ADAPTIVE OPTIMIZATION OF CONTINUOUS BIOREACTOR USING NEURAL NETWORK MODEL
作者:
DEBASIS SARKAR,
JAYANTM. MODAK,
期刊:
Chemical Engineering Communications
(Taylor Available online 1996)
卷期:
Volume 143,
issue 1
页码: 99-116
ISSN:0098-6445
年代: 1996
DOI:10.1080/00986449608936436
出版商: Taylor & Francis Group
关键词: Adaptive optimization;Neural network;Continuous bioreactor
数据来源: Taylor
摘要:
An adaptive optimization algorithm using backpropogation neural network model for dynamic identification is developed. The algorithm is applied to maximize the cellular productivity of a continuous culture of baker's yeast. The robustness of the algorithm is demonstrated in determining and maintaining the optimal dilution rate of the continuous bioreactor in presence of disturbances in environmental conditions and microbial culture characteristics. The simulation results show that a significant reduction in time required to reach optimal operating levels can be achieved using neural network model compared with the traditional dynamic linear input-output model. The extension of the algorithm for multivariable adaptive optimization of continuous bioreactor is briefly discussed.
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