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<p>The increasing global demand for sustainable and low-carbon energy sources has driven significant interest in biomass-based biofuels. Industrial waste algae, an abundant, renewable, and non-food bioresource, presents a promising feedstock for thermochemical conversion due to its high lipid content, rapid growth rate, and carbon sequestration potential. This study investigates the hydrothermal liquefaction (HTL) and hydrothermal gasification (HTG) processes for converting industrial waste algae into biofuels, focusing on optimizing bio-oil yield, hydrogen production, and pollution index reduction. The influence of key process parametersincluding temperature, pressure, reaction time, catalyst loading, and solvent-to-biomass (S/B) ratioon bio-oil and hydrogen yield was systematically analyzed through experimental and computational approaches.HTL experiments were conducted at temperatures between 200 and 420&#xa0;°C, pressures up to 20&#xa0;MPa, and reaction times ranging from 30 to 90&#xa0;min, utilizing Ca(OH)₂ as a catalyst. The optimal conditions (300&#xa0;°C, 50&#xa0;bar, 60&#xa0;min, 3% catalyst, and an S/B ratio of 13.3) resulted in a bio-oil yield of 39.6% with a higher heating value (HHV) of 35.8&#xa0;MJ/kg. Similarly, HTG experiments performed at temperatures &gt; 375&#xa0;°C and pressures &gt; 22&#xa0;MPa demonstrated that 350&#xa0;°C, 20&#xa0;MPa, and 60&#xa0;min reaction time yielded 81.6% hydrogen with a pollution index (PI) of 9.3% when processed with 5% ZnO catalyst. The syngas composition was characterized using gas chromatography-mass spectrometry (GC-MS), revealing an H₂-rich gas phase with minimal CO₂ emissions.To enhance process efficiency, an AI-driven Tunable Decision Support System (TDSS) and Tunable Recommendation System (TRS) were developed, integrating Supervised Multivariate Random Forest (SMVRF) and Adaptive Multivariate Random Forest (AMVRF) models. These machine learning models analyzed large-scale experimental datasets and demonstrated &gt; 94% accuracy in predicting optimal process conditions. The AI framework effectively correlated biomass composition with conversion efficiency, enabling real-time decision-making for improved biofuel yield and energy recovery.This study establishes that industrial waste algae is a viable and sustainable feedstock for biofuel production through HTL and HTG, offering a renewable energy alternative with lower carbon emissions. The integration of machine learning-driven optimization significantly enhances process efficiency, reducing experimental costs while maximizing biofuel yield. These findings contribute to the advancement of biorefinery technologies and support the scalability of hydrothermal biofuel production, paving the way for sustainable, AI-assisted industrial bioconversion.</p>

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Hydrothermal liquefaction and gasification of industrial waste algae: experimental and AI-Assisted optimization for biofuel and hydrogen production

  • 期刊论文

  • 发表日期：

    2026年1月30日

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  • Deepankumar S,
  • Senthil Kumar K L

摘要

The increasing global demand for sustainable and low-carbon energy sources has driven significant interest in biomass-based biofuels. Industrial waste algae, an abundant, renewable, and non-food bioresource, presents a promising feedstock for thermochemical conversion due to its high lipid content, rapid growth rate, and carbon sequestration potential. This study investigates the hydrothermal liquefaction (HTL) and hydrothermal gasification (HTG) processes for converting industrial waste algae into biofuels, focusing on optimizing bio-oil yield, hydrogen production, and pollution index reduction. The influence of key process parametersincluding temperature, pressure, reaction time, catalyst loading, and solvent-to-biomass (S/B) ratioon bio-oil and hydrogen yield was systematically analyzed through experimental and computational approaches.HTL experiments were conducted at temperatures between 200 and 420 °C, pressures up to 20 MPa, and reaction times ranging from 30 to 90 min, utilizing Ca(OH)₂ as a catalyst. The optimal conditions (300 °C, 50 bar, 60 min, 3% catalyst, and an S/B ratio of 13.3) resulted in a bio-oil yield of 39.6% with a higher heating value (HHV) of 35.8 MJ/kg. Similarly, HTG experiments performed at temperatures > 375 °C and pressures > 22 MPa demonstrated that 350 °C, 20 MPa, and 60 min reaction time yielded 81.6% hydrogen with a pollution index (PI) of 9.3% when processed with 5% ZnO catalyst. The syngas composition was characterized using gas chromatography-mass spectrometry (GC-MS), revealing an H₂-rich gas phase with minimal CO₂ emissions.To enhance process efficiency, an AI-driven Tunable Decision Support System (TDSS) and Tunable Recommendation System (TRS) were developed, integrating Supervised Multivariate Random Forest (SMVRF) and Adaptive Multivariate Random Forest (AMVRF) models. These machine learning models analyzed large-scale experimental datasets and demonstrated > 94% accuracy in predicting optimal process conditions. The AI framework effectively correlated biomass composition with conversion efficiency, enabling real-time decision-making for improved biofuel yield and energy recovery.This study establishes that industrial waste algae is a viable and sustainable feedstock for biofuel production through HTL and HTG, offering a renewable energy alternative with lower carbon emissions. The integration of machine learning-driven optimization significantly enhances process efficiency, reducing experimental costs while maximizing biofuel yield. These findings contribute to the advancement of biorefinery technologies and support the scalability of hydrothermal biofuel production, paving the way for sustainable, AI-assisted industrial bioconversion.