What does this research mean for the field?

Machine learning models can accurately predict energy demand in hydrothermal liquefaction of biomass, with temperature being the most significant predictor. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The study aims to create robust machine learning models to predict energy demand during the hydrothermal liquefaction of biomass.

March 12, 2026Open Access

Predictive modeling of energy demand in hydrothermal liquefaction of biomass using machine learning algorithms

Key Points

The study aims to create robust machine learning models to predict energy demand during the hydrothermal liquefaction of biomass.
Utilized a dataset of 653 experimental records from literature.
Included input features like elemental and biochemical composition as well as process conditions.
Optimized seven machine learning algorithms through hyperparameter tuning.
Employed 5-fold cross-validation with metrics such as R² and MSE for evaluation.
Random Forest model achieved a test R² of 0.936 and low MSE of 142,229.
Temperature was identified as the most influential predictor of energy demand.
Protein-rich feedstocks showed a negative impact on energy requirements.

Abstract

Hydrothermal liquefaction (HTL) of biomass is a promising thermochemical pathway for sustainable biofuel production, yet accurately predicting the process energy demand remains challenging due to the nonlinear interplay between feedstock properties and operating parameters. This study aims to develop robust machine learning models to predict HTL energy consumption (MJ/t biomass) using a comprehensive dataset of 653 experimental records drawn from peer‑reviewed literature. Input features include elemental composition (C, H, N, S, O, ash), biochemical composition (protein, lipid, carbohydrate), and process conditions (temperature, reaction time, solid loading ratio). Seven algorithms, Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K‑Nearest Neighbor (KNN), Ensemble Learning (EL), Convolutional Neural Network (CNN), and Multilayer Perceptron‑ANN (MLP‑ANN), were optimized through targeted hyperparameter tuning and evaluated via 5‑fold cross‑validation using R², MSE, and AARE%. RF achieved superior performance with a test R² of 0.936, low MSE (142,229), and minimal AARE% (3.19), followed closely by KNN. SHAP analysis revealed temperature as the overwhelmingly dominant predictor (impact magnitude ≈1150), with reaction time and protein content playing secondary but consistent roles. Positive SHAP values for high temperature and prolonged reaction times confirmed their contribution to higher energy demand, while protein‑rich feedstocks exhibited negative SHAP trends, reducing predicted requirements. Findings underscore the capacity of optimized tree‑based algorithms to deliver accurate, generalizable energy demand forecasts across diverse biomass types and process regimes. These models offer actionable insights for HTL operational optimization, reducing energy intensity and supporting scalable bioenergy deployment. • Modeling Energy Demand in Hydrothermal Liquefaction of Biomass. • temperature as the overwhelmingly dominant predictor. • Random Forest achieved the highest generalization capability.

Predictive modeling of energy demand in hydrothermal liquefaction of biomass using machine learning algorithms

Key Points

Abstract

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