A novel method for rapid and reliable estimation of Gross Calorific Value (GCV) of Coal using mid-infrared FTIR Spectroscopy and a multi-model Machine Learning Approach

The Gross Calorific Value (GCV) indicates coal quality by measuring the total heat released during the complete combustion of the coal. Accurate GCV estimation is crucial for efficient pricing, processing, and energy performance assessment in industries. Conventional oxygen bomb calorimetry, though precise, is relatively slow and expensive for large-scale analyses. Since coal’s organic and elemental composition strongly affects its heating value, understanding this relationship can help with reliable GCV evaluation. In this study, we analyzed the mid-infrared FTIR spectra of coal and selected 56 absorption bands associated with the relevant organic and elemental constituents of coal. These were used as input features for various machine learning (ML) models to predict the GCV of coal from the Johilla coal basin in India. The ML models tested included piecewise linear regression (PLR), partial least squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), artificial neural networks (ANN), and extreme gradient boosting regression (XGB). By combining the predictions from the three models (PLSR, RFR, and XGB) through a simple average, we achieved the highest accuracy (R² = 0.951, RMSE = 19.05%, MBE = 1.42%, MAE = 4.053 cal/g), indicating strong agreement between the predicted and measured values. Overall, the FTIR-based method yields results that match or surpass those of traditional laboratory techniques reported in earlier research. The GCV values predicted from the FTIR models were statistically tested using t-tests (test for mean) and F-tests (test for variance) at a 1% significance level and were found to be statistically similar to the results from the standard bomb calorimeter method. The study demonstrates that the FTIR-based approach is independent and reliable and can be used as a faster and more convenient alternative method for determining GCV, making it highly useful for quick coal quality analysis in industry.