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Optimal control of batch processes using particle swam optimisation with stacked neural network models

Lookup NU author(s): Fernando Herrera Elizalde, Dr Jie ZhangORCiD


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An optimal control strategy for batch processes using particle swam optimisation (PSO) and stacked neural networks is presented in this paper. Stacked neural networks are used to improve model generalisation capability, as well as provide model prediction confidence bounds. In order to improve the reliability of the calculated optimal control policy, an additional term is introduced in the optimisation objective function to penalise wide model prediction confidence bounds. PSO can cope with multiple local minima and could generally find the global minimum. Application to a simulated fedbatch process demonstrates that the proposed technique is very effective. © 2008 Elsevier B.V. All rights reserved.

Publication metadata

Author(s): Herrera F, Zhang J

Publication type: Article

Publication status: Published

Journal: Computer Aided Chemical Engineering

Year: 2008

Volume: 25

Pages: 375-380

Print publication date: 01/01/2008

ISSN (print): 1570-7946

ISSN (electronic):

Publisher: Elsevier BV


DOI: 10.1016/S1570-7946(08)80067-3


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