Self-optimizing control technologies are a well-known study field of control structure design, having a robust mathematical background. With the aid of commercial process simulators and numerical packages, process modeling became an easier task. However, dealing with extremely large and complex systems still is a tedious task, and sometimes not feasible, even with these innovative tools. Surrogate models, also called metamodels, can be used to substitute partially or totally the original mathematical models for prediction and optimization purposes, reducing the complexity of evaluating large-scale and highly nonlinear processes. This work aims at applying recent self-optimizing control techniques to surface responses of processes using the Kriging method as a reduced model builder. A procedure to apply self-optimizing control to surrogate responses was described in detail, together with how the optimization can be done. Well-known case studies had their surface responses successfully built and analyzed to generate using the techniques cited, the optimal selection of controlled variables that minimizes the worst-case loss, and the same results were found when compared with the implementation in the original models from previous authors. The results indicate the effectiveness of the reduced models when applied to design self-optimizing control structures, simplifying the task.

中文翻译：

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基于元模型的自优化控制数值技术

自优化控制技术是控制结构设计的一个著名研究领域，具有强大的数学背景。借助商业过程仿真器和数值软件包，过程建模变得更加容易。但是，即使使用这些创新工具，处理极其庞大和复杂的系统仍然是一项繁琐的任务，有时甚至是不可行的。替代模型（也称为元模型）可用于部分或全部替代原始数学模型以进行预测和优化，从而降低了评估大规模和高度非线性过程的复杂性。这项工作旨在将最近的自优化控制技术应用于使用Kriging方法作为简化模型构建器的过程的表面响应。详细介绍了将自我优化控制应用于替代响应的过程，以及如何进行优化。著名的案例研究使用所引用的技术成功构建并分析了它们的表面响应，使用了控制变量的最佳选择以最大程度地减少了最坏情况的损失，并且与原始模型中的实现相比，发现了相同的结果以前的作者。结果表明，简化后的模型在设计自优化控制结构时的有效性，简化了任务。最佳选择的控制变量可以最大程度地减少最坏情况的损失，并且与以前作者在原始模型中的实现相比，发现了相同的结果。结果表明，简化后的模型在设计自优化控制结构时的有效性，简化了任务。优化控制变量的选择以最大程度地减少最坏情况的损失，并且与以前作者在原始模型中的实现相比，发现了相同的结果。结果表明，简化后的模型在设计自优化控制结构时的有效性，简化了任务。