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Multi-scale local LSSVM based spatiotemporal modeling and optimal control for the goethite process

多尺度的本地 LSSVM 基于为 goethite 过程的空间与时间的建模和最佳的控制

作     者:Dai, Jiayang Chen, Ning Luo, Biao Gui, Weihua Yang, Chunhua 

作者机构:Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China 

出 版 物:《NEUROCOMPUTING》 (神经计算)

年 卷 期:2020年第385卷第0期

页      面:88-99页

核心收录:

学科分类:08[工学] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

基  金:Program of National Natural Science Foundation of China [61673399, 61873350] Foundation for Innovative Research Groups of the National Natural Science Foundation of China Natural Science Foundation of Hunan Province [2017JJ2329] 

主  题:Distributed parameter system Iron removal process Least squares Multi-scale kernel learning Spatiotemporal modeling Support vector machine 

摘      要:The iron removal process by goethite is an important part of zinc hydrometallurgy. In existing works, the goethite process is often modeled as a lumped parameter system, where the spatial distribution information of reactants is not involved. In this paper, the spatiotemporal modeling of the goethite process and its optimal control problem are studied. To make the infinite-dimensional distributed parameter system easier to solve, space-time separation is adopted to transform it into a finite-dimensional system. Then, a multi-scale local least squares support vector machine is proposed to establish the temporal model. This method uses multi-scale kernel learning to deal with different trends of the process and establish a local model to track the state change of the system. Through space-time synthesis, the established spatiotemporal model can approximate the distributed parameter system of the goethite process. Moreover, an optimal control strategy based on the spatiotemporal model is designed to reduce the cost of oxygen and zinc oxide consumed in the process. Finally, simulation experiments on the goethite process demonstrate the effectiveness of the proposed modeling method and optimal control strategy. (C) 2019 Elsevier B.V. All rights reserved.

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