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

TL;DR

This paper reviews and classifies hybrid science-guided machine learning models that integrate scientific knowledge with data analytics to improve modeling and optimization in chemical and bioprocessing, illustrating with polymer process examples.

Contribution

It provides a systematic classification of hybrid SGML models and discusses their applications, advantages, and limitations in chemical engineering.

Findings

Hybrid models enhance prediction accuracy and scientific consistency.

Classification framework aids in selecting appropriate hybrid modeling strategies.

Examples demonstrate the effectiveness of hybrid SGML in polymer process modeling.

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.We also present several examples to illustrate different hybrid SGML methodologies for modeling polymer processes.