A Hybrid Science-Guided Machine Learning Approach for Modeling and Optimizing Chemical Processes

• URL: http://arxiv.org/abs/2112.01475v1
• Date: Thu, 2 Dec 2021 18:24:13 GMT
• Title: A Hybrid Science-Guided Machine Learning Approach for Modeling and Optimizing Chemical Processes
• Authors: Niket Sharma, Y. A. Liu
• Abstract summary: Hybrid process modeling and optimization is combined with a science-guided machine learning (SGML) approach. For applying ML to improve models, we present expositions of the sub-categories of direct serial and parallel hybrid modeling. For applying scientific principles to improve ML models, we discuss the sub-categories of science-guided design, learning and refinement.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study presents a broad perspective of hybrid process modeling and optimization combining the scientific knowledge and data analytics in bioprocessing and chemical engineering with a science-guided machine learning (SGML) approach. We divide the approach into two major categories. The first refers to the case where a data-based ML model compliments and makes the first-principle science-based model more accurate in prediction, and the second corresponds to the case where scientific knowledge helps make the ML model more scientifically consistent. We present a detailed review of scientific and engineering literature relating to the hybrid SGML approach, and propose a systematic classification of hybrid SGML models. For applying ML to improve science-based models, we present expositions of the sub-categories of direct serial and parallel hybrid modeling and their combinations, inverse modeling, reduced-order modeling, quantifying uncertainty in the process and even discovering governing equations of the process model. For applying scientific principles to improve ML models, we discuss the sub-categories of science-guided design, learning and refinement. For each sub-category, we identify its requirements, advantages and limitations, together with their published and potential areas of applications in bioprocessing and chemical engineering.

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