Active Perturbation in Modifier Adaptation for Real Time Optimization to Cope with Measurement Delays

Abstract

Iterative optimization is a technique where the model-based optimization problem is iteratively updated to drive the plant to its optimal operating point in the presence of plant-model mismatch. Modifier adaptation (MA) is a state-of-the-art iterative optimization approach which can be used in the presence of both structural and parametric plant-model mismatch. Availability of plant measurements is key for the successful application of MA. In the process industries, analytical sensors cannot always be installed directly at the location of interest. A remote positioning of a measurement device leads to a significant delay in receiving the measurements, since the transportation of the sample to the sensor requires additional time. This leads to a waiting time between two successive iterations during which the standard iterative optimization techniques remain idle. In this contribution, the issue of a pure time delay caused by the remote positioning of a sensor is addressed. We propose an active perturbation strategy to obtain more information by perturbing the plant during the waiting time to improve the estimation of the plant gradient, which reduces the time to reach the plant-optimum. The performance of the proposed strategy is analyzed based on the simulation results from a chemical engineering case study.