A deep learning system for collotelinite segmentation and coal reflectance determination

Abstract

Coal is widely used in industrial applications such as carbonization and coke production for steel making, combustion, or gasification to generate electricity, and liquefaction to generate petrochemical feedstock. The utility of a coal is dictated by its properties, commonly referred to as its rank, type, and grade. Coal type or maceral composition and coal rank determination by vitrinite (collotelinite) reflectance are traditionally conducted manually by trained petrographers using reflected light optical microscopy and bulk chemical tests. This study developed an automatic method based on machine learning for rank determination on petrographic images of coal that might improve the efficiency of this process and potentially eliminate operator subjectivity. Firstly, a Mask R-CNN-based architecture deep learning approach was developed to identify and segment the collotelinite maceral, which is fundamental for rank analysis, as rank can be assessed by the reflectance of this maceral. Secondly, an image processing method was developed to analyze the collotelinite segmented images and determine coal rank by associating the grey values with the reflectance. For the segmentation, five samples were used to train the network, 174 images were used for training, and 86 were used in the test set, with over 80% success rates. Four of those five samples were used alongside another eight to determine the rank. The samples ranged from 0.97% to 1.8% reflectance. The root mean square error calculated between the proposed method and the reference values of reflectance was 0.0978%.