Limitations Associated with Proximate Analysis-Based Gross Calorific Value Modeling for Coals

Abstract

ABSTRACT This study aims at investigating the limitations associated with proximate analysis-based gross calorific value (GCV) modeling for coals. Toward this, a dataset comprising proximate analysis and GCV data of 4792 coal samples collected from various Indian coal basins was generated, and then a GCV prediction model was developed using the popular multivariate linear regression (MLR) technique. Although the developed model appeared to be acceptable in terms of prediction R2 value of 0.934, through a rigorous statistical analysis, it has been shown that grade misclassification and source specific biases are inherent limitations associated with such GCV prediction models. It was found that the grade classification accuracy associated with a GCV prediction model was inversely and linearly proportional to the model’s associated mean absolute error (MAE) value. It has further been demonstrated that even well-validated GCV prediction models available in literature may perform sub-optimally when utilized for grade classification tasks. The analysis presented in this study also confirms that a source-specific bias can be introduced in the GCV prediction models developed using coal samples from varied geographical sources. It has further been shown that the incorporation of a categorical representation of the sample sources in the GCV prediction model could successfully eliminate the source-specific biases.