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Intelligent Estimation of Vitrinite Reflectance of Coal from Photomicrographs Based on Machine Learning

Author

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  • Hongdong Wang

    (School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Meng Lei

    (School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
    Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada)

  • Ming Li

    (School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Yilin Chen

    (School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

  • Jin Jiang

    (School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Liang Zou

    (School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
    Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada)

Abstract

The accurate measurement of vitrinite reflectance (especially for mean maximum vitrinite reflectance, MMVR) is an important issue in the fields of coal mining and processing. However, the application of MMVR has been somewhat hampered by the subjective and the time-consuming characteristic of manual measurements. Semi-automated methods that are oversimplified might affect the accuracy in measuring MMVR values. To address these concerns, we propose a novel MMVR measurement strategy based on machine learning (MMVRML). Considering the complex nature of coal, adaptive K-means clustering is firstly employed to automatically detect the number of clusters (i.e., maceral groups) in photomicrographs. Furthermore, comprehensive features along with a support vector machine are utilized to intelligently identify the regions with vitrinite. The largest region with vitrinite in each photomicrograph is gridded for further regression analysis. Evaluations on 78 photomicrographs show that the model based on random forest and 15 simplified grayscale features achieves the state-of-the-art root mean square error of 0.0424. In addition, to facilitate the usage of petrologists without strong expertise in the machine learning domain, we released the first non-commercial standalone software for estimating MMVR.

Suggested Citation

  • Hongdong Wang & Meng Lei & Ming Li & Yilin Chen & Jin Jiang & Liang Zou, 2019. "Intelligent Estimation of Vitrinite Reflectance of Coal from Photomicrographs Based on Machine Learning," Energies, MDPI, vol. 12(20), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3855-:d:275636
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    References listed on IDEAS

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    1. Tommy Löfstedt & Patrik Brynolfsson & Thomas Asklund & Tufve Nyholm & Anders Garpebring, 2019. "Gray-level invariant Haralick texture features," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-18, February.
    2. Patric H. Hendershott & Bryan D. MacGregor & Charles W.R. Ward, 2004. "Introduction," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 32(2), pages 181-182, June.
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