<b>Comparative Evaluation of Ensemble Machine Learning Models for Predicting the Gross Calorific Value of North-Central Nigeria Coal</b>

Accurate determination of the gross calorific value (GCV) of coal is essential for fuel quality assessment, blending, pricing, and industrial energy management. Conventional laboratory determination using bomb calorimetry, although highly accurate, is time-consuming, costly, and unsuitable for rapid large-scale evaluation. This study comparatively evaluates three ensemble machine learning approaches, namely ensemble artificial neural network (ensemble-ANN), ensemble Gaussian process regression (ensemble-GPR), and ensemble support vector machine (ensemble-SVM), for predicting gross calorific value (GCV) of North-Central Nigeria coal using readily obtainable proximate analysis parameters, namely air-dry moisture (ADM), residual moisture (RM), volatile matter (VM), and ash content (AC). A total of 350 laboratory-tested coal samples were randomly partitioned using a hold-out split method into a calibration dataset comprising 300 data points to develop the models, and a verification dataset comprising 50 data points to compare the model performance.