Accelerating process control and optimization via machine learning: A review
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 27 of its 93 references that are in the map. First posted on arXiv 24 Dec 2024.
Builds on
27 of 93 references.
References · 27
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15 more
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Citing papers per year: 2026: 3.
3 papers.