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

Aishwarya Aishwarya and Arun Goyal

Energy & Environment, 2025, vol. 36, issue 7, 3166-3183

Abstract: The present study explores the potential of wild elephant grass (EG), for co-production of ethanol and xylitol. Alkaline H 2 O 2 -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 glucose ) ethanol and 5.1 g/L (0.44 g/g xylose ) xylitol titres with K L a 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.

Keywords: elephant grass; fermentation; ethanol yield; xylitol yield; artificial neural network; genetic algorithm (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:engenv:v:36:y:2025:i:7:p:3166-3183

DOI: 10.1177/0958305X251367113

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