Dynamic Models for L-Histidine Fed-Batch Fermentation by Corynebacterium glutamicum

Abstract:

To predict and control feed batch fermentations of Corynebacterium glutamicun TQ2226 which can produce L-histidine , in this paper , we use a recurrent neural network model(RNNM).The control variables are the limiting substrate and the feeding conditions. The multi-input and multi-output RNNM proposed has seven outputs, nineteen neurons, twelve inputs, 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 RNNM generalization was carried out reproducing a C. glutamicum fermentation not included in the learning process. It attains an error approximation of 1.8%.

• Periodical: Advanced Materials Research (Volumes 160-162)
• Pages: 1749-1755
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.160-162.1749
• Online since: November 2010
• Authors: Ning Chen, Jian Tao Du, Xi Xian Xie, Qing Yang Xu
• Keywords: Corynebacterium glutamicum, Dynamic Model, Fed-Batch Fermentation, L-Histidine

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