Recent trends on hybrid modeling for Industry 4.0

A B S T R A C T The chemical processing industry has relied on modeling techniques for process monitoring, control, di-agnosis, optimization, and design, especially since the third industrial revolution and the emergence of Process Systems Engineering. The fourth industrial revolution, connected to massive digitization, made it possible to collect and process large volumes of data triggering the development of data-driven frame-works for knowledge extraction. However, one must not leave behind the successful solutions developed over decades based on first principle mechanistic modeling approaches. At present, both industry and researchers are realizing the need for new ways to incorporate process and phenomenological knowledge in big data and machine learning frameworks, leading to more robust and intelligible artificial intelli-gence solutions, capable of assisting the target stakeholders in their activities and decision processes. In this article, we review hybrid modeling techniques, associated system identification methodologies and model assessment criteria. Applications in chemical and biochemical processes are also referred. (C) 2021 Published by Elsevier Ltd. & nbsp;

Automated summary

This article discusses the chemical processing industry's reliance on modeling techniques and the impact of emerging data-driven frameworks on knowledge extraction, emphasizing the importance of integrating traditional mechanistic models with data-driven frameworks, and pointing out the need to incorporate process and phenomenological knowledge into big data and machine learning frameworks.