Real-time optimization (RTO) has the ability to boost the performance of a process whilst satisfying the constraints by using process measurements, driving the operating conditions towards optimality. Modifier adaptation (MA) is a methodology of RTO which can find the optimal operating point of a process even in the presence of plant-model mismatch. This work presents an extension to MA through the combination of two established frameworks, allowing for the optimization of a controlled process using transient measurements whilst using a steady-state open-loop model. In addition, an approach for model-based gradient estimation, despite the mismatch between the degrees of freedom of the closed-loop plant and the available open loop model is suggested that does not necessitate amending the model. The proposed scheme is illustrated on a case study of a CSTR and a distillation column, detailing how the gradient can be estimated.

实时优化（RTO）能够提高过程的性能，同时通过使用过程测量来满足约束条件，从而将操作条件推向最佳状态。修改器适应（MA）是RTO的一种方法，即使在存在工厂模型不匹配的情况下，它也可以找到过程的最佳操作点。这项工作通过两个已建立的框架的组合提出了对MA的扩展，允许使用瞬态测量同时使用稳态开环模型来优化受控过程。另外，尽管闭环设备的自由度和可用的开环模型之间不匹配，但提出了一种基于模型的梯度估计方法，该方法无需修改模型。