Prediction of HHV of fuel by Machine learning Algorithm: Interpretability analysis using Shapley Additive Explanations (SHAP)

This study presents a novel approach using machine learning techniques to estimate waste materials' higher heating value (HHV), which plays a crucial role in waste-to-energy generation efficiency. The study utilizes a dataset comprising ultimate and proximate analysis of 16 different waste types. It employs six machine learning models: Extra Trees, Random Forest, Support Vector, Decision Tree, Extreme Gradient Boosting, and Multivariate Linear Regressors. The investigation explores the relationships between the features and outcomes through Spearman correlation, feature importance analysis, SHAP dependence, and decision plots, providing the interpretability of the model's predictions. The models are fine-tuned with hyperparameters for six feature sets, enabling researchers to anticipate HHV based on their specific input. The results demonstrate high accuracy in predicting HHV, with R2 ranging from 0.83 to 0.98, RMSE from 2.25 to 0.79, and MAPE from 6.01 to 0.92%. The study further reveals that higher carbon and hydrogen content increases HHV, while higher oxygen and ash content results in decreased HHV. Notably, Carbon, Ash content and Hydrogen content are the significant features with mean absolute SHAP values of 2.17, 0.65, and 0.37, respectively. The proposed alternative prediction method has practical implications for waste-to-energy generation research and practice, facilitating informed decision-making in this field.

Automated summary

This study proposes a novel approach using machine learning techniques to predict the higher heating value (HHV) of waste materials. The results show that increased carbon and hydrogen content leads to higher HHV, while increased oxygen and ash content results in decreased HHV. The prediction models demonstrate high accuracy and have practical implications for waste-to-energy generation research and practice.