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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.
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
URL: http://dx.doi.org/10.1016/j.jprocont.2007.11.008
DOI: 10.1016/j.jprocont.2007.11.008
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