Abstract
A combination of a cascade RNN model and a modified GA for optimizing a fed-batch bioreactor is investigated in this chapter. The complex nonlinear relationship between the manipulated feed rate and the biomass product is described by two recurrent neural sub-models. Based on the neural model, the modified GA is employed to determine a smooth optimal feed rate profile. The final biomass quantity yields from the optimal feed rate profile based on the neural network model reaches 99.8% of the “real” optimal value obtained based on a mechanistic model.
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© 2006 Springer
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(2006). Optimization of Fed-batch Fermentation Processes using Genetic Algorithms based on Cascade Dynamic Neural Network Models. In: Modelling and Optimization of Biotechnological Processes. Studies in Computational Intelligence, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32493-5_5
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DOI: https://doi.org/10.1007/978-3-540-32493-5_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30634-4
Online ISBN: 978-3-540-32493-5
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