In the present work, growth and cellulase production by the cellulolytic fungus Aspergillus niger in fed-batch culture using an agricultural residue as the substrate have been investigated. The Windows application of Artificial Neural Network (ANN) to the estimation of bioprocess variables is presented. A neural network methodology is discussed, which uses environmental and physiological information available from on-line sensors, to estimate the cellulase production in a fed-batch bioprocess. An efficient optimization algorithm that reduces the number of iterations required for convergence is proposed. Results are presented for different training sets and different training methodologies.
intelligent techniques, neural network, biological process, enzyme production
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Cite this paper
M. Caramihai, Irina Severin. (2018) Enzyme Production Modeling Simulation Using Neural Techniques. International Journal of Biology and Biomedicine, 3, 26-29
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