Accelerating process control and optimization via machine learning: A review
Systems and Control
2024-12-25 v1 Machine Learning
Systems and Control
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.
Cite
@article{arxiv.2412.18529,
title = {Accelerating process control and optimization via machine learning: A review},
author = {Ilias Mitrai and Prodromos Daoutidis},
journal= {arXiv preprint arXiv:2412.18529},
year = {2024}
}