Data-driven computational approaches to estimate gross calorific value of coal using proximate and ultimate analyses

A crucial factor in determining coal's quality is its gross calorific value (GCV). GCV is generally determined experimentally using an adiabatic bomb calorimeter. But the laboratory measurement process is complicated, tedious, expensive, and time-consuming. To address these problems, this study has developed five machine learning techniques, such as multilayer perceptron artificial neural network (MLP-ANN), multiple variable regression (MVR), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM). ANN is further regularized using early stopping to avoid overfitting, whereas the MVR is regularized by employing L1, L2, and elastic networks. An inter-item correlation matrix, scatterplots, and sensitivity analysis are used to rank the parameters of the ultimate and proximate analyses according to their significance. A total of 6582 datapoints from the US. Geological Survey Coal Quality (COALQUAL) database are utilized for this study. The models' performance is measured using coefficient of determinant (${{\rm{R}}<^>2}$R2), mean squared error (MSE), explained error (EV), maximum error (max_error), minimum error (min_error), mean absolute error (MAE), and mean absolute percentage error (MAPE), respectively. The study's findings demonstrate that MVR, MLP-ANN, SVM, and ANFIS are highly efficient concerning both prediction and computation effectiveness. The prediction performances of these models are somewhat similar, with a coefficient of determinant of over 0.99. Hence, simple MVR is sufficient to predict GCV accurately instead of complex modeling. Parameters ranking indicates that oxygen (O), carbon (C), and moisture (M) are the most significant input variables for GCV prediction.