One of the difficulties in practical implementations of the classic Real-Time Optimization (RTO) strategy is the integration between optimization and control layers, mainly due to the differences between the models used in each layer, which may result in unreachable setpoints coming from optimization to the control layer. In this context, Economic Model Predictive Control (EMPC) is a strategy where optimization and control problems are solved simultaneously. However, this strategy is based on the assumption that a nonlinear dynamic model is available, which may not be valid. Also, when considering a first-principles nonlinear model, the computational cost and convergence may be relevant issues. The present work presents an RTO framework based on an EMPC structure considering a Hammerstein model for the plant. This modeling approach can be applied even in the absence of first-principles models. The proposed EMPC considers the minimization of the economic objective function gradient calculated through a steady-state model based on a Gaussian Process. This strategy was applied to the Willians–Otto Reactor benchmark and presented superior results than the classic RTO and Hybrid RTO (H-RTO) approaches in closed-loop and a lower average iteration time than these other approaches.

经典实时优化 (RTO) 策略在实际实现中的难点之一是优化层和控制层之间的集成，主要是由于各层使用的模型之间存在差异，这可能导致从优化到控制的设定点不可达。控制层。在这种情况下，经济模型预测控制 (EMPC) 是一种同时解决优化和控制问题的策略。但是，此策略基于非线性动态模型可用的假设，这可能无效。此外，在考虑第一性原理非线性模型时，计算成本和收敛性可能是相关问题。目前的工作提出了一个基于 EMPC 结构的 RTO 框架，考虑了工厂的 Hammerstein 模型。即使在没有第一性原理模型的情况下，也可以应用这种建模方法。所提出的 EMPC 考虑了通过基于高斯过程的稳态模型计算的经济目标函数梯度的最小化。该策略应用于 Willians-Otto Reactor 基准测试，并在闭环中呈现出比经典 RTO 和混合 RTO (H-RTO) 方法更好的结果，并且比这些其他方法具有更低的平均迭代时间。