Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/2245
Title: Optimal control of a fed-batch fermenter using parameterized data-driven models
Authors: Rani, K Yamuna
Keywords: Artificial neural networks;Fed-batch fermentation;Linear model;Optimal control;Orthonormal polynomial approximations;Parameterized data-driven modeling;Quadratic model
Issue Date: Oct-2008
Publisher: CSIR
Abstract: An optimal control approach is proposed for semi-batch processes based on parameterized data-driven (PDD) model structures. Orthonormally parameterized input trajectories, initial states and process parameters are inputs to the model, which predicts output trajectories in terms of Fourier coefficients. Two model structures (linear and quadratic) are incorporated into PDD modeling approach, and a previously proposed model structure of artificial neural networks (ANNs) is considered for comparison. Proposed PDD modeling approach using newly proposed model structures is capable of capturing nonlinear and time-varying behavior inherent in fed-batch systems fairly accurately, and results of operating trajectory optimization using all models are found to be comparable to the results obtained using exact first principles model.
Page(s): 759-773
URI: http://hdl.handle.net/123456789/2245
ISSN: 0022-4456
Appears in Collections:JSIR Vol.67(10) [October 2008]

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