Real-time optimization via modifier adaptation of closed-loop processes using transient measurements

Abstract

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.

Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes

“…This approach has shown to be very efficient in some scenarios, nevertheless, function gradients from process measurements are needed, which are difficult to estimate in practice. Variants of modifier adaptation to resolve this issue include recursive update schemes [8], [9], directional derivatives [10], transient process measurements [11].…”