ORIGINAL PAPER
The identification of coal and gangue and the prediction of the degree of coal metamorphism based on the EDXRD principle and the PSO-SVM model
1
School of Mechanical Engineering, Anhui University of Science and Technology, China
Submission date: 2022-03-30
Final revision date: 2022-05-06
Acceptance date: 2022-05-23
Publication date: 2022-06-28
Corresponding author
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2022;38(2):113-129
KEYWORDS
TOPICS
ABSTRACT
In order to improve the utilization rate of coal resources, it is necessary to classify coal and gangue, but the classification of coal is particularly important. Nevertheless, the current coal and gangue sorting technology mainly focus on the identification of coal and gangue, and no in-depth research has been carried out on the identification of coal species. Accordingly, in order to preliminary screen coal types, this paper proposed a method to predict the coal metamorphic degree while identifying coal and gangue based on Energy Dispersive X-Ray Diffraction (EDXRD) principle with 1/3 coking coal, gas coal, and gangue from Huainan mine, China as the research object. Differences in the phase composition of 1/3 coking coal, gas coal, and gangue were analyzed by combining the EDXRD patterns with the Angle Dispersive X-Ray Diffraction (ADXRD) patterns. The calculation method for characterizing the metamorphism degree of coal by EDXRD patterns was investigated, and then a PSO-SVM model for the classification of coal and gangue and the prediction of coal metamorphism degree was developed. Based on the results, it is shown that by embedding the calculation method of coal metamorphism degree into the coal and gangue identification model, the PSO-SVM model can identify coal and gangue and also output the metamorphism degree of coal, which in turn achieves the purpose of preliminary screening of coal types. As such, the method provides a new way of thinking and theoretical reference for coal and gangue identification.
ACKNOWLEDGEMENTS
The authors declare that they have no conflict of interest.
This work was supported by the National Natural Science Foundation of China under Grant (No. 51904007), in part by Collaborative Innovation Project of Colleges and Universities of Anhui Province (No. GXXT-2020-054, GXXT-2021-076), in part by the Anhui Natural Science Foundation Project under Grant (No. 1908085QE227), in part by Graduate Innovation Fund of Anhui University of Science and Technology under Grant (No. 2021CX1008). The authors are very grateful for this generous support.
METADATA IN OTHER LANGUAGES:
Polish
Identyfikacja węgla i skały płonnej oraz prognozowanie stopnia metamorfizmu węgla w oparciu o zasadę EDXRD i model PSO-SVM
identyfikacja węgla i skały płonnej, dyfrakcja rentgenowska, dyspersja energii, stopień metamorfizmu, PSO-SVM
W celu poprawy stopnia wykorzystania zasobów węgla konieczna jest klasyfikacja węgla i skały płonnej, ale to klasyfikacja węgla jest szczególnie ważna. Niemniej jednak obecna technologia separacji węgla i skały płonnej koncentruje się głównie na identyfikacji węgla i skały płonnej, ale nie przeprowadzono dogłębnych badań dotyczących identyfikacji gatunków węgla. W związku z tym, w celu wstępnego przesiewu rodzajów węgla, w niniejszym artykule zaproponowano metodę przewidywania stopnia metamorfizmu węgla przy identyfikacji węgla i skały płonnej w oparciu o zasadę dyfrakcji rentgenowskiej z dyspersją energii (EDXRD) z 1/3 węglem koksującym, węglem gazowym i skałą płonną z kopalni Huainan w Chinach jako obiektem badawczym. Różnice w składzie fazowym 1/3 węgla koksowego, węgla gazowego i skały płonnej analizowano przez połączenie wzorców EDXRD z wzorcami dyfrakcji rentgenowskiej z dyspersją kątową (ADXRD). Zbadano metodę obliczeniową charakteryzującą stopień metamorfizmu węgla za pomocą wzorców EDXRD, a następnie opracowano model PSO-SVM do klasyfikacji węgla i skały płonnej oraz przewidywania stopnia metamorfizmu węgla. Na podstawie uzyskanych wyników wykazano, że poprzez wbudowanie metody obliczania stopnia metamorfizmu węgla w model identyfikacji węgla i skały płonnej, model PSO-SVM może identyfikować węgiel i skałę płonną, a także wyprowadzać stopień metamorfizmu węgla, co z kolei spełnia cel wstępnego przesiewania rodzajów węgla. Jako taka, metoda ta zapewnia nowy sposób myślenia i teoretyczne odniesienie do identyfikacji węgla i skał płonnych.
REFERENCES (33)
1.
Alfarzaeai et al.2020 – Alfarzaeai, M., Niu, Q., Zhao, J., Eshaq, R. and Hu, E. 2020. Coal/Gangue Recognition Using Convolutional Neural Networks and Thermal Images. IEEE Access 8, pp. 76780–76789.
2.
Barnakov et al. 2016 – Barnakov, C., Khokhlova, G., Popova, A., Sozinov, S. and Ismagilov, Z. 2016. X-Ray diffraction technique: structure determination of carbonaceous materials (Review). Chemistry for Sustainable Development 24(4), pp. 569–576.
3.
Bassett et al. 2006 – Bassett, K., Ettmuller, F. and Bemet, M. 2006. Provenance analysis of the Paparoa and Brunner coal measures using integrated SEM-cathodoluminescence and optical microscopy. New Zealand Journal of Geology and Geophysics 49(2), pp. 241–254, DOI: 10.1080/00288306.2006.9515163.
4.
Cao et al. 2020 – Cao, Y., Liu, M., Xing, Y., Li, G., Luo, J. and Gui, X. 2020. Current situation and prospect of underground coal preparation technology. Journal of Mining and Safety Engineering 37(01), pp. 192–201 (in Chinese).
5.
Chang et al. 2011 – Chang, C. and Lin, C. 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3), pp. 1–27, DOI: 10.1145/1961189.1961199.
6.
Chen et al. 2019 – Chen, Y., Wang, X., Song, Q., Xu, J. and Mu, B. 2019. Development of a high-energy-resolution EDXRD system with a CdTe detector for security inspection. AIP Advances 8(10), DOI: 10.1063/1.5052027.
7.
Cook et al. 2007 – Cook, E., Fong, R., Horrocks, J., Wilkinson, D. and Speller, R. 2007. Energy dispersive X-ray diffraction as a means to identify illicit materials: A preliminary optimisation study. Applied radiation and isotopes 65, pp. 959–967, DOI: 10.1016/j.apradiso.2007.02.010.
8.
Energy/2000/12 – (E/ECE/)Energy/2000/12, International Codification System for Low-rank Coal Utilization.
9.
Jiang et al. 1998 – Jiang, B., Qin, Y., Song, D. and Wang, C. 1998. XRD structure of high rank tectonic coals and its implication to structural geology. Journal of China University of mining and Technology 27(2), pp. 115–118. (in Chinese).
10.
Ju et al. 2014 – Ju, Y., Luxbacher, K., Li, X., Wang, G., Yan, Z., Wei, M. and Yu, L. 2014. Micro-structural evolution and their effects on physical properties in different types of tectonically deformed coals. International Journal of Coal Science and Technology 1, pp. 364–375. DOI: 10.1007/s40789-014-0042-1.
11.
Kuerten, A. 2017. Coal preconcentration in Moatize mine based on sensor-SBS technology. Rio de Janeiro: Federal University of Rio Grande Do Sul.
12.
Luggar et al. 1998 – Luggar, R., Farquharson, M., Horrocks, J. and Lacey, R. 1998. Multivariate Analysis of Statistically Poor EDXRD Spectra for the Detection of Concealed Explosives. X-ray Spectrometry 27, pp. 87–94, DOI: 10.1002/(SICI)1097-4539(199803/04)27:2<87::AID-XRS256>3.0.CO;2-0.
13.
Luo et al. 2004 – Luo, Y. and Li, W. 2004. X- ray diffraction analysis on the different macerals of several low-to-medium metamorpic grade coals. Journal of China Coal Society 03, pp. 338–341 (in Chinese).
14.
Qian et al. 2017 – Qian, J., Liu, Z., Lin, S., Li, X., Hong, S. and Li, D. 2017. Characteristics analysis of post-explosion coal dust samples by X-ray diffraction. Combustion Science and Technology 190(1), pp. 1–15, DOI: 10.1080/00102202.2017.1407317.
15.
Ranjan et al. 2017 – Ranjan, R., Patel, V. and Chellappa, R. 2017. Hyper Face: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 41(1), pp. 121–135, DOI: 10.48550/arXiv.1603.01249.
16.
Robben et al. 2019 – Robben, C. and Hermann, W. 2019. Sensor-Based Ore Sorting Technology in Mining-Past, Present and Future. Minerals 9(9), pp. 523–426, DOI: 10.3390/min9090523.
17.
Saikia, B. 2010. Inference on carbon atom arrangement in the turbostatic graphene layers in Tikak coal (India) by X-ray pair distribution function analysis. International Journal of Oil, Gas and Coal Technology 3(4), pp. 362–372, DOI: 10.1504/IJOGCT.2010.037465.
18.
Saikia et al. 2008 – Saikia, B., and Boruah, R. 2008. Structural studies of some Indian coals by using X-ray diffraction techniques. Journal of X-Ray Science and Technology 16(2), pp. 89–94.
19.
Shrivastava et al. 2015 – Shrivastava, N., Khosravi, A. and Panigrahi, B. 2015. Prediction interval estimation of electricity prices using PSO-tuned support vetor machines. IEEE Transactions on Industrial Informatics 11(2), pp. 322–331, DOI: 10.1109/TII.2015.2389625.
20.
Silver et al. 2016 – Silver, D., Huang, A., Maddison, C., Guez, A., Sifre, L., Driessche, G., Schittwieser, J., Antonoglou, L., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T. and Hassabia, D. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), pp. 484–489, DOI: 10.1038/nature16961.
21.
Singh, V. and Rao, S. 2006. Application of image processing in mineral industry: a case study of ferruginous manganese ores. Mineral Processing and Extractive Metallurgy Review 115, pp. 155–160, DOI: 10.1179/174328506X109130.
22.
Song et al. 2017 – Song, L., Liu, S., Yu, M., Mao, Y. and Wu, L. 2017. A Classification Method Based on the Combination of Visible, Near-Infrared and Thermal Infrared Spectrum for Coal and Gangue Distinguishment. Spectroscopy and Spectral Analysis 37(02), pp. 416–422, DOI: 10.3964/j.issn.1000-0593(2017)02-0416-07.
23.
Sun et al. 2019 – Sun, Z., Lu, W., Xuan, P., Li, H., Zhang, S., Niu, S. and Jia, R. 2019. Separation of gangue from coal based on supplementary texture by morphology. International Journal of Coal Preparation and Utilization, pp. 1–17, DOI: 10.1080/19392699.2019.1590346.
24.
Von Ketelhodt, L. and Carl, B. 2010. Dual energy X-ray transmission sorting of coal. Journal of the southern African Institute of Mining and Metallurgy 110(7), pp. 371–378.
25.
Wang, R. 2021. Research on Coal and Gangue Smart Sorting System Base on X-Ray and Structured Light Camera. North University of China (in Chinese).
26.
Wang et al. 1998 – Wang, T., Xiao, Y., Yang, Y. and Chase, H. 1998. Fourier transform surface-enhanced Raman spectra of fulvic acid from weathered coal adsorbed on gold electrodes. Journal of Environmental Science and Health: Part A 34(3), pp. 749–765, DOI: 10.1080/00387019608007136.
27.
Wang et al. 2021 – Wang, X., Wang, S., Guo, Y., Hu, K. and Wang, W. 2021. Dielectric and geometric feature extraction and recognition method of coal and gangue based on VMD-SVM. Powder Technology 392, pp. 241–250.
28.
Xu et al. 2020 – Xu, Z., Lv, Z., Wang, W., Zhang, K. and Lv, H. 2020. Machine vision recognition method and optimization for intelligent separation of coal and gangue. Journal of China Coal Society 45(06), pp. 2207–2216 (in Chinese).
29.
Xin et al. 2014 – Xin, H., Wang, D., Dou, G., Qi, X., Xu, T. and Qi, G. 2014. The infrared characterization and mechanism of oxygen adsorption in coal. Spectroscopy Letters 47(9), pp. 664–675, DOI: 10.1080/00387010.2013.833940.
30.
Xiang et al. 2016 – Xiang, J., Zeng, F., Liang, H., Li, M., Song, X. and Zhao, Y. 2016. Carbon structure characteristics and evolution mechanism of different rank coals. Journal of China Coal Society 41(6), pp. 1498–1506, DOI: 10.3390/min8020049.
31.
Zhang et al. 2020 – Zhang, Z., Liu, Y., Hu, Q., Zhang, Z., Wang, L., Liu, X. and Xia, X. 2020. Multi-information online detection of coal quality based on machine vision. Powder Technology 374, pp. 250–262, DOI: 10.1016/j.powtec.2020.07.040.
32.
Zhang et al. 2022 – Zhang, J., Han, X. and Cheng, D. 2022. Improving coal/gangue recognition efficiency based on liquid intervention with infrared imager at low emissivity. Measurement 189.
33.
Zhu et al. 1987 – Zhu, X., Birringer, R., Herr, U. and Gleiter, H. 1987. X-ray diffraction studies of the structure of nanometer-sized crystalline materials. Physical Review B 35(17), pp. 9085–9090.
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