Pattern recognition in chemical process flowsheets

Zhang, Tong; Sahinidis, Nikolaos V.; Siirola, Jeffrey J.

doi:10.1002/aic.16443

Cited by 28 publications

(13 citation statements)

References 19 publications

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“…In addition to these applications, GNNs have also been extended to recognize higher-order features from graphs [113]. There have also been some first efforts to represent reaction networks [94,114] and flowsheets [115,116] as graphs and apply ML to this data. Venkatasubramanian [7] identified information representation as promising building blocks to further advance the field of CE.…”

Section: Heterogeneity Of Datamentioning

confidence: 99%

Machine Learning in Chemical Engineering: A Perspective

Schweidtmann

Esche

Fischer

et al. 2021

Chemie Ingenieur Technik

129

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The transformation of the chemical industry to renewable energy and feedstock supply requires new paradigms for the design of flexible plants, (bio-)catalysts, and functional materials. Recent breakthroughs in machine learning (ML) provide unique opportunities, but only joint interdisciplinary research between the ML and chemical engineering (CE) communities will unfold the full potential. We identify six challenges that will open new methods for CE and formulate new types of problems for ML: (1) optimal decision making, (2) introducing and enforcing physics in ML, (3) information and knowledge representation, (4) heterogeneity of data, (5) safety and trust in ML applications, and (6) creativity. Under the umbrella of these challenges, we discuss perspectives for future interdisciplinary research that will enable the transformation of CE.

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Section: Heterogeneity Of Datamentioning

confidence: 99%

Machine Learning in Chemical Engineering: A Perspective

Schweidtmann

Esche

Fischer

et al. 2021

Chemie Ingenieur Technik

129

View full text Add to dashboard Cite

show abstract

“…In this contribution, we represent flowsheets as graphs consisting of unit operations as nodes and streams as edges (cf., References 44,45). The developed agent architecture features a flowsheet fingerprint, which is learned by processing flowsheet graphs in GNNs.…”

Section: Introductionmentioning

confidence: 99%

Flowsheet generation through hierarchical reinforcement learning and graph neural networks

et al. 2022

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Process synthesis experiences a disruptive transformation accelerated by artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state-of-the-art actor-critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from process graphs. In particular, the graph neural networks are implemented within the agent architecture to process the states and make decisions. We implement a hierarchical and hybrid decision-making process to generate flowsheets, where unit operations are placed iteratively as discrete decisions and corresponding design variables are selected as continuous decisions. We demonstrate the potential of our method to design economically viable flowsheets in an illustrative case study comprising equilibrium reactions, azeotropic separation, and recycles. The results show quick learning in discrete, continuous, and hybrid action spaces. The method is predestined to include large action-state spaces and an interface to process simulators in future research.

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“…In this contribution, we represent flowsheets as graphs consisting of unit operations as nodes and streams as edges (c.f. 44,45 ). The developed agent architecture features a flowsheet fingerprint, which is learned by processing flowsheet graphs in GNNs.…”

Section: Introductionmentioning

confidence: 99%

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks

Stops¹,

Roel²,

Gao³

et al. 2022

Preprint

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Process synthesis experiences a disruptive transformation accelerated by digitization and artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state-of-the-art actor-critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from process graphs. In particular, the graph neural networks are implemented within the agent architecture to process the states and make decisions. Moreover, we implement a hierarchical and hybrid decision-making process to generate flowsheets, where unit operations are placed iteratively as discrete decisions and corresponding design variables are selected as continuous decisions. We demonstrate the potential of our method to design economically viable flowsheets in an illustrative case study comprising equilibrium reactions, azeotropic separation, and recycles. The results show quick learning in discrete, continuous, and hybrid action spaces. Due to the flexible architecture of the proposed reinforcement learning agent, the method is predestined to include large action-state spaces and an interface to process simulators in future research.

show abstract

Pattern recognition in chemical process flowsheets

Cited by 28 publications

References 19 publications

Machine Learning in Chemical Engineering: A Perspective

Machine Learning in Chemical Engineering: A Perspective

Flowsheet generation through hierarchical reinforcement learning and graph neural networks

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks

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