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In this study, the gross calorific value (GCV) of coal has been estimated using two types of coal analysis data. These are proximate data and spectral reflectance data in visible to near infrared range (wavelength: 400–2500 nm). The present study aims in developing two GCV prediction models and compare the accuracy of the predicted values using those models with the experimental value. Multiple linear regression (MLR) method is used to develop these models. In the first model, different properties of coal such as ash, moisture, and volatile matter contents are correlated with the GCV. Whereas in the second model, the GCV is predicted from the maximum absorption band depth at five different wavelength ranges. The performance accuracy has been evaluated using  R2 , RMSE, and MAPE values of the two regression models. These two models yield  R2 , RMSE, and MAPE values of 0.84, 2.26, 5.24 and 0.92, 1.6, 4.84, respectively. It has been concluded that the GCV predicted from the spectral reflectance data provides better accuracy than those predicted from the proximate data. Therefore, hyperspectral sensor based technology could be used for rapid determination of the GCV of coal with greater accuracy.

Estimation of Gross Calorific Value of Bituminous Coal using various Coal Properties and Reflectance Spectra