用机器学习自动选优化算法，提速化工流程控制。
Accelerating process control and optimization via machine learning: A review
- 从数据中学习求解器行为，自动化算法选择与配置。
- 针对单体与分解式算法，提升求解效率与精度。
- 适合做流程优化的工程师和算法研究者参考。
过程控制与优化广泛应用于化学工程决策问题。但识别并调优最优求解算法既具挑战性又耗时。机器学习可通过从数据中学习数值求解器的行为来自动化这一过程。本文综述了近期在（i）决策问题的机器学习表征、（ii）算法选择，以及（iii）单体与分解式算法的配置方面的进展。最后，讨论了机器学习加速过程优化与控制应用中的开放问题。
原文摘要 · Abstract (English)
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.
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