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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Vydáno v:	Canadian journal of chemical engineering Ročník 102; číslo 1; s. 307 - 323
Hlavní autoři:	Wang, Qianqian, An, Aimin, Tang, Minan, Lu, Jiawei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Hoboken, USA John Wiley & Sons, Inc 01.01.2024 Wiley Subscription Services, Inc
Témata:	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
On-line přístup:	Získat plný text
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