Document Type : Original Research Paper
Authors
1 Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran
2 Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
10.22044/jme.2025.15288.2929
Abstract
One of the most prevalent risks in coal mines is spontaneous combustion (spon com) of coal, which is a major source of coal loss in these environments. Therefore, to avoid coal loss and preventing the potential risks, a criterion for predicting the spon com of coal is essential. The main purpose of this work is to present a new model for predicting the spon com of coal potential using a decision tree technique, known as the Spon com of coal decision Tree (SCCDT). In this research work, after identifying the effectiveness of each parameter on the spon com of coal, several parameters were examined, including characteristics such as moisture, ash, pyrite, volatile matter, fixed carbon, mineralogy, and petrography. Subsequently, the primary phases of applying the decision tree model were analyzed, and the probability of the spon com of coal potential was determined based on intrinsic parameters. Finally, the mentioned parameters were categorized, and an appropriate model for classifying the spon com of coal potential was developed. In the SCCDT model, the spon com of coal potential was divided into three classes: low, medium, and high. The model was then applied to Parvadeh I to IV coal mines in Tabas. A comparison of the study's findings showed relatively good agreement with the SCCDT model. Using the proposed model can help to predict the spon com hazard and prevent the various life-threatening/mortal and financial risks.
Keywords
Main Subjects
[1]. Akgün, F. and A. Arisoy, (1994). Effect of particle size on the spontaneous heating of a coal stockpile. Combustion and Flame, 99(1): p. 137-146.
[2]. Nugroho, Y.S., A. McIntosh, and B. Gibbs, (2000). Low-temperature oxidation of single and blended coals. Fuel, 79(15): p. 1951-1961.
[3]. Smith, M.A. and D. Glasser, (2005). Spontaneous combustion of carbonaceous stockpiles. Part II. Factors affecting the rate of the low-temperature oxidation reaction. Fuel, 84(9): p. 1161-1170.
[4]. Beamish, B.B. and A. Arisoy, (2008). Effect of mineral matter on coal self-heating rate. Fuel, 87(1): p. 125-130.
[5]. Saffari, A., et al., (2017). Presenting an engineering classification system for coal spontaneous combustion potential. International Journal of Coal Science & Technology, 4: p. 110-128.
[6]. Saffari, A., et al., (2013). Applying rock engineering systems (RES) approach to evaluate and classify the coal spontaneous combustion potential in Eastern Alborz coal mines. International Journal of Mining and Geo-Engineering, 47(2): p. 115-127.
[7]. Demir, B., Ö. Ören, and C. Şensöğüt, (2020). Investigation of the effect of reactor size on spontaneous combustion properties of coals with different coalification degrees. Arabian Journal of Geosciences, 13(15): p. 730.
[8]. Wang, H., B.Z. Dlugogorski, and E.M. Kennedy, (2003). Coal oxidation at low temperatures: oxygen consumption, oxidation products, reaction mechanism and kinetic modelling. Progress in energy and combustion science, 29(6): p. 487-513.
[9]. Michaylov, M., (2002). Expert system for assessment of risk from spontaneous combustion. Mining and Mineral Processing, p. 44-45.
[10]. Lang, L. and Z. Fu-Bao, (2010). A comprehensive hazard evaluation system for spontaneous combustion of coal in underground mining. International journal of coal geology, 82(1-2): p. 27-36.
[11]. Wang, L. and R. Tong. (2020). An Improved Evaluation of Comprehensive for Mine Fire Risk. in IOP Conference Series: Materials Science and Engineering. IOP Publishing.
[12]. Ahamed, S., et al., (2016). Investigation the risk of spontaneous combustion in Barapukuria coal mine, Dinajpur, Bangladesh. Journal of Geoscience and Environment Protection, 4(4): p. 74-79.
[13]. Tripathy, D. and B. Pal, (2001). Spontaneous heating susceptibility of coals-evaluation based on experimental techniques. Journal of mines, Metals and Fuels, 49.
[14]. Parr, S. and E. Hilgard, (1925). Oxidation of Sulfur as a Factor in Coal Storage. Industrial & Engineering Chemistry, 17(2): p. 117-118.
[15]. Guney, M., (1968). Oxidation and spontaneous combustion of coal: Review of individual factors. Colliery Guardian, 216(105-110): p. 137-143.
[16]. Feng, K., R. Chakravorty, and T. Cochrane, (1973). Spontaneous combustion-coal mining hazard. CIM BULLETIN, 66(738): p. 75-84.
[17]. Şensöğüt, C., (2011). Spontaneous combustion related fire ratios. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 5(1).
[18]. Banerjee, S.C., (1985). Spontaneous combustion of coal and mine fires.
[19]. Smith, A.C. and C.P. Lazzara, (1987). Spontaneous combustion studies of US coals. Vol. 9079. US Department of the Interior, Bureau of Mines.
[20]. Kaymakci, E. and V. Didari, (2002). Relations between coal properties and spontaneous combustion parameters.
[21]. Song, Z. and C. Kuenzer, (2014). Coal fires in China over the last decade: A comprehensive review. International Journal of Coal Geology, 133: p. 72-99.
[22]. Tosun, A., (2017). Development of a technology to prevent spontaneous combustion of coal in underground coal mining. Journal of the Southern African Institute of Mining and Metallurgy, 117(12): p. 1133-1138.
[23]. Lei, C., et al., (2018). A random forest approach for predicting coal spontaneous combustion. Fuel, 223: p. 63-73.
[24]. Saffari, A., F. Sereshki, and M. Ataei, (2020). A comprehensive study on the effect of moisture content on coal spontaneous combustion tendency. Iranian Journal of Earth Sciences, 12(3): p. 194-204.
[25]. Onifade, M. and B. Genc, (2018). Spontaneous combustion of coals and coal-shales. International Journal of Mining Science and Technology, 28(6): p. 933-940.
[26]. Onifade, M. and B. Genc, (2020). A review of research on spontaneous combustion of coal. International Journal of Mining Science and Technology, 30(3): p. 303-311.
[27]. Said, K.O., et al., (2021). An artificial intelligence-based model for the prediction of spontaneous combustion liability of coal based on its proximate analysis. Combustion Science and Technology, 193(13): p. 2350-2367.
[28]. Deng, J., et al., (2021). Prediction model for coal spontaneous combustion based on SA-SVM. ACS omega, 6(17): p. 11307-11318.
[29]. Zhang, L., et al., (2022). Prediction of coal self-ignition tendency using machine learning. Fuel, 325: p. 124832.
[30]. Mishra, A. and S.K. Gupta, (2024). Intelligent classification of coal seams using spontaneous combustion susceptibility in IoT paradigm. International Journal of Coal Preparation and Utilization, 44(7): p. 757-779.
[31]. Yetkin, M.E., et al., (2024). The prediction of the risks of spontaneous combustion in underground coal mines using a fault tree analysis method. MethodsX, 13: p. 102835.
[32]. Wang, C., et al., (2023). Study on spontaneous combustion characteristics and early warning of coal in a deep mine. Fire, 6(10): p. 396.
[33]. Xu, X. and F. Zhang, (2023). Evaluation and optimization of multi-parameter prediction index for coal spontaneous combustion combined with temperature programmed experiment. Fire, 6(9): p. 368.
[34]. Kursunoglu, N. and M. Gogebakan, (2022). Prediction of spontaneous coal combustion tendency using multinomial logistic regression. International Journal of Occupational Safety and Ergonomics, 28(4): p. 2000-2009.
[35]. Zhou, X., et al., (2022). Study on the spontaneous combustion characteristics and prevention technology of coal seam in overlying close goaf. Combustion Science and Technology, 194(11): p. 2233-2254.
[36]. Saffari, A., F. Sereshki, and M. Ataei, (2022). Evaluation effect of macerals petrographic and pyrite contents on spontaneous coal combustion in Tabas Parvadeh and Eastern Alborz coal mines in Iran. International Journal of Coal Preparation and Utilization, 42(1): p. 12-29.
[37]. Ghosh, A.K. and D. Choudhury, (2022). Spontaneous combustion in relation to drying of low rank coal. International journal of coal preparation and Utilization, 42(5): p. 1435-1448.
[38]. Zeng, J., et al., (2021). Assessment of coal spontaneous combustion prediction index gases for coal with different moisture contents. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, p. 1-13.
[39]. Shen, L. and Q. Zeng, (2021). Investigation of the kinetics of spontaneous combustion of the major coal seam in Dahuangshan mining area of the Southern Junggar coalfield, Xinjiang, China. Scientific reports, 11(1): p. 876.
[40]. Gbadamosi, A., et al., (2021). Spontaneous combustion liability indices of coal. Combustion Science and Technology, 193(15): p. 2659-2671.
[41]. Shao, H., Q. Lu, and S. Jiang, (2021). Effect of frequency conversion ventilation on coal spontaneous combustion. Combustion Science and Technology, 193(10): p. 1766-1781.
[42]. Onifade, M., B. Genc, and S. Bada, (2020). Spontaneous combustion liability between coal seams: A thermogravimetric study. International Journal of Mining Science and Technology, 30(5): p. 691-698.
[43]. Zhang, Y., et al., (2020). Study on the characteristics of coal spontaneous combustion during the development and decaying processes. Process Safety and Environmental Protection, 138: p. 9-17.
[44]. Saffari, A., M. Ataei, and F. Sereshki, (2020). Studying relationship between coal intrinsic characteristics in spontaneous combustion of coal potential using crossing point temperature test method. Journal of Mining and Environment, 11(1): p. 315-333.
[45]. Saffari, A., F. Sereshki, and M. Ataei, (2020). Effect of maceral content on tendency of spontaneous coal combustion using the R70 method. International Journal of Mining and Geo-Engineering, 54(2): p. 93-99.
[46]. Wu, Y., et al., (2020). Study on the effect of extraneous moisture on the spontaneous combustion of coal and its mechanism of action. Energies, 13(8): p. 1969.
[47]. Tutak, M., et al., (2020). The impact of the ventilation system on the methane release hazard and spontaneous combustion of coal in the area of exploitation—a case study. Energies, 13(18): p. 4891.
[48]. Więckowski, M., N. Howaniec, and A. Smoliński, (2020). Assessment of Heating and Cooling of a Spontaneous Fire Source in Coal Deposits—Effect of Coal Grain Size. Minerals, 10(10): p. 907.
[49]. Ma, L., et al., (2020). Prediction indices and limiting parameters of coal spontaneous combustion in the Huainan mining area in China. Fuel, 264: p. 116883.
[50]. Mohalik, N.K., E. Lester, and I.S. Lowndes, (2020). Development of a petrographic technique to assess the spontaneous combustion susceptibility of Indian coals. International Journal of Coal Preparation and Utilization, 40(3): p. 186-209.
[51]. Fan, J., G. Wang, and J. Zhang, (2019). Study on spontaneous combustion tendency of coals with different metamorphic grade at low moisture content based on TPO-DSC. Energies, 12(20): p. 3890.
[52]. Golubev, D.D., P.N. Dmitriyev, and A.A. Sankovsky, (2019). Influence of a state of interpanel pillars on spontaneous combustion of coal. Technology, 10(3): p. 3240-3248.
[53]. Saffari, A., F. Sereshki, and M. Ataei, (2019). The simultaneous effect of moisture and pyrite on coal spontaneous combustion using CPT and R70 test methods. Rudarsko-geološko-naftni zbornik, 34(3).
[54]. Saffari, A., M. Ataei, and F. Sereshki, (2019). PROCJENA SPONTANOGA IZGARANJA UGLJENA UPORABOM METODA TESTIRANJA R70 NA TEMELJU KORELACIJE KONSTITUCIJSKIH SVOJSTAVA UGLJENA (PRIMJER RUDNIKA UGLJENA TABAS PARVADEH, IRAN). Rudarsko-geološko-naftni zbornik, 34(3): p. 49-60.
[55]. Saffari, A., M. Ataei, and F. Sereshki, (2019). ISPITIVANJE ULOGE SADRŽAJA VLAGE NA SPONTANO IZGARANJE UGLJENA. Rudarsko-geološko-naftni zbornik, 34(3): p. 61-71.
[56]. Saffari, A., F. Sereshki, and M. Ataei, (2019). A comprehensive study of effect of maceral content on tendency of spontaneous coal combustion occurrence. Journal of the Institution of Engineers (india): Series D, 100: p. 1-13.
[57]. Yang, F., Y. Lai, and Y. Song, (2019). Determination of the influence of pyrite on coal spontaneous combustion by thermodynamics analysis. Process Safety and Environmental Protection, 129: p. 163-167.
[58]. Meshkov, S. (2019). Determination of the Parameters of Technological Schemes for the Development of Coal Seams Prone to Spontaneous Combustion using Computer Simulation. in E3S Web of Conferences. EDP Sciences.
[59]. Saffari, A., M. Ataei, and F. Sereshki, (2019). Evaluation of the spontaneous combustion of coal (SCC) by using the R70 test method based on the correlation among intrinsic coal properties (Case study: Tabas Parvadeh coal mines, Iran). Rudarsko-geološko-naftni zbornik, 34(3).
[60]. Zhai, X., et al., (2019). Oxygen distribution and air leakage law in gob of working face of U+ L ventilation system. Mathematical Problems in Engineering, 2019(1): p. 8356701.
[61]. Onifade, M. and B. Genc, (2018). Modelling spontaneous combustion liability of carbonaceous materials. International Journal of Coal Science & Technology, 5: p. 191-212.
[62]. Zhang, Y., et al., (2018). Experimental study on the effects of drying methods on the stabilities of lignite. Chinese Journal of Chemical Engineering, 26(7): p. 1545-1554.
[63]. Liu, W. and Y. Qin, (2017). Multi-physics coupling model of coal spontaneous combustion in longwall gob area based on moving coordinates. Fuel, 188: p. 553-566.
[64]. Zhao, H., et al., (2016). Effects of drying method on self-heating behavior of lignite during low-temperature oxidation. Fuel Processing Technology, 151: p. 11-18.
[65]. Hetao, S., et al., (2016). Risk analysis of coal self-ignition in longwall gob: A modeling study on three-dimensional hazard zones. Fire Safety Journal, 83: p. 54-65.
[66]. Xia, T., et al., (2015). Evolution of coal self-heating processes in longwall gob areas. International Journal of Heat and Mass Transfer, 86: p. 861-868.
[67]. Panigrahi, D. and S. Ray, (2014). Assessment of self-heating susceptibility of Indian coal seams–a neural network approach. Archives of Mining Sciences, 59(4).
[68]. Sahu, H. and S. Mahapatra, (2013). Forecasting spontaneous heating susceptibility of Indian coals using neuro fuzzy system. Geotechnical and Geological Engineering, 31: p. 683-697.
[69]. Yuan, L. and A.C. Smith, (2012). The effect of ventilation on spontaneous heating of coal. Journal of Loss Prevention in the Process Industries, 25(1): p. 131-137.
[70]. Taraba, B. and Z. Michalec, (2011). Effect of longwall face advance rate on spontaneous heating process in the gob area–CFD modelling. Fuel, 90(8): p. 2790-2797.
[71]. Beamish, B. and R. Beamish, (2010). Benchmarking moist coal adiabatic oven testing.
[72]. Qin, B.-t., et al., (2009). Analysis and key control technologies to prevent spontaneous coal combustion occurring at a fully mechanized caving face with large obliquity in deep mines. Mining Science and Technology (China), 19(4): p. 446-451.
[73]. Beamish, B.B. and W. Sainsbury, (2008). Development of a site specific self-heating rate prediction equation for a high volatile bituminous coal.
[74]. Mitchell, P., (2003). Controlling and reducing heat on longwall faces.
[75]. Cotterell, M.E., (1997). The effect of coal maceral composition on spontaneous combustion. University of Queensland.
[76]. Ramlu, M.A., (1991). Mine disasters and mine rescue.
[77]. Chandra, D. and Y. Prasad, (1990). Effect of coalification on spontaneous combustion of coals. International Journal of Coal Geology, 16(1-3): p. 225-229.
[78]. Hodges, D., (1963). The influence of moisture on the spontaneous heating of coal. University of Nottingham.
[79]. Diehr, G. and E.B. Hunt, (1968). A comparison of memory allocation algorithms in a logical pattern recognizer. Department of Psychology, University of Washington.
[80]. Michie, D., (1982). The state of the art in machine learning. Introductory readings in expert systems, p. 208-229.
[81]. Li, B., et al., (1984). Classification and regression trees (CART). Biometrics, 40(3): p. 358-361.
[82]. Priyam, A., et al., (2013). Comparative analysis of decision tree classification algorithms. International Journal of current engineering and technology, 3(2): p. 334-337.
[83]. Buntine, W., (2020). Learning classification trees, in Artificial Intelligence frontiers in statistics. Chapman and Hall/CRC. p. 182-201.
[84]. Li, R.-H. and G.G. Belford. (2002). Instability of decision tree classification algorithms. in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining.
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