Optimization of enzymatic saccharification of water hyacinth biomass for bio-ethanol: Comparison between artificial neural network and response surface methodology

Abstract

Response surface methodology (RSM) is commonly used for optimising process parameters affecting enzymatic hydrolysis. However, artificial neural network–genetic algorithm hybrid model can also serve as an effective option, primarily for non-linear polynomial systems. The present study compares these approaches for enzymatic hydrolysis of water hyacinth biomass to maximise total reducing sugar (TRS) for bio-ethanol production. Maximum TRS (0.5672 g/g) was obtained using 9.92 (% w/w) substrate concentrations, 49.56 U/g cellulase concentrations, 280.33 U/g xylanase concentrations and 0.13 (% w/w) surfactant concentrations. The average % error for artificial neural networking (ANN) and RSM were 3.08 and 4.82 and the prediction percentage errors in optimum output are 0.95 and 1.41, respectively, which showed the supremacy of ANN in illustrating the non-linear behaviour of the system. Fermentation of the hydrolysate yielded a maximum ethanol concentration of 10.44 g/l using Pichia stipitis, followed by 8.24 and 6.76 g/l for Candida shehatae and Saccharomyces cerevisiae.