Establishing a generalized model for accurate prediction of higher heating values of substances with large ash fractions

The higher heating value (HHV) of biomass is a crucial property for design calculations and numerical simulations in bioenergy utilization. However, existing models for HHV prediction faced challenges in terms of predictive accuracy and generalization capability across various solid waste types, especially for those with high ash content. This work proposed a novel HHV prediction model based on its reduction degree (D_R) and ash content (C_ash). First, ultimate analysis of biomass was applied to establish the calculation method of D_R; then, the correlation between D_R, C_ash, and HHV was analyzed using the Pearson Correlation Coefficient; subsequently, the HHV = f (D_R, C_ash) model was developed using regression analysis. Furthermore, the accuracy was compared to previous literature in terms of correlation coefficient (R²), root mean square error (RMSE), and mean absolute error (MAE). Results revealed that this model provided attractive accuracy with R² = 0.854, RMSE = 0.900, and MAE = 0.773 within a wide range of ash content from 0 to 83.32 wt%. Even higher accuracy was achieved with this model in predicting the HHV of coal, biochar, and bio-oil, with R² of 0.961, 0.989, and 0.939, respectively. Conclusively, this work proposed the use of D_R for HHV estimation, which was not only a simple and accurate approach but also widely applicable to various fuels.