Artificial neural network and regression coupled genetic algorithm to optimize parameters for enhanced xylitol production by Debaryomyces nepalensis in bioreactor

In this study, prediction ability and optimization ability of regression and artificial neural network (ANN) Models were compared. The input variables used to predict xylose consumption, biomass and xylitol production by regression analysis and neural network model are temperature, fermentation time, pH, k(L)a, biomass and glycerol of previous data points. Determination of coefficient (R-2) was used to assess the adequacy of the regression model and R-2 for xylitol and biomass were 86.56% and 96.43% respectively. A multi layered feed forward neural network (ANN) of 5-10-2 topology has been developed to predict the xylitol production. Results showed that prediction accuracy of ANN was apparently higher when compared to regression model. Genetic algorithm (GA) was used to find the optimum parameters to enhance xylitol production. Optimization of fermentation parameters was carried out using hybrid regression GA and hybrid ANN GA with an optimum prediction error of 10% and 3.5% respectively. ANN coupled GA is considered to be the better optimization method due to its high accuracy and low prediction error and recommended to be employed for optimization of fermentative process. (C) 2017 Elsevier B.V. All rights reserved.