Himmel, Andreas ; Matschek, Janine ; Kok (Louis), Rudolph ; Morabito, Bruno ; Nguyen, Hoang Hai ; Findeisen, Rolf
Hrsg.: Soroush, Masoud ; Braatz, Richard D. (2024)
Machine learning for control of (bio)chemical manufacturing systems.
In: Artificial Intelligence in Manufacturing - Concepts and Methods
doi: 10.1016/B978-0-323-99134-6.00009-8
Buchkapitel, Bibliographie

Kurzbeschreibung (Abstract)

The control of manufacturing processes must satisfy high-quality and efficiency requirements while meeting safety requirements. A broad spectrum of monitoring and control strategies, such as model and optimization-based controllers, is utilized to address these issues. Driven by rising demand for flexible yet energy and resource-efficient operations existing approaches are challenged due to high uncertainties and changes. Machine learning algorithms are becoming increasingly important in tackling these challenges, especially due to the growing amount of available data. The ability for automatic adaptation and learning from human operators offer new opportunities to increase efficiency yet provide flexible operation. Combining machine learning algorithms with safe or robust controls offers novel reliable operation methods. This chapter highlights ways to fuse machine learning and control for the safe and improved operation of chemical and biochemical processes. We outline and summarize both learning models for control and learning the control components. The main objective is to provide a structured, general overview, including a literature review, thus providing a guideline for utilizing machine learning techniques in control structures.

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