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A reliable multi-objective control strategy for batch processes based on bootstrap aggregated neural network models

Lookup NU author(s): Dr Ankur Mukherjee, Dr Jie ZhangORCiD



This paper presents a reliable multi-objective optimal control method for batch processes based on bootstrap aggregated neural networks. In order to overcome the difficulty in developing detailed mechanistic models, bootstrap aggregated neural networks are used to model batch processes. Apart from being able to offer enhanced model prediction accuracy, bootstrap aggregated neural networks can also provide prediction confidence bounds indicating the reliability of the corresponding model predictions. In addition to the process operation objectives, the reliability of model prediction is incorporated in multi-objective optimisation in order to improve the reliability of the obtained optimal control policy. The standard error of the individual neural network predictions is taken as the indication of model prediction reliability. The additional objective of enhancing model prediction reliability forces the calculated optimal control policies to be within the regions where the model predictions are reliable. By such a means, the resulting control policies are reliable. The proposed method is demonstrated on a simulated fed-batch reactor and a simulated batch polymerisation process. It is shown that by incorporating model prediction reliability in the optimisation criteria, reliable control policy is obtained. © 2007 Elsevier Ltd. All rights reserved.

Publication metadata

Author(s): Mukherjee A, Zhang J

Publication type: Article

Publication status: Published

Journal: Journal of Process Control

Year: 2008

Volume: 18

Issue: 7-8

Pages: 720-734

Print publication date: 01/08/2008

Date deposited: 05/06/2014

ISSN (print): 0959-1524

ISSN (electronic): 1873-2771

Publisher: Elsevier Ltd


DOI: 10.1016/j.jprocont.2007.11.008


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