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

Huaiwei Shi, Teng Zhou

PDF(1679 KB)
PDF(1679 KB)
Front. Chem. Sci. Eng. ›› 2021, Vol. 15 ›› Issue (1) : 49-59. DOI: 10.1007/s11705-020-1959-0
REVIEW ARTICLE
REVIEW ARTICLE

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

Author information +
History +

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.

Graphical abstract

Keywords

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

Cite this article

Download citation ▾
Huaiwei Shi, Teng Zhou. Computational design of heterogeneous catalysts and gas separation materials for advanced chemical processing. Front. Chem. Sci. Eng., 2021, 15(1): 49‒59 https://doi.org/10.1007/s11705-020-1959-0

References

[1]
Bartholomew C H, Farrauto R J. Fundamentals of Industrial Catalytic Processes. 2nd ed. Hoboken: Wiley-Interscience, 2006, 1–59
[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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef Google scholar
[8]
Herron J A, Mavrikakis M, Maravelias C T. Optimization methods for catalyst design. Computer-Aided Chemical Engineering, 2016, 38: 295–300
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef 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
CrossRef Google scholar
[62]
Erucar I, Keskin S. Computational modeling of bio-MOFs for CO2/CH4 separations. Chemical Engineering Science, 2015, 130: 120–128
CrossRef 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
CrossRef 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
CrossRef Google scholar
[65]
Azar A N V, Keskin S. Computational screening of MOFs for acetylene separation. Frontiers in Chemistry, 2018, 6: 36
CrossRef Google scholar

Funding Information

Open access funding provided by Projekt DEAL.

Open Access

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

RIGHTS & PERMISSIONS

2020 The Author(s) 2020. This article is published with open access at link.springer.com and journal.hep.com.cn
AI Summary AI Mindmap
PDF(1679 KB)

Accesses

Citations

Detail

Sections
Recommended

/