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Computational design of heterogeneous catalysts and gas separation materials for advanced chemical processing

Abstract

Functional materials are widely used in chemical industry in order to reduce the process cost while simultaneously increase the product quality. Considering their significant effects, systematic methods for the optimal selection and design of materials are essential. The conventional synthesis-and-test method for materials development is inefficient and costly. Additionally, the performance of the resulting materials is usually limited by the designer’s expertise. During the past few decades, computational methods have been significantly developed and they now become a very important tool for the optimal design of functional materials for various chemical processes. This article selectively focuses on two important process functional materials, namely heterogeneous catalyst and gas separation agent. Theoretical methods and representative works for computational screening and design of these materials are reviewed.

References

  1. Bartholomew C H, Farrauto R J. Fundamentals of Industrial Catalytic Processes. 2nd ed. Hoboken: Wiley-Interscience, 2006, 1–59

    Google Scholar 

  2. Dumesic J A, Milligan B A, Greppi L A, Balse V R, Sarnowski K T, Beall C E, Kataoka T, Rudd D F, Trevino A A. A kinetic modeling approach to the design of catalysts—formulation of a catalyst design advisory program. Industrial & Engineering Chemistry Research, 1987, 26(7): 1399–1407

    CAS  Google Scholar 

  3. Bligaard T, Nørskov J K, Dahl S, Matthiesen J, Christensen C H, Sehested J. The Brønsted-Evans-Polanyi relation and the volcano curve in heterogeneous catalysis. Journal of Catalysis, 2004, 224(1): 206–217

    CAS  Google Scholar 

  4. Katare S, Caruthers J M, Delgass W N, Venkatasubramanian V. An intelligent system for reaction kinetic modeling and catalyst design. Industrial & Engineering Chemistry Research, 2004, 43(14): 3484–3512

    CAS  Google Scholar 

  5. Linic S, Jankowiak J, Barteau M A. Selectivity driven design of bimetallic ethylene epoxidation catalysts from first principles. Journal of Catalysis, 2004, 224(2): 489–493

    CAS  Google Scholar 

  6. Lee C J, Yang Y, Prasad V, Lee J M. Sample-based approaches to decision making problems under uncertainty. Canadian Journal of Chemical Engineering, 2012, 90(2): 385–395

    CAS  Google Scholar 

  7. Xu Y, Lausche A C, Wang S G, Khan T S, Abild-Pedersen F, Studt F, Norskov J K, Bligaard T. In silico search for novel methane steam reforming catalysts. New Journal of Physics, 2013, 15(12): 125021

    Google Scholar 

  8. Herron J A, Mavrikakis M, Maravelias C T. Optimization methods for catalyst design. Computer-Aided Chemical Engineering, 2016, 38: 295–300

    CAS  Google Scholar 

  9. Rangarajan S, Maravelias C T, Mavrikakis M. Sequential-optimization-based framework for robust modeling and design of heterogeneous catalytic systems. Journal of Physical Chemistry C, 2017, 121(46): 25847–25863

    CAS  Google Scholar 

  10. Wang Z Y, Hu P. Towards rational catalyst design: a general optimization framework. Philosophical Transactions -Royal Society. Mathematical, Physical, and Engineering Sciences, 2016, 374(2061): 20150078

    Google Scholar 

  11. Jacobsen C J H, Dahl S, Clausen B S, Bahn S, Logadottir A, Norskov J K. Catalyst design by interpolation in the periodic table: bimetallic ammonia synthesis catalysts. Journal of the American Chemical Society, 2001, 123(34): 8404–8405

    CAS  PubMed  Google Scholar 

  12. Jacobsen C J H, Dahl S, Boisen A, Clausen B S, Topsoe H, Logadottir A, Norskov J K. Optimal catalyst curves: connecting density functional theory calculations with industrial reactor design and catalyst selection. Journal of Catalysis, 2002, 205(2): 382–387

    CAS  Google Scholar 

  13. Nørskov J K, Abild-Pedersen F, Studt F, Bligaard T. Density functional theory in surface chemistry and catalysis. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108(3): 937–943

    PubMed  PubMed Central  Google Scholar 

  14. Thybaut J W, Sun J, Olivier L, Van Veen A C, Mirodatos C, Marin G B. Catalyst design based on microkinetic models: oxidative coupling of methane. Catalysis Today, 2011, 159(1): 29–36

    CAS  Google Scholar 

  15. Huang K, Zhan X L, Chen F Q, Lu D W. Catalyst design for methane oxidative coupling by using artificial neural network and hybrid genetic algorithm. Chemical Engineering Science, 2003, 58 (1): 81–87

    CAS  Google Scholar 

  16. Baumes L, Farrusseng D, Lengliz M, Mirodatos C. Using artificial neural networks to boost high-throughput discovery in heterogeneous catalysis. QSAR & Combinatorial Science, 2004, 23(9): 767–778

    CAS  Google Scholar 

  17. Baumes L A, Serra J M, Serna P, Corma A. Support vector machines for predictive modeling in heterogeneous catalysis: a comprehensive introduction and overfitting investigation based on two real applications. Journal of Combinatorial Chemistry, 2006, 8(4): 583–596

    CAS  PubMed  Google Scholar 

  18. Corma A, Serra J M, Serna P, Moliner M. Integrating high-throughput characterization into combinatorial heterogeneous catalysis: unsupervised construction of quantitative structure/property relationship models. Journal of Catalysis, 2005, 232(2): 335–341

    CAS  Google Scholar 

  19. Fernandez M, Barron H, Barnard A S. Artificial neural network analysis of the catalytic efficiency of platinum nanoparticles. RSC Advances, 2017, 7(77): 48962–48971

    CAS  Google Scholar 

  20. Li Z, Ma X F, Xin H L. Feature engineering of machine-learning chemisorption models for catalyst design. Catalysis Today, 2017, 280: 232–238

    CAS  Google Scholar 

  21. Goldsmith B R, Esterhuizen J, Liu J X, Bartel C J, Sutton C. Machine learning for heterogeneous catalyst design and discovery. AIChE Journal. American Institute of Chemical Engineers, 2018, 64 (7): 2311–2323

    CAS  Google Scholar 

  22. Zhou T, McBride K, Linke S, Song Z, Sundmacher K. Computer-aided solvent selection and design for efficient chemical processes. Current Opinion in Chemical Engineering, 2020, 27: 35–44

    Google Scholar 

  23. Ng L Y, Chong F K, Chemmangattuvalappil N G. Challenges and opportunities in computer-aided molecular design. Computers & Chemical Engineering, 2015, 81: 115–129

    CAS  Google Scholar 

  24. Struebing H, Ganase Z, Karamertzanis P G, Siougkrou E, Haycock P, Piccione P M, Armstrong A, Galindo A, Adjiman C S. Computer-aided molecular design of solvents for accelerated reaction kinetics. Nature Chemistry, 2013, 5(11): 952–957

    CAS  PubMed  Google Scholar 

  25. Zhou T, Wang J Y, McBride K, Sundmacher K. Optimal design of solvents for extractive reaction processes. AIChE Journal. American Institute of Chemical Engineers, 2016, 62(9): 3238–3249

    CAS  Google Scholar 

  26. Zhou T, Lyu Z X, Qi Z W, Sundmacher K. Robust design of optimal solvents for chemical reactions—a combined experimental and computational strategy. Chemical Engineering Science, 2015, 137: 613–625

    CAS  Google Scholar 

  27. Song Z, Zhang C Y, Qi Z W, Zhou T, Sundmacher K. Computer-aided design of ionic liquids as solvents for extractive desulfurization. AIChE Journal. American Institute of Chemical Engineers, 2018, 64(3): 1013–1025

    CAS  Google Scholar 

  28. Zhou T, Song Z, Zhang X, Gani R, Sundmacher K. Optimal solvent design for extractive distillation processes: a multiobjective optimization-based hierarchical framework. Industrial & Engineering Chemistry Research, 2019, 58(15): 5777–5786

    CAS  Google Scholar 

  29. Bardow A, Steur K, Gross J. Continuous-molecular targeting for integrated solvent and process design. Industrial & Engineering Chemistry Research, 2010, 49(6): 2834–2840

    CAS  Google Scholar 

  30. Burger J, Papaioannou V, Gopinath S, Jackson G, Galindo A, Adjiman C S. A hierarchical method to integrated solvent and process design of physical CO2 absorption using the SAFT-Mie approach. AIChE Journal. American Institute of Chemical Engineers, 2015, 61(10): 3249–3269

    CAS  Google Scholar 

  31. Zhou T, McBride K, Zhang X, Qi Z W, Sundmacher K. Integrated solvent and process design exemplified for a Diels-Alder reaction. AIChE Journal. American Institute of Chemical Engineers, 2015, 61 (1): 147–158

    CAS  Google Scholar 

  32. Zhou T, Zhou Y, Sundmacher K. A hybrid stochastic-deterministic optimization approach for integrated solvent and process design. Chemical Engineering Science, 2017, 159: 207–216

    CAS  Google Scholar 

  33. Chong F K, Foo D C Y, Eljack F T, Atilhan M, Chemmangattuvalappil N G. A systematic approach to design task-specific ionic liquids and their optimal operating conditions. Molecular Systems Design & Engineering, 2016, 1(1): 109–121

    CAS  Google Scholar 

  34. Papadopoulos A I, Badr S, Chremos A, Forte E, Zarogiannis T, Seferlis P, Papadokonstantakis S, Galindo A, Jackson G, Adjiman C S. Computer-aided molecular design and selection of CO2 capture solvents based on thermodynamics, reactivity and sustainability. Molecular Systems Design & Engineering, 2016, 1(3): 313–334

    CAS  Google Scholar 

  35. Ahmad M Z, Hashim H, Mustaffa A A, Maarof H, Yunus N A. Design of energy efficient reactive solvents for post combustion CO2 capture using computer aided approach. Journal of Cleaner Production, 2018, 176: 704–715

    CAS  Google Scholar 

  36. Jensen N, Coll N, Gani R. An integrated computer aided system for generation and evaluation of sustainable process alternatives. Technological Choices for Sustainability, 2004, 183–214

  37. Chong F K, Foo D C Y, Eljack F T, Atilhan M, Chemmangattuvalappil N G. Ionic liquid design for enhanced carbon dioxide capture by computer-aided molecular design approach. Clean Technologies and Environmental Policy, 2015, 17(5): 1301–1312

    CAS  Google Scholar 

  38. Lei Z G, Dai C N, Wang W, Chen B H. UNIFAC model for ionic liquid-CO2 systems. AIChE Journal. American Institute of Chemical Engineers, 2014, 60(2): 716–729

    CAS  Google Scholar 

  39. Valencia-Marquez D, Flores-Tlacuahuac A, Vasquez-Medrano R. An optimization approach for CO2 capture using ionic liquids. Journal of Cleaner Production, 2017, 168: 1652–1667

    CAS  Google Scholar 

  40. Peng D L, Zhang J A, Cheng H Y, Chen L F, Qi Z W. Computer-aided ionic liquid design for separation processes based on group contribution method and COSMO-SAC model. Chemical Engineering Science, 2017, 159: 58–68

    CAS  Google Scholar 

  41. Lin S T, Sandler S I. A priori phase equilibrium prediction from a segment contribution solvation model. Industrial & Engineering Chemistry Research, 2002, 41(5): 899–913

    CAS  Google Scholar 

  42. Mortazavi-Manesh S, Satyro M A, Marriott R A. Screening ionic liquids as candidates for separation of acid gases: solubility of hydrogen sulfide, methane, and ethane. AIChE Journal. American Institute of Chemical Engineers, 2013, 59(8): 2993–3005

    CAS  Google Scholar 

  43. Klamt A, Eckert F. COSMO-RS: a novel and efficient method for the a priori prediction of thermophysical data of liquids. Fluid Phase Equilibria, 2000, 172(1): 43–72

    CAS  Google Scholar 

  44. Zhao Y S, Gani R, Afzal R M, Zhang X P, Zhang S J. Ionic liquids for absorption and separation of gases: an extensive database and a systematic screening method. AIChE Journal. American Institute of Chemical Engineers, 2017, 63(4): 1353–1367

    CAS  Google Scholar 

  45. Hasan M M F, First E L, Floudas C A. Cost-effective CO2 capture based on in silico screening of zeolites and process optimization. Physical Chemistry Chemical Physics, 2013, 15(40): 17601–17618

    CAS  PubMed  Google Scholar 

  46. First E L, Gounaris C E, Wei J, Floudas C A. Computational characterization of zeolite porous networks: an automated approach. Physical Chemistry Chemical Physics, 2011, 13(38): 17339–17358

    CAS  PubMed  Google Scholar 

  47. First E L, Hasan M M F, Floudas C A. Discovery of novel zeolites for natural gas purification through combined material screening and process optimization. AIChE Journal. American Institute of Chemical Engineers, 2014, 60(5): 1767–1785

    CAS  Google Scholar 

  48. Liu T T, First E L, Hasan M M F, Floudas C A. Discovery of new zeolites for H2S removal through multi-scale systems engineering. Computer-Aided Chemical Engineering, 2015, 37: 1025–1030

    CAS  Google Scholar 

  49. Erucar I, Keskin S. High-throughput molecular simulations of metal organic frameworks for CO2 separation: opportunities and challenges. Frontiers in Materials, 2018, 5: 4

    Google Scholar 

  50. Willems T F, Rycroft C H, Kazi M, Meza J C, Haranczyk M. Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials. Microporous and Mesoporous Materials, 2012, 149(1): 134–141

    CAS  Google Scholar 

  51. Bae Y S, Snurr R Q. Development and evaluation of porous materials for carbon dioxide separation and capture. Angewandte Chemie International Edition, 2011, 50(49): 11586–11596

    CAS  PubMed  Google Scholar 

  52. Wu D, Yang Q Y, Zhong C L, Liu D H, Huang H L, Zhang W J, Maurin G. Revealing the structure-property relationships of metal-organic frameworks for CO2 capture from flue gas. Langmuir, 2012, 28(33): 12094–12099

    CAS  PubMed  Google Scholar 

  53. Wu D, Wang C C, Liu B, Liu D H, Yang Q Y, Zhong C L. Large-scale computational screening of metal-organic frameworks for CH4/H2 separation. AIChE Journal. American Institute of Chemical Engineers, 2012, 58(7): 2078–2084

    CAS  Google Scholar 

  54. Haldoupis E, Nair S, Sholl D S. Finding MOFs for highly selective CO2/N2 adsorption using materials screening based on efficient assignment of atomic point charges. Journal of the American Chemical Society, 2012, 134(9): 4313–4323

    CAS  PubMed  Google Scholar 

  55. Li Z J, Xiao G, Yang Q Y, Xiao Y L, Zhong C L. Computational exploration of metal-organic frameworks for CO2/CH4 separation via temperature swing adsorption. Chemical Engineering Science, 2014, 120: 59–66

    CAS  Google Scholar 

  56. Qiao Z W, Zhang K, Jiang J W. In silico screening of 4764 computation-ready, experimental metal-organic frameworks for CO2 separation. Journal of Materials Chemistry. A, Materials for Energy and Sustainability, 2016, 4(6): 2105–2114

    CAS  Google Scholar 

  57. Qiao Z W, Peng C W, Zhou J, Jiang J W. High-throughput computational screening of 137953 metal-organic frameworks for membrane separation of a CO2/N2/CH4 mixture. Journal of Materials Chemistry. A, Materials for Energy and Sustainability, 2016, 4(41): 15904–15912

    CAS  Google Scholar 

  58. Wilmer C E, Farha O K, Bae Y S, Hupp J T, Snurr R Q. Structure-property relationships of porous materials for carbon dioxide separation and capture. Energy & Environmental Science, 2012, 5 (12): 9849–9856

    CAS  Google Scholar 

  59. Li S, Chung Y G, Simon C M, Snurr R Q. High-throughput computational screening of multivariate metal-organic frameworks (MTV-MOFs) for CO2 capture. Journal of Physical Chemistry Letters, 2017, 8(24): 6135–6141

    CAS  Google Scholar 

  60. Chung Y G, Gomez-Gualdron D A, Li P, Leperi K T, Deria P, Zhang H D, Vermeulen N A, Stoddart J F, You F Q, Hupp J T, Farha O K, Snurr R Q. In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm. Science Advances, 2016, 2(10): e1600909

    PubMed  PubMed Central  Google Scholar 

  61. Gurdal Y, Keskin S. Atomically detailed modeling of metal organic frameworks for adsorption, diffusion, and separation of noble gas mixtures. Industrial & Engineering Chemistry Research, 2012, 51 (21): 7373–7382

    CAS  Google Scholar 

  62. Erucar I, Keskin S. Computational modeling of bio-MOFs for CO2/CH4 separations. Chemical Engineering Science, 2015, 130: 120–128

    CAS  Google Scholar 

  63. Altintas C, Keskin S. Computational screening of MOFs for C2H6/C2H4 and C2H6/CH4 separations. Chemical Engineering Science, 2016, 139: 49–60

    CAS  Google Scholar 

  64. Sumer Z, Keskin S. Ranking of MOF adsorbents for CO2 separations: a molecular simulation study. Industrial & Engineering Chemistry Research, 2016, 55(39): 10404–10419

    CAS  Google Scholar 

  65. Azar A N V, Keskin S. Computational screening of MOFs for acetylene separation. Frontiers in Chemistry, 2018, 6: 36

    Google Scholar 

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Funding

Funding Information Open access funding provided by Projekt DEAL.

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Correspondence to Teng Zhou.

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Shi, H., Zhou, T. Computational design of heterogeneous catalysts and gas separation materials for advanced chemical processing. Front. Chem. Sci. Eng. 15, 49–59 (2021). https://doi.org/10.1007/s11705-020-1959-0

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  • DOI: https://doi.org/10.1007/s11705-020-1959-0

Keywords

  • heterogeneous catalyst
  • gas separation
  • solvent
  • porous adsorbent
  • material screening and design