文章基本信息

标题：Active Perturbation in Modifier Adaptation for Real Time Optimization to Cope with Measurement Delays ⁎
本地全文：下载
作者：Anwesh Reddy Gottu Mukkula ; Simon Wenzel ; Sebastian Engell 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：1
页码：124-129
DOI：10.1016/j.ifacol.2018.05.021
语种：English
出版社：Elsevier
摘要：AbstractIterative 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.
关键词：KeywordsReal-time optimizationPlant-model mismatchModifier adaptationTime delayActive perturbation strategy