Accelerating process control and optimization via machine learning: A review

TL;DR

This review discusses how machine learning can enhance process control and optimization in chemical engineering by automating decision-making and tuning algorithms, highlighting recent advances and open challenges.

Contribution

It provides a comprehensive overview of recent machine learning methods applied to decision problem representation, algorithm selection, and configuration in process optimization.

Findings

Machine learning automates solver behavior learning from data.

Advances in problem representation improve ML application in process control.

Open problems remain in applying ML to accelerate process optimization.

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.