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《工程(英文)》 >> 2021年 第7卷 第9期 doi: 10.1016/j.eng.2020.12.022

多尺度材料与过程设计的数据驱动和机理混合建模方法

a Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg D-39106, Germany
b Process Systems Engineering, Otto-von-Guericke University Magdeburg, Magdeburg D-39106, Germany
c PSE for SPEED Co. Ltd., Allerod DK 3450, Denmark
d Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea

收稿日期: 2020-09-09 修回日期: 2020-10-22 录用日期: 2020-12-15 发布日期: 2021-04-01

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摘要

世界人口的不断增长要求加工业以更高效和更可持续的方式生产食品、燃料、化学品和消费品。功能性过程材料是这一挑战的核心。传统上,人们根据经验或者通过反复试验的方法来发现新型先进材料。随着理论方法和相关工具的不断改进和计算机能力的提高,现在流行使用计算方法来指导材料选择和设计,这种方法也非常有效。由于材料选择与材料使用的过程操作之间存在很强的相互作用,必须同时进行材料设计和过程设计。尽管有这种重要联系,但由于通常需要使用不同规模的多个模型,材料和过程的集成设计并不容易。混合建模为解决此类复杂的设计问题提供了一个有前景的选择。在混合建模中,用数据驱动模型描述原本计算成本高昂的材料特性,而用机理模型表示众所周知的过程相关原理。本文重点介绍了混合建模在多尺度材料和过程设计中的重要性。首先介绍通用设计方法,然后选择了六个重要的应用领域:四个来自化学工程领域,两个来自能源系统工程领域。对于选定的每个领域,讨论了使用混合建模进行多尺度材料和过程设计的最新研究。最后,本文给出了结论,指出当前研究的局限性和未来的发展空间。

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参考文献

[ 1 ] Grossmann IE, Westerberg AW. Research challenges in process systems engineering. AIChE J 2000;46(9):1700–3. 链接1

[ 2 ] Shi H, Zhou T. Computational design of heterogeneous catalysts and gas separation materials for advanced chemical processing. Front Chem Sci Eng 2021;15(1):49–59. 链接1

[ 3 ] Zhou T, Song Z, Sundmacher K. Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design. Engineering 2019;5(6):1017–26. 链接1

[ 4 ] McBride K, Sundmacher K. Overview of surrogate modeling in chemical process engineering. Chem Ing Tech 2019;91(3):228–39. 链接1

[ 5 ] Von Stosch M, Oliveira R, Peres J, Feyo de Azevedo S. Hybrid semi-parametric modeling in process systems engineering: past, present and future. Comput Chem Eng 2014;60:86–101. 链接1

[ 6 ] Zhang S, Wang F, He D, Jia R. Batch-to-batch control of particle size distribution in cobalt oxalate synthesis process based on hybrid model. Powder Technol 2012;224:253–9. 链接1

[ 7 ] Zahedi G, Lohi A, Mahdi KA. Hybrid modeling of ethylene to ethylene oxide heterogeneous reactor. Fuel Process Technol 2011;92(9):1725–32. 链接1

[ 8 ] Rall D, Schweidtmann AM, Aumeier BM, Kamp J, Karwe J, Ostendorf K, et al. Simultaneous rational design of ion separation membranes and processes. J Membr Sci 2020;600:117860. 链接1

[ 9 ] Zhou T, McBride K, Zhang X, Qi Z, Sundmacher K. Integrated solvent and process design exemplified for a Diels–Alder reaction. AIChE J 2015;61 (1):147–58. 链接1

[10] Huster WR, Schweidtmann AM, Mitsos A. Working fluid selection for organic Rankine cycles via deterministic global optimization of design and operation. Optim Eng 2020;21(2):517–36. 链接1

[11] Zendehboudi S, Rezaei N, Lohi A. Applications of hybrid models in chemical, petroleum, and energy systems: a systematic review. Appl Energy 2018;228:2539–66. 链接1

[12] McBride K, Sanchez Medina EI, Sundmacher K. Hybrid semi-parametric modeling in separation processes: a review. Chem Ing Tech 2020;92 (7):842–55. 链接1

[13] Yang S, Navarathna P, Ghosh S, Bequette BW. Hybrid modeling in the era of smart manufacturing. Comput Chem Eng 2020;140:106874. 链接1

[14] Stephanopoulos G, Reklaitis GV. Process systems engineering: from Solvay to modern bio- and nanotechnology: a history of development, successes and prospects for the future. Chem Eng Sci 2011;66(19):4272–306. 链接1

[15] Papadopoulos AI, Tsivintzelis I, Linke P, Seferlis P. Computer-aided molecular design: fundamentals, methods, and applications. Ref Module Chem Mol Sci Chem Eng 2018. 链接1

[16] Zhang L, Babi DK, Gani R. New vistas in chemical product and process design. Annu Rev Chem Biomol Eng 2016;7(1):557–82. 链接1

[17] Uhlemann J, Costa R, Charpentier JC. Product design and engineering—past, present, future trends in teaching, research and practices: academic and industry points of view. Curr Opin Chem Eng 2020;27:10–21. 链接1

[18] Fung KY, Ng KM, Zhang L, Gani R. A grand model for chemical product design. Comput Chem Eng 2016;91:15–27. 链接1

[19] Alshehri AS, Gani R, You F. Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: state-of-theart and future directions. Comput Chem Eng 2020;141:107005. 链接1

[20] Austin ND, Sahinidis NV, Trahan DW. Computer-aided molecular design: an introduction and review of tools, applications, and solution techniques. Chem Eng Res Des 2016;116:2–26. 链接1

[21] Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics: recent applications and prospects. NPJ Comput Mater 2017;3:54. 链接1

[22] Zhou T, Song Z, Zhang X, Gani R, Sundmacher K. Optimal solvent design for extractive distillation processes: a multiobjective optimization-based hierarchical framework. Ind Eng Chem Res 2019;58(15):5777–86. 链接1

[23] Lek-utaiwan P, Suphanit B, Douglas PL, Mongkolsiri N. Design of extractive distillation for the separation of close-boiling mixtures: solvent selection and column optimization. Comput Chem Eng 2011;35(6):1088–100. 链接1

[24] Kossack S, Kraemer K, Gani R, Marquardt W. A systematic synthesis framework for extractive distillation processes. Chem Eng Res Des 2008;86(7):781–92. 链接1

[25] Burger J, Papaioannou V, Gopinath S, Jackson G, Galindo A, Adjiman CS. A hierarchical method to integrated solvent and process design of physical CO2 absorption using the SAFT-c Mie approach. AIChE J 2015;61(10):3249–69. 链接1

[26] McBride K, Sundmacher K. Data driven conceptual process design for the hydroformylation of 1-dodecene in a thermomorphic solvent system. Ind Eng Chem Res 2015;54(26):6761–71. 链接1

[27] Austin ND, Samudra AP, Sahinidis NV, Trahan DW. Mixture design using derivative-free optimization in the space of individual component properties. AIChE J 2016;62(5):1514–30. 链接1

[28] Reichardt C. Solvents and solvent effects: an introduction. Org Process Res Dev 2007;11(1):105–13. 链接1

[29] Reichardt C, Welton T. Solvents and solvent effects in organic chemistry. 4th ed. Weinheim: Wiley-VCH; 2011. 链接1

[30] Song Z, Shi H, Zhang X, Zhou T. Prediction of CO2 solubility in ionic liquids using machine learning methods. Chem Eng Sci 2020;223:115752. 链接1

[31] Zhao Y, Gao J, Huang Y, Afzal RM, Zhang X, Zhang S. Predicting H2S solubility in ionic liquids by the quantitative structure–property relationship method using Sr-profile molecular descriptors. RSC Adv 2016;6(74):70405–13. 链接1

[32] Zhao Y, Huang Y, Zhang X, Zhang S. A quantitative prediction of the viscosity of ionic liquids using Sr-profile molecular descriptors. Phys Chem Chem Phys 2015;17(5):3761–7. 链接1

[33] Zhou T, Jhamb S, Liang X, Sundmacher K, Gani R. Prediction of acid dissociation constants of organic compounds using group contribution methods. Chem Eng Sci 2018;183:95–105. 链接1

[34] Holderbaum T, Gmehling J. PSRK: a group contribution equation of state based on UNIFAC. Fluid Phase Equilib 1991;70(2–3):251–65. 链接1

[35] Valencia-Marquez D, Flores-Tlacuahuac A, Vasquez-Medrano R. An optimization approach for CO2 capture using ionic liquids. J Clean Prod 2017;168:1652–67. 链接1

[36] Hasan MMF, First EL, Floudas CA. Cost-effective CO2 capture based on in silico screening of zeolites and process optimization. Phys Chem Chem Phys 2013;15 (40):17601–18. 链接1

[37] First EL, Hasan MMF, Floudas CA. Discovery of novel zeolites for natural gas purification through combined material screening and process optimization. AIChE J 2014;60(5):1767–85. 链接1

[38] Liu T, First EL, Faruque Hasan MM, Floudas CA. A multi-scale approach for the discovery of zeolites for hydrogen sulfide removal. Comput Chem Eng 2016;91:206–18. 链接1

[39] Wiersum AD, Chang JS, Serre C, Llewellyn PL. An adsorbent performance indicator as a first step evaluation of novel sorbents for gas separations: application to metal–organic frameworks. Langmuir 2013;29(10):3301–9. 链接1

[40] Leperi KT, Chung YG, You F, Snurr RQ. Development of a general evaluation metric for rapid screening of adsorbent materials for postcombustion CO2 capture. ACS Sustainable Chem Eng 2019;7(13):11529–39. 链接1

[41] Khurana M, Farooq S. Adsorbent screening for postcombustion CO2 capture: a method relating equilibrium isotherm characteristics to an optimum vacuum swing adsorption process performance. Ind Eng Chem Res 2016;55 (8):2447–60. 链接1

[42] Qiao Z, Peng C, Zhou J, Jiang J. High-throughput computational screening of 137953 metal–organic frameworks for membrane separation of a CO2/N2/CH4 mixture. J Mater Chem A 2016;4(41):15904–12. 链接1

[43] Altintas C, Erucar I, Keskin S. High-throughput computational screening of the metal organic framework database for CH4/H2 separations. ACS Appl Mater Interfaces 2018;10(4):3668–79. 链接1

[44] Aghaji MZ, Fernandez M, Boyd PG, Daff TD, Woo TK. Quantitative structureproperty relationship models for recognizing metal organic frameworks (MOFs) with high CO2 working capacity and CO2/CH4 selectivity for methane purification. Eur J Inorg Chem 2016;2016(27):4505–11. 链接1

[45] Gharagheizi F, Tang D, Sholl DS. Selecting adsorbents to separate diverse nearazeotropic chemicals. J Phys Chem C 2020;124(6):3664–70. 链接1

[46] Shi Z, Yang W, Deng X, Cai C, Yan Y, Liang H, et al. Machine-learning-assisted high-throughput computational screening of high performance metal–organic frameworks. Mol Syst Des Eng 2020;5(4):725–42. 链接1

[47] Abetz V, Brinkmann T, Dijkstra M, Ebert K, Fritsch D, Ohlrogge K, et al. Developments in membrane research: from material via process design to industrial application. Adv Eng Mater 2006;8(5):328–58. 链接1

[48] Park HB, Kamcev J, Robeson LM, Elimelech M, Freeman BD. Maximizing the right stuff: the trade-off between membrane permeability and selectivity. Science 2017;356(6343):eaab0530. 链接1

[49] Labban O, Liu C, Chong TH, Lienhard JH. Relating transport modeling to nanofiltration membrane fabrication: navigating the permeability–selectivity trade-off in desalination pretreatment. J Membr Sci 2018;554:26–38. 链接1

[50] Tula AK, Befort B, Garg N, Camarda KV, Gani R. Sustainable process design & analysis of hybrid separations. Comput Chem Eng 2017;105:96–104. 链接1

[51] Srivastava RD. Heterogeneous catalytic science. Boca Raton: CRC Press; 1988. 链接1

[52] Cheng J, Hu P. Utilization of the three-dimensional volcano surface to understand the chemistry of multiphase systems in heterogeneous catalysis. J Am Chem Soc 2008;130(33):10868–9. 链接1

[53] Lausche AC, Hummelshøj JS, Abild-Pedersen F, Studt F, Nørskov JK. Application of a new informatics tool in heterogeneous catalysis: analysis of methanol dehydrogenation on transition metal catalysts for the production of anhydrous formaldehyde. J Catal 2012;291:133–7. 链接1

[54] Wang Z, Cao XM, Zhu J, Hu P. Activity and coke formation of nickel and nickel carbide in dry reforming: a deactivation scheme from density functional theory. J Catal 2014;311:469–80. 链接1

[55] Bligaard T, Nørskov JK, Dahl S, Matthiesen J, Christensen CH, Sehested J. The Brønsted–Evans–Polanyi relation and the volcano curve in heterogeneous catalysis. J Catal 2004;224(1):206–17. 链接1

[56] Wang Z, Wang HF, Hu P. Possibility of designing catalysts beyond the traditional volcano curve: a theoretical framework for multi-phase surfaces. Chem Sci 2015;6(10):5703–11. 链接1

[57] Schumann J, Medford AJ, Yoo JS, Zhao ZJ, Bothra P, Cao A, et al. Selectivity of synthesis gas conversion to C2+ oxygenates on fcc(111) transition-metal surfaces. ACS Catal 2018;8(4):3447–53. 链接1

[58] Jacobsen CJH, Dahl S, Boisen A, Clausen BS, Topsøe H, Logadottir A, et al. Optimal catalyst curves: connecting density functional theory calculations with industrial reactor design and catalyst selection. J Catal 2002;205(2):382–7. 链接1

[59] Thybaut JW, Sun J, Olivier L, Van Veen AC, Mirodatos C, Marin GB. Catalyst design based on microkinetic models: oxidative coupling of methane. Catal Today 2011;159(1):29–36. 链接1

[60] Goldsmith BR, Esterhuizen J, Liu JX, Bartel CJ, Sutton C. Machine learning for heterogeneous catalyst design and discovery. AIChE J 2018;64(7):2311–23. 链接1

[61] Quoilin S, Broek MVD, Declaye S, Dewallef P, Lemort V. Techno-economic survey of organic Rankine cycle (ORC) systems. Renew Sustain Energy Rev 2013;22:168–86. 链接1

[62] Lampe M, Stavrou M, Bücker HM, Gross J, Bardow A. Simultaneous optimization of working fluid and process for organic Rankine cycles using PC-SAFT. Ind Eng Chem Res 2014;53(21):8821–30. 链接1

[63] Wang ZQ, Zhou NJ, Guo J, Wang XY. Fluid selection and parametric optimization of organic Rankine cycle using low temperature waste heat. Energy 2012;40(1):107–15. 链接1

[64] Schilling J, Lampe M, Gross J, Bardow A. 1-Stage CoMT-CAMD: an approach for integrated design of ORC process and working fluid using PC-SAFT. Chem Eng Sci 2017;159:217–30. 链接1

[65] Gross J, Sadowski G. Perturbed-chain SAFT: an equation of state based on a perturbation theory for chain molecules. Ind Eng Chem Res 2001;40(4):1244–60. 链接1

[66] Mourah M, NguyenHuynh D, Passarello JP, de Hemptinne JC, Tobaly P. Modelling LLE and VLE of methanol + n-alkane series using GC-PC-SAFT with a group contribution kij. Fluid Phase Equilib 2010;298(1):154–68. 链接1

[67] Chimowitz EH, Anderson TF, Macchietto S, Stutzman LF. Local models for representing phase-equilibria in multicomponent, nonideal vapor–liquid and liquid–liquid systems. 1. Thermodynamic approximation functions. Ind Eng Chem Process Des Dev 1983;22(2):217–25. 链接1

[68] Schweidtmann AM, Huster WR, Lüthje JT, Mitsos A. Deterministic global process optimization: accurate (single-species) properties via artificial neural networks. Comput Chem Eng 2019;121:67–74. 链接1

[69] Bell IH, Wronski J, Quoilin S, Lemort V. Pure and pseudo-pure fluid thermophysical property evaluation and the open-source thermophysical property library CoolProp. Ind Eng Chem Res 2014;53(6):2498–508. 链接1

[70] Abhat A. Low temperature latent heat thermal energy storage: heat storage materials. Sol Energy 1983;30(4):313–32. 链接1

[71] Sharma A, Tyagi VV, Chen CR, Buddhi D. Review on thermal energy storage with phase change materials and applications. Renew Sustain Energy Rev 2009;13(2):318–45. 链接1

[72] Sharma RK, Ganesan P, Tyagi VV, Metselaar HSC, Sandaran SC. Developments in organic solid–liquid phase change materials and their applications in thermal energy storage. Energy Convers Manage 2015;95:193–228. 链接1

[73] Kenisarin M, Mahkamov K. Solar energy storage using phase change materials. Renew Sustain Energy Rev 2007;11(9):1913–65. 链接1

[74] Terasawa N, Tsuzuki S, Umecky T, Saito Y, Matsumoto H. Alkoxy chains in ionic liquid anions; effect of introducing ether oxygen into perfluoroalkylborate on physical and thermal properties. Chem Commun 2010;46(10):1730–2. 链接1

[75] Zhu J, Bai L, Chen B, Fei W. Thermodynamical properties of phase change materials based on ionic liquids. Chem Eng J 2009;147(1):58–62. 链接1

[76] Plechkova NV, Seddon KR. Applications of ionic liquids in the chemical industry. Chem Soc Rev 2008;37(1):123–50. 链接1

[77] Vijayraghavan R, Rana UA, Elliott GD, MacFarlane DR. Protic ionic solids and liquids based on the guanidinium cation as phase-change energy-storage materials. Energy Technol 2013;1(10):609–12. 链接1

[78] Boukouvala F, Floudas CA. ARGONAUT: algorithms for global optimization of constrained grey-box computational problems. Optim Lett 2017;11 (5):895–913. 链接1

[79] Schweidtmann AM, Mitsos A. Deterministic global optimization with artificial neural networks embedded. J Optim Theory Appl 2019;180(3):925–48. 链接1

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