Paper Info
Title
Multi-scale local LSSVM based spatiotemporal modeling and optimal control for the goethite process
Abstract
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.
Year
DOI
Venue
2020
10.1016/j.neucom.2019.12.008
Neurocomputing
Keywords
Field
DocType
Distributed parameter system,Iron removal process,Least squares,Multi-scale kernel learning,Spatiotemporal modeling,Support vector machine
Kernel (linear algebra),Goethite,Hydrometallurgy,Optimal control,Least squares support vector machine,Pattern recognition,Algorithm,Artificial intelligence,Distributed parameter system,Mathematics
Journal
Volume
ISSN
Citations 
385
0925-2312
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Jiayang Dai100.34
Ning Chen201.01
Biao Luo3584.38
Weihua Gui457790.82
Chunhua Yang543571.63