Accelerating process control and optimization via machine learning: A review

2024/12/24 by Mitrai, Ilias, Daoutidis, Prodromos · 2 citations
#FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2412.18529

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.

Cited by

• Learning-Based Hierarchical Approach for Fast Mixed-Integer Optimization
• Graph-Based Imitation and Reinforcement Learning for Efficient Benders Decomposition
• Learning to control inexact Benders decomposition via reinforcement learning

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