Journal
SUSTAINABLE MATERIALS AND TECHNOLOGIES
Volume 3, Issue -, Pages 17-28Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.susmat.2015.01.001
Keywords
Response surface methodology; Artificial neural network; Genetic algorithm; Enzymatic saccharification; Water hyacinth biomass; Bio-ethanol
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Funding
- EPSRC [EP/K036548/2, EP/K036548/1, EP/J020184/2] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/J020184/2, EP/K036548/2, EP/K036548/1] Funding Source: researchfish
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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 tomaximise 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. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
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