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<p>Sustainable biofuel production from renewable feedstocks remains a key challenge in the development of low-carbon transportation fuels. This paper outlines an effective approach to biofuel production using marine macroalgal oil produced from <i>Ulva fasciata</i> as a sustainable non-edible feedstock, with focus on environmental suitability and efficiency of the process. The lipids that were obtained after the macroalgal biomass were subjected to proximate and physicochemical analyses to determine the suitability of the feedstock. Zn-doped CaO nano catalyst was developed and systematically characterized to elucidate its structural, morphological and textural properties. Biofuel production was systematically modeled and optimized using response surface methodology (RSM), artificial neural networks (ANN), and a genetic algorithm (GA) to understand process interactions and maximize conversion efficiency. Under optimized conditions, a biofuel yield of 80.78% was achieved, demonstrating effective catalytic performance. Gas chromatography–mass spectrometry analysis confirmed the predominance of C16–C18 fatty acid methyl esters, indicating efficient transesterification. The integration of marine macroalgal biomass, heterogeneous nanocatalyst and hybrid modeling–optimization tools highlight a promising and sustainable route for biofuel production.</p> Graphical Abstract <p></p>

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Integrated Statistical and Machine-Learning Optimization of Biofuel Extraction from Ulva fasciata Incorporated Zn-Doped CaO Nanoparticle

  • 期刊论文

  • 发表日期：

    2026年5月7日

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  • R. Suganya,
  • V. Karthik,
  • S. Muralidharan,
  • S. Mariaamalraj

摘要

Sustainable biofuel production from renewable feedstocks remains a key challenge in the development of low-carbon transportation fuels. This paper outlines an effective approach to biofuel production using marine macroalgal oil produced from Ulva fasciata as a sustainable non-edible feedstock, with focus on environmental suitability and efficiency of the process. The lipids that were obtained after the macroalgal biomass were subjected to proximate and physicochemical analyses to determine the suitability of the feedstock. Zn-doped CaO nano catalyst was developed and systematically characterized to elucidate its structural, morphological and textural properties. Biofuel production was systematically modeled and optimized using response surface methodology (RSM), artificial neural networks (ANN), and a genetic algorithm (GA) to understand process interactions and maximize conversion efficiency. Under optimized conditions, a biofuel yield of 80.78% was achieved, demonstrating effective catalytic performance. Gas chromatography–mass spectrometry analysis confirmed the predominance of C16–C18 fatty acid methyl esters, indicating efficient transesterification. The integration of marine macroalgal biomass, heterogeneous nanocatalyst and hybrid modeling–optimization tools highlight a promising and sustainable route for biofuel production.

Graphical Abstract