Abstract
The study of the petrographic structure of medium- and high-rank coals is important from both a cognitive and a utilitarian point of view. The petrographic constituents and their individual characteristics and features are responsible for the properties of coal and the way it behaves in various technological processes. This paper considers the application of convolutional neural networks for coal petrographic images segmentation. The U-Net-based model for segmentation was proposed. The network was trained to segment inertinite, liptinite, and vitrinite. The segmentations prepared manually by a domain expert were used as the ground truth. The results show that inertinite and vitrinite can be successfully segmented with minimal difference from the ground truth. The liptinite turned out to be much more difficult to segment. After usage of transfer learning, moderate results were obtained. Nevertheless, the application of the U-Net-based network for petrographic image segmentation was successful. The results are good enough to consider the method as a supporting tool for domain experts in everyday work.
Highlights
Coal petrography is a science that, despite the passage of many years, is developing and updating its knowledge with a view to new directions for use in the energy industry
Particular emphasis is placed on clean coal technologies as well as the recovery of critical elements from coal [1,2,3,4,5,6]
The study of the petrographic structure of coal is important from both a cognitive and a utilitarian point of view [7,8,9,10,11]. It is the petrographic constituents and their individual characteristics and features that are responsible for the property of coal and the way it behaves in various technological processes [7,8,12,13,14,15]
Summary
Coal petrography is a science that, despite the passage of many years, is developing and updating its knowledge with a view to new directions for use in the energy industry. The highlights of the presented results include the development of the coal petrographic images database, the method of image preparation and augmentation, and the development of a U-Net [69]-based convolutional neural network for the semantic segmentation of coal petrographic images. The analysis of the results shows that the quality of segmentation is similar to that for inertinite; a slightly higher level of artifacts was observed, which is visible in the presented images (see Figure 7).
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