Title
ANN and RSM based optimization of cellulase production by Hypocrea sp. Z28 by submerged fermentation
Authors
YU ZHANG XIAOHUAN ZHANG WEI QI JINGLIANG XU ZHENHONG YUAN and ZHONGMING WANG

Received September 14, 2016
Published Volume 52 Issue 3-4 March-April
Keywords cellulase production, submerged fermentation, artificial neural network-genetic algorithm, bioprocess optimization, response surface methodology, Hypocrea sp.

Abstract
Response surface methodology (RSM) and artificial neural network (ANN) were used to simulate and optimize cellulase production by Hypocrea sp. Z28 by submerged fermentation. Results showed ANN had higher simulation accuracy than RSM. Cellulase production optimized by RSM was 5.48 U/mL, while the corresponding experimental value was 5.67±0.32 U/mL. Using ANN as a prediction function, a maximum cellulase production of 5.96 U/mL was searched by the genetic algorithm, and the corresponding experimental value was 6.01±0.43 U/mL. Compared to RSM, ANN brought higher and more accurate cellulase production values. The application of ANN to optimize cellulase production proved successful.