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
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.