Machine learning in process systems engineering: Challenges and opportunities
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          | Published in | Computers & chemical engineering Vol. 181; p. 108523 | 
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| Main Authors | , , , , , , , , , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
          
        01.02.2024
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| Online Access | Get full text | 
| ISSN | 0098-1354 1873-4375  | 
| DOI | 10.1016/j.compchemeng.2023.108523 | 
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| ArticleNumber | 108523 | 
    
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| Author | Rangarajan, Srinivas Gopaluni, Bhushan Schweidtmann, Artur M. Lee, Jay H. Chiang, Leo Georgakis, Christos Harjunkoski, Iiro Lima, Fernando V. Mercangöz, Mehmet Mesbah, Ali Boukouvala, Fani del Rio Chanona, Antonio Daoutidis, Prodromos  | 
    
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| Cites_doi | 10.1021/jacs.1c08794 10.1016/j.compchemeng.2021.107252 10.1016/j.compchemeng.2020.106844 10.1016/j.matt.2021.01.008 10.1002/wcms.1603 10.1016/j.ces.2014.10.030 10.1021/acs.iecr.3c01565 10.1016/j.cherd.2021.10.042 10.1016/j.compchemeng.2021.107567 10.1016/j.ces.2022.117964 10.1021/acs.iecr.9b02282 10.1016/j.ejor.2020.07.063 10.1002/aic.16489 10.1103/PhysRevLett.120.145301 10.1016/j.compchemeng.2017.09.017 10.1016/j.coche.2022.100843 10.1038/s41586-021-03819-2 10.1146/annurev-chembioeng-060816-101555 10.1038/s42256-020-00284-w 10.1007/s12532-018-0139-4 10.1109/TAC.2020.2966717 10.1186/s13321-020-00460-5 10.1038/s42256-023-00628-2 10.1002/cctc.202000953 10.1021/acs.jpcc.9b08431 10.1039/C9ME00108E 10.1016/j.compchemeng.2022.107770 10.1016/j.automatica.2021.109947 10.1016/j.compchemeng.2020.106886 10.1002/cite.202100083 10.1016/j.compchemeng.2021.107470 10.1021/acs.jcim.0c00502 10.1021/acs.jpcc.7b08089 10.1038/s41586-020-2242-8 10.1016/j.cej.2023.142089 10.1021/acs.jctc.2c01018 10.1016/j.sysconle.2020.104831 10.1016/j.arcontrol.2021.10.006 10.1109/TCYB.2020.2999556 10.1021/acscatal.9b01234 10.1016/j.compchemeng.2021.107365 10.1016/j.cherd.2023.04.028 10.1126/sciadv.aax9324 10.1002/aic.16689 10.1021/acs.jpclett.7b02010 10.1016/j.compchemeng.2019.05.029 10.1016/j.apenergy.2017.03.081 10.1016/j.compchemeng.2022.107898 10.1016/j.compchemeng.2021.107291 10.1038/nmat4717 10.1002/aic.17190 10.1016/j.ces.2022.117469 10.1016/j.compchemeng.2023.108162 10.1016/j.compchemeng.2019.04.003 10.1016/j.compchemeng.2018.11.020 10.1038/s41586-021-03213-y 10.1021/jacs.2c13467 10.1038/s42254-021-00314-5 10.1016/j.coche.2022.100831 10.1007/s10957-018-1396-0 10.1021/acs.iecr.0c02032 10.1016/j.compchemeng.2017.10.008 10.1016/j.compchemeng.2022.107956 10.1016/j.compchemeng.2019.106649 10.1016/j.compchemeng.2017.09.015 10.1021/acscentsci.9b00576 10.1073/pnas.1517384113 10.1126/science.1165893 10.1016/j.compchemeng.2022.108110 10.1002/aic.14418  | 
    
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| References | Ceccon (10.1016/j.compchemeng.2023.108523_b11) 2022; 23 Hirschfeld (10.1016/j.compchemeng.2023.108523_b32) 2020; 60 Gao (10.1016/j.compchemeng.2023.108523_b26) 2018; 108 Jumper (10.1016/j.compchemeng.2023.108523_b39) 2021; 596 Bradley (10.1016/j.compchemeng.2023.108523_b8) 2022 Nian (10.1016/j.compchemeng.2023.108523_b57) 2020 González (10.1016/j.compchemeng.2023.108523_b28) 2023; 170 Zhong (10.1016/j.compchemeng.2023.108523_b95) 2020; 581 Karg (10.1016/j.compchemeng.2023.108523_b41) 2020; 50 Petsagkourakis (10.1016/j.compchemeng.2023.108523_b60) 2020; 133 Bennett (10.1016/j.compchemeng.2023.108523_b4) 2022; 36 Chen (10.1016/j.compchemeng.2023.108523_b15) 2022; 135 Lanzetti (10.1016/j.compchemeng.2023.108523_b46) 2019 Karniadakis (10.1016/j.compchemeng.2023.108523_b42) 2021; 3 Cappart (10.1016/j.compchemeng.2023.108523_b10) 2023; 24 Andersson (10.1016/j.compchemeng.2023.108523_b1) 2019; 11 Isenberg (10.1016/j.compchemeng.2023.108523_b35) 2020; 5 Van Waarde (10.1016/j.compchemeng.2023.108523_b86) 2020; 65 Mesbah (10.1016/j.compchemeng.2023.108523_b53) 2022 Shin (10.1016/j.compchemeng.2023.108523_b77) 2019; 121 Yoon (10.1016/j.compchemeng.2023.108523_b93) 2021; 2 Kang (10.1016/j.compchemeng.2023.108523_b40) 2023; 5 Mitrai (10.1016/j.compchemeng.2023.108523_b54) 2023 Paulson (10.1016/j.compchemeng.2023.108523_b59) 2023 Chakraborty (10.1016/j.compchemeng.2023.108523_b12) 2021; 154 del Rio Chanona (10.1016/j.compchemeng.2023.108523_b19) 2021; 147 Fioretto (10.1016/j.compchemeng.2023.108523_b23) 2021 Chen (10.1016/j.compchemeng.2023.108523_b14) 2022; 37 Tang (10.1016/j.compchemeng.2023.108523_b82) 2021; 147 Ingolfsson (10.1016/j.compchemeng.2023.108523_b34) 2023; 19 Jinnouchi (10.1016/j.compchemeng.2023.108523_b38) 2017; 8 Mann (10.1016/j.compchemeng.2023.108523_b51) 2021; 67 Esche (10.1016/j.compchemeng.2023.108523_b22) 2022; 177 Schmidt (10.1016/j.compchemeng.2023.108523_b70) 2009; 324 Bhosekar (10.1016/j.compchemeng.2023.108523_b6) 2018; 108 Hirtreiter (10.1016/j.compchemeng.2023.108523_b33) 2023 Harjunkoski (10.1016/j.compchemeng.2023.108523_b31) 2020; 59 Lee (10.1016/j.compchemeng.2023.108523_b48) 2018; 114 Rangarajan (10.1016/j.compchemeng.2023.108523_b66) 2017; 121 Hanselman (10.1016/j.compchemeng.2023.108523_b30) 2019; 123 Bradford (10.1016/j.compchemeng.2023.108523_b7) 2020; 139 Kim (10.1016/j.compchemeng.2023.108523_b43) 2020; 6 Lee (10.1016/j.compchemeng.2023.108523_b47) 2023 Cozad (10.1016/j.compchemeng.2023.108523_b17) 2014; 60 Gómez-Bombarelli (10.1016/j.compchemeng.2023.108523_b27) 2016; 15 Tang (10.1016/j.compchemeng.2023.108523_b83) 2022 Zavala (10.1016/j.compchemeng.2023.108523_b94) 2023; 62 Berahas (10.1016/j.compchemeng.2023.108523_b5) 2021 Sun (10.1016/j.compchemeng.2023.108523_b81) 2021; 4 Venkatasubramanian (10.1016/j.compchemeng.2023.108523_b87) 2019; 65 Makrygiorgos (10.1016/j.compchemeng.2023.108523_b50) 2022; 162 Mitrai (10.1016/j.compchemeng.2023.108523_b56) 2023 Qin (10.1016/j.compchemeng.2023.108523_b65) 2019; 126 Shin (10.1016/j.compchemeng.2023.108523_b76) 2019; 127 Brunton (10.1016/j.compchemeng.2023.108523_b9) 2016; 113 David (10.1016/j.compchemeng.2023.108523_b18) 2020; 12 Deshwal (10.1016/j.compchemeng.2023.108523_b20) 2020 Ouyang (10.1016/j.compchemeng.2023.108523_b58) 2018; 2 Shields (10.1016/j.compchemeng.2023.108523_b75) 2021; 590 Shin (10.1016/j.compchemeng.2023.108523_b78) 2017; 195 Chiang (10.1016/j.compchemeng.2023.108523_b16) 2017; 8 Vogel (10.1016/j.compchemeng.2023.108523_b88) 2023; 171 Yao (10.1016/j.compchemeng.2023.108523_b91) 2020 Thebelt (10.1016/j.compchemeng.2023.108523_b84) 2022; 252 Spielberg (10.1016/j.compchemeng.2023.108523_b80) 2019; 65 Xie (10.1016/j.compchemeng.2023.108523_b90) 2018; 120 Schwaller (10.1016/j.compchemeng.2023.108523_b72) 2021; 3 Galagali (10.1016/j.compchemeng.2023.108523_b24) 2015; 123 Pistikopoulos (10.1016/j.compchemeng.2023.108523_b61) 2021; 147 Proctor (10.1016/j.compchemeng.2023.108523_b63) 2023 Tsay (10.1016/j.compchemeng.2023.108523_b85) 2019; 58 Polak (10.1016/j.compchemeng.2023.108523_b62) 2023 Gao (10.1016/j.compchemeng.2023.108523_b25) 2023 Kumar (10.1016/j.compchemeng.2023.108523_b44) 2021; 150 Sansana (10.1016/j.compchemeng.2023.108523_b68) 2021; 151 Wigh (10.1016/j.compchemeng.2023.108523_b89) 2022; 12 Döppel (10.1016/j.compchemeng.2023.108523_b21) 2022; 262 Schwaller (10.1016/j.compchemeng.2023.108523_b71) 2019; 5 10.1016/j.compchemeng.2023.108523_b55 Schweidtmann (10.1016/j.compchemeng.2023.108523_b74) 2019; 180 Sitapure (10.1016/j.compchemeng.2023.108523_b79) 2023; 194 Anstine (10.1016/j.compchemeng.2023.108523_b2) 2023; 145 Matera (10.1016/j.compchemeng.2023.108523_b52) 2019; 9 Savara (10.1016/j.compchemeng.2023.108523_b69) 2020; 12 Gusmão (10.1016/j.compchemeng.2023.108523_b29) 2022 Pulsipher (10.1016/j.compchemeng.2023.108523_b64) 2022; 156 Bengio (10.1016/j.compchemeng.2023.108523_b3) 2021; 290 Lan (10.1016/j.compchemeng.2023.108523_b45) 2021; 143 Chen (10.1016/j.compchemeng.2023.108523_b13) 2021 Ren (10.1016/j.compchemeng.2023.108523_b67) 2022 Yoo (10.1016/j.compchemeng.2023.108523_b92) 2021; 52 Jahani (10.1016/j.compchemeng.2023.108523_b36) 2021 Lejarza (10.1016/j.compchemeng.2023.108523_b49) 2023; 462 Schweidtmann (10.1016/j.compchemeng.2023.108523_b73) 2021; 93 Jiang (10.1016/j.compchemeng.2023.108523_b37) 2022  | 
    
| References_xml | – year: 2020 ident: 10.1016/j.compchemeng.2023.108523_b20 – start-page: 36 issue: May year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b63 article-title: Data science and digitalisation for chemical engineers publication-title: IChemE Chem. Eng. (TCE) Mag. – volume: 143 start-page: 16804 issue: 40 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b45 article-title: Discovering catalytic reaction networks using deep reinforcement learning from first-principles publication-title: J. Am. Chem. Soc. doi: 10.1021/jacs.1c08794 – year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b47 – volume: 147 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b61 article-title: Process systems engineering - The generation next? publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2021.107252 – volume: 139 year: 2020 ident: 10.1016/j.compchemeng.2023.108523_b7 article-title: Stochastic data-driven model predictive control using Gaussian processes publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2020.106844 – volume: 4 start-page: 1305 issue: 4 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b81 article-title: A data fusion approach to optimize compositional stability of halide perovskites publication-title: Matter doi: 10.1016/j.matt.2021.01.008 – year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b36 – volume: 12 issue: 5 year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b89 article-title: A review of molecular representation in the age of machine learning publication-title: WIREs Comput. Mol. Sci. doi: 10.1002/wcms.1603 – volume: 123 start-page: 170 year: 2015 ident: 10.1016/j.compchemeng.2023.108523_b24 article-title: Bayesian inference of chemical kinetic models from proposed reactions publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2014.10.030 – volume: 24 start-page: 1 issue: 130 year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b10 article-title: Combinatorial optimization and reasoning with graph neural networks publication-title: J. Mach. Learn. Res. – volume: 62 start-page: 8995 issue: 23 year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b94 article-title: Outlook: How I learned to Love machine learning (a personal perspective on machine learning in process systems engineering) publication-title: Ind. Eng. Chem. Res. doi: 10.1021/acs.iecr.3c01565 – volume: 177 start-page: 184 year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b22 article-title: Architectures for neural networks as surrogates for dynamic systems in chemical engineering publication-title: Chem. Eng. Res. Des. doi: 10.1016/j.cherd.2021.10.042 – volume: 156 year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b64 article-title: A unifying modeling abstraction for infinite-dimensional optimization publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2021.107567 – volume: 262 year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b21 article-title: Efficient machine learning based surrogate models for surface kinetics by approximating the rates of the rate-determining steps publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2022.117964 – year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b37 – year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b25 – volume: 58 start-page: 16696 issue: 36 year: 2019 ident: 10.1016/j.compchemeng.2023.108523_b85 article-title: 110Th anniversary: Using data to bridge the time and length scales of process systems publication-title: Ind. Eng. Chem. Res. doi: 10.1021/acs.iecr.9b02282 – volume: 290 start-page: 405 issue: 2 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b3 article-title: Machine learning for combinatorial optimization: a methodological tour d’horizon publication-title: European J. Oper. Res. doi: 10.1016/j.ejor.2020.07.063 – volume: 2 issue: 8 year: 2018 ident: 10.1016/j.compchemeng.2023.108523_b58 article-title: SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates publication-title: Phys. Rev. Mater. – volume: 65 start-page: 466 issue: 2 year: 2019 ident: 10.1016/j.compchemeng.2023.108523_b87 article-title: The promise of artificial intelligence in chemical engineering: Is it here, finally? publication-title: AIChE J. doi: 10.1002/aic.16489 – volume: 120 issue: 14 year: 2018 ident: 10.1016/j.compchemeng.2023.108523_b90 article-title: Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.120.145301 – volume: 108 start-page: 250 year: 2018 ident: 10.1016/j.compchemeng.2023.108523_b6 article-title: Advances in surrogate based modeling, feasibility analysis, and optimization: A review publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2017.09.017 – volume: 37 year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b14 article-title: Recent progress toward catalyst properties, performance, and prediction with data-driven methods publication-title: Curr. Opin. Chem. Eng. doi: 10.1016/j.coche.2022.100843 – volume: 596 start-page: 583 issue: 7873 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b39 article-title: Highly accurate protein structure prediction with AlphaFold publication-title: Nature doi: 10.1038/s41586-021-03819-2 – volume: 8 start-page: 63 year: 2017 ident: 10.1016/j.compchemeng.2023.108523_b16 article-title: Big data analytics in chemical engineering publication-title: Annu. Rev. Chem. Biomol. Eng. doi: 10.1146/annurev-chembioeng-060816-101555 – volume: 3 start-page: 144 issue: 2 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b72 article-title: Mapping the space of chemical reactions using attention-based neural networks publication-title: Nat. Mach. Intell. doi: 10.1038/s42256-020-00284-w – volume: 11 start-page: 1 year: 2019 ident: 10.1016/j.compchemeng.2023.108523_b1 article-title: CasADi: a software framework for nonlinear optimization and optimal control publication-title: Math. Program. Comput. doi: 10.1007/s12532-018-0139-4 – volume: 65 start-page: 4753 issue: 11 year: 2020 ident: 10.1016/j.compchemeng.2023.108523_b86 article-title: Data informativity: a new perspective on data-driven analysis and control publication-title: IEEE Trans. Automat. Control doi: 10.1109/TAC.2020.2966717 – volume: 12 start-page: 56 issue: 1 year: 2020 ident: 10.1016/j.compchemeng.2023.108523_b18 article-title: Molecular representations in AI-driven drug discovery: a review and practical guide publication-title: J. Cheminformatics doi: 10.1186/s13321-020-00460-5 – volume: 5 start-page: 309 issue: 3 year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b40 article-title: A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks publication-title: Nat. Mach. Intell. doi: 10.1038/s42256-023-00628-2 – volume: 12 start-page: 5385 issue: 21 year: 2020 ident: 10.1016/j.compchemeng.2023.108523_b69 article-title: CheKiPEUQ intro 1: Bayesian parameter estimation considering uncertainty or error from both experiments and theory publication-title: ChemCatChem doi: 10.1002/cctc.202000953 – volume: 123 start-page: 29209 issue: 48 year: 2019 ident: 10.1016/j.compchemeng.2023.108523_b30 article-title: Optimization-based design of active and stable nanostructured surfaces publication-title: J. Phys. Chem. C doi: 10.1021/acs.jpcc.9b08431 – volume: 2 issue: 4 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b93 article-title: Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy publication-title: Mach. Learn.: Sci. Technol. – volume: 5 start-page: 232 issue: 1 year: 2020 ident: 10.1016/j.compchemeng.2023.108523_b35 article-title: Identification of optimally stable nanocluster geometries via mathematical optimization and density-functional theory publication-title: Mol. Syst. Des. Eng. doi: 10.1039/C9ME00108E – volume: 162 year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b50 article-title: Performance-oriented model learning for control via multi-objective Bayesian optimization publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2022.107770 – volume: 135 year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b15 article-title: Large scale model predictive control with neural networks and primal active sets publication-title: Automatica doi: 10.1016/j.automatica.2021.109947 – year: 2020 ident: 10.1016/j.compchemeng.2023.108523_b57 article-title: A review on reinforcement learning: Introduction and applications in industrial process control publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2020.106886 – volume: 93 start-page: 2029 issue: 12 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b73 article-title: Machine learning in chemical engineering: A perspective publication-title: Chem. Ing. Tech. doi: 10.1002/cite.202100083 – volume: 154 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b12 article-title: AI-DARWIN: A first principles-based model discovery engine using machine learning publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2021.107470 – volume: 60 start-page: 3770 issue: 8 year: 2020 ident: 10.1016/j.compchemeng.2023.108523_b32 article-title: Uncertainty quantification using neural networks for molecular property prediction publication-title: J. Chem. Inf. Model. doi: 10.1021/acs.jcim.0c00502 – year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b33 article-title: Toward automatic generation of control structures for process flow diagrams with large language models publication-title: AIChE J. – year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b13 – start-page: 1295 year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b59 article-title: A tutorial on derivative-free policy learning methods for interpretable controller representations – volume: 121 start-page: 25847 issue: 46 year: 2017 ident: 10.1016/j.compchemeng.2023.108523_b66 article-title: Sequential-optimization-based framework for robust modeling and design of heterogeneous catalytic systems publication-title: J. Phys. Chem. C doi: 10.1021/acs.jpcc.7b08089 – volume: 581 start-page: 178 issue: 7807 year: 2020 ident: 10.1016/j.compchemeng.2023.108523_b95 article-title: Accelerated discovery of CO2 electrocatalysts using active machine learning publication-title: Nature doi: 10.1038/s41586-020-2242-8 – volume: 462 year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b49 article-title: A dynamic nonlinear optimization framework for learning data-driven reduced-order microkinetic models publication-title: Chem. Eng. J. doi: 10.1016/j.cej.2023.142089 – year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b5 – volume: 19 start-page: 2658 issue: 9 year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b34 article-title: Machine learning-driven multiscale modeling: Bridging the scales with a next-generation simulation infrastructure publication-title: J. Chem. Theory Comput. doi: 10.1021/acs.jctc.2c01018 – volume: 147 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b82 article-title: Dissipativity learning control (DLC): theoretical foundations of input–output data-driven model-free control publication-title: Systems Control Lett. doi: 10.1016/j.sysconle.2020.104831 – volume: 52 start-page: 108 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b92 article-title: Reinforcement learning for batch process control: Review and perspectives publication-title: Annu. Rev. Control doi: 10.1016/j.arcontrol.2021.10.006 – volume: 50 start-page: 3866 issue: 9 year: 2020 ident: 10.1016/j.compchemeng.2023.108523_b41 article-title: Efficient representation and approximation of model predictive control laws via deep learning publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2020.2999556 – volume: 9 start-page: 6624 issue: 8 year: 2019 ident: 10.1016/j.compchemeng.2023.108523_b52 article-title: Progress in accurate chemical kinetic modeling, simulations, and parameter estimation for heterogeneous catalysis publication-title: ACS Catal. doi: 10.1021/acscatal.9b01234 – volume: 151 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b68 article-title: Recent trends on hybrid modeling for Industry 4.0 publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2021.107365 – start-page: 1048 year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b83 article-title: Data-driven control: Overview and perspectives – volume: 147 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b19 article-title: Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation publication-title: Comput. Chem. Eng. – volume: 194 start-page: 461 year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b79 article-title: Exploring the potential of time-series transformers for process modeling and control in chemical systems: an inevitable paradigm shift? publication-title: Chem. Eng. Res. Des. doi: 10.1016/j.cherd.2023.04.028 – volume: 6 start-page: eaax9324 issue: 1 year: 2020 ident: 10.1016/j.compchemeng.2023.108523_b43 article-title: Inverse design of porous materials using artificial neural networks publication-title: Sci. Adv. doi: 10.1126/sciadv.aax9324 – year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b62 – volume: 65 issue: 10 year: 2019 ident: 10.1016/j.compchemeng.2023.108523_b80 article-title: Toward self-driving processes: A deep reinforcement learning approach to control publication-title: AIChE J. doi: 10.1002/aic.16689 – volume: 23 issue: 1 year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b11 article-title: OMLT: Optimization & machine learning toolkit publication-title: J. Mach. Learn. Res. – volume: 8 start-page: 4279 issue: 17 year: 2017 ident: 10.1016/j.compchemeng.2023.108523_b38 article-title: Predicting catalytic activity of nanoparticles by a DFT-aided machine-learning algorithm publication-title: J. Phys. Chem. Lett. doi: 10.1021/acs.jpclett.7b02010 – volume: 127 start-page: 282 year: 2019 ident: 10.1016/j.compchemeng.2023.108523_b76 article-title: Reinforcement learning – Overview of recent progress and implications for process control publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2019.05.029 – start-page: 342 year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b53 article-title: Fusion of machine learning and MPC under uncertainty: What advances are on the horizon? – volume: 195 start-page: 616 year: 2017 ident: 10.1016/j.compchemeng.2023.108523_b78 article-title: Operational planning and optimal sizing of microgrid considering multi-scale wind uncertainty publication-title: Appl. Energy doi: 10.1016/j.apenergy.2017.03.081 – year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b8 article-title: Perspectives on the integration between first-principles and data-driven modeling publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2022.107898 – volume: 150 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b44 article-title: Industrial, large-scale model predictive control with structured neural networks publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2021.107291 – volume: 15 start-page: 1120 issue: 10 year: 2016 ident: 10.1016/j.compchemeng.2023.108523_b27 article-title: Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach publication-title: Nat. Mater. doi: 10.1038/nmat4717 – volume: 67 issue: 3 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b51 article-title: Predicting chemical reaction outcomes: A grammar ontology-based transformer framework publication-title: AIChE J. doi: 10.1002/aic.17190 – volume: 252 year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b84 article-title: Maximizing information from chemical engineering data sets: Applications to machine learning publication-title: Chem. Eng. Sci. doi: 10.1016/j.ces.2022.117469 – start-page: 118 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b23 article-title: Lagrangian duality for constrained deep learning – ident: 10.1016/j.compchemeng.2023.108523_b55 – volume: 171 year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b88 article-title: Learning from flowsheets: A generative transformer model for autocompletion of flowsheets publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2023.108162 – volume: 126 start-page: 465 year: 2019 ident: 10.1016/j.compchemeng.2023.108523_b65 article-title: Advances and opportunities in machine learning for process data analytics publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2019.04.003 – volume: 121 start-page: 556 year: 2019 ident: 10.1016/j.compchemeng.2023.108523_b77 article-title: Multi-timescale, multi-period decision-making model development by combining reinforcement learning and mathematical programming publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2018.11.020 – volume: 590 start-page: 89 issue: 7844 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b75 article-title: Bayesian reaction optimization as a tool for chemical synthesis publication-title: Nature doi: 10.1038/s41586-021-03213-y – volume: 145 start-page: 8736 issue: 16 year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b2 article-title: Generative models as an emerging paradigm in the chemical sciences publication-title: J. Am. Chem. Soc. doi: 10.1021/jacs.2c13467 – volume: 3 start-page: 422 issue: 6 year: 2021 ident: 10.1016/j.compchemeng.2023.108523_b42 article-title: Physics-informed machine learning publication-title: Nat. Rev. Phys. doi: 10.1038/s42254-021-00314-5 – year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b56 – start-page: 581 year: 2020 ident: 10.1016/j.compchemeng.2023.108523_b91 article-title: Pyhessian: Neural networks through the lens of the hessian – volume: 36 year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b4 article-title: Autonomous chemical science and engineering enabled by self-driving laboratories publication-title: Curr. Opin. Chem. Eng. doi: 10.1016/j.coche.2022.100831 – volume: 180 start-page: 925 issue: 3 year: 2019 ident: 10.1016/j.compchemeng.2023.108523_b74 article-title: Deterministic global optimization with artificial neural networks embedded publication-title: J. Optim. Theory Appl. doi: 10.1007/s10957-018-1396-0 – volume: 59 start-page: 16684 issue: 38 year: 2020 ident: 10.1016/j.compchemeng.2023.108523_b31 article-title: Synergistic and intelligent process optimization: First results and open challenges publication-title: Ind. Eng. Chem. Res. doi: 10.1021/acs.iecr.0c02032 – volume: 114 start-page: 111 year: 2018 ident: 10.1016/j.compchemeng.2023.108523_b48 article-title: Machine learning: Overview of the recent progresses and implications for the process systems engineering field publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2017.10.008 – year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b67 article-title: A tutorial review of neural network modeling approaches for model predictive control publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2022.107956 – volume: 133 year: 2020 ident: 10.1016/j.compchemeng.2023.108523_b60 article-title: Reinforcement learning for batch bioprocess optimization publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2019.106649 – volume: 108 start-page: 268 year: 2018 ident: 10.1016/j.compchemeng.2023.108523_b26 article-title: Application and comparison of derivative-free optimization algorithms to control and optimize free radical polymerization simulated using the kinetic Monte Carlo method publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2017.09.015 – volume: 5 start-page: 1572 issue: 9 year: 2019 ident: 10.1016/j.compchemeng.2023.108523_b71 article-title: Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction publication-title: ACS Cent. Sci. doi: 10.1021/acscentsci.9b00576 – volume: 113 start-page: 3932 issue: 15 year: 2016 ident: 10.1016/j.compchemeng.2023.108523_b9 article-title: Discovering governing equations from data by sparse identification of nonlinear dynamical systems publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.1517384113 – volume: 324 start-page: 81 issue: 5923 year: 2009 ident: 10.1016/j.compchemeng.2023.108523_b70 article-title: Distilling free-form natural laws from experimental data publication-title: Science doi: 10.1126/science.1165893 – volume: 170 year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b28 article-title: New paradigms for exploiting parallel experiments in Bayesian optimization publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2022.108110 – start-page: 655 year: 2023 ident: 10.1016/j.compchemeng.2023.108523_b54 article-title: A graph classification algorithm to determine when to decompose optimization problems – volume: 60 start-page: 2211 issue: 6 year: 2014 ident: 10.1016/j.compchemeng.2023.108523_b17 article-title: Learning surrogate models for simulation-based optimization publication-title: AIChE J. doi: 10.1002/aic.14418 – year: 2022 ident: 10.1016/j.compchemeng.2023.108523_b29 article-title: Kinetics-informed neural networks publication-title: Catal. Today – start-page: 1005 year: 2019 ident: 10.1016/j.compchemeng.2023.108523_b46 article-title: Recurrent neural network based MPC for process industries  | 
    
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