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Artificial neural network modelling-coupled genetic algorithm optimization for co-production of bioethanol and xylitol from delignified elephant grass.

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Title: Artificial neural network modelling-coupled genetic algorithm optimization for co-production of bioethanol and xylitol from delignified elephant grass.
Authors: Aishwarya, Aishwarya, Goyal, Arun
Source: Energy & Environment; Nov2025, Vol. 36 Issue 7, p3166-3183, 18p
Subject Terms: ARTIFICIAL neural networks, GENETIC algorithms, CENCHRUS purpureus, MATHEMATICAL optimization, FERMENTATION, ETHANOL as fuel, BIOCHEMICAL engineering, XYLITOL
Abstract: The present study explores the potential of wild elephant grass (EG), for co-production of ethanol and xylitol. Alkaline H2O2-pretreated-EG was hydrolyzed by a tailor-made cocktail of recombinant bacterial crude cellulolytic and xylanolytic enzymes, used for co-fermentation. Candida tropicalis (MTCC 230) was adapted in medium having both C5 and C6 sugars. Three significant parameters, inoculum size, S:N in medium and orbital shaking speed (rpm), were optimized using response surface methodology (RSM) and artificial neural network linked genetic algorithm (ANN-GA) for bioethanol and xylitol production. The predictive capabilities of both models were compared. ANN-GA predicted optimum conditions were 10% (v/v) initial inoculum size, the S:N ratio 37.4 and rpm 250 gave 27.4 g/L (0.42 g/gglucose) ethanol and 5.1 g/L (0.44 g/gxylose) xylitol titres with KLa of 194 h−1. The ANN-GA optimized parameters gave 22.3% and 13.3% higher ethanol and xylitol yields, respectively, than those predicted by the RSM-based model. The current innovative method of co-producing ethanol and xylitol from EG offers a promising alternative to traditional bioethanol production. [ABSTRACT FROM AUTHOR]
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Abstract:The present study explores the potential of wild elephant grass (EG), for co-production of ethanol and xylitol. Alkaline H<subscript>2</subscript>O<subscript>2</subscript>-pretreated-EG was hydrolyzed by a tailor-made cocktail of recombinant bacterial crude cellulolytic and xylanolytic enzymes, used for co-fermentation. Candida tropicalis (MTCC 230) was adapted in medium having both C5 and C6 sugars. Three significant parameters, inoculum size, S:N in medium and orbital shaking speed (rpm), were optimized using response surface methodology (RSM) and artificial neural network linked genetic algorithm (ANN-GA) for bioethanol and xylitol production. The predictive capabilities of both models were compared. ANN-GA predicted optimum conditions were 10% (v/v) initial inoculum size, the S:N ratio 37.4 and rpm 250 gave 27.4 g/L (0.42 g/g<subscript>glucose</subscript>) ethanol and 5.1 g/L (0.44 g/g<subscript>xylose</subscript>) xylitol titres with K<subscript>L</subscript>a of 194 h<sup>−1</sup>. The ANN-GA optimized parameters gave 22.3% and 13.3% higher ethanol and xylitol yields, respectively, than those predicted by the RSM-based model. The current innovative method of co-producing ethanol and xylitol from EG offers a promising alternative to traditional bioethanol production. [ABSTRACT FROM AUTHOR]
ISSN:0958305X
DOI:10.1177/0958305X251367113