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Explaining and Integrating Machine Learning Models with Rigorous Simulation
Author(s) -
Schöneberger Jan C.,
Aker Burcu,
Fricke Armin
Publication year - 2021
Publication title -
chemie ingenieur technik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 36
eISSN - 1522-2640
pISSN - 0009-286X
DOI - 10.1002/cite.202100089
Subject(s) - workflow , process (computing) , computer science , resource (disambiguation) , process simulation , process modeling , work (physics) , work in process , industrial engineering , machine learning , engineering , mechanical engineering , computer network , operations management , database , operating system
Abstract First‐principle flowsheet simulation is a reliable data resource for training machine learning (ML) models for process industry, especially when plant data is not available. Process simulators play an even more important role for evaluation and validation of ML models. In this work, we present a workflow for building and evaluating ML models based on data generated by a commercial flowsheet simulator. The resulting hybrid models, combining data‐driven predictions with mass and energy balances, have much lower calculation times than the rigorous models. The implementation of such models shows great potential for solving more complex process engineering problems on the high‐dimensional space in the future, while saving the process engineer's time in the present.