Multiscale process systems engineering—analysis and design of chemical and energy systems from molecular design up to process optimization

Graphical abstract

References

[1] Grossmann I E, Westerberg A W. Research challenges in process systems engineering. AIChE Journal, 2000, 46(9): 1700–1703

[2] 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): 1017–1026

[3] Zhou T, Gani R, Sundmacher K. Hybrid data-driven and mechanistic modeling approaches for multiscale material and process design. Engineering, 2021, 7(9): 1231–1238

[4] Shi H, Zhou T. Computational design of heterogeneous catalysts and gas separation materials for advanced chemical processing. Frontiers of Chemical Science and Engineering, 2021, 15(1): 49–59

[5] You L, Guo Y, He Y, Huo F, Zeng S, Li C, Zhang X, Zhang X. Molecular level understanding of CO₂ capture in ionic liquid/polyimide composite membrane. Frontiers of Chemical Science and Engineering, 2022, 16(2): 141–151

[6] Liu Q, Jiang Y, Zhang L, Du J. A computational toolbox for molecular property prediction based on quantum mechanics and quantitative structure-property relationship. Frontiers of Chemical Science and Engineering, 2022, 16(2): 152–167

[7] Chong J W, Thangalazhy-Gopakumar S, Muthoosamy K, Chemmangattuvalappil N G. Design of bio-oil additives via molecular signature descriptors using a multi-stage computer-aided molecular design framework. Frontiers of Chemical Science and Engineering, 2022, 16(2): 168–182

[8] Ludl P O, Heese R, Höller J, Asprion N, Bortz M. Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet simulations with constraints. Frontiers of Chemical Science and Engineering, 2022, 16(2): 183–197

[9] Li J, Wang T, Liu P, Li Z. Dynamic modelling and simulation of a post-combustion CO₂ capture process for coal-fired power plants. Frontiers of Chemical Science and Engineering, 2022, 16(2): 198–209

[10] Asprion N, Böttcher R, Schwientek J, Höller J, Schwartz P, Vanaret C, Bortz M. Decision support for the development, simulation and optimization of dynamic process models. Frontiers of Chemical Science and Engineering, 2022, 16(2): 210–220

[11] Dong Y, Georgakis C, Santos-Marques J, Du J. Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis. Frontiers of Chemical Science and Engineering, 2022, 16(2): 221–236

[12] Chee E, Wong W C, Wang X. An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system. Frontiers of Chemical Science and Engineering, 2022, 16(2): 237–250

[13] Vollmer N I, Al R, Gernaey K V, Sin G. Synergistic optimization framework for the process synthesis and design of biorefineries. Frontiers of Chemical Science and Engineering, 2022, 16(2): 251–273

[14] Fang H, Zhou J, Wang Z, Qiu Z, Sun Y, Lin Y, Chen K, Zhou X, Pan M. Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations. Frontiers of Chemical Science and Engineering, 2022, 16(2): 274–287

[15] Göttl Q, Grimm D G, Burger J. Automated synthesis of steady-state continuous processes using reinforcement learning. Frontiers of Chemical Science and Engineering, 2022, 16(2): 288–302

[16] Yang A, Su Y, Shi T, Ren J, Shen W, Zhou T. Energy-efficient recovery of tetrahydrofuran and ethyl acetate by triple-column extractive distillation: entrainer design and process optimization. Frontiers of Chemical Science and Engineering, 2022, 16(2): 303–315

[17] Patraşcu I, Bîldea C S, Kiss A A. Novel eco-efficient reactive distillation process for dimethyl carbonate production by indirect alcoholysis of urea. Frontiers of Chemical Science and Engineering, 2022, 16(2): 316–331

RIGHTS & PERMISSIONS

2021 Higher Education Press