A Regression-Based Model for Estimating the Lower Heating Value of Fuels from Elemental Composition

The lower heating value (LHV) is one of the most important fuel properties in thermochemical conversion systems, directly affecting energy balances, process efficiency, and fuel selection. Although several empirical correlations have been proposed in the literature, most existing models are limited to specific fuel classes or focused primarily on higher heating value (HHV) prediction. In this work, generalized regression models were developed to estimate the LHV of solid, liquid, and gaseous fuels from ultimate analysis data. A comprehensive database containing 520 fuels was used to construct both a full polynomial model and a simplified polynomial formulation. The predictive performance of the models was evaluated using multiple statistical metrics, while robustness and cross-validated predictive performance were assessed through K-fold cross-validation, residual analysis, Bland–Altman analysis, and variance inflation factor diagnostics (VIF). The full polynomial model achieved the highest fitting accuracy, whereas the simplified formulation demonstrated improved statistical stability and reduced multicollinearity with only minor reductions in predictive performance. Compared with representative literature correlations, the proposed models showed competitive or superior predictive capability, demonstrating their applicability as practical preliminary correlations for practical engineering and energy-related applications.