Identification of the rank and kind of ten coals including bituminous and anthracite based on near-infrared spectroscopy and interpretable GASF-ResNet18 method

Different ranks and kinds of coal have different properties (volatile, ash, etc.), which affect the process parameters in clean utilization of coal. Based on near-infrared (NIR) spectroscopy data, the 1D spectral data of 670 coal samples were encoded into 2D images with Gramian Angular Summation Field (GASF) method as input data, and a convolutional neural network (CNN) known as ResNet18 was applied to identify the rank and kind of coal. On a dataset containing seven kinds of bituminous coal and three kinds of anthracite coal, the proposed GASF-ResNet18 method achieves an accuracy of 96.3% for coal rank identification, and 94.8% for coal kind identification, even in the presence of moisture interference. Compared with traditional machine learning methods (partial least squares discriminant analysis, back propagation network, logistic regression, and support vector machine), GASF-ResNet18 method has a higher accuracy. It was attributed to ResNet18 catching the key information provided by the following GASF image regions: the combination of the absorption rate in 1250–1700 nm in column and 1550–1700 nm in row, and the combination of 900–1150 nm in column and 1150–1450 nm in row. The results showed GASF-ResNet18 method based on NIR data can be used to rapidly identify the rank and kind of coal.