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Chemical-free processing of Bermuda grass for bioethanol production: Hybrid optimization of simultaneous C5/C6 sugar utilization using response surface methodology, genetic algorithm, and artificial neural network

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  • Saravanan, Harini
  • Uppuluri, Kiran Babu

Abstract

The growing global demand for renewable and sustainable energy sources has intensified interest in lignocellulosic biomass as an alternative to fossil fuels. However, the recalcitrant structure of lignocellulose and the reliance on harsh chemical pretreatments remain major barriers to efficient bioethanol production. The present study addresses the challenge of efficiently converting lignocellulosic biomass into bioethanol without relying on harsh chemicals, focusing on Cynodon dactylon (Bermuda grass), an underutilized and abundant feedstock. A novel microwave-assisted deep eutectic solvent pretreatment system, composed of potassium carbonate and glycerol, was developed and applied to the biomass. Subsequently, simultaneous saccharification and co-fermentation (SSCF) using Saccharomyces cerevisiae NCIM 3219 and Kluyveromyces marxianus MTCC 1389 facilitated effective conversion of both C5 and C6 sugars into ethanol. Process optimization was conducted using response surface methodology (RSM) and a hybrid artificial neural network-genetic algorithm (ANN-GA) modeling approach. The ANN-GA model outperformed RSM in predictive capability, achieving a maximum ethanol of 23.84 ± 0.45 g/L compared to 14.85 ± 0.32 g/L with RSM. Overall, this work presents a novel, chemical-free valorization route for Bermuda grass and offers a promising framework for optimizing lignocellulosic bioethanol production through advanced modeling, contributing to Sustainable Development Goals on clean energy and responsible resource utilization.

Suggested Citation

  • Saravanan, Harini & Uppuluri, Kiran Babu, 2026. "Chemical-free processing of Bermuda grass for bioethanol production: Hybrid optimization of simultaneous C5/C6 sugar utilization using response surface methodology, genetic algorithm, and artificial neural network," Renewable Energy, Elsevier, vol. 258(C).
    • Handle: RePEc:eee:renene:v:258:y:2026:i:c:s0960148125026230
    DOI: 10.1016/j.renene.2025.124959
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    References listed on IDEAS

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