Process expert knowledge is essential in creating value from data-driven industrial soft sensors

• Published in: Computers & chemical engineering Vol. 183; p. 108602
• Main Authors: Offermans, Tim, Szymańska, Ewa, Souza, Francisco A.A., Jansen, Jeroen J.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2024
• Subjects: Industry 5.0; Optimization; Soft-sensoring; Statistical modelling; Industry 5.0; Statistical modelling; Optimization; Soft-sensoring
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2024.108602

•Process variable selection greatly improves industrial soft-sensors.•Expert knowledge-based selections outperform purely data-driven selections.•Data-driven selections can be improve expert knowledge-based selections.•A new human-centered data modelling oppertunity (Industry 5.0) is described. The objective of Industry 5.0 is to (re)centre the human operator amidst digital process automation. This requires new data processing technologies that extract human expertise and integrate it with advanced data modelling techniques to enhance human-computer collaboration. In this work, we present an integrated and systematic approach that combines contemporary data modelling technology with process knowledge gained from engineers. Specifically, we develop, investigate, and compare data- and expert-driven approaches for selecting process variables for a real-time product quality prediction for three parallel-operated dairy processing lines. We show how and why data-driven selections are outperformed by expert-driven selections, but that they can improve upon expert-driven selections when used as a secondary selection step. This leads to explainable soft-sensors that are optimal both in accuracy and in parsimony. This highlights the significance of capturing and preserving human expertise, which will enhance the quality and sustainability of industrial processing.