Automated generation of mechanistic models for chemical process digital twins using reinforcement learning part I: Conceptual framework and equation generation

Computers & Chemical Engineering, vol. 202, pp. 109281

Abstract

Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent model updates and ask for automation of this modeling process. In this work, we propose an automated workflow that generates accurate mechanistic reactor models from experimental concentration data of a given reactor. At the core of this workflow, a reinforcement learning agent assembles an interpretable reactor model by iteratively simplifying general differential balance equations and fitting the resulting candidate model to experimental data. We demonstrate the performance of our workflow in two case studies. An in silico case study shows that the workflow correctly reconstructs the model underlying a synthetic data set, is robust against noise in the input data, and has favorable scaling properties. The agent accelerates the model derivation process significantly compared to an exhaustive enumerative search. Secondly, an experimental case study is conducted employing a Taylor-Couette prototype reactor. A liquid-phase esterification reaction of (2-bromophenyl)methanol and acetic anhydride was used as a test system. Based on the experimental data, the workflow derives meaningful mechanistic models, with the most accurate model showing a normalized root mean squared error of 2.4%. Future work encompasses the integration of automated experiments into the workflow and the transfer of our workflow to process units beyond chemical reactors.

Authors 5

1. Mathis Heyer Aachen Process Systems Engineering (AVT.SVT)

   RWTH Aachen University · University of Cambridge

   Affiliation as printed

   Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, 52074 Aachen, Germany

2. Jiyizhe Zhang corresponding

   University of Cambridge

   Affiliation as printed

   Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom

   Innovation Centre in Digital Molecular Technologies, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom

3. Naoto Sugisawa

   University of Cambridge · Nagoya University

   Affiliation as printed

   Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya University, Nagoya, 464-8601, Japan

   Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom

4. Jan-Frederic Laub Aachen Process Systems Engineering (AVT.SVT)

   RWTH Aachen University · University of Cambridge

   Affiliation as printed

   Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, 52074 Aachen, Germany

5. Alexei A. Lapkin corresponding

   University of Cambridge · The Cambridge Centre for Advanced Research and Education in Singapore

   Affiliation as printed

   Cambridge Centre for Advanced Research and Education in Singapore Ltd., 1 Create Way, CREATE Tower #05-05, Singapore 138602, Singapore

   Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom

   Innovation Centre in Digital Molecular Technologies, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom