Paper Title:
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%.

  Info
Periodical
Advanced Materials Research (Volumes 160-162)
Edited by
Guojun Zhang and Jessica Xu
Pages
1749-1755
DOI
10.4028/www.scientific.net/AMR.160-162.1749
Citation
N. Chen, J. T. Du, X. X. Xie, Q. Y. Xu, "Dynamic Models for L-Histidine Fed-Batch Fermentation by Corynebacterium glutamicum", Advanced Materials Research, Vols. 160-162, pp. 1749-1755, 2011
Online since
November 2010
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