work requirement, though not much lower than the SU-MAC sorbent.

The work required for the ZIF-78 sorbent is the highest of the three systems.

Figure 22. Specific work required using ZIF-78, Zeolite 5A and SU-MAC in a 1-stage PSA/VSA

process.

3.2.1.5 Results for the 2-stage PSA/VSA performance model

To increase the CO2 purity required for pipeline transport of CO2 (>95%) a 2-stage system was

also analyzed. Here, the product from the first stage is sent to a second stage where further

separation of CO2 occurs. The operating conditions of both the stages are assumed to be the

same. Though purity increases in a 2-stage process, the overall CO2 recovery decreases because

some amount of CO2 is vented in the second stage as well as in the first stage.

Figures 23 and 24 show the effect of operating conditions on CO2 product purity and recovery,

respectively, using the three sorbents. In general, purity is significantly higher than for the 1-

stage process, with levels above 95% achievable across the operating conditions shown.

25

However, recovery rates are lower, and generally insufficient to achieve the 90% removal goal

for CO2 capture systems based on the data currently available for these sorbents.


process.


PSA/VSA process.

3.2.1.6 Designs for constant CO2 capture efficiency

CO2 capture systems in a power plant are designed to achieve a given CO2 capture rate, typically

90%, which is also the GCEP target removal rate. However, as seen in the performance analyses

of the three sorbents, 90% capture can be achieved only at very low desorption pressures. Lower

capture efficiencies also may be viable, however, depending on future emission control

requirements and policies. In this section, the performance of the three sorbents is evaluated for

constant CO2 capture efficiencies of 90% or less.

1-Stage Systems

Figure 25 shows the combination of adsorption and desorption pressures required to achieve

specific CO2 capture efficiencies (recovery) for 1-stage systems using the three sorbents. While

all sorbents can achieve 90% CO2 removal efficiency, the required desorption pressure is highest

for Zeolite 5A, which means that the required vacuum pump size (and related cost) is also lowest

for Zeolite 5A. As adsorption pressures increase, the required desorption pressure first increases

and then decreases for ZIF-78, while continuing to increase for Zeolite 5A and SU-MAC.

26

However, for all sorbents, it can be seen that very low desorption pressures are needed to achieve

90% CO2 capture. For 75% capture efficiency the desorption pressures are higher. Note,

however, that for all 1-stage systems with high removal rates the product purity is typically well

below the levels required for CO2 pipeline transport. Thus, in the overall plant designs presented

later in this report, an additional purification unit (with additional energy and costs penalty) is

required to achieve an acceptable product purity for pipeline transport and storage.

Figure 25. Combination of adsorption and desorption pressures for fixed CO2 capture efficiency in

a 1-stage PSA/VSA system using ZIF-78, Zeolite 5A and SU-MAC.

2-Stage Systems

Similarly, Figure 26 show the combination of adsorption and desorption pressures required to

achieve specific CO2 capture efficiencies (recovery) for 2-stage systems using ZIF-78, Zeolite

5A and SU-MAC, respectively. For 2-stage systems, no sorbent can reach 90% CO2 capture.

ZIF-78 and SU-MAC can achieve a capture efficiency of 80%, but at very low desorption

pressures. For a lower CO2 capture efficiency (60%), SU-MAC has the highest required

desorption pressure, indicating lower vacuum pump energy requirement.

Figure 26. Combination of adsorption and desorption pressures for fixed CO2 capture efficiency in

a 2-stage PSA/VSA system using ZIF-78, Zeolite 5A and SU-MAC.

3.2.1.7 Summary of Performance Model Results

The analysis so far showed that the performance of solid sorbents-based PSA/VSA CO2 capture

process is very sensitive to the operating conditions such as temperature, pressures and inlet CO2

concentration. Based on the results for purity and recovery, it can be concluded that in general

for 1-stage systems, Zeolite 5A has better performance characteristics than SU-MAC and ZIF-

27

78, even though its adsorption capacity is lower than the other two sorbents (Figure 18). The

main difference between the sorbents appears to be the N2 adsorption capacity, which is much

almost zero for Zeolite 5A. Thus it can be concluded that for a PSA/VSA process, it is the

absolute adsorption capacity of N2 on a sorbent, rather than CO2 adsorption capacity and CO2/N2

selectivity, which determines CO2 purity and recovery. Hence, research should focus on

developing sorbents that have a high CO2 adsorption capacity and very low N2 adsorption

capacity. It was also seen that attaining high capture rates in a 2-stage system is difficult for all

sorbents because both stages of the process release CO2 that is not captured.

3.2.2 Engineering-Economic Models

The performance models discussed above are linked to engineering-economic models that

estimate the capital cost, annual operating and maintenance (O&M) costs, and total annual

levelized cost of electricity (LCOE) for the capture system as well as for the entire power plant.

The methodological details of cost calculations were provided earlier in Section 3.1.2.

Table 6 shows the major components of the PSA system that go into estimating the PFC and

TCR of the capture system. Table 7 summarizes the major variable and fixed O&M cost

components. Cost model details are provided in Appendix D.

Table 6. Capital cost components of the PSA/VSA CO2 capture process

CO2 Capture Process Area Costs CO2 Capture Plant Costs

Flue Gas Cooler and Condenser Process facilities capital

PSA System General facilities capital

Flue Gas Blower Engineering. & home office fees

Heat Exchangers Project contingency cost

Exhaust Flue Gas Expander Process contingency cost

Vacuum Pump Interest charges

Compressing CO2 Product Stream Royalty fees

CO2 Purification and Compression Preproduction (startup) cost

Inventory capital

Process Facilities Capital (sum of above) Total Capital Requirement (sum of above)

Table 7. O&M cost components of the PSA/VSA CO2 capture process

Variable Cost Component Fixed Cost Component

Cooler and Condenser Operating labor

Sorbent Maintenance labor

Electricity Maintenance material

Caustic (NaOH) Admin. & support labor

Water Taxes and Insurance

CO2 Transport and Storage

Total Variable Cost (sum of above) Total O&M Cost (sum of above)

28

3.2.3 Total Power Plant System Analysis

The performance and cost models of the CO2 capture process were integrated into the IECM to

enable analysis of the overall power plant system. This section demonstrates the use of IECM for

techno-economic analysis of solid sorbents-based PSA CO2 capture system.

3.2.3.1 Base case assumptions

Table 8 shows the overall input assumptions used for the power plant case study. A typical new

supercritical PC plant using Illinois #6 coal and generating 650 MW of gross power was used as

the base case. For CO2 capture case studies, the three sorbents discussed earlier—ZIF-78, Zeolite

5A and SU-MAC—are modeled. Both 1-stage and 2-stage PSA/VSA systems are analyzed. For

all sorbents and process configurations, the performance and cost of the CO2 capture process as

well as the entire power plant were evaluated. The PSA/VSA-based CO2 capture is also

compared to a conventional amine-based CO2 capture system in terms of performance and cost.

Table 8. Technical and economic assumptions and parameters for PC Plants with CCS

Parameter Value

Power Plant Design Parameters

Coal type Illinois #6

Boiler type Supercritical

Capacity factor (%) 75

Ambient air temperature (oC) 18.9

CO2 control Solid sorbents

Solid sorbents-based CO2 Capture System

Sorbents ZIF-78, Zeolite 5A and SU-MAC

CO2 removal efficiency (%) Depends on sorbent

CO2 product final pressure (MPa) 13.79

CO2 product compressor efficiency (%) 80

Flue gas blower efficiency (%) 85

Flue gas expander efficiency (%) 85

Vacuum pump efficiency (%) 85

Economic and Financial Parameters

Cost year 2012

Dollar type Constant

Fixed charge factor (fraction) 0.113

Coal price ($/t) 42

CO2 capture system:

Sorbent price ($/t) 1000 (ZIF-78, Zeolite 5A)

2450 (SU-MAC)

Total transport & storage cost ($/t) 10

General facilities capital (% of PFC) 15

Engineering & home office fees (E) (% of PFC) 10

Project contingency (C) (% of PFC) 20

Process contingency (% of (PFC+E+C) 20

Total maintenance cost (% of TPC) 2.5

29

Table 9 shows the overall results for the case study power plants. As can be seen, the net plant

efficiency of power plants with the 1-stage capture configuration ranges between 26% and 31%

(HHV basis), compared with close to 40% for the reference plant without CCS. The plant using

Zeolite 5A has the highest net plant efficiency (30.5%)—three percentage points higher than the

conventional amine-based plant with a net efficiency of 27.6% for 90% CO2 capture.

Table 9. Performance and costs of PC power plants with and without CCS. For the CCS

cases, the final CO2 product purity is 99.5%, achieved using the CO2 purification unit

(CPU).

Parameter PC w/o

CCS

PC

w/CCS

PC w/ CCS

(1-stage capture)

PC w/ CCS

(2-stage capture)

CO2 Capture Material MEA ZIF-78 Z-5A SU-

MAC ZIF-78 Z-5A

SU-

MAC

Gross power output (MW) 650 650 650 650 650 650 650 650

Adsorber CO2 Capture Rate (%) 90 90 90 90 80 60 80

Net power output (MW) 608 525 416 483 425 417 331 351

Net plant efficiency (HHV, %) 39.9 27.6 26.3 30.5 26.9 26.4 20.9 22.2

Feed Gas Temperature (oC) 35 35 25 35 35 25

Adsorption Pressure (bar) 1.2 1.2 2.5 1.2 2.5 2.5

Desorption Pressure (bar) 0.02 0.03 0.03 0.06 0.01 0.02

CO2 purity out of PSA (%) 65.2 84.6 75.6 82.1 96.8 93.3

CO2 emission rate (kg/kWh) 0.82 0.11 0.23 0.20 0.22 0.33 0.76 0.40

CO2 capture system use (MW) 181 113 172 179 265 245

Feed Blower Power Use (MW) 15.0 15.0 33.2 18.3 96.3 94.5

Expander Power Recovery (MW) 7.42 7.67 15.5 9.14 39.3 38.3

Vacuum Pump Use (MW) 38.3 34.4 29.1 47.4 54.6 38.4

CO2 Compression Use (MW) 66.7 42.1 78.2 92.1 150 140

CO2 purification unit (MW) 68.2 29.7 47.6 30.7 3.6 10.9

Total plant capital cost

(2012$/kWnet)

1,985 3,651 5,646 4,333 5,340 6,550 8,680 8,258

Plant LCOE (2012$/MWh) 61.5 111 158 127 151 177 229 218

Added LCOE for CCS

(2012$/MWh)

49.2 96.4 65 89.3 116 197 157

Cost of CO2 avoided (2012$/t CO2) 69.9 148 94.7 136 216 1300 337

Cost of CO2 captured (2012$/t CO2) 43.4 89.7 68.1 84.3 125 196 144

For the case study assumptions, the capital cost and LCOE of the plants with solid sorbent-based

CO2 capture are more than twice that of the base plant without CO2 capture. Furthermore, as seen

from Table 9 and Figure 27, the LCOE and cost of CO2 avoided are higher for the plants with

solid sorbent-based CO2 capture than for the plant with conventional amine-based capture. Thus,

although the configuration using the Zeolite 5A sorbent has a higher overall plant efficiency than

the plant with the amine-based sorbent, the higher capital cost of the solid sorbent-based plant

results in a higher cost of electricity generation for the base case assumptions.

For the 2-stage capture process configurations, also shown in Table 9 and Figure 27, the CO2

removal efficiency is much lower and the total process costs much higher than the corresponding

1-stage configurations for a given sorbent. This reflects the process performance results shown

30

earlier. Thus, even though the 2-stage systems results in a higher-purity product than the 1-stage

systems, the additional vacuum work and capital cost required for an additional stage results in a

much higher plant capital cost and LCOE compared to the 1-stage configurations. Even though a

large CPU is needed for the 1-stage systems in order to achieve pipeline purity specifications, it

is still cheaper than an additional PSA stage. Note that Zeolite 5A case is not shown in Figure 27,

because the adsorber capture efficiency that can be achieved is much lower (60%, as shown in

Table 9).

Figure 27. Plant LCOE and cost of CO2 avoided for the plants with CCS. Adsorber CO2 capture

efficiency is 90% for 1-stage systems and 80%for 2-stage systems.

Figure 28 shows a further breakdown of the process facilities cost (PFC) for the 1-stage capture

system using the ZIF-78 sorbent. As seen, the PSA reactor system constitutes up to 43% of PFC.

As a result of very low desorption pressures, the next highest contributor to capital cost comes

from the vacuum pumps.

Figure 28. Distribution of direct capital costs for the 1-stage CO2 capture system with ZIF-78

31

3.2.3.2 Sensitivity Analysis

A sensitivity analysis was performed using the enhanced IECM to investigate the effects of

major plant and system parameters on the performance and cost of the PC plant with solid

sorbents-based CCS. When a parameter is evaluated, other parameters were held at their base

values given in Table 8. As with the analysis of ionic liquid systems in Section 3.1, the major

parameters considered include CO2 removal efficiency, plant size, coal type, capacity factor,

fixed charge factor, and the capture system's process and project contingency cost factors. For

illustration, ZIF-78 with a 1-stage PSA system is used.

Figure 29 shows the effects of CO2 capture rate on net plant efficiency for a plant using ZIF-78

in a 1-stage PSA. As can be expected, net plant efficiency reduces with increasing capture rate.

Figure 30 shows the effect of CO2 capture rate on the plant capital cost, LCOE and cost of CO2

captured and avoided. In all cases the costs are much higher than the cost of a plant with MEA-

based CO2 capture system.

Figure 29. Effect of CO2 capture efficiency on net plant efficiency. (ZIF-78 in a 1-stage system).

Figure 30. Effect of CO2 capture efficiency on the total plant capital cost and plant LCOE. The CO2

capture process uses ZIF-78 in a 1-stage system.

Figure 31 shows the effect of plant size and coal type on the plant LCOE and the cost of CO2

avoided. Three coal types are modeled: Illinois #6 bituminous coal, Wyoming Power River

Basin (PRB) sub-bituminous coal, and North Dakota (ND) lignite. The coal prices in the IECM

fuel database are $42, $9.6 and $16.8 per tonne, respectively. For a given coal, larger plant size

32

leads to lower costs. For a given plant size, plants using ND lignite have higher costs than plants

using higher quality coals. The cost of plant using PRB coal is comparable to that using

Illinois#6 coal because of the much lower coal price of PRB.

Figure 31. Effect of plant size and coal type on plant LCOE and cost of CO2 avoided. The CO2

capture process uses ZIF-78 in a 1-stage system.

Given that CCS is still in early stages of commercialization, there is uncertainty in their

operation and financing (Rubin et al 2007). To account for the effects of both factors, further

parametric analysis was conducted for capacity factor and fixed charge factor. Figure 32 shows

that LCOE and CO2 avoidance costs are very sensitive to these two factors, with higher capacity

factors and lower FCFs leading to lower costs.

Figure 32. Effect fixed charge factor and capacity factor on plant LCOE and cost of CO2 avoided.

The CO2 capture process uses ZIF-78 in a 1-stage system.

Since solid sorbents-based CO2 capture is currently in the early stages of research and

development there is high uncertainty in both the process and project contingency cost factors

associated with the proposed system. As can be expected, lower contingency factors lead to

lower costs. However, systems with low commercial experience tend to have higher contingency

values. Figure 33 shows the effects on plant LCOE and cost of CO2 avoided of project and

process contingencies varying from 10% to 30% of PFC.

33

Figure 33. Effect project and process contingencies on plant LCOE and cost of CO2 avoided. The

CO2 capture process uses ZIF-78 in a 1-stage system.

3.2.4 Evaluation of Improved Sorbents and Process

As seen in the results so far, the power plants modeled here with solid sorbent-based PSA/VSA

CO2 capture system are higher in cost than plants with conventional amine-based capture

systems. In this section, sensitivity analyses are performed to understand how to improve the cost

characteristics of solid sorbent-based capture systems. The 1-stage capture system with Zeolite

5A sorbent is used as the base case for these sensitivity studies since it is the lowest-cost system

of the various options analyzed. All other plant-level assumptions are held constant.

There are two ways of improving the performance and cost of the solid sorbent-based capture

system. One is to improve the material properties such that the required CO2 purity and recovery

can be obtained at much higher desorption pressures, in turn leading to a decrease in the energy

penalty of the capture system. The other is to reduce the capital and O&M costs of the capture

system components.

3.2.4.1 Effect of energy penalty reduction

Table 9 showed that the energy requirements of PSA systems ranges from 17% to 40% of gross

power output. Figure 34 shows the effect of varying the PSA CO2 capture system energy penalty

on the plant performance and capital cost. It can be seen that if a sorbent were designed to

achieve energy penalties lower than about 10% of gross power output, the total plant capital cost

would be lower than that for a plant with an amine-based capture system, all other parameters

held constant.

Figure 35 shows the additional effect of energy penalty on the plant LCOE and the cost of CO2

avoided. Again it can be seen that at lower energy penalties it is possible for the solid sorbent-

based plants to cost less than the MEA-based plants. As has been discussed before, the main way

of improving the performance (achieving the required purity and recovery at high desorption

pressure) is to increase the CO2 adsorption capacity and at the same time have near zero N2

adsorption capacity.

34

Figure 34. Effect of PSA CO2 capture system energy penalty on net plant efficiency and total plant

capital cost. (For this comparison, 1-stage Zeolite 5A models with 90% CO2 capture are used as

basis).

Figure 35. Effect of PSA CO2 capture system energy penalty on plant LCOE and cost of CO2

avoided. (For this comparison, 1-stage Zeolite 5A models with 90% CO2 capture are used as basis).

3.2.4.2 Effect of capital cost reduction

From the results in the previous section, it was seen that the PSA CO2 capture systems constitute

at least 40% of the overall plant capital cost. Any decrease in this number would lead to a

decrease in the total plant capital cost and LCOE. Figure 36 shows the effect of reduction in TCR

(including direct and indirect costs) of the PSA CO2 capture system on the total plant capital cost

and LCOE. It can be seen that there should be at least a 30% decrease in the PSA CO2 capture

system TCR to make it comparable to an amine-based CO2 capture system. Similarly, there

should be at least a 50% reduction in TCR to reduce the LCOE below MEA-capture system

level. Improvements in sorbent materials, as explained before, will also lead to a decrease in

capital cost by reducing the costs of vacuum pump, CO2 compression from vacuum to

atmospheric pressure and CPU. Improvements in the PSA reactor system design could also

contribute to reductions in capital costs. Since indirect costs are also included in this analysis,

capital cost reduction could also be achieved by increased technological maturity (which reduces

indirect costs).

35

Figure 36. Effect of reduction in total capital requirement (TCR) of the PSA CO2 capture system

total plant capital cost and LCOE. (For this comparison, 1-stage Zeolite 5A models with 90% CO2

capture are used as basis).

3.2.4.3 Effect of O&M cost reduction

The O&M cost of the capture system affects the overall plant LCOE. O&M cost depends on

sorbent replacement rate and the cost of sorbent. Results in Table 9 were obtained assuming a

sorbent replacement rate of 0.005%/year. Figure 37 shows the effect of sorbent replacement rate

and cost of sorbent on the plant LCOE and the cost of CO2 avoided. Even a modest increase in

sorbent replacement rate leads to a large increase in plant LCOE. As explained before, because

the sorbent requirement itself is very high, replacement costs are also very high. In order to

minimize the cost of replacement, sorbents have to be extremely stable in power plant

conditions. Interestingly, the cost of sorbent has only a modest effect on the LCOE. Hence it is

clear that the main variables in minimizing LCOE are sorbents with low sorbent requirement

(such as SU-MAC, as seen in Figure 21), extremely high stability and low cost. Similarly, it is

clear that a combination of improved sorbent material design as well as system design is required

to bring down the cost of solid sorbent-based PSA/VSA CO2 capture systems.

Figure 37. Effect of sorbent replacement rate and cost of sorbent on plant LCOE and cost of CO2

avoided. (For this comparison, 1-stage Zeolite 5A models with 90% CO2 capture are used as basis).

36

3.2.5 Summary of Results for Solid Sorbent-Based Systems

Performance and cost models for a PSA/VSA process using three solid sorbents (ZIF-78, Zeolite

5A and SU-MAC) were developed and incorporated into the plant-level IECM simulation model.

For all of the solid sorbent capture materials studied, a 1-stage PSA/VSA systems coupled with a

CO2 purification unit had lower costs than 2-stage systems. Among the sorbents, Zeolite 5A had

the lowest overall cost for 90% CO2 capture. Sensitivity analyses also were conducted to show

the effect of different performance, cost and financial parameters on the overall power plant cost.

The systems analyses also indicated that in general the cost of power plants with a PSA/VSA

CO2 capture system employing the solid sorbents of interest in this project are higher than the

cost of plants using conventional amine-based CO2 capture systems.

4. Conclusions

This project developed an integrated performance and cost model that links electric power

generation systems designs with the process parameters and material properties that influence the

overall performance and cost of a carbon capture system. Coupled performance and cost models

were formulated for several advanced carbon capture processes employing novel sorbent

materials being developed in three separate projects supported by the Stanford Global Climate

and Energy Program (GCEP). The capture process models were then implemented in the

Integrated Environmental Control Model (IECM) framework to assess the performance and cost

of a complete power plant with carbon capture and storage (CCS). This systems analysis

capability provides a common platform for conducting comparative analyses of emergent capture

technology options for different types of power plants. Thus, it is a powerful tool for identifying

whether a specific scientific approach for carbon capture has the potential to be a breakthrough

when applied in a full-scale power generation system.

The specific systems studied in this project included a process employing certain ionic liquids for

pre-combustion CO2 capture (in an integrated gasification combined cycle power plant), and

processes employing three types of solid sorbents, including metal organic frameworks (MOFs)

and an activated carbon, for post-combustion CO2 capture (in a pulverized coal combustion

power plant). The performance and cost models developed for each process were embedded into

the IECM to facilitate plant-level analyses of the type sought by GCEP.

The results of this study show that the cost of carbon capture depends not only on the design and

operation of the CO2 capture process, but also strongly on the design and operation of the power

plant to which the capture unit is applied. Results from a range of plant-level modeling and

analyses show that the addition of a CO2 capture system using either the chemical ILs for pre-

combustion capture or novel physical sorbents for post-combustion capture at a power plant

would (as expected) decrease the net plant efficiency and increase the overall cost of electricity

generation in order to avoid emitting 90% of the potential CO2 emissions to the atmosphere. The

magnitude of the plant-level impact from CCS deployment varies with the carbon capture

materials, the capture process configuration and operating conditions, and the overall power

plant design, operation and financing, as well as the fuel type and quality.

For the currently synthesized new materials for CO2 capture identified in this project, the cost of

electricity generated and CO2 avoided for the IGCC plant with IL-based pre-combustion CCS

was found to be significantly larger than for the same plant using current Selexol-based CCS.

37

Costs for the plants with the solid sorbent-based systems for post-combustion CCS were also

significantly larger than that for PC plants with current amine-based CCS. These results were

due in part to the properties and CO2 capture characteristics of the sorbent materials modeled, but

also to the significant capital costs incurred in the design of processes to utilize these materials

for carbon capture at a large coal-fired power plant.

Lowering the chemical ILs' heat capacity and reaction heat by about 50% to decrease the thermal

energy use for pre-combustion CO2 capture would be needed in order to improve the viability of

IL-based capture technology in competition with Selexol-based capture technology, based on the

plant-level analysis. The process performance by temperature swing could be improved by

incorporation of some pressure swing. For sorbent-based post-combustion CCS, improved

material properties (such that the required CO2 purity and recovery are attained at higher

desorption pressures) and system designs could lead to a significant decrease in the overall cost

of the power plant, making such designs competitive with or better than current amine-based

capture systems. Future research should especially focus on material designs with improved CO2

adsorption capacity together with near-zero N2 adsorption capacity. In all cases, systems analysis

tools such as the IECM can be used to understand and quantify the effects of different design and

operating parameters on overall plant costs, thereby providing a directional basis for future

development of improved materials and processes for carbon capture in support of climate

change mitigation goals.

5. References

5.1 References for Section 3.1 (Ionic Liquids)

Chen, C. and Rubin, E. S. (2009). CO2 control technology effects on IGCC plant performance

and cost. Energy Policy, 37(3), 915-924.

Electric Power Research Institute (EPRI). TAG, Technical Assessment Guide, Volume 1:

Electricity Supply (Revision 7); EPRI: Palo Alto, CA, June 1993; Report TR-102276-V1R7.

Electric Power Research Institute (EPRI). Updated Cost and Performance Estimates for

Advanced Coal Technologies Including CO2 Capture; EPRI: Palo Alto, CA, December 2009;

Report 1017495.

Gurkan, B., Goodrich, B. F., Mindrup, E. M., Ficke, L. E., Massel, M., Seo, S., ... & Schneider,

W. F. (2010). Molecular design of high capacity, low viscosity, chemically tunable ionic liquids

for CO2 capture. The Journal of Physical Chemistry Letters, 1(24), 3494-3499.

Integrated Environmental Control Model, Version 8.0.2, Carnegie Mellon University, available

at: www.iecm-online.com.

Integrated Environmental Control Model, Version 10-beta, Carnegie Mellon University.

Onda, K., Takeuchi, H., and Okumoto, Y. (1968). Mass transfer coefficients between gas and

liquid phases in packed columns. Journal of Chemical Engineering of Japan, 1(1), 56-62.

Seader, J. D., Henley, E. J., & Roper, D. K. (2011). Separation process principles: chemical and

biochemical operations. Hoboken.

38

Seo, S., DeSilva, M. A., Xia, H., and Brennecke, J. F. (2015). Effect of Cation on Physical

Properties and CO2 Solubility for Phosphonium-Based Ionic Liquids with 2-Cyanopyrrolide

Anions. The Journal of Physical Chemistry B, 119(35), 11807-11814.

Silla, H. (2003). Chemical process engineering: design and economics. CRC Press.

Strigle Jr, R. F. (1994). Packed tower design and applications. Gulf Pub. Co.

Rao, A. B. and Rubin, E. S. (2002). A technical, economic, and environmental assessment of

amine-based CO2 capture technology for power plant greenhouse gas control. Environmental

Science & Technology, 36(20), 4467-4475.

Rubin, E. S., Chen, C., and Rao, A. B. (2007). Cost and performance of fossil fuel power plants

with CO2 capture and storage. Energy Policy, 35(9), 4444-4454.

Maginn, E.J., et al (2013). Ionic Liquids: Breakthrough Absorption Technology for Post-

Combustion CO2 Capture. Final Report for Project DE-FC26-07NT43091, University of Notre

Dame, Notre Dame, IN.

Wankat, P. C. (1988). Separations in chemical engineering: equilibrium staged separations. New

York: Elsevier.

5.2 References for Section 3.2 (Solid Sorbents)

Grande, C.A. (2012). Advances in pressure swing adsorptions for gas separation. ISRN Chemical

Engineering, 1-13.

Krishna, R., and van Bate, J.M. (2012). A comparison of the CO2 capture characteristics of

zeolites and metal-organic frameworks. Separation and Purification Technology, 87, 120-126.

Leperi, K.T., Snurr, R.Q., and You, F. (2014). Modeling and optimization of a two-stage MOF-

based pressure/vacuum swing adsorption process coupled with material selection. Chemical

Engineering Transactions, 39, 277-282.

Leperi, K.T., Snurr, R.Q., and You, F. (2016). Optimization of two-stage pressure/vacuum swing

adsorption with variable dehydration level for post-combustion carbon capture. Industrial &

Engineering Chemistry Research, 55 (12), 3338-3350.

Maring, J.M., and Webley, P.A. (2013). A new simplified pressure/vacuum swing adsorption

model for rapid-adsorbent screening for CO2 capture applications. International Journal of

Greenhouse Gas Control, 15, 16-31.

Rubin, E. S., Chen, C., and Rao, A. B. (2007). Cost and performance of fossil fuel power plants

with CO2 capture and storage. Energy Policy, 35(9), 4444-4454.

To, J.W.F., He, J., Mei, J., Haghpanah, R., Chen, Z., Kurosawa, T., Chen, S., Bae, W-G., Pan, L.,

Tok, J.B-H., Wilcox, J., and Bao, Z. (2016). Hierarchical N-doped carbon as CO2 adsorption

with high CO2 selectivity from rationally designed polypyrrole precursor. Journal of American

Chemical Society, 138(3), 1001-1009.

39

Appendix A: Reduced-Order Performance Models for Pre-Combustion CO2 Capture

Using [P2228][ 2-CNpyr]

A wide range of process scenarios are designed to explore the potential operational space of an

isothermal ionic liquid-based capture process and then characterize key input-output response

relations. Reduced-order models (ROMs) are formulated based on the response relations and

then embedded into the IECM. Table A1 summarizes the major input and output variables

included in the ROMs. Each of the input parameters is varied over a range to cover possible

operation conditions. For the given ranges of key input variables shown in Table A1, there are a

total of 8784 scenarios designed and modeled for quantifying input-output response relations

among the major process parameters.

Table A1. Summary of key input and output variables for reduced-order models

Parameter Symbol Unit Parameter Type Range

CO2 removal efficiency fraction Input 0.50-0.95

Inlet CO2 Concentration fraction Input 0.20-0.45

Absorption Pressure bar Input 20-45

Absorption Temperature oC Input 25-50

CO2 Lean Loading Capacity

mole fraction Input 0.15-0.40

Liquid-to-Gas Ratio mole ratio Output

Absorption Vessel per

Train

m3/tonne CO2

captured/hr Output

Total Cooling Duty for

Lean Solvent

kJ/kmole lean

solv. Output

Regeneration Pressure bar Output

Regeneration

Temperature o

C Output

Thermal Duty of Rich-Lean Solvent Heat

Exchanger per Train

kJ/kmole lean

solv. Output

LMTD of Solvent Heat

Exchanger

oC Output

Steam Thermal

Requirement for Solvent

Regeneration

kJ/kg CO2 captured

Output

Regeneration Vessel per

Train

m3/tonne CO2

captured/hr Output

Results of the regression analyses for the nine output parameters in Table A1 are as follows:

[1] Liquid-to-Gas Ratio:

when the CO2 lean loading capacity is less than 0.35 mole fraction,

R-sq (adj)=98.17%

Otherwise,

40

R-sq (adj)=98.21%

[2] Absorption Vessel per Train:


R-sq (adj)=96.75%

Otherwise,

R-sq (adj)=97.73%

[3] Total Cooling Duty for Lean Solvent:

R-sq (adj)=100.00%

[4] Regeneration Pressure:

[5] Regeneration Temperature:

R-sq (adj)=99.70%

[6] Thermal Duty of Rich-Lean Solvent Heat Exchanger per Train:

R-sq (adj)=99.90%

[7] Logarithmic Mean Temperature Difference of Solvent Heat Exchanger:

R-sq (adj)=95.64%

[8] Steam Thermal Requirement for Solvent Regeneration:


R-sq (adj)=98.93%

Otherwise,

R-sq (adj)=93.52%

41

[9] Regeneration Vessel per Train:


R-sq (adj)=97.85%

Otherwise,

R-sq (adj)=96.57%

42

Appendix B: Direct Capital Cost Estimation for Pre-combustion CO2 Capture Using

[P2228][ 2-CNpyr]

The direct costs of various components are scaled based on the major sizing parameters using the

common 6/10th

power law and are estimated as follows:

In addition,

43

Table A2 summarizes the reference sizing parameters and costs for all the direct cost

components. The sources of cost information are also available in Table A2.

Table A2. Equipment Reference Costs

Equipment Variable Unit Reference Value of Variable

Source of Ref Value

Gas stream heat

exchangers,

2007$M 2.948 *2.01*(1+0.30) Maginn et al

2013 MMBtu/hr 232*1.055e6

# 2

Absorbers,

2007$M 44.865*2.01*(1.0+0.30) Maginn et al 2013

m3 (13.1*13.1)*15.2

# 2

Solvent circulation

pumps,

2011$M 15.89 IECM

v8.0.2 t/hr 8308

Absorption intercoolers,

2007$M 3.216 *2.01*(1.0+0.30) Maginn et al

2013 MMBtu/hr 358.0

# 2

Lean solvent coolers

2007$M 2.948 *2.01*(1.0+0.30) Maginn et al

2013 MMBtu/hr 232*1.055e6

# 2

Solvent regenerators,

2007$M 3.525*2.01*(1.+0.30) Maginn et al

2013 m

3 (1./4.*3.14*5.9**2)*10.7

# 3

Rich & lean solvent heat

exchangers,

2007$M 21.976*2.01*(1+0.30) Maginn et al

2013 MMBtu/hr 2112*1.055e6

oC 8.3

Reboilers, 2011$M 27.33 IECM

v8.0.2 kJ/hr 3533*541.4*1000

Solvent reclaimers,

2011$M 1.337 IECM

v8.0.2 kJ/hr 54.93

Solvent processing,

2011$M 1.462 IECM v8.0.2

kJ/hr 54.93

Steam extraction, 2011$M 3.701 IECM

44

kJ/hr 3533*541.4*1000 v8.0.2

CO2 product heat

exchangers,

2007$M 2.948 *2.01*(1+0.30) Maginn et al

2013 MMBtu/hr 232*1.055e6

# 2

CO2 product compressors,

kW

45

Appendix C: Solid Sorbent Pressure Swing Adsorption Process Performance Model

The following are the mass balance equations for the MOFs-based PSA process for CO2 capture,

depicted in the above figure. Three MOFs are considered - ZIF-78, Zeolite 5A and SU-MAC.

Cooler and Condensor (C&C)

The cooler and condenser is used to remove water vapor from the flue gas (after FGD) before it

enters the PSA process. Pressurized flue gas flows through cross flow heat exchangers where

cooling water is used to cool the flue gas through indirect contact. The amount of water removed

depends on the inlet pressure and outlet temperature. The following regression equation was

obtained after Aspen simulations:

ηH2O,removed = 0.9882 + 0.04526*pH – 0.006095*Tfeed (R2 = 92%)

Water removed = MH2O,out,FGD*ηH2O,removed

Cooling duty required for this is obtained from the following regression equation:

Cooling duty (kJ/kmol H2O removed) = 33932 - 3151*pH – 446.7*Tfeed (R2 = 91%)

From this, outlet concentration of water vapor can be calculated. This will be the inlet

concentration of water vapor to the PSA unit.

yH2O,in,PSA = yH2O,out,FGD*(1 - ηH2O,removed)

Basically, MH2O,in,PSA (kmol/hr) = MH2O,out,FGD(1 – ηH2O,removed)

Mole flows of all other components remain the same. Mole fractions can be calculated, for

example, as:

yCO2,in,PSA = MCO2,out,FGD/(Mfluegas,out,FGD – ηH2O,removed *MH2O,out,FGD)

(This is the same as yfeed in the following sections)

PSA Unit Mass Balance

The performance model uses regression equations for five quantities - Moles of CO2 in the

product (MCO2,prod), Moles of N2 in the product (MN2,prod), Moles of feed flow (Mfeed), Moles of

feed in the repressurization step (MRP) and Work required for the vacuum pump (Wvac). The

variables in the regression equation are - Feed temperature (Tfeed, deg C), Adsorption pressure

(pH, bar), Desorption pressure (pL, bar) and CO2 molar concentration in the feed (yCO2,feed). User

Water, out

46

inputs are pH and Tfeed. Calculate PSA inlet flow rates from the C&C mass balance. The

following tables show the coefficients for different terms in the regression equations for the five

quantities.

ZIF-78

Coefficients MCO2,prod MN2,prod Mfeed MRP Wvac

ηCO2

1 Tfeed^2 -1.18E-04 1.00E-06 -4.38E-04 -3.00E-06 -1.0650 -6.90E-05

2 Tfeed*pL 0.0055 7.90E-05 0.0478 0.0036 -346.3 0.0224

3 Tfeed*pH 0.0041 -1.63E-04 0.0139 -5.90E-05 28.6 0.0024

4 Tfeed*yCO2,feed 0.0161 5.51E-04 0.0517 3.37E-04 117.8 -0.0131

5 Tfeed 0.0653 -8.44E-04 0.2359 0.0013 648.0 0.0033

6 pL^2 70.7770 0.0229 177.4300 2.9425 1784157.0 19.9820

7 pL*pH 0.0244 6.41E-04 -0.1440 -0.3675 5372.0 0.6783

8 pL*yCO2,feed -3.0234 0.0931 37.0200 -0.3887 -53107.0 47.4150

9 pL -18.0720 -0.0780 -71.4800 -1.7899 -237223.0 -16.4518

10 pH^2 -0.1092 4.00E-05 -0.3329 -0.0056 -529.3 -0.0685

11 pH*yCO2,feed 0.0819 -0.4779 -1.7175 0.0086 -2418.8 -0.2503

12 pH -0.6617 0.5290 -1.8210 0.5896 -6460.0 0.1984

13 yCO2,feed^2 -3.0176 -0.0043 12.6350 -0.0589 -23682.0 3.9120

14 yCO2,feed -0.6480 -0.1744 -32.2490 -0.0297 -3796.0 -2.5050

15 Constant -8.8000 0.1716 -23.4000 -0.1306 -84828.0 0.9765

R2

(%)

98.93 100 97.3 99.99 95.96

Zeolite 5A

Coefficients MCO2,prod MN2,prod Mfeed MRP Wvac

ηCO2

1 Tfeed^2 0 0.000001 -5.50E-05 -9.00E-06 0 9.10E-05

2 Tfeed*pL -0.04641 0 -0.09578 -0.005383 -821.1 -0.066728

3 Tfeed*pH 0.001499 -0.000042 0.004017 0.000513 0 -0.001017

4 Tfeed*yCO2,feed 0.008911 -0.000719 -0.061264 -0.000131 16.8 -0.017418

5 Tfeed 0.00604 -0.000151 0.0875 0.005972 124.33 0.005439

6 pL^2 33.074 0 80.28 10.785 680849 7.0222

7 pL*pH 0.1484 0 0 -0.3597 3362 0.49587

8 pL*yCO2,feed 0.3484 0 26.085 -0.0173 3821 11.7588

9 pL 5.261 0 -2.73 -0.2234 95156 -2.3658

10 pH^2 -0.025692 -0.000345 -0.05447 -0.005938 -77.15 -0.015246

11 pH*yCO2,feed -0.06572 -0.049237 -0.3652 -0.001914 -793.3 -0.44671

12 pH -0.2786 0.06191 -0.7603 -0.01593 388.2 0.241008

13 yCO2,feed^2 -0.81377 0.158469 9.6113 0.02758 -3867.5 25.061

14 yCO2,feed -1.5863 0.0382 6.16 0.00879 -23 -8.5612

15 Constant -1.4792 0.0043 -16.3 -0.929 -29115 0.84562

R2

(%)

98.36 99.68 97.28 99.61 96.8 99.77

47

SU-MAC

Coefficients

(ONLY for 25oC)

MCO2,prod MN2,prod Mfeed MRP Wvac ηCO2

1 Tfeed^2 0 0 0 0 0 0

2 Tfeed*pL 0 0 0 0 0 0

3 Tfeed*pH 0 0 0 0 0 0

4 Tfeed*yCO2,feed 0 0 0 0 0 0

5 Tfeed 0 0 0 0 0 0

6 pL^2 14.754 -0.2662 33.781 0.91776 245904 3.3686

7 pL*pH -0.4079 0.03314 -1.303 -0.153 -1280 0.06407

8 pL*yCO2,feed -4.2688 0.36979 25.183 -0.29721 -26126 10.788

9 pL -9.7798 -0.18755 -35.517 -0.73707 -143098 -4.7947

10 pH^2 -0.11982 -0.001985 -0.4944 -0.000786 -266.4 -0.040798

11 pH*yCO2,feed 0.08891 -0.505286 -5.2128 0.018137 -2165.2 -0.3745

12 pH 1.1542 0.498794 6.3349 0.599414 2931 0.35272

13 yCO2,feed^2 -5.2969 -0.15608 32.564 -0.21421 -19328 4.55

14 yCO2,feed 8.4459 0.14823 -33.552 0.26422 33013 -1.966

15 Constant -0.5848 -0.03359 11.437 -0.07053 14037 0.5413

R2 (%) 98.57 99.68 94.41 99.97 96.07 95.84

All the quantities are calculated assuming 1 kg of sorbent in the bed. The following are the

regression equations. The number in the subscript indicates the coefficient listed in the tables.

For example, MCO2,prod,2 is the coefficient number 2 in the MCO2,prod column. For ZIF-78, this

value is 0.0833.

Moles of CO2 in the product stream (mol/kg sorbent):

Eqn (1)

Moles of N2 in the product stream (mol/kg sorbent):

Eqn (2)

Moles of inlet gas in the feed step (mol/kg sorbent):

48

Eqn (3)

Moles of inlet gas in the repressurization step (mol/kg sorbent)

Eqn (4)

Specific work required for vacuum pump (J/kg sorbent):

Eqn (5)

Using these quantities, performance metrics such as recovery, purity and work required can be

calculated.

(6)

(7)

1-Stage Model Calculation of Vacuum Pressure (pL)

For IECM, the operating conditions needed to get a given capture efficiency should be

calculated. Here, the desorption pressure (pL) needed to achieve a given recovery (ηCO2) is

calculated by rearranging Eqn 6. The solution requires solving a quadratic equation in terms of

pL:

(8)

The coefficients of the quadratic equation are derived as:

49

Solution of the quadratic equation is

(9)

Note: This gives two results. The lower one is the solution. Sometimes MATLAB gives an

imaginary solution with the imaginary part as 0. Only the real part has to be kept. If a negative or

zero solution happens, the pL should be made as 0.001 bar.

2-Stage Model Calculation of Vacuum Pressure (pL)

For 2-stage model, overall recovery and purity was calculated is calculated using a regression

equation as a function of overall capture rate (limit the maximum value to 80% for 2-stage).

The coefficients of the quadratic equation are derived as:

Solution of the quadratic equation is:

(9)

Note: This gives two results. The lower one is the solution. Sometimes MATLAB gives an

imaginary solution with the imaginary part as 0. Only the real part has to be kept. If a negative or

zero solution happens, the pL should be made as 0.001 bar.

50

1-Stage Mass Flow Rates

Once pL is known, the other quantities can be calculated using Equations 1 – 5.

Sorbent required can be calculated as follows:

(10)

The other energy consumers are blower and CO2 compressor to compress CO2 from the vacuum

condition to atmospheric pressure (J/kg sorbent).

(11)

(12)

“n” is the number of stages needed and rp is the pressure ratio. Since the higher pressure is

atmospheric pressure,

(13)

Compressor efficiency can be assumed to be 0.8, the same as the CO2 product compressor in

IECM. The number of stages is determined in the following way:

% No. of stages of compression

if (r_p>=1) && (r_p<=10)

n=1;

elseif (r_p>10) && (r_p<=100)

n=2;

else

n=3;

end

Flue gas flow rate to the stack can be calculated as:

(14)

Work can be recovered using a flue gas expander. This will be useful only if pH is greater than

1.2 bar. The output pressure can be assumed to be atmospheric (1 bar). So the pressure ratio of

the expander is pH. The temperature of exhaust gases is assumed to be the same as adsorber

temperature.

(15)

Total specific work (kWh/tonne CO2) of the PSA process can be calculated as follows:

51

(16)

Power required for CO2 product compressor is not included here. The standard IECM models

can be used there (93kWh/tonne CO2).

2-Stage Mass Flow Rates

Once pL is known, the other quantities for the first stage can be calculated using Equations 1 – 5.

Purity after first stage is calculated as:

(17)

This is used as yfeed for the second stage.



(18)

(19)

“n” is the number of stages needed and rp is the pressure ratio. Since the higher pressure is


(20)




if (r_p>=1) && (r_p<=10)

n=1;

elseif (r_p>10) && (r_p<=100)

n=2;

else

n=3;

end

Flue gas flow rate to the stack can be calculated as:

(21)




temperature.

52

(22)

Specific work (kWh/tonne CO2) for the first stage of the PSA process can be calculated as

follows:

(23)

The following calculations are for the second stage:

Using the same pH, pL,Tfeed from the first stage and yCO2,out,1st (from eqn 17), MCO2,prod,2nd,

MN2,prod,2nd, MRP,2nd, Mfeed,2nd and Wvac,2nd can be calculated using Equations 1-5.

Purity after second stage is calculated as:

(24)

CO2 capture efficiency of 2nd

stage can be calculated as:

(25)

Since overall CO2 capture efficiency is fixed, the capture efficiency of 1st stage can be back-

calculated as follows:

(26)



For the blower to 2nd

stage, it is assumed that the outlet gas from first stage is expanded to

atmospheric pressure from pL and then pressurized to pH. This work has already been accounted

for in Eqn 20. The blower work for second stage is given by:

(27)

After the 2nd

stage, CO2 product is again compressed to atmospheric pressure and then is com

pressed to pipeline pressure either in a CO2 compressor or a CPU.

(28)

where,“n” is the number of stages needed and rp is the pressure ratio. Since the higher pressure is


(29)

53




if (r_p>=1) && (r_p<=10)

n=1;

elseif (r_p>10) && (r_p<=100)

n=2;

else

n=3;

end

Flue gas flow rate to the stack from the 2nd

stage can be calculated as:

(30)




temperature.

(31)

Specific work (kWh/tonne CO2) of the 2nd

stage of PSA process can be calculated as follows:

(32)

Total specific work (kWh/tonne CO2 captured) of the PSA process can be calculated as follows:

(33)

Sorbent required for the first stage can be calculated as follows:

(34)

Sorbent required for the second stage can be calculated as follows:

(35)

Total sorbent required for both stages of the PSA process can be calculated as follows:

(36)

54

Power required for CO2 product compressor is not included here. The standard IECM models

can be used there (93kWh/tonne CO2).

CO2 Purification and Compression

Since the purity of PSA process is generally low, a CPU is needed. The CPU model from oxy-

combustion model can be used here to calculate the mass and energy requirement as well as cost

of the CPU unit.

From the CPU model (developed by Kyle), the following regression equation was developed for

the CPU work requirement (including CO2 compressor) as a function of CO2 product purity and

product recovery:

WCPU+Compr (kWh/tonne CO2 product) =

factor*(15.81+0.23031*recovery(%)+1.0567*purity(%))

(R2 = 99.46%)

Factor = (purityout(%) – purityin(%))/27.03

(27.03% is the improvement in purity in the oxy-fuel model).

To get just the CPU work, CO2 compressor work should be subtracted from the above equation.

WCPU = WCPU+Compr – WCO2,compr

Integration with Power Plant

In a power plant, flue gas flow rate is known. For fixed capture efficiency, CO2 in the product

stream is known. That can be used to calculate the sorbent requirement.

(38)

(This should be the same as MCO2,prod,2,act)

The ideal sorbent required is given as

(39)

Presence of water may affect sorbent performance. This is accounted for in the model using an

adjustment or loss factor (closs). The actual sorbent flow rate is given by:

(40)

The default value of closs is 0%. The maximum can be 10%.

Equations 38 – 40 are the same for both 1-stage and 2-stage models. All the quantities estimated

before are based on one kg of sorbent. This (msorbent,act) can be multiplied with the mass and

energy quantities estimated before to calculate the overall mass and energy requirements of the

CCS system.

55

For the 2-stage system, this should be done for each stage. For example:

(41)

(42)

(43)

(44)

(45)

(46)

(47)

(48)

(49)

All other gases are assumed not to be captured by the PSA system. Hence they all go out in the

stack. Hence, the flow rate of stack gas is given by:

(50)

Composition of stack gases:

Mole flows of each gas going through the stack are given as:

(51)

(52)

All other gases:

(53)

Composition of each gas can be calculated as:

(54)

56

Appendix D: Solid Sorbent Pressure Swing Adsorption Process Cost Model

Equipment used in the PSA process:

FG blower

C&C unit

PSA system (fixed-bed)

Vacuum pump

CO2 compressor (or CPU from oxy-combustion model)

Flue gas expander

Capital cost of FG blower – same as used in IECM

Capital cost of C&C unit (will be modified later) (Levy et al, 2011, NETL):

Cref = $4,140,000 (2010)

mref = 64,288 kg/hr

Capital cost of the 1-stage PSA system (Heyne and Harvey, 2013):

Capital cost of the 2-stage PSA system (Heyne and Harvey, 2013):

Cref = $31,971,840 (2002)

Mref = 9,600 kmol/hr

Capital cost of vacuum pump (estimated from IECM membrane model):

For 1-stage model:

Cvac ($M, 2012) = 0.0072*(MCO2,prod,1,act + MN2,prod,1,act)

For 2-stage model:

Cvac ($M, 2012) = 0.0072*(MCO2,prod,1,act + MN2,prod,act + MCO2,prod,2 + MN2,prod,2,act)

57

Capital cost of expander (estimated from IECM membrane model):

For 1-stage

Cvac ($M, 2012) = 0.0007*(Mexhaust,act)

For 2-stage

Cvac ($M, 2012) = 0.0007*(Mexhaust,1,act+Mexhuast,2,act)

Capital cost of CPU (including compressor)

Capital cost is only a function of recovery. The following exponential regression equation was

obtained:

CCPU+Compr ($M, 2014) = mCO2,product (tonne/hr) *factor*(0.22282 + 0.000612*recovery -

0.002039*purity) (R2 = 98.3%)

factor=100/recovery*(1/yCO2,prod,2nd - 1) – (100/purity-1)

O&M costs –

There is no data on degradation of MOFs. A nominal value of 0.005 %/year replacement rate is

assumed as the default value here.

Cost of ZIF-78 and Zeolite 5A is $1/kg (Leperi et al, 2016)and SU-MAC is $2.5/kg.

Annual make-up MOF cost = (capacity factor) x Replc rate (%/year)/100 x (msorbent.act/100) x

CMOF x 8760

O&M cost of C&C unit = $0.315/tonne of H2O removed. (Levy et al, 2011, NETL).

systems analysis of advanced power plant carbon capture

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