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<p>The growing demand for sustainable energy calls for efficient and accurate methods to optimize biofuel production processes. Hydrothermal liquefaction (HTL) is a promising thermochemical technique to convert wet biomass into biocrude oil, but estimating yield across diverse feedstocks and conditions remains challenging. In this study, we develop and benchmark a series of machine learning models to predict biocrude oil yield from HTL, using a comprehensive dataset of 650 biomass samples and process parameters, including elemental composition and higher heating value (HHV). Notably, this is the first study to incorporate HHV as a predictive feature at this scale. Seven ML models—including XGBoost, Random Forest, and Gaussian Process Regressor—were optimized via Bayesian hyperparameter tuning and evaluated through a dual-validation strategy combining tenfold cross-validation with a hold-out test set. XGBoost achieved the highest performance (R<sup>2</sup> = 0.97, RMSE = 0.033). To ensure model interpretability, SHAP and SAGE techniques were applied, identifying HHV, carbon content, and pressure as key yield predictors. These results provide a transparent, data-driven framework for enhancing reactor design and feedstock selection in bio-oil production systems. The study underscores the potential of interpretable ML in advancing the predictive capabilities of renewable fuel technologies.</p>

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Prediction of Biocrude Oil Yield From Biomass Hydrothermal Liquefaction Via Interpretable Machine Learning Using Higher Heating Value and Process Parameters

  • 期刊论文

  • 发表日期：

    2025年10月22日

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  • Serafettin Gungor,
  • Mert Akin Insel,
  • Hasan Sadikoglu,
  • Mehmet Melikoglu

摘要

The growing demand for sustainable energy calls for efficient and accurate methods to optimize biofuel production processes. Hydrothermal liquefaction (HTL) is a promising thermochemical technique to convert wet biomass into biocrude oil, but estimating yield across diverse feedstocks and conditions remains challenging. In this study, we develop and benchmark a series of machine learning models to predict biocrude oil yield from HTL, using a comprehensive dataset of 650 biomass samples and process parameters, including elemental composition and higher heating value (HHV). Notably, this is the first study to incorporate HHV as a predictive feature at this scale. Seven ML models—including XGBoost, Random Forest, and Gaussian Process Regressor—were optimized via Bayesian hyperparameter tuning and evaluated through a dual-validation strategy combining tenfold cross-validation with a hold-out test set. XGBoost achieved the highest performance (R2 = 0.97, RMSE = 0.033). To ensure model interpretability, SHAP and SAGE techniques were applied, identifying HHV, carbon content, and pressure as key yield predictors. These results provide a transparent, data-driven framework for enhancing reactor design and feedstock selection in bio-oil production systems. The study underscores the potential of interpretable ML in advancing the predictive capabilities of renewable fuel technologies.