Automated synthesis of steady-state continuous processes using reinforcement learning

Abstract

Automated flowsheet synthesis is an important field in computer-aided process engineering. The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis without any heuristics or prior knowledge of conceptual design. The environment consists of a steady-state flowsheet simulator that contains all physical knowledge. An agent is trained to take discrete actions and sequentially build up flowsheets that solve a given process problem. A novel method named SynGameZero is developed to ensure good exploration schemes in the complex problem. Therein, flowsheet synthesis is modelled as a game of two competing players. The agent plays this game against itself during training and consists of an artificial neural network and a tree search for forward planning. The method is applied successfully to a reaction-distillation process in a quaternary system.

Graphical abstract

Keywords

automated process synthesis / flowsheet synthesis / artificial intelligence / machine learning / reinforcement learning

References

[1] Westerberg A W. A retrospective on design and process synthesis. Computers & Chemical Engineering, 2004, 28(4): 447–458

[2] Stephanopoulos G, Reklaitis G V. Process systems engineering: from solvay to modern bio- and nanotechnology. A history of development, successes and prospects for the future. Chemical Engineering Science, 2011, 66(19): 4272–4306

[3] Siirola J J. Strategic process synthesis: advances in the hierarchical approach. Computers & Chemical Engineering, 1996, 20(S2): S1637–S1643

[4] Chen Q, Grossmann I E. Recent developments and challenges in optimization-based process synthesis. Annual Review of Chemical and Biomolecular Engineering, 2017, 8(1): 249–283

[5] Yeomans H, Grossmann I E. A systematic modeling framework of superstructure optimization in process synthesis. Computers & Chemical Engineering, 1999, 23(6): 709–731

[6] Stephanopoulos G, Westerberg A W. Studies in process synthesis II, evolutionary synthesis of optimal process flowsheets. Chemical Engineering Science, 1976, 31(3): 195–204

[7] Zhang T, Sahinidis N V, Siirola J J. Pattern recognition in chemical process flowsheets. AIChE Journal. American Institute of Chemical Engineers, 2019, 65(2): 592–603

[8] Gani R, O’Connell J P. A knowledge based system for the selection of thermodynamic models. Computers & Chemical Engineering, 1989, 13(4-5): 397–404

[9] Kirkwood R L, Locke M H, Douglas J M. A prototype expert system for synthesizing chemical process flowsheets. Computers & Chemical Engineering, 1988, 12(4): 329–343

[10] Tula A K, Eden M R, Gani R. Process synthesis, design and analysis using a process-group contribution method. Computers & Chemical Engineering, 2015, 81: 245–259

[11] Daichendt M M, Grossmann I E. Integration of hierarchical decomposition and mathematical programming for the synthesis of process flowsheets. Computers & Chemical Engineering, 1997, 22(1-2): 147–175

[12] Martin M, Adams T A II. Challenges and future directions for process and product synthesis and design. Computers & Chemical Engineering, 2019, 128: 421–436

[13] Grossmann I E, Harjunkoski I. Process systems engineering: academic and industrial perspectives. Computers & Chemical Engineering, 2019, 126: 474–484

[14] Stephanopoulos G. Artificial intelligence in process engineering—current state and future trends. Computers & Chemical Engineering, 1990, 14(11): 1259–1270

[15] Stephanopoulos G, Han C. Intelligent systems in process engineering: a review. Computers & Chemical Engineering, 1996, 20(6-7): 143–191

[16] Dimiduk D M, Holm E A, Niezgoda S R. Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering. Integrating Materials and Manufacturing Innovation, 2018, 7(3): 157–172

[17] Venkatasubramanian V. The promise of artificial intelligence in chemical engineering: is it here, finally? AIChE Journal. American Institute of Chemical Engineers, 2019, 65(2): 466–478

[18] Lee J H, Shin J, Realff M J. Machine learning: overview of the recent progresses and implications for the process systems engineering field. Computers & Chemical Engineering, 2018, 114: 111–121

[19] Eason J, Cremaschi S. Adaptive sequential sampling for surrogate model generation with artificial neural networks. Computers & Chemical Engineering, 2014, 68: 220–232

[20] Fahmi I, Cremaschi S. Process synthesis of biodiesel production plant using artificial neural networks as the surrogate models. Computers & Chemical Engineering, 2012, 46: 105–123

[21] Fernandes F A N. Optimization of fischer‐tropsch synthesis using neural networks. Chemical Engineering & Technology, 2006, 29(4): 449–453

[22] Sutton R S, Barto A G. Reinforcement Learning: An Introduction. 2nd ed. Cambridge, MA: The MIT Press, 2018

[23] Lapan M. Deep Reinforcement Learning Hands-On. 1st ed. Birmingham, E.K.: Packt Publishing Ltd., 2018

[24] Shin J, Badgwell T A, Liu K H, Lee J H. Reinforcement learning—overview of recent progress and implications for process control. Computers & Chemical Engineering, 2019, 127: 282–294

[25] Zhou Z, Li X, Zare R N. Optimizing chemical reactions with deep reinforcement learning. ACS Central Science, 2017, 3(12): 1337–1344

[26] Khan A, Lapkin A. Searching for optimal process routes: a reinforcement learning approach. Computers & Chemical Engineering, 2020, 141: 107027

[27] Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A, Chen Y, Lillicrap T, Hui F, Sifre L, van den Driessche G, Graepel T, Hassabis D. Mastering the game of Go without human knowledge. Nature, 2017, 550(7676): 354–359

[28] Silver D, Hubert T, Schrittwieser J, Antonoglou I, Lai M, Guez A, Lanctot M, Sifre L, Kumaran D, Graepel T, Lillicrap T, Simonyan K, Hassabis D. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 2018, 362(6419): 1140–1144

[29] Wang Y, Li Y, Song Y, Rong X. The influence of the activation function in a convolution neural network model of facial expression recognition. Applied Sciences (Basel, Switzerland), 2020, 10(5): 1897

[30] Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado G S, Davis A, Dean J, Devin M, . TensorFlow: large-scale machine learning on heterogeneous systems. 2015, arXiv: 1603.04467

[31] Alpaydin E. Introduction to Machine Learning. 2nd ed. Cambridge, MA: The MIT Press, 2010

[32] Zinser A, Rihko-Struckmann L, Sundmacher K. Computationally efficient steady-state process simulation by applying a simultaneous dynamic method. Computer-Aided Chemical Engineering, 2016, 38: 517–522

[33] Hoffmann A, Bortz M, Burger J, Hasse H, Küfer K H. A new scheme for process simulation by optimization: distillation as an example. Computer-Aided Chemical Engineering, 2016, 38: 205–210

[34] Hausknecht M, Stone P. Deep reinforcement learning in parameterized action space. 2015, arXiv: 1511.04143

[35] Xiong J, Wang Q, Yang Z, Sun P, Han L, Zheng Y, Fu H, Zhang T, Liu J, Liu H. Parametrized deep q-networks learning: reinforcement learning with discrete-continuous hybrid action space. 2018, arXiv: 1810.06394

[36] Neunert M, Abdolmaleki A, Wulfmeier M, Lampe T, Springenberg J T, Hafner R, Romano F, Buchli J, Heess N, Riedmiller M. Continuous-discrete reinforcement learning for hybrid control in robotics. 2020, arXiv: 2001.00449v1

Funding note

Open Access funding enabled and organized by Projekt DEAL.

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