Browse by author
Lookup NU author(s): Dr Jie ZhangORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
A neural network based batch to batch optimal control strategy is proposed in this paper. To overcome the difficulty in developing mechanistic models for batch processes, neural network models are developed from process operational data. The developed neural network model can only approximate the batch process and model plant mismatches usually exist. Thus the optimal control policy calculated based on a neural network model may not be optimal when applied to the true process. Due to the repetitive nature of batch processes, it is possible to improve the operation of the next batch using the information of the current and previous batch runs. A batch to batch optimal control strategy based on the linearization of the neural network model is proposed in this paper. Applications to a simulated batch polymerisation reactor demonstrate that the proposed method can improve process performance from batch to batch in the presence of model plant mismatches and unknown disturbances.
Author(s): Zhang J
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: IEEE International Symposium on Intelligent Control
Year of Conference: 2003
Pages: 352-357
Publisher: IEEE
URL: http://dx.doi.org/10.1109/ISIC.2003.1254659
DOI: 10.1109/ISIC.2003.1254659
Library holdings: Search Newcastle University Library for this item
ISBN: 0780378911
