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
Figure 23. CO2 product purity using ZIF-78, Zeolite 5A and SU-MAC in a 2-stage PSA/VSA
process. ..................................................................................................................................... 25
Figure 24. CO2 recovery (capture rate) using ZIF-78, Zeolite 5A and SU-MAC in a 2-stage
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
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
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
Plant & ProcessPerformance
- Efficiency
- Resource use
EnvironmentalEmissions
- Air, water, land
Plant & ProcessCosts - Capital
- 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
)
Operating Pressure (bar)
3000
3100
3200
3300
3400
3500
25 30 35 40 45
Th
erm
al E
ner
gy
Use
(kJ/
kg C
O2)
Operating Pressure (bar)
50
75
100
125
150
25 30 35 40 45
To
tal S
epar
atio
n W
ork
(MW
e)
Operating Pressure (bar)
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
)
CO2 Lean Loading Capacity (mole frac.)
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)
CO2 Lean Loading Capacity (mole frac.)
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 (
$/M
Wh
)
CO2 Removal Efficiency (%)
50
55
60
65
70
50 60 70 80 90
Co
st o
f C
O2
Cap
ture
d
($/t
)
CO2 Removal Efficiency (%)
80
90
100
110
120
50 60 70 80 90
Co
st o
f C
O2
Av
oid
ed
($/t
)
CO2 Removal Efficiency (%)
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
Actual IL 50% of Actual
Heat Capacity
50% of Actual
Heat Capacity + 50% of
Actual Reaction Heat
Net
Pla
nt
Eff
icie
ncy
(HH
V,%
)
Selexol
125
130
135
140
145
150
Actual IL 50% of Actual
Heat Capacity
50% of Actual
Heat Capacity + 50% of Actual
Reaction Heat
Pla
nt L
CO
E (
$/M
Wh
)
Selexol50
60
70
80
90
Actual IL 50% of Actual
Heat Capacity
50% of Actual
Heat Capacity + 50% of
Actual Reaction Heat
Co
st o
f C
O2
Av
oid
ed
($/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
Figure 19. CO2 product purity using ZIF-78, Zeolite 5A and SU-MAC in a 1-stage PSA/VSA
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.
Figure 20. CO2 recovery (capture rate) using ZIF-78, Zeolite 5A and SU-MAC in a 1-stage
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.
Figure 23. CO2 product purity using ZIF-78, Zeolite 5A and SU-MAC in a 2-stage PSA/VSA
process.
Figure 24. CO2 recovery (capture rate) using ZIF-78, Zeolite 5A and SU-MAC in a 2-stage
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)
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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:
when the CO2 lean loading capacity is less than 0.35 mole fraction,
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:
when the CO2 lean loading capacity is less than 0.35 mole fraction,
R-sq (adj)=98.93%
Otherwise,
R-sq (adj)=93.52%
41
[9] Regeneration Vessel per Train:
when the CO2 lean loading capacity is less than 0.35 mole fraction,
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.
The other energy consumers are blower and CO2 compressor to compress CO2 from the vacuum
condition to atmospheric pressure (J/kg sorbent).
(18)
(19)
“n” is the number of stages needed and rp is the pressure ratio. Since the higher pressure is
atmospheric pressure,
(20)
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:
(21)
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.
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)
The other energy consumers are blower and CO2 compressor to compress CO2 from the vacuum
condition to atmospheric pressure (J/kg sorbent).
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
atmospheric pressure,
(29)
53
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 from the 2nd
stage can be calculated as:
(30)
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.
(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).