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Optimal control of a fed-batch bioreactor based upon an augmented recurrent neural network model

Lookup NU author(s): Dr Jie ZhangORCiD


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The paper presents a structured approach to building neural network models for a fed batch bioreactor to allow the development of reactor optimal control policy. Since the ultimate interest in batch bioreactor control is on the end-of-batch product quality, accurate long range predictions are essential in developing optimal control policy. To address the long range prediction issue, an augmented recurrent neural network is used to build long range prediction models which can predict the product quality over the batch trajectory. The augmented recurrent neural network contains two recurrent neural networks in series and utilises available process knowledge to improve long range prediction accuracy. The first network predicts an important process state variable while the second network predicts the product quality variable. Based on the augmented neural network model, constrained optimisation techniques are used to find the optimal control policy. The proposed technique is applied to a simulated fed-batch bioreactor. The results obtained are shown to be comparable to those computed using a full phenomenological model which is usually difficult to obtain, demonstrating that the proposed approach can contribute to the optimal control of some batch processes where detailed mechanistic models are difficult or infeasible to develop. © 2002 Elsevier Science B.V. All rights reserved.

Publication metadata

Author(s): Tian Y, Zhang J, Morris J

Publication type: Review

Publication status: Published

Journal: Neurocomputing

Year: 2002

Volume: 48

Pages: 919-936

Print publication date: 01/10/2002

ISSN (print): 0925-2312

ISSN (electronic): 1872-8286


DOI: 10.1016/S0925-2312(01)00680-4