Distributed nonlinear model predictive control for cobalt removal process in zinc hydrometallurgy considering error compensation modelling

To address the strong nonlinearity, uncertainty, and mutual coupling in the cobalt removal process of zinc hydrometallurgy, an algorithm based on an improved genetic algorithm (GA) backpropagation (BP) neural network combined with distributed nonlinear model predictive control (NMPC) is proposed. Th...

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Veröffentlicht in:	Canadian journal of chemical engineering Jg. 102; H. 1; S. 307 - 323
Hauptverfasser:	Wang, Qianqian, An, Aimin, Tang, Minan, Lu, Jiawei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 01.01.2024 Wiley Subscription Services, Inc
Schlagworte:	Algorithms Back propagation networks Cobalt cobalt removal Continuously stirred tank reactors Control methods distributed nonlinear model predictive control Error compensation Genetic algorithms genetic algorithm‐backpropagation neural network Hydrometallurgy Mutual coupling Neural networks Nonlinear control Nonlinearity Parameterization Predictive control Quadratic programming synergistic CSTR Zinc
ISSN:	0008-4034, 1939-019X
Online-Zugang:	Volltext
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