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

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.