通过机器学习加速过程控制与优化：综述
Accelerating process control and optimization via machine learning: A review
AI总结：
本文综述了如何利用机器学习自动化化学工程中过程控制与优化的算法选择和配置，总结了决策问题表示、算法选择与配置的最新进展及未来开放问题。
摘要：
过程控制与优化已广泛应用于解决化学工程应用中的决策问题。然而，识别和调整最佳求解算法极具挑战且耗时。机器学习工具可通过从数据中学习数值求解器的行为来自动化这些步骤。本文讨论了以下方面的最新进展：用于机器学习任务的决策问题表示、算法选择，以及针对单一算法和基于分解的算法的算法配置。最后，我们讨论了应用机器学习加速过程优化与控制的开放问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
