The rising number of occurrences of coal grade slippage among coal suppliers and users is causing worry in the
Indian coal industry.One of the most important metrics for determining coal quality is the Gross Calorific Value (GCV). As a
result, good GCV prediction is one of the essential techniques to boost heating value and coal output. This system aims to
estimate the GCV of the coal samples from proximate and ultimate parameters of coal using machine learning regression
algorithm: the Multiple Linear Regression (MLR) and Local Polynomial Regression (LPR). The performance of this system is
evaluated using Coefficient of determination (R2
), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE)
parameters.
