Gross Calorific Value Estimation in Coal Using Multi-Model FTIR and Machine Learning Approach

The Gross Calorific Value (GCV) is a key indicator used to assess the energy potential and quality of coal. Conventional oxygen bomb calorimetry, though widely used, is inherently time-consuming due to the combustion process involved. Similarly, regression models for GCV prediction based on ultimate or proximate analyses require extensive laboratory procedures and sample preparation. To address these challenges, this study investigates the use of mid-infrared Fourier Transform Infrared (FTIR) spectroscopy coupled with supervised variable selection to enable rapid, non-destructive, and cost-effective assessment of coal properties. In this work, a detailed mid-infrared FTIR spectral analysis of coal was conducted to identify fifty-six selective absorption bands (supervised input variables) sensitive to the organic functional group content in coal, coupled with several machine learning (ML) techniques to model the GCV of coal samples from the Johilla coal basin, India. The ML techniques employed here are 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). A multi-model estimation of GCV using the simple average output of the three models (PLSR, RFR, and XGB) achieved the best predictive performance (R2 = 0.951, RMSE = 19.050%, MBE = 1.420%, MAE = 4.053 cal/g), reflecting strong consistency between predictions and actual measurements. The FTIR-based approach achieves competitive or improved results relative to conventional methods and models documented in prior studies. The GCVs derived through modeling of FTIR data are also statistically proven (using t-test and F-test at alpha = 0.01) to be significantly similar to those of the bomb calorimeter, an industry standard for GCV measurements. Consequently, this novel FTIR-based methodology establishes an efficient, dependable tool for GCV determination that operates independently of conventional techniques, thereby enabling rapid quality assessment critical for industrial applications.