Biomass energy transformation: Harnessing the power of explainable ai to unlock the potential of ultimate analysis data

Abstract

Biomass fuels are a promising renewable energy source for both heat and electricity generation. When weighing the economics of biomass energy, the higher heating value (HHV) is a significant factor to consider. To estimate HHVs, a full understanding of the chemical constituents of biomass is required, which can be costly and time-consuming to find experimentally. To solve this issue, this study applies three modern and robust machine learning algorithms XGBoost, Random Forest (RF), and Adaptive Boosting (AdaBoost), to predict HHVs reliably based on final analysis data, which includes carbon (C, %), hydrogen (H, %), nitrogen (N, %), and oxygen (O, %) content in biomass. The linear regression (LR) was used for measuring baseline performance. The XGBoost, AdaBoost, and RF models exhibit endurance in model prediction, however, the XGBoost model outperforms all other models. A Bayesian strategy for hyperparameter optimization was employed to optimize the prediction outcomes. The R2 values achieved during the model testing for LR, RF, XGBoost, and AdaBoost-based models were 0.8425, 0.9822, 0.9967, and 0.8367, respectively. Once the best-performing model (XGBoost) was determined, it was evaluated with Shapley additive explanations (SHAP) for feature analysis. This study report adds to informed decision-making and instills trust in biomass energy applications by putting light on the fundamental mechanisms driving HHVs.