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Accelerating process control and optimization via machine learning
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AI
DOI：10.1515/revce-2024-0060.png)
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Abstract
En 中文
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.
Keywords:
process control
process optimization
machine learning
Journal
IF:
55.8
Papers:
558
Citations:
24.7W