Self-optimizing control (SOC) aims to maintain near-optimal process operation by judiciously selecting controlled variables (CVs). In this series of work, the generalized global SOC (g²SOC) approach is proposed, which extends the concept of SOC to the whole operation space and uses general nonlinear functions to design CVs instead of linear combinations. In the first part of this series work, two numerical approaches for g²SOC are proposed: the optimization-based approach and the regression-based approach, based on a theoretical analysis of the existence of perfect self-optimizing CVs. The CVs designed by the former perform better, but are usually infeasible for large-scale problems. In this paper, we propose an algorithm called objective-guided controlled variable learning (OGCVL) that combines the advantages of both and has a better scalability. OGCVL is proposed for efficient CV design that seamlessly integrates symbolic and numerical computation techniques. Finally, the effectiveness of the OGCVL method is verified in two numerical examples. Both examples illustrate show that the OGCVL method is able to achieve good results while maintaining computational efficiency and is also feasible in large-scale problems.

中文翻译：

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化学过程的广义全局自优化控制：第 II 部分 目标引导的受控变量学习方法


自我优化控制 （SOC） 旨在通过明智地选择受控变量 （CV） 来保持接近最优的过程操作。在这一系列工作中，提出了广义全局 SOC （g²SOC） 方法，该方法将 SOC 的概念扩展到整个操作空间，并使用一般非线性函数来设计 CV 而不是线性组合。在本系列工作的第一部分，提出了两种 g²SOC 的数值方法：基于优化的方法和基于回归的方法，基于对完美自优化 CV 存在的理论分析。前者设计的 CV 性能更好，但通常不适用于大规模问题。在本文中，我们提出了一种称为目标引导受控变量学习（OGCVL）的算法，它结合了两者的优点，具有更好的可扩展性。OGCVL 用于无缝集成符号和数值计算技术的高效 CV 设计。最后，通过两个数值算例验证了OGCVL方法的有效性。这两个例子都表明，OGCVL 方法能够在保持计算效率的同时获得良好的结果，并且在大规模问题中也是可行的。