Machine Learning for Process Control of (Bio)Chemical Processes

TL;DR

This paper reviews how machine learning can enhance the control of chemical and biochemical processes by improving safety, efficiency, and adaptability through data-driven models and control strategies.

Contribution

It provides a comprehensive overview and guidelines for integrating machine learning techniques into process control systems, emphasizing safety and robustness.

Findings

Machine learning enables adaptive control in uncertain environments.

Combining ML with traditional control improves process safety.

The review offers a structured guideline for applying ML in process control.

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, are 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. We offer a general overview, including a literature review, to provide a guideline for utilizing machine learning techniques in control structures.