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Molecular descriptor-based artificial intelligence frameworks for the estimation of bio-oil yield from agricultural waste

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  • Yeole, Shreya P.
  • Prasad, Tanishq
  • Kundu, Debashis

Abstract

The study highlights the potential of using molecular descriptors and artificial intelligence (AI) frameworks to enhance the predictive accuracy of bio-oil yields, emphasizing the need for AI formulations in handling complex and non-linear data scenarios. An exhaustive dataset of bio-oil yield derived from various feedstocks, including wheat straw, coconut shell, rice husk, bagasse, and sawdust, is collected for the training validation and testing of AI models. Altogether, 2926 molecular descriptors with a combination of two-dimensional and three-dimensional parameters are considered for estimating bio-oil yield using Artificial Neural Network (ANN) and ANN integrated with Grey Wolf Optimization (ANN-GWO) models. Parameters for ANN and ANN-GWO are optimized using a grid approach to demonstrate the correlation coefficient of 0.959 and 0.929, respectively. Among the feedstocks, rice husk and saw dust show remarkable accuracy in the bio-oil estimation, while others perform poorly.

Suggested Citation

  • Yeole, Shreya P. & Prasad, Tanishq & Kundu, Debashis, 2025. "Molecular descriptor-based artificial intelligence frameworks for the estimation of bio-oil yield from agricultural waste," Renewable Energy, Elsevier, vol. 239(C).
    • Handle: RePEc:eee:renene:v:239:y:2025:i:c:s0960148124021827
    DOI: 10.1016/j.renene.2024.122114
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