Valorizing Residue Biomass into Bioenergy: An Explainable Hybrid Machine Learning Model for Predicting Higher Heating Value (HHV) from Elemental Composition

Transforming waste and agricultural-residue biomass into bioenergy is central to the circular bioeconomy, yet routing such heterogeneous residues to the right thermochemical pathway depends on the higher heating value (HHV), which is conventionally measured by slow, resource-intensive bomb calorimetry. Here, we present an explainable alternative that predicts HHV from inexpensive elemental inputs. We used a publicly archived compilation of 344 literature-reported biomass samples retrieved from an open data repository rather than assembled by the authors, including carbon (C), hydrogen (H), oxygen (O), nitrogen (N) and sulfur (S). Measured HHV was the target. The samples spanned woody, herbaceous and agricultural-residue biomass, and they were standardized through duplicate removal, consistency verification and outlier assessment. On these features, we developed a stacked hybrid model combining Random Forest, eXtreme Gradient Boosting and Artificial Neural Networks, which estimated the HHV with R2 = 0.99, RMSE = 0.45 MJ/kg and MAE = 0.30 MJ/kg. SHAP and LIME analyses showed that carbon exerts the strongest positive influence on HHV, whereas oxygen contributes negatively, which is consistent with established thermochemical principles. Within the compositional range covered by the training data, and subject to the absence of external validation, the framework offers a fast and interpretable complement to bomb calorimetry for screening residue biomass.