Automated Flowsheet Synthesis Using Hierarchical Reinforcement Learning: Proof of Concept

被引:15
|
作者
Gottl, Quirin [1 ]
Tonges, Yannic [1 ]
Grimm, Dominik G. [2 ,3 ,4 ]
Burger, Jakob [1 ]
机构
[1] Tech Univ Munich, Lab Chem Proc Engn, Campus Straubing Biotechnol & Sustainabil, D-94315 Straubing, Germany
[2] Tech Univ Munich, Bioinformat, Campus Straubing Biotechnol & Sustainabil, D-94315 Straubing, Germany
[3] Weihenstephan Triesdorf Univ Appl Sci, Petersgasse 18, D-94315 Straubing, Germany
[4] Tech Univ Munich, Dept Informat, Boltzmannstr 3, D-85748 Garching, Germany
关键词
Artificial intelligence; Automated process synthesis; Flowsheet synthesis; Machine learning; Reinforcement learning; ARTIFICIAL-INTELLIGENCE; DESIGN; STATE; OPTIMIZATION; CHALLENGES; GO;
D O I
10.1002/cite.202100086
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Recently we showed that reinforcement learning can be used to automatically generate process flowsheets without heuristics or prior knowledge. For this purpose, SynGameZero, a novel two-player game has been developed. In this work we extend SynGameZero by structuring the agent's actions in several hierarchy levels, which improves the approach in terms of scalability and allows the consideration of more sophisticated flowsheet problems. We successfully demonstrate the usability of our novel framework for the fully automated synthesis of an ethyl tert-butyl ether process.
引用
收藏
页码:2010 / 2018
页数:9
相关论文
共 50 条
  • [1] Automated synthesis of steady-state continuous processes using reinforcement learning
    Goettl, Quirin
    Grimm, Dominik G.
    Burger, Jakob
    FRONTIERS OF CHEMICAL SCIENCE AND ENGINEERING, 2022, 16 (02) : 288 - 302
  • [2] Automated synthesis of steady-state continuous processes using reinforcement learning
    Quirin Göttl
    Dominik G. Grimm
    Jakob Burger
    Frontiers of Chemical Science and Engineering, 2022, 16 : 288 - 302
  • [3] Automated synthesis of steady-state continuous processes using reinforcement learning
    Gttl Quirin
    Grimm Dominik G
    Burger Jakob
    Frontiers of Chemical Science and Engineering, 2022, 16 (02) : 288 - 302
  • [4] Flowsheet generation through hierarchical reinforcement learning and graph neural networks
    Stops, Laura
    Leenhouts, Roel
    Gao, Qinghe
    Schweidtmann, Artur M.
    AICHE JOURNAL, 2023, 69 (01)
  • [5] Hierarchical reinforcement learning for efficient and effective automated penetration testing of large networks
    Mohamed C. Ghanem
    Thomas M. Chen
    Erivelton G. Nepomuceno
    Journal of Intelligent Information Systems, 2023, 60 : 281 - 303
  • [6] Hierarchical reinforcement learning for efficient and effective automated penetration testing of large networks
    Ghanem, Mohamed C.
    Chen, Thomas M.
    Nepomuceno, Erivelton G.
    JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 2023, 60 (02) : 281 - 303
  • [7] Scaling intelligent agent combat behaviors through hierarchical reinforcement learning
    Black, Scotty E.
    Darken, Christian J.
    DISRUPTIVE TECHNOLOGIES IN INFORMATION SCIENCES VII, 2023, 12542
  • [8] Automated Concept Drift Handling for Fault Prediction in Edge Clouds Using Reinforcement Learning
    Shayesteh, Behshid
    Fu, Chunyan
    Ebrahimzadeh, Amin
    Glitho, Roch H.
    IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 2022, 19 (02): : 1321 - 1335
  • [9] Using machine learning to classify temporomandibular disorders: a proof of concept
    Zatt, Fernanda Pretto
    Cordeiro, Joao Victor Cunha
    Bohner, Lauren
    de Souza, Beatriz Dulcineia Mendes
    Caldas, Victor Emanoel Armini
    Caldas, Ricardo Armini
    JOURNAL OF APPLIED ORAL SCIENCE, 2024, 32
  • [10] Reinforcement learning of simplex pivot rules: a proof of concept
    Varun Suriyanarayana
    Onur Tavaslıoğlu
    Ankit B. Patel
    Andrew J. Schaefer
    Optimization Letters, 2022, 16 : 2513 - 2525