Accelerating process control and optimization via machine learning: A review
It provides a review of recent advances for researchers in chemical engineering and optimization, but is incremental as it synthesizes existing work.
This paper reviews how machine learning can automate algorithm selection and configuration to accelerate process control and optimization in chemical engineering, addressing challenges in identifying and tuning solution algorithms.
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.