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

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.

arXiv comment29 pages 12 figures, 1 table

References in corpus (4)

• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
• Physically-informed artificial neural networks for atomistic modeling of materials
• Uncertainty Quantification in First-Principles Predictions of Harmonic Vibrational Frequencies of Molecules and Molecular Complexes

Cited by in corpus (4)

• Physics-informed AI and ML-based sparse system identification algorithm for discovery of PDE's representing nonlinear dynamic systems
• ROM-Based Stochastic Optimization for a Continuous Manufacturing Process
• Applications of Machine Learning to Optimizing Polyolefin Manufacturing
• Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering