Sarkar, Debasis and Modak, Jayant M (1996) Adaptive optimization of continuous bioreactor using neural network model. In: Chemical Engineering Communications, 143 (1). pp. 99-116.Full text not available from this repository.
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.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Taylor and Francis Group.|
|Department/Centre:||Division of Mechanical Sciences > Chemical Engineering|
|Date Deposited:||19 May 2011 08:06|
|Last Modified:||19 May 2011 08:06|
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