Neural networks applied to the prediction of fed-batch fermentation kinetics of Bacillus thuringiensis |
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Authors: | Valdez-Castro L. Baruch I. Barrera-Cortés J. |
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Affiliation: | Departamento Biotecnología y Bioingeniería, CINVESTAV-IPN, Av. IPN No 2508, Col San Pedro Zacatenco, México D.F., C.P. 07360. |
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Abstract: | This paper proposes using a new recurrent neural network model (RNNM) to predict and control fed batch fermentations of Bacillus thuringiensis. The control variables are the limiting substrate and the feeding conditions. The multi-input multi-output RNNM proposed has twelve inputs, seven outputs, nineteen neurons in the hidden layer, and global and local feedbacks. The weight update learning algorithm designed is a version of the well known backpropagation through time algorithm directed to the RNNM learning. The error approximation for the last epoch of learning is 2% and the total learning time is 51 epochs, where the size of an epoch is 162 iterations. The RNNM generalization was carried out reproducing a B. thuringiensis fermentation not included in the learning process. It attains an error approximation of 1.8%. |
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