Automated coal petrography using random forest

Article Type

Research Article

Publication Title

International Journal of Coal Geology

Abstract

Coal is the backbone of the steel industry because of its manifold use in coke making, pulverised coal injection and electricity generation. Coal behaviour is a factor of its phase fractions (organic and inorganic) along with its maturity level. Coal petrography is an important tool for maceral determination and rank measurement based on its optical properties. Manual calculation of phase fractions is time-consuming and depends on operator's expertise and efficiency. To add value to plant operations, a faster, accurate and repetitive data is required. As a result, an attempt has been made to develop a machine learning based method for the automatic calculation of different phases present in coal. A random forest based model is developed to classify different phases of coal macerals (organic constituents) and minerals (inorganic constituents). The efficacy of the proposal is improved after introducing a hierarchical classification approach wherein random forest based classifier is used to segment macerals ignoring background. Features related to micro-structures of the coal microscopic images are extracted and utilized in random forest based classification. Methodology developed provides a better and quick alternative for manual petrographic analysis. A comparative analysis suggest that the final output shows better than 90% classification accuracy compared to ground truth. Its industrial application will save time, money and labour with the increase in efficiency level.

DOI

10.1016/j.coal.2020.103629

Publication Date

12-1-2020

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