Accelerating process control and optimization via machine learning: A review

Ilias Mitrai; Prodromos Daoutidis

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arXiv · 2412.18529

Accelerating process control and optimization via machine learning: A review

Ilias Mitrai·Prodromos Daoutidis

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Abstract

Process control and optimization have been widely used to solve decision-making problems in chemical engineering applications. However, identifying and tuning the best solution algorithm is challenging and time-consuming. Machine learning tools can be used to automate these steps by learning the behavior of a numerical solver from data. In this paper, we discuss recent advances in (i) the representation of decision-making problems for machine learning tasks, (ii) algorithm selection, and (iii) algorithm configuration for monolithic and decomposition-based algorithms. Finally, we discuss open problems related to the application of machine learning for accelerating process optimization and control.

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BibTeX RIS

Ilias Mitrai, Prodromos Daoutidis. 2024-12-24. Accelerating process control and optimization via machine learning: A review. https://arxiv.org/abs/2412.18529

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