1932

Abstract

Advanced multiscale modeling and simulation have the potential to dramatically reduce the time and cost to develop new carbon capture technologies. The Carbon Capture Simulation Initiative is a partnership among national laboratories, industry, and universities that is developing, demonstrating, and deploying a suite of such tools, including basic data submodels, steady-state and dynamic process models, process optimization and uncertainty quantification tools, an advanced dynamic process control framework, high-resolution filtered computational-fluid-dynamics (CFD) submodels, validated high-fidelity device-scale CFD models with quantified uncertainty, and a risk-analysis framework. These tools and models enable basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to synthesize and optimize a process. The resulting process informs the development of process control systems and more detailed simulations of potential equipment to improve the design and reduce scale-up risk. Quantification and propagation of uncertainty across scales is an essential part of these tools and models.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-chembioeng-060713-040321
2014-06-07
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/chembioeng/5/1/annurev-chembioeng-060713-040321.html?itemId=/content/journals/10.1146/annurev-chembioeng-060713-040321&mimeType=html&fmt=ahah

Literature Cited

  1. Chu S. 1.  2009. Carbon capture and sequestration. Science 325:1599 [Google Scholar]
  2. Chu S, Majumdar A. 2.  2012. Opportunities and challenges for a sustainable energy future. Nature 488:294–303 [Google Scholar]
  3. Rubin ES. 3.  2008. CO2 capture and transport. Elements 4:311–17 [Google Scholar]
  4. Ciferno JP, Fout TE, Jones AP, Murphy JT. 4.  2009. Capturing carbon from existing coal-fired power plants. Chem. Eng. Prog. April:33–41 [Google Scholar]
  5. Jenkins J, Mansur S. 5.  2011. Bridging the Clean Energy Valleys of Death Oakland, CA: Breakthrough Inst http://thebreakthrough.org/archive/bridging_the_clean_energy_vall
  6. Haszeldine RS. 6.  2009. Carbon capture and storage: How green can black be?. Science 325:1647–52 doi:10.1126/science.1172246 [Google Scholar]
  7. Hules KR, Yilmaz A. 7.  2002. From bunker to stack: the cost-reduction and problem-solving benefits of computational fluid dynamics for utility and industrial power generation Presented at POWER-GEN Int., Orlando, FL
  8. Syamlal M, Guenther C, Cugini A, Ge W, Wang W. 8.  et al. 2011. Computational science: enabling technology development. CEP Magazine January
  9. Mebane DS, Bhat KS, Kress JD, Fauth DJ, Gray ML. 9.  et al. 2013. Bayesian calibration of thermodynamic models for the uptake of CO2 in supported amine sorbents using ab initio priors. Phys. Chem. Chem. Phys. 15:4355–66 [Google Scholar]
  10. US Energy Inf. Adm 2013. Annual Energy Outlook 2013 Early Release Overview. Report no. DOE/EIA-0383ER, US Dep. Energy, Washington, DC. http://www.eia.gov/forecasts/aeo/er/pdf/0383er%282013%29.pdf
  11. Nichols C. 11.  2010. Coal-fired power plants in the United States: examination of the cost of retrofitting with CO2 capture technology and the potential for improvements in efficiency Report no. DOE/NETL-402/102309, US Dep. Energy, Natl. Energy Technol. Lab., Pittsburgh, PA
  12. Sjostrom S, Krutka H. 12.  2010. Evaluation of solid sorbents as a retrofit technology for CO2 capture. Fuel 89:1298–306 [Google Scholar]
  13. Sjostrom S. 13.  2010. Evaluation of solid sorbents as a retrofit technology for CO2 capture Presented at 2010 NETL CO2 Capture Technol. Meet., Pittsburgh, PA
  14. Kim H, Haranczyk M, Epperly T, Abouelnasr M, Swisher JA. 14.  et al. 2012. Integrating the carbon capture materials database with the process simulation tools of the carbon capture simulation initiative Presented at AIChE Annu. Meet., Pittsburgh, PA
  15. Lee A, Mebane D, Fauth DJ, Miller DC. 15.  2011. A model for the adsorption kinetics of CO2 on amine-impregnated mesoporous sorbents in the presence of water Presented at 28th Int. Pittsburgh Coal Conf., Pittsburgh, PA
  16. Salciccioli M, Stamatakis M, Caratzoulas S, Vlachos DG. 16.  2011. A review of multiscale modeling of metal-catalyzed reactions: mechanism development for complexity and emergent behavior. Chem. Eng. Sci. 66:4319–55 [Google Scholar]
  17. Kennedy MC, O'Hagan A. 17.  2001. Bayesian calibration of computer models. J. R. Stat. Soc. Ser. B Stat. Methodol. 63:425–50 [Google Scholar]
  18. Higdon D, Kennedy MC, Cavendish JC, Cafeo JA, Ryne RD. 18.  2004. Combining field data and computer simulations for calibration and prediction. SIAM J. Sci. Comput. 26:448–66 [Google Scholar]
  19. Reich BJ, Storlie CB, Bondell HD. 19.  2009. Variable selection in Bayesian smoothing spline ANOVA models: application to deterministic computer codes. Technometrics 51:110–20 [Google Scholar]
  20. Miller DC, Sahinidis NV, Cozad A, Lee A, Kim H. 20.  et al. 2013. Computational tools for accelerating carbon capture process development Presented at 38th Int. Tech. Conf. Clean Coal Fuel Syst., Clearwater, FL
  21. Lee A, Miller DC. 21.  2013. A one-dimensional, (1-D) three-region model for a bubbling fluidised bed adsorber. Ind. Eng. Chem. Res. 52:469–84 [Google Scholar]
  22. Modekurti S, Bhattacharyya D, Zitney SE. 22.  2013. Dynamic modeling and control studies of a two-stage bubbling fluidized bed adsorber-reactor for solid-sorbent CO2 capture. Ind. Eng. Chem. Res. 52:10250–60 [Google Scholar]
  23. Modekurti S, Bhattacharyya D, Zitney SE. 23.  2012. Dynamic modeling and transient studies of a solid-sorbent adsorber for CO2 capture Presented at 29th Annu. Int. Pittsburgh Coal Conf., Pittsburgh, PA
  24. Kim H, Miller DC. 24.  2011. Development of a moving bed simulation model for carbon capture from fossil energy systems Presented at AIChE Annu. Meet., Minneapolis, MN
  25. Kunii D, Levenspiel O. 25.  1991. Fluidization Engineering Boston: Butterworth-Heinemann
  26. Lüdtke KH. 26.  2004. Process Centrifugal Compressors: Basics, Function, Operation, Design, Application Berlin: Springer
  27. Biegler LT, Grossmann IE, Westerberg AW. 27.  1997. Systematic Methods of Chemical Process Design Upper Saddle River, NJ: Prentice Hall
  28. Siirola JJ. 28.  1996. Industrial applications of chemical process synthesis. Advances in Chemical Engineering JL Anderson 2–62 San Diego, CA: Academic [Google Scholar]
  29. Grossmann IE. 29.  1996. Mixed-integer optimization techniques for algorithmic process synthesis. Advances in Chemical Engineering JL Anderson 172–239 San Diego, CA: Academic [Google Scholar]
  30. Seider WD, Seader JD, Lewin DR, Widagdo S. 30.  2008. Product and Process Design Principles: Synthesis, Analysis and Design New York: Wiley, 3rd ed..
  31. Black JB, Haslbeck JL, Jones AP, Lundberg WL, Shah V. 31.  2013. Cost and performance of PC and IGCC plants for a range of carbon dioxide capture Report no. DOE/NETL-2011/1498, US Dep. Energy, Natl. Energy Technol. Lab., Pittsburgh, PA. http://www.netl.doe.gov/File%20Library/Research/Energy%20Analysis/Publications/Gerdes-08022011.pdf
  32. Seader JD, Seider WD, Pauls AC, Hughes RR. 32.  1977. FLOWTRAN Simulation: An Introduction Austin, TX: CACHE
  33. Evans LB, Boston JF, Britt HI, Gallier PW, Gupta PK. 33.  et al. 1979. Aspen: an advanced system for process engineering. Comput. Chem. Eng. 3:319–27 [Google Scholar]
  34. Pantelides CC. 34.  1988. Speedup—recent advances in process simulation. Comput. Chem. Eng. 12:745–55 [Google Scholar]
  35. Rios LM, Sahinidis NV. 35.  2013. Derivative-free optimization: a review of algorithms and comparison of software implementations. J. Glob. Optim. 56:1247–93 [Google Scholar]
  36. Wang GG, Shan S. 36.  2007. Review of metamodeling techniques in support of engineering design optimization. J. Mech. Des. 129:370–80 [Google Scholar]
  37. Cozad A, Sahinidis NV, Miller DC. 37.  2014. Learning surrogate models for simulation-based optimization. AIChE J. 602211–27
  38. Caballero JA, Grossmann IE. 38.  2008. An algorithm for the use of surrogate models in modular flowsheet optimization. AIChE J. 54:2633–50 [Google Scholar]
  39. Henao CA, Maravelias CT. 39.  2011. Surrogate-based superstructure optimization framework. AIChE J. 57:1216–32 [Google Scholar]
  40. Hurvich CM, Tsai CL. 40.  1993. A corrected Akaike information criterion for vector autoregressive model selection. J. Time Ser. Anal. 14:271–79 [Google Scholar]
  41. Tawarmalani M, Sahinidis NV. 41.  2005. A polyhedral branch-and-cut approach to global optimization. Math. Program. 103:225–49 [Google Scholar]
  42. Eslick JC, Miller DC. 42.  2011. A multi-objective analysis for the retrofit of a pulverized coal power plant with a CO2 capture and compression process. Comput. Chem. Eng. 35:1488–500 [Google Scholar]
  43. Deng G. 43.  2007. Simulation-based optimization PhD Thesis, Univ. Wis.-Madison
  44. Mele FD, Guillén G, Espuña A, Puigjaner L. 44.  2006. A simulation-based optimization framework for parameter optimization of supply-chain networks. Ind. Eng. Chem. Res. 45:3133–48 [Google Scholar]
  45. Rajasree R, Moharir AS. 45.  2000. Simulation based synthesis, design and optimization of pressure swing adsorption (PSA) processes. Comput. Chem. Eng. 24:2493–505 [Google Scholar]
  46. Wan X, Pekny JF, Reklaitis GV. 46.  2005. Simulation-based optimization with surrogate models—application to supply chain management. Comput. Chem. Eng. 29:1317–28 [Google Scholar]
  47. Conn AR, Scheinberg K, Vicente LN. 47.  2009. Introduction to Derivative-Free Optimization Philadelphia: Soc. Ind. Appl. Math.
  48. Hansen N. 48.  2006. The CMA evolution strategy: a comparing review. Towards a New Evolutionary Computation. Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing) JA Lozano, P Larrañaga, I Inza, E Bengoetxea 75–102 Berlin: Springer [Google Scholar]
  49. Huyer W, Neumaier A. 49.  2008. SNOBFIT—stable noisy optimization by branch and fit. ACM Trans. Math. Softw. 35: 9:1–9:25 [Google Scholar]
  50. Boverhof J, Leek J, Eslick JC, Agarwal D. 50.  2013. Turbine and Sinter: enabling management of parallel process simulations on demand CCSI Tech. Rep. Ser. https://www.acceleratecarboncapture.org/drupal/sites/default/files/elfinder/CCSI_Directory/CCSI-TurbineSinter.pdf
  51. US Dep. Energy/Off. Foss. Energy 2012. Clean Coal Research Program: 2012 Technology Readiness Assessment. Pathway for Readying the Next Generation of Affordable Clean Energy Technology—Carbon Capture, Utilization, and Storage (CCUS). Washington, DC: US Dep. Energy [Google Scholar]
  52. Lawal A, Wang M, Stephenson P, Koumpouras G, Yeung H. 52.  2010. Dynamic modelling and analysis of post-combustion CO2 chemical absorption process for coal-fired power plants. Fuel 89:2791–801 [Google Scholar]
  53. Lin Y, Pan T, Wong DS, Jang S, Chi Y, Yeh C. 53.  2011. Plantwide control of CO2 capture by absorption and stripping using monoethanolamine solution. Ind. Eng. Chem. Res. 50:1338–45 [Google Scholar]
  54. Ziaii S, Rochelle GT, Edgar TF. 54.  2009. Dynamic modeling to minimize energy use for CO2 capture in power plants by aqueous monoethanolamine. Ind. Eng. Chem. Res. 48:6105–11 [Google Scholar]
  55. Braatz RD, Alkire RC, Seebauer E, Rusli E, Gunawan R. 55.  et al. 2006. Perspectives on the design and control of multiscale systems. J. Process Control 16:193–204 [Google Scholar]
  56. Nandong J, Samyudia Y, Tade MO. 56.  2007. Control of multi-scale dynamics system Presented at 16th IEEE Int. Conf. Control Appl., Singapore
  57. Ilchman A. 57.  1993. Non-Identifier-Based High-Gain Adaptive Control New York: Springer
  58. Ilchman A, Ryan EP. 58.  1994. Universal-tracking for nonlinearly-perturbed systems in the presence of noise. Automatica 30:337–46 [Google Scholar]
  59. Sentoni GB, Guiver JP, Zhao H, Biegler LT. 59.  1998. A state space approach to nonlinear process modeling: identification and universality. AIChE J. 44:2229–39 [Google Scholar]
  60. Agrawal K, Loezos PN, Syamlal M, Sundaresan S. 60.  2001. The role of meso-scale structures in rapid gas-solid flows. J. Fluid Mech. 445:151–85 [Google Scholar]
  61. Igci Y, Andrews AT, Sundaresan S, Pannala S, O'Brien T. 61.  2008. Filtered two-fluid models for fluidized gas-particle suspensions. AIChE J. 54:1431–48 [Google Scholar]
  62. Parmentier JF, Simonin O, Delsart O. 62.  2012. A functional subgrid drift velocity model for filtered drag prediction in dense fluidized bed. AIChE J. 58:1084–98 [Google Scholar]
  63. Wang W, Lu B, Zhang N, Shi Z, Li J. 63.  2010. A review of multiscale CFD for gas–solid CFB modeling. Int. J. Multiph. Flow 36:109–18 [Google Scholar]
  64. Igci Y, Sundaresan S. 64.  2011. Constitutive models for filtered two-fluid models of fluidized gas–particle flows. Ind. Eng. Chem. Res. 50:13190–201 [Google Scholar]
  65. Milioli CC, Milioli FE, Holloway W, Agrawal K, Sundaresan S. 65.  2013. Filtered two-fluid models of fluidized gas-particle flows: new constitutive relations. AIChE J. 59:3265–75 [Google Scholar]
  66. Agrawal K, Holloway W, Milioli CC, Milioli FE, Sundaresan S. 66.  2013. Filtered models for scalar transport in gas-particle flows. Chem. Eng. Sci. 95:291–300 [Google Scholar]
  67. Holloway W, Sundaresan S. 67.  2012. Filtered models for reacting gas-particle flows. Chem. Eng. Sci. 82:132–43 [Google Scholar]
  68. Sarkar A, Sun X, Sundaresan S. 68.  2013. Sub-grid drag models for horizontal cylinder arrays immersed in gas-particle multiphase flows. Chem. Eng. Sci. 104:399–412 [Google Scholar]
  69. Igci Y, Pannala S, Benyahia S, Sundaresan S. 69.  2011. Validation studies on filtered model equations for gas-particle flows in risers. Ind. Eng. Chem. Res. 51:2094–103 [Google Scholar]
  70. Igci Y, Sundaresan S. 70.  2011. Verification of filtered two-fluid models for gas-particle flows in risers. AIChE J. 57:2691–707 [Google Scholar]
  71. Ryan EM, Montgomery C, Storlie C, Wendelberger J. 71.  2012. CCSI validation and uncertainty quantification hierarchy for CFD models CCSI Tech. Rep. Ser. http://www.acceleratecarboncapture.org/drupal/sites/default/files/elfinder/CCSI_Directory/Deliverables/Sept_30_2012/CCSI_V-UQ%20Hierarchy_FINAL.pdf
  72. Spenik J. 72.  2011. Development of a circulating fluidized bed for flue gas carbon capture using solid sorbent Presented at NETL 2011 Workshop Multiph. Flow Sci., Pittsburgh, PA
  73. Lane WA, Storlie C, Montgomery C, Ryan EM. 73.  2013. Numerical modeling and uncertainty quantification of a bubbling fluidized bed with immersed horizontal tubes. Powder Technol. 253733–43
  74. Storlie C, Lane WA, Ryan EM. 74.  2013. Calibration of computational models with categorical parameters and correlated outputs via Bayesian smoothing spline ANOVA. J. Am. Stat. Assoc. Submitted
  75. Kim SW, Ahn JY, Kim SD, Lee DH. 75.  2003. Heat transfer and bubble characteristics in a fluidized bed with immersed horizontal tube bundle. Int. J. Heat Mass Transf. 46:399–409 [Google Scholar]
  76. Helton JC. 76.  1997. Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty. J. Stat. Comput. Simul. 57:3–76 [Google Scholar]
  77. Saltelli A, Chan K, Scott EM. 77.  2000. Sensitivity Analysis New York: Wiley
  78. Oakley J, O'Hagan A. 78.  2002. Bayesian inference for the uncertainty distribution of computer model outputs. Biometrika 89:769–84 [Google Scholar]
  79. Storlie CB, Swiler LP, Helton JC, Sallaberry CJ. 79.  2009. Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models. Reliab. Eng. Syst. Saf. 94:1735–63 [Google Scholar]
  80. Konomi BA, Karagiannis G, Sarkar A, Sun X, Lin G. 80.  2013. Bayesian treed multivariate Gaussian process with adaptive design: application to a carbon capture unit. Technometrics. In press. doi:10.1080/00401706.2013.879078
  81. Wen CY, Yu YH. 81.  1966. Mechanics of fluidization. Chem. Eng. Prog. Symp. Ser. 62:100–11 [Google Scholar]
  82. Engel D, Dalton A, Anderson K, Sivaramakrishnan C, Lansing C. 82.  2012. Development of Technology Readiness Level (TRL) Metrics and Risk Measure Richland, WA: Pac. Northwest Natl. Lab http://www.osti.gov/bridge/product.biblio.jsp?osti_id=1067968
  83. Dale C, Thompson J, Engel D, Dalton A, Jones E. 83.  2013. Risk analysis and decision making CCSI Tech. Rep. Ser. www.acceleratecarboncapture.org
  84. Zongxue X, Jinno K, Kawamura A, Takesaki S, Ito K. 84.  1989. Performance risk analysis for Fukuoka water supply system. Water Resour. Manag. 12:13–30 [Google Scholar]
  85. Engel D, Letellier B, Edwards B, LeClaire R, Jones E. 85.  2012. New technical risk management development for carbon capture process Presented at 11th Annu. Conf. Carbon Capture Sequestration, Pittsburgh, PA
/content/journals/10.1146/annurev-chembioeng-060713-040321
Loading
/content/journals/10.1146/annurev-chembioeng-060713-040321
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error