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Vogel G, Schulze Balhorn L, Schweidtmann AM. Learning from flowsheets: A generative transformer model for autocompletion of flowsheets. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Göttl Q, Tönges Y, Grimm DG, Burger J. Automated Flowsheet Synthesis Using Hierarchical Reinforcement Learning: Proof of Concept. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100086] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Quirin Göttl
- Technical University of Munich Campus Straubing for Biotechnology and Sustainability Laboratory of Chemical Process Engineering Schulgasse 16 94315 Straubing Germany
| | - Yannic Tönges
- Technical University of Munich Campus Straubing for Biotechnology and Sustainability Laboratory of Chemical Process Engineering Schulgasse 16 94315 Straubing Germany
| | - Dominik G. Grimm
- Technical University of Munich Campus Straubing for Biotechnology and Sustainability Bioinformatics Schulgasse 22 94315 Straubing Germany
- Weihenstephan-Triesdorf University of Applied Sciences Petersgasse 18 94315 Straubing Germany
- Technical University of Munich Department of Informatics Boltzmannstraße 3 85748 Garching Germany
| | - Jakob Burger
- Technical University of Munich Campus Straubing for Biotechnology and Sustainability Laboratory of Chemical Process Engineering Schulgasse 16 94315 Straubing Germany
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Abstract
AbstractAutomated 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.
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