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Baseline Flowsheet Model for IGCC with Carbon Capture Randall P. Field* and Robert Brasington MIT Energy Initiative, Massachusetts Institute of Technology (MIT), 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States

bS Supporting Information ABSTRACT: Integrated gasification combined cycle (IGCC) processes have the potential for high thermal efficiency with a low energy penalty for carbon capture. Many researchers have proposed various innovations to improve upon the efficiency of the IGCC process. However, the analysis methods of most publications are generally not transparent and these published results are exceedingly difficult to reproduce. The National Energy Technology Laboratory (NETL) report [Cost and Performance Baseline for Fossil Energy Plants: Bituminous Coal and Natural Gas to Electricity Final Report; U.S. Department of Energy, Office of Fossil Energy, NETL, DOE/NETL-2010/1397, 2010] is widely used as a reference by researchers and by industry. To enable researchers to have a consistent and transparent framework for analyzing IGCC flowsheets and potential innovations, a baseline model derived from the NETL report is described herein and the corresponding flowsheet model and its full documentation are available online from this journal for use and modification.

’ INTRODUCTION Power generation accounts for 41% of the CO2 emissions in the United States1 and represents the single largest sector contributing to global warming. Similarly, electrification of the developing countries is expected to represent the largest growth in CO2 emissions. For this reason, many researchers have been examining alternative technologies for power generation and carbon capture. A frequent goal in these studies is to identify technologies that will be competitive when there is a price signal for CO2 emissions. It is common practice to analyze the thermal efficiency of new technologies by developing a material and energy balance of the process using a commercially available flowsheet simulation software program such as Aspen Plus.2 6 Within such flowsheet simulation programs, there are alternative modeling approaches for representing any process technology. One can model the performance with great modeling granularity and rigor if one has sufficient data. However, there are also many cases when the reaction kinetics and mass transfer characteristics are not wellestablished for a new technology. For these cases, researchers have developed material and energy balances of their processes, based on modeling assumptions. Such models can be of great value in assessing the potential benefits of new technologies, provided that the modeling assumptions are well-documented and transparent to the people who assess the results of the model. The challenge in comparing the performance of innovative technologies analyzed by different researchers is that the detailed modeling assumptions and modeling methods are never consistent and are rarely transparent in the publications. Therefore, reproducing the results by anyone who does not have direct access to the flowsheet simulation files is very challenging. Consequently, the comparison of results from multiple researchers has a large margin of error, unless one rebuilds the simulations on a common basis. The proposed best practice for publication of a proposed new process technology is to thoroughly document the modeling assumptions and provide the flowsheet simulation r 2011 American Chemical Society

file to enable others to reproduce the work and understand the more-subtle modeling assumptions and methods. In 2007, the NETL issued their report,7 titled “Cost and Performance Baseline for Fossil Energy Plants, Volume 1”. This report was subsequently revised in 2010.8 These reports compared the cost and performance of different IGCC technologies, and pulverized coal (PC) plants, and natural gas combined cycle (NGCC) plants, each with and without carbon capture. These studies were focused on representing current technology and some of the subsystem models were based on performance specifications provided by vendors. Each of these configurations was modeled with Aspen Plus to determine the material and energy balance. These excellent reports have rich content with substantial process and economic detail within the 516-page and 626-page documents. Yet, these reports did not document many of the fundamental modeling assumptions and methods used in generating these results. The NETL models are not available to U.S. corporations or universities that do not have a special relationship with NETL. The widely referenced NETL report was selected as the basis for MIT’s analysis of alternative technologies. The 2010 report’s Case 2 for the GEE gasifier with a two-stage Selexol unit for CO2 and H2S removal was selected as the baseline system for MIT’s research program. The NETL report on Case 2 was thoroughly examined. Based on the text, diagrams, and stream tables in the report, an Aspen Plus flowsheet model was generated initially to mimic the results presented in the NETL report. This initial mimic model is referenced hereafter as the Version 1 model. In the process of generating the Version 1 model, the MIT researchers identified opportunities for improving upon the modeling assumptions. Received: February 10, 2011 Accepted: August 19, 2011 Revised: July 8, 2011 Published: August 19, 2011 11306

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Table 1. Sour Water Electrolyte Reactionsa Reaction Equilibrium Parameters reaction

A

B

2 H2O T H3O + OH

132.89888

13445.9

22.4773

214.582443

12995.4

33.5471

HS + H2O T H3O+ + S2

9.741963

8585.47

D 0 0

0

0

CO2 + 2H2O T H3O+ + HCO3

231.465439

12092.1

36.7816

HCO3 + H2O T H3O+ + CO32

216.050446

12431.7

35.4819

NH3 + H2O T NH4+ + OH

1.256563

3335.7

1.4971

0.0370566

NH3 + HCO3 T NH2COO + H2O

4.583437

2900

0

0

HCl + H2O T H3O+ + Cl a

C

H2S + H2O T H3O+ + HS

+

0 0

based on calculated thermodynamic properties

Reaction equilibrium = exp(A + B/T + C ln(T) + DT), where T is given in Kelvin.

The model has since evolved through a series of enhancements to produce Version 2 of the IGCC flowsheet model. The major improvements in the Version 2 modeling assumptions are documented herein.

’ BASELINE MODEL PHYSICAL PROPERTIES METHODS A variety of property methods are required to meet the needs of the complex IGCC process, with requirements ranging from sour water systems (which require electrolyte modeling) to hightemperature, high-pressure syngas processing (which requires an appropriate equation-of-state model). The Peng Robinson equation of state with the Boston Mathias alpha function (PR-BM) is used throughout many sections of the flowsheet. The Boston Mathias alpha function was selected over the standard Peng Robinson equation, because it is more accurate for high reduced temperatures (>5) for the light gases in the system. The STEAMNBS steam tables are used to model the boiler feedwater heaters, steam boilers, and steam turbine unit operations. For the Claus section, the Version 2 model uses PR-BM with a modification to determine the molten sulfur mixture liquid molar volume, based on the mole fraction weighted-average molar volume of the individual components in the mixture. Most of the parameters for the sulfur species come from the INORGANICS databank in Aspen Plus. The data in this databank are licensed copies of the well-known Barin databank.9 To model the sulfur species S2 S8 in the Claus process, Version 2 uses the following Barin thermodynamic methods and parameters: • For liquid heat capacity, the Barin equation is used with the parameters CPLXP1 and CPLXP2. • For ideal gas heat capacity, the Barin equation is used with the parameters CPIXP1, CPIXP2, and CPIXP3. • For liquid density, the DIPPR density value for monoatomic sulfur was used. In Version 2, all blocks involving sour water are modeled using the electrolyte non-random two-liquid activity coefficient model for the liquid phase and the Redlich Kwong equation of state for the vapor phase. This property method is referred to as ELECNRTL. The following sour water chemistry is included in the model. The reaction equilibrium constants for these electrolyte reactions are based on the Aspen Plus default values, as documented in Table 1. As part of using the ELECNRTL model to represent the vapor liquid equilibrium (VLE) for systems containing sour

Table 2. Components Modeled with Henry’s Law for Subsystems Containing Sour Water CO2

O2

H2S

N2

Cl2

Ar

SO2

CO

SO3

NO

NH3

NO2

H2 COS

CH4

water, the gases listed in Table 2 are modeled using Henry’s Law. The Henry correlation parameters applied in this model are all default values from the Aspen Plus BINARY and HENRY databanks. Note that the built-in Henry correlation parameters were all regressed from VLE data below 100 °C, but the IGCC model requires VLE calculations at temperatures in excess of 200 °C. Therefore, the Henry values must be extrapolated above the maximum temperature for these Henry correlations. These high-temperature phase equilibrium calculations represent an opportunity for improving the accuracy of the property modeling for the IGCC flowsheet. However, the gases are all sparingly soluble at elevated temperatures, so the inaccuracy of the current VLE calculations is acceptable in the current model. For the mechanistic dimethyl ether polyethylene glycol (DEPG) process model, the perturbed-chain statistical associating fluid theory (PC-SAFT) property method is used. The PCSAFT model is based on perturbation theory. The underlying idea is to divide the total intermolecular forces into repulsive and attractive contributions. The model uses a hard-chain reference system to account for the repulsive interactions. The attractive forces are further divided into different contributions, including dispersion and association. Its applicability covers fluid systems from small to large molecules, including normal fluids, water, hydrogen, alcohols, polymers and copolymers, and their mixtures. For IGCC with carbon capture, DEPG is a candidate solvent for removing H2S and CO2 from syngas. For this application, DEPG generally refers to a mixture of dimethyl ethers of polyethylene glycol. One technique for modeling this solvent is to represent the DEPG mixture as a single component with properties that represent the mixture. An alternative method is to represent each component in the mixture with different properties for each component. AspenTech has used both of these 11307

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Table 3. Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) Binary Interaction Parameters for Key Components in the Acid Gas Removal Subsystem component i

CO2

H2S

H2

N2

Ar

CO

CH4

component j

Selexol

Selexol

Selexol

Selexol

Selexol

Selexol

Selexol

Selexol

temperature units aij

K 0.218926

K 0.007871

K 12.12819

K 2.520704

K 0

K 2.207

K 1.634816

K

bij

0.17102

0.07673

11.6218

2.25299

0.2

2.00698

1.45164

H2 O

1.12006 1.022798

cij

0

0

0

0

0

0

0

0

dij

0

0

0

0

0

0

0

0

eij Tref

0 298.15

0 298.15

0 298.15

0 298.15

0.4 298.15

0 298.15

0 298.15

0 298.15

Figure 1. High-level IGCC process flowsheet.

techniques, but the work described in this paper is based on using a single DEPG component to represent the mixture with PCSAFT parameters regressed from DEPG VLE data.10 The PCSKIJ parameters shown in Table 3 were verified using solubility numbers reported by NETL.11 Work has continued to improve the representation of the DEPG VLE data over the temperature range of interest, and Aspen Technology has issued a revised data package to represent DEPG. This most recent DEPG data package from AspenTech has not been applied in the model described in this paper. DEPG is commonly referred to by its commercial name, Selexol. Dow Corporation owns the trademark for this name, and UOP LLC has licensing rights for this process technology. DEPG will be referenced by its commercial name throughout the remainder of this paper.

’ OVERALL FLOWSHEET MODEL The overall IGCC process model consists of 10 major process sections. Each process section has its own material and energy balance and consists of multiple unit operation models (not shown in the high-level flowsheet diagram in Figure 1). The air separation unit (denoted as “ASU” in the figure) represents the production of high-pressure oxygen and nitrogen for use in the various sections of the IGCC process. The coal gasification section (“GASIFIER”) is used to represent gasification, syngas radiant cooling, and quench. The gas scrubbing section (“SCRUBBER”) is used to remove chlorides and some of

the water vapor. The water-gas-shift section (“SHIFT”) represents the packed-bed reactors for converting most of the CO to CO2 and H2, and for the hydrolysis of COS to H2S. The gas cooling section (“COOL”) reduces the syngas temperature to enable H2S and CO2 to be removed. The acid gas removal section (“SELEXOL”) removes H2S and CO2 from the cool syngas. The sulfur plant (“CLAUS”) converts the H2S to elemental sulfur for sale as a byproduct. The CO2 compressor section (“CO2”) compresses and pumps the CO2 to the supercritical pressures required for transportation and sequestration. The gas turbine section (“GTURBINE”) generates electricity from syngas expansion and from syngas combustion. The steam island (“STEAM”) recovers heat from the flue gas and other process heat sources to generate steam for driving the stream turbines and to meet various process demands. The GASIFIER, SHIFT, and SELEXOL process sections demonstrate major divergence in the modeling methods and assumptions between the Version 1 and Version 2 models. The specific differences between these models are described below. The full details for all 10 subsystems are provided in the Supporting Information.

’ GASIFIER MODEL The chemical reactions, mass transfer, and heat transfer that occur in the gasifier can been modeled at different levels of fidelity. Researchers at MIT have been collaborating on a spectrum of gasifier modeling methods, ranging from 0-D equilibrium models 11308

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Figure 2. Version 2 model configuration for the two-stage Selexol flowsheet.

to multiscale models, which include homogeneous and heterogeneous kinetics, particle microstructure models, radiative and convective heat transfer, and 3-D large eddy simulation models.12,13 The gasifier model applied in the baseline IGCC flowsheet model uses the 0-D equilibrium representation. For entrained-flow slagging gasifiers, the syngas composition is close to equilibrium for most of the gasification homogeneous reactions. However, the water-gas-shift (WGS) reaction (CO + H2O T CO2 + H2) is typically not at equilibrium at the gasifier temperature, because the WGS reaction does not stop when the syngas exits the gasifier. The syngas leaving the gasifier may be cooled at different rates, depending on the design of the gasification system and the associated syngas cooling system. A common approach in representing rate-limited reactions without modeling the reaction kinetics is to identify the apparent temperature at which the reaction equilibrium is satisfied. This modeling method is appropriate for systems in which there is sufficient empirical data to support the determination of the “equilibrium temperature.” The difference between the reactor temperature and the equilibrium temperature is strongly dependent on the rate of cooling of the syngas.14 When the gasifier (such as the GEE gasifier) uses a radiant cooler, the typical cooling rate is ∼100 K/s, which is much slower than the cooling rate of ∼28 000 K/s achieved with the quench-only configuration. From these cooling rates and the approximate gasification temperature (1370 °C), approximate values of the equilibrium approach temperature (gasifier temperature equilibrium temperature) were obtained from the work of Bochelie et al.14 for the WGS reaction. The Version 2 model sets the equilibrium approach temperature for the WGS reaction to be 200 K below the gasifier temperature for the radiant cooler configuration and

10 K below the gasifier temperature for the quench-only configuration. All other reactions are assumed to be at equilibrium at the gasifier exit temperature. The Version 2 gasifier model differs from Version 1, in terms of the WGS assumptions. This difference is examined in terms of the WGS equilibrium constant (KWGS, which is defined as KWGS = yH2yCO2/(yH2OyCO)). The Version 1 model has a KWGS value of 0.96, whereas the Version 2 model with the temperature approach for the WGS reaction set to 200 K has a calculated equilibrium KWGS value of 0.426, which is consistent with the measured syngas composition from the Texaco gasifier9 with a KWGS value of 0.427. The other difference between the Version 1 and Version 2 models for the gasifier pertains to the energy balance. The energy balance around the Version 1 model has an apparent heat loss of 3%. For the Version 2 model, the assumed heat loss is 1% of the HHV of the feed coal, which is a typical value for a commercialscale gasifier.15

’ WATER-GAS-SHIFT (WGS) REACTOR The water-gas-shift (WGS) reactors are required to enable most of the carbon to be captured in the SELEXOL unit prior to combustion. In these reactors, the WGS reaction is catalyzed within packed-bed reactors. To achieve 90% carbon capture, two stages of WGS reactors are required with interstage cooling to drive the reaction equilibrium forward. For the baseline process configuration, the catalyst must be sulfurtolerant, because the shift is taking place prior to sulfur removal. The WGS catalyst also catalyzes the COS hydrolysis reaction (COS + H2O T H2S + CO2). 11309

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Industrial & Engineering Chemistry Research Some of the requirements for maintaining catalyst life and activity include maintaining a 2:1 H2O:CO ratio in the feed and ensuring that the reactor feed conditions are at least 15 K above the water dew point, to avoid carbon deposition.16 These constraints were applied to the Version 2 model of the SHIFT subsystem. The Version 1 model also included the 2:1 H2O:CO ratio, but the results from the two models were significantly different. One reason is the difference in the WGS reaction taking place in the gasifier subsystem, as described in the prior section of this paper. That is, the Version 1 model shifts more CO to CO2 in the gasifier/radiant cooler, producing a CO flow rate that is 9.7% less than the CO flow rate for the Version 2 model. Another contributor to the discrepancy between the Version 1 and Version 2 models is the composition of the syngas from the scrubber. In Version 2, the water mole fraction is 0.07 more than that in the Version 1 model. One reason for this difference is related to the scrubber modeling assumptions, which dictate the syngas temperature at the exit from the scrubber. The Version 2 model assumes that the scrubber will drop the syngas temperature by 5 °C below the feed gas saturated temperature for an exit temperature of 211 °C, while the Version 1 model scrubber temperature is 206 °C. The combined effect of these discrepancies is that the Version 1 model requires 19% more shift steam to attain the required 2:1 steam:CO ratio. There are additional consequences for the downstream cooling section and for the steam power generation. This finding highlights the importance of the reactor models and process thermodynamics. MIT has developed high-fidelity models for the WGS reactor including a two-dimensional (2D) dynamic model,17 but the model incorporated into the MIT baseline flowsheet model is based on temperature approach to equilibrium. The Version 2 model uses the Aspen Plus REQUIL model and sets the WGS temperature approach to 25 °C for the first stage and 10 °C for the second stage.18

’ ACID GAS REMOVAL MODEL Physical solvents are currently the most common commercial technology for removing H2S and CO2 from syngas at elevated pressures. Dimethyl ether polyethylene glycol (DEPG) is one of the established solvents for this application. Selexol is the commercial name for DEPG. There are many variations on the two-stage process for removal of H2S and CO2 from syngas. Each of these configurations takes advantage of the selective absorption of H2S relative to CO2 to produce a relatively clean CO2 product and an acid gas stream that is sufficiently rich (+40%) in H2S. The separation units common in all two-stage Selexol configurations include the following: (1) H2S Absorber — uses CO2-rich Selexol to absorb H2S. (2) CO2 Absorber — uses lean and semilean Selexol to absorb CO2. (3) Flash Drums — regenerates the CO2-rich Selexol via a series of pressure drops. (4) H2S Concentrator — removes most of the CO2 from the H2S-rich Selexol. (5) Stripper — regenerates the H2S-rich Selexol. The primary difference between the various two-stage Selexol configurations is with respect to the management of vapor recycles. The two vapor recycles come from the flashing of the CO2-rich Selexol and from the H2S concentrator.

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Table 4. Comparison of NETL and MIT Results NETL 2010

Version 2

Report

Model

gas turbine

464 000

464 300

sweet gas expander

6500

8200

steam turbine

263 500

264 500

total power generation

734 000

737 000

67 330 10 640

71 300 11 300

power generation (kWe)

major power consumers (kWe) ASU air compressor oxygen compressor nitrogen compressor

35 640

34 800

CO2 compressor

31 160

28 300

boiler feedwater pumps

4180

4900

acid gas removal

19 230

16 700

190 750

187 100

net power (kWe)

total power consumption

543 250

549 900

net plant efficiency (% HHV) net plant heat rate (kJ/kWh)

32.6 11 034

32.6 11 050

coal feed rate (kg/h)

220 904

223 910

thermal input (kWt)

1 665 074

1 687 700

condenser duty (kWt)

419 167

431 306

CO2 emissions (kg/MWh)

93

93

CO2 capture (%)

90.3

90.0

oxygen to gasifier (kg/h)

168 055

176 669

shift steam (kg/h) composition before quench

7193

6048

CO

0.3576

0.3893

CO2

0.1380

0.1051

H2

0.3406

0.2942

H2 O

0.1369

0.1862

composition after scrubber CO

0.2823

0.2918

CO2 H2

0.1089 0.2689

0.0787 0.2204

H2 O

0.3190

0.3908

CO2 product composition CO

0.0002

0.0002

CO2

0.9948

0.9963

H2

0.0048

0.0022

H2 O

0

0.0005

Although H2 solubility in Selexol is almost 100 times less than CO2, the mass flow rate of Selexol in the CO2 absorber is sufficient to absorb a significant quantity of H2. Consequently, the vapor coming off the first flash drum may contain more than 3% of the H2 from the syngas. This H2 content would result in an unacceptable loss in efficiency if it were not recovered. The widely adopted approach is to compress and recycle the vapor from the high-pressure flash drum to the CO2 absorber. The vapor from the H2S concentrator has substantial mole fractions of CO2 and H2S. A common configuration for the H2S concentrator vapor is the direct recycle of the vapor into the H2S absorber. There are two basic strategies for this recycle. One is to compress the vapor to feed it back to the absorber. The other approach is to operate the H2S concentrator at a higher pressure than the H2S absorber. By doing so, the vapor from the H2S 11310

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Industrial & Engineering Chemistry Research concentrator does not need to be compressed before returning it to the absorber. Of course, we still need to compress the N2 purge to the H2S concentrator pressure. However, the capital and energy to compress the N2 purge is substantial less than the alternative configuration. This elevated-pressure concentrator strategy was used as the basis for the Version 2 model, as shown in Figure 2. However, we may revert to the recompression of vapor from the concentrator after we have a more complete and accurate model of the Selexol VLE.

’ RESULTS Key results from the Version 2 model are compared against the NETL report issued in November 2010 in Table 4. The overall plant performance results are relatively consistent between the NETL report and the Version 2 model, with an overall thermal efficiency of 32.6% for both; however, the power required by major subsystems differ by more than 10%. Both were built around the capacity of a pair of GE Advanced F Class gas turbines with a combined net output of 464 MW. One reason why the sweet syngas expander generates more power in the Version 2 model is because the nitrogen purge used in the Selexol unit gives a modest boost to the gas flow through the expander. The Version 2 model has a higher oxygen flow to the gasifier and, correspondingly, higher power consumption in the ASU. The power consumption for the SELEXOL unit and for the CO2 compressor is lower for the Version 2 model, by ∼10%. The reasons for each of these differences cannot be discerned from the limited information available regarding the NETL modeling assumptions. Note that the NETL report is partially based upon vendor data for subsystem packages, because the objective of the NETL study was to examine currently available technologies. These vendor data packages do not provide the underlying thermodynamics, equipment characteristics, and reaction kinetics used by the vendors to develop these subsystems. As such, there are natural gaps in knowledge and expected discrepancies between the NETL report and the MIT model. ’ BASELINE MODEL APPLICATIONS The purpose of the baseline model is to provide a foundation upon which various process technologies can be compared on a fair and consistent basis. MIT researchers have already applied this baseline model for various technology studies. A few of these studies are described in depth in various publications, regarding gasification-fuel cell integration,19 heat integration,20 CO2 as a slurry medium,21 warm syngas cleanup,22 and uncertainty analysis.23 ’ ASSOCIATED CONTENT

bS

Supporting Information. The authors have provided access to the Version 2 model in the spirit of openness and collaboration. By sharing models across the research community, the quality of shared models can be further enhanced and the mutual objective of innovating process technologies for moreefficient low carbon energy can be advanced more rapidly. There are deficiencies in the current model, as identified in the detailed documentation. The authors welcome direct contact with any organization, which has suggestions or improvements to the Version 2 model. The baseline IGCC model and the corresponding detailed documentation are available for download. This model was built with Aspen Plus 7.1 CP2; it will open and run

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successfully in Aspen Plus versions 7.2 and 7.3. This information is available free of charge via the Internet at http://pubs.acs.org/.

’ AUTHOR INFORMATION Corresponding Author

*E-mail: rpfi[email protected].

’ ACKNOWLEDGMENT The authors thank BP for funding this research. We also thank AspenTech for the use of Aspen Plus in the execution of this research and for the advice of experts, including Chau-Chyun Chen, Suphat Watanasiri, David Tremblay, Mike Mendez, and Paul Talley. ’ REFERENCES (1) U.S. Carbon Emissions from Energy Sources 2008 Flash Estimate; Energy Information Administration, 2009. (2) Bhattacharyya, D.; Turton, R.; Zitney, S. Steady-State Simulation and Optimization of an Integrated Gasification Combined Cycle Power Plant with CO2 Capture. Ind. Eng. Chem. Res. 2011, 50, 1674–1690. (3) Zheng, L.; Furinsky, E. Comparison of Shell, Texaco, BGL and KRW Gasifiers as Part of IGCC Plant Computer Simulations. Energy Convers. Manage. 2005, 46, 1767–1779. (4) Kunze, C.; Spliethoff, H. Modelling of an IGCC Plant with Carbon Capture in 2020. Fuel Process. Technol. 2010, 91, 934–941. (5) Emun, F.; Gadalla, M.; Majozi, T.; Boer, D. Integrated Gasification Combined Cycle (IGCC) Process Simulation and Optimization. Comput. Chem. Eng. 2010, 34, 331–338. (6) Perez-Fortes, M.; Bojarski, A. D.; Velo, E.; Nougues, J. M.; Puigjaner, L. Conceptual Model and Evaluation of Generated Power and Emissions in an IGCC Plant. Energy 2009, 34, 1721–1732. (7) Cost and Performance Baseline for Fossil Energy Plants: Bituminous Coal and Natural Gas to Electricity Final Report; U.S. Department of Energy, Office of Fossil Energy, NETL, DOE/NETL-2007/1281, 2007. (8) Cost and Performance Baseline for Fossil Energy Plants: Bituminous Coal and Natural Gas to Electricity Final Report; U. S. Department of Energy, Office of Fossil Energy, NETL, DOE/NETL-2010/1397, 2010. (9) Barin, I. Thermochemical Data of Pure Substances; VCH Verlagsgesellschaft: Weinheim, Germany, 1989. (10) Aspen Plus Model of the CO2 Capture Process by DEPG; Aspen Technology, Inc.: Burlington, MA, 2008. (11) Ciferno, J. CO2 Capture: Comparison of Cost and Performance of Gasification and Combustion-based Plants. Presented at the Workshop on Gasification Technologies, Denver, CO, 2007. (12) Monaghan, R. F. D.; Kumar, M.; Singer, S. L.; Zhang, C.; Ghoniem, A. F. Reduced Order Modeling of Entrained Flow Solid Fuel Gasification. In Proceedings of the ASME International Mechanical Engineering Congress & Exhibition, Lake Buena Vista, FL, 2009. (13) Kumar, M.; Zhang, C.; Monaghan, R. F. D.; Singer, S. L.; Ghoniem, A. F. CFD Simulation of Entrained Flow Gasification with Improved Devolatilization and Char Consumption Submodels. In Proceedings of the ASME International Mechanical Engineering Congress & Exhibition, Lake Buena Vista, FL, 2009. (14) Bochelie, M. J.; Denison, M. K.; Chen, Z.; Senior, C. L.; Sarofim, A. F. Using Models to Select Operating Conditions for Gasifiers; Reaction Engineering International, http://www.reaction-eng.com. (15) Watkinson, A. P.; Lucas, J. P.; Lim, C. J. A Prediction of Performance of Commercial Gasifiers. Fuel 1991, 70, 522. (16) SSK catalyst Sulphur resistant/sour water-gas shift catalyst; Haldor Topsoe, http://www.topsoe.com. (17) Adams, T. A., II; Barton, P. I. A Dynamic Two-Dimensional Heterogeneous Model for Water Gas Shift Reactors. Int. J. Hydrogen Energy 2009, 34 (21), 8877–8891. 11311

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(18) Chen, C. A Technical and Economic Assessment of CO2 Capture Technology for IGCC Power Plants, Ph.D. Thesis, Carnegie Mellon University, Pittsburgh, PA, 2005 (19) Botros, B. B.; Brisson, J. G., II. Steam optimization with increased flexibility in steam power island design. Presented at the 13th Conference on Process Integration, Modeling and Optimisation for Energy Saving and Pollution Reduction, Prague, Czech Republic, 2010. (20) Adams, T. A., II; Barton, P. I. Clean Coal: A new power generation process with high efficiency, carbon capture, and zero emissions. Presented at the 20th European Symposium on Computer Aided Process Engineering, Naples, Italy, 2010. (21) Botero, C.; Brasington, R.; Field, R.; Herzog, H.; Ghoniem, A. Assessment of CO2(l) as Slurrying Medium for Entrained Flow Gasifiers in IGCC Plants with CO2 Capture. Submitted to Energy. (22) Couling, D.; Prakash, K.; Green, W. H. Analysis of Membrane and Adsorbent Processes for Warm Syngas Cleanup in Integrated Gasification Combined-Cycle Power with CO2 Capture and Sequestration. Ind. Eng. Chem. Res., doi: 10.1021/ie200291j. (23) Berkelmans, I. Development and Application of a Framework for Technology and Model Selection Under Uncertainty. Ph.D. Thesis, Massachusetts Institute of Technology (MIT), Cambridge, MA, 2010.

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