Coal identification based on a deep network and reflectance spectroscopy.

The rapid identification of coal types in the field is an important task. This research combines spectroscopy with deep learning algorithms and proposes a method for quickly identifying coal types in the field. First, we collect field spectral data of various coals and preprocess the spectra. Then, a coal identification model that uses a convolutional neural network in combination with an extreme learning machine is proposed. The two-dimensional spectral features of coal are extracted through the convolutional neural network, and the extreme learning machine is used as a classifier to identify the features. To further improve the identification performance of the model, we use the whale optimization algorithm to optimize the parameters of the model. The experimental results show that the proposed method can quickly and accurately identify types of coal. It provides a low-cost, convenient, and effective method for the rapid identification of coal in the field.