A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model Identification Platforms

作     者：Arun Pankajakshan Conor Waldron Marco Quaglio Asterios Gavriilidis Federico Galvanin 

作者机构：Department of Chemical EngineeringUniversity College LondonLondon WC1E 7JEUK 

基　　金：Department of Chemical Engineering, University College London University College London, UCL Department of Chemical Engineering, Monash University 

出 版 物：《Engineering》 (工程（英文）)

年 卷 期：2019年第5卷第6期

页      码：1049-1059页

摘      要：Recent advances in automation and digitization enable the close integration of physical devices with their virtual counterparts, facilitating the real-time modeling and optimization of a multitude of processes in an automatic way. The rich and continuously updated data environment provided by such systems makes it possible for decisions to be made over time to drive the process toward optimal targets. In many man- ufacturing processes, in order to achieve an overall optimal process, the simultaneous assessment of mul- tiple objective functions related to process performance and cost is necessary. In this work, a multi- objective optimal experimental design framework is proposed to enhance the ef ciency of online model-identi cation platforms. The proposed framework permits exibility in the choice of trade-off experimental design solutions, which are calculated online that is, during the execution of experiments. The application of this framework to improve the online identi cation of kinetic models in ow reactors is illustrated using a case study in which a kinetic model is identi ed for the esteri cation of benzoic acid and ethanol in a microreactor.

主 题 词：Multi-objective optimization Optimal design of experiments Online 

学科分类：08[工学] 0812[工学-测绘类] 

核心收录：

D　O　I：10.1016/j.eng.2019.10.003

馆 藏 号：203860675...