Enzyme Production Modeling Simulation Using Neural Techniques

Abstract: 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.