A sorting method for coal and gangue based on surface grayness and glossiness
Yifan Wei 1,2
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School of Mechanical Engineering, Anhui University of Science and Technology
State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan, China
Submission date: 2023-04-16
Final revision date: 2023-06-11
Acceptance date: 2023-08-23
Publication date: 2023-09-22
Corresponding author
Yifan Wei   

School of Mechanical Engineering, Anhui University of Science and Technology
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2023;39(3):173–198
Sorting coal and gangue is important in raw coal production; accurately identifying coal and gangue is a prerequisite for effectively separating coal and gangue. The method of extracting coal and gangue using image grayscale information can effectively identify coal and gangue, but the recognition rate of the sorting process based on image grayscale information needs to substantially higher than that which is needed to meet production requirements. A sorting method of coal and gangue using object surface grayscale-gloss characteristics is proposed to improve the recognition rate of coal and gangue. Using different comparative experiments, bituminous coal from the Huainan area was used as the experimental object. It was found that the number of pixel points corresponding to the highest level grey value of the grayscale moment and illumination component of the coal and gangue images were combined into a total discriminant value and used as input for the best classification of coal and gangue using the GWO-SVM classification model. The recognition rate could reach up to 98.14%. This method sorts coal and gangue by combining surface greyness and glossiness features, optimizes the traditional greyness-based recognition method, improves the recognition rate, makes the model generalizable, enriches the research on coal and gangue recognition, and has theoretical and practical significance in enterprise production operations.
This work was supported by the Anhui Provincial University System Innovation Project of China (grant no. GXXT-2021-076), Open Research Fund of Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining (grant no. EC2021010), and Open Project Program of Anhui Province Key Laboratory of Metallurgical Engineering & Resources Recycling (grant no. JKF22-06).
Metoda sortowania węgla i skały płonnej na podstawie szarości i połysku powierzchni
połysk, rozpoznawanie skały płonnej, rozpoznawanie obrazu, klasyfikacja nadzorowana, algorytm szarych wilków, maszyna wektorów nośnych
Sortowanie węgla i skały płonnej jest ważne w produkcji węgla surowego; dokładna identyfikacja węgla i skały płonnej jest warunkiem wstępnym skutecznego oddzielenia tych surowców. Metoda rozdzielenia węgla i skały płonnej przy użyciu informacji w skali szarości obrazu może skutecznie identyfikować węgiel i skałę płonną, ale stopień rozpoznawania procesu sortowania w oparciu o te informacje być znacznie wyższy niż wymagany do spełnienia wymagań produkcyjnych. W artykule zaproponowano metodę sortowania węgla i skały płonnej wykorzystującą charakterystykę połysku i skali szarości powierzchni obiektu w celu poprawy szybkości rozpoznawania węgla i skały płonnej. W badaniach wykorzystano próbki węgla kamiennego z obszaru Huainan. Stwierdzono, że liczbę punktów pikseli odpowiadającą najwyższemu poziomowi szarości momentu w skali szarości i składowej oświetlenia obrazów węgla i skały płonnej połączono w całkowitą wartość dyskryminującą i wykorzystano jako dane wejściowe dla najlepszej klasyfikacji węgla i skały płonnej przy użyciu modelu klasyfikacji GWO-SVM. Wskaźnik rozpoznawalności może osiągnąć nawet 98,14%. Ta metoda sortowania węgla i skały płonnej poprzez połączenie cech szarości i połysku powierzchni, optymalizuje tradycyjną metodę rozpoznawania w oparciu o szarość, poprawia współczynnik rozpoznawania, umożliwia uogólnienie modelu, wzbogaca badania nad rozpoznawaniem węgla i skały płonnej, ma znaczenie teoretyczne i praktyczne w operacjach produkcyjnych przedsiębiorstwa.
Alfarzaeai et al. 2020 – Alfarzaeai, M.S., Niu, Q., Zhao, J.Q., Eshaq, R. and Hu, E. 2020. Coal/Gangue Recognition Using Convolutional Neural Networks and Thermal Images. IEEE Access 8, pp. 76780–76789, DOI: 10.1109/ACCESS.2020.2990200.
Atkinson, G.A. and Hancock, E.R. 2006. Recovery of surface orientation from diffuse polarization. IEEE Transactions on Image Processing 15(6), pp. 1653–1664, DOI: 10.1109/TIP.2006.871114.
Bharat et al. 2017 – Bharat, R., Deepak, G., Shakti, P. and Kamalini, A. 2017. A Review on Support Vector Machines for Classification Problems. Artificial Intelligent Systems and Machine Learning 9(7).
Cheng et al. 2022 – Cheng, G., Chen, J., Wei, Y., Chen, S., Pan, Z. 2023. A Coal Gangue Identification Method Based on HOG Combined with LBP Features and Improved Support Vector Machine. Symmetry 15, DOI: 10.3390/sym15010202.
Congo et al. 2023 – Congo, T., Rodrigues, S., Esterle, J., Steel, K. and Maranha, S. 2023. Before and after: A visual glimpse into the coking behaviour of coal macerals. Fuel 343, DOI: 10.1016/j.fuel.2023.127979.
Doan, M. and Carpenter, A.E. 2019. Leveraging machine vision in cell-based diagnostics to do more with less. Nature Materials 18(5), pp. 414–418, DOI: 10.1038/s41563-019-0339-y.
Fan, C.L. and Chung, Y.J. 2022. Supervised Machine Learning-Based Detection of Concrete Efflorescence. Symmetry-Basel 14(11), DOI: 10.3390/sym14112384.
Gao et al. 2020 – Gao, R., Sun, Z., Li, W., Pei, L., Hu, Y. and Xiao, L. 2020. Automatic Coal and Gangue Segmentation Using U-Net Based Fully Convolutional Networks. Energies 13(4), DOI: 10.3390/en13040829.
Hu et al. 2022 – Hu, F., Zhou, M., Dai, R. and Liu, Y. 2022. Recognition method of coal and gangue based on multispectral spectral characteristics combined with one-dimensional convolutional neural network. Front. Earth Sci. Frontiers In Earth Science 10, DOI: 10.3389/feart.2022.893485.
Huang et al. 2022 – Huang, H.X., Dou, D.Y. and Zhang, C.L. 2022. Detecting coal-carrying rate in gangue based on binocular machine vision and particle queuing method. International Journal of Coal Preparation and Utilization, DOI: 10.1080/19392699.2022.2104265.
Jia et al. 2022 – Jia X.R., Ding Y.Q., Zhao Y.B., Huo, X., Liu, S. and Yun, F. 2022. Investigation of the pollutant emission characteristics of blends of biomass and coal gangue in a fluidized bed. Thermal Science 26(5), pp. 4333–4343, DOI: 10.2298/TSCI211030042J.
Kayalvizhi et al. 2022 – Kayalvizhi, R., Kumar, A., Malarvizhi, S., Topkar, A. and Vijayakumar, P. 2022. Raw data processing techniques for material classification of objects in dual energy X-ray baggage inspection systems. Radiation Physics and Chemistry 193, DOI: 10.1016/j.radphyschem.2021.109512.
Lin et al. 2022 – Lin, X.B., Zhang, P.S., Meng, F.B. and Liu, C. 2022. A Coal Seam Thickness Prediction Model Based on CPSAC and WOA-LS-SVM: A Case Study on the ZJ Mine in the Huainan Coalfield. Energies 15(19), DOI: 10.3390/en15197324.
Mirjalili et al. 2014 – Mirjalili, S., Mirjalili, S.M. and Lewis, A. 2014. Grey Wolf Optimizer. Advances in Engineering Software 69, pp. 46–61, DOI: 10.1016/j.advengsoft.2013.12.007.
Miyazaki et al. 2003 – Miyazaki, D., Kagesawa, M. and Ikeuchi, K. 2003. Polarization-based transparent surface modeling from two views. 9th IEEE International Conference on Computer Vision, DOI: 10.1109/ICCV.2003.1238651.
Miyazaki et al. 2004 – Miyazaki, D., Kagesawa, M. and Ikeuchi, K. 2004. Transparent Surface Modeling from a Pair of Polarization Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), pp. 73–82, DOI: 10.1109/TPAMI.2004.1261080.
Osipov et al. 2020 – Osipov, S.P., Chakhlov, S., Udod, V.A. and Usachev, E.J.U. 2020. Estimation of the effective mass thickness and effective atomic number of the test object material by the dual energy method. Radiation Physics and Chemistry 168, DOI: 10.1016/j.radphyschem.2019.108543.
Pu et al 2019 – Pu, Y.Y., Apel, D.B., Szmigiel, A. and Chen, J. 2019. Image Recognition of Coal and Coal Gangue Using a Convolutional Neural Network and Transfer Learning. Energies 12(9).
Shen et al. 2017 – Shen, X., Niu, L.F., Qi, Z.Q. and Tian, Y. 2017. Support vector machine classifier with truncated pinball loss. Pattern Recognition 68, pp. 199–210, DOI: 10.1016/j.patcog.2017.03.011.
Shi et al. 2017 – Shi, P., Liang, K., Han, D. and Zhang, Y. 2017. A novel intelligent fault diagnosis method of rotating machinery based on deep learning and PSO-SVM. Journal of Vibroengineering 19(8), pp. 5932–5946, DOI: 10.21595/jve.2017.18380.
Singla et al. 2019 – Singla, M., Ghosh, D. and Shukla, K.K. 2019. A survey of robust optimization based machine learning with special reference to support vector machines. International Journal of Machine Learning and Cybernetics 11, pp. 1359–1385, DOI: 10.1007/s13042-019-01044-y.
Subasi, A. 2013. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput Biol Med 43(5), pp. 576–586, DOI: 10.1016/j.compbiomed.2013.01.020.
Wang, W.D. and Zhang, C. 2017. Separating coal and gangue using three-dimensional laser scanning. International Journal of Mineral Processing 169, pp. 79–84, DOI: 10.1016/j.minpro.2017.10.010.
Wang, W.D., Lv, Z.Q., Lu, H.R. 2021. Research on methods to differentiate coal and gangue using image processing and a support vector machine. International Journal of Coal Preparation snd Utilization 41(8), pp. 603–616, DOI: 10.1080/19392699.2018.1496912.
Wang et al. 2022 – Wang, B.J., Huang, H.X., Dou, D.Y. and Qiu, Z. 2022. Detection of coal content in gangue via image analysis and particle swarm optimization-support vector machine. International Journal of Coal Preparation and Utilization 42(7), pp. 1915–1924, DOI: 10.1080/19392699.2021.1932842.
Zhang et al. 2018 – Zhang, J.H., Zhang, Y. and Shi, Z.G. 2018. Enhancement of dim targets in a sea background based on long-wave infrared polarisation features. IET Image Processing 12(11), pp. 2042–2050, DOI: 10.1049/iet-ipr.2018.5607.
Zou et al. 2020 – Zou, L., Yu, X., Li, M., Lei, M. and Yu, H. 2020. Nondestructive Identification of Coal and Gangue via Near-Infrared Spectroscopy Based on Improved Broad Learning. IEEE Transactions on Instrumentation and Measurement 69(10), pp. 8043–8052, DOI: 10.1109/TIM.2020.2988169.
Yin, H. and Ren, H. 2021. Direct symbol decoding using GA-SVM in chaotic baseband wireless communication system. Journal of the Franklin Institute 358(12), DOI: 10.48550/arXiv.2103.10855.