An Artificial Intelligence-based Model for the Prediction of Spontaneous Combustion Liability of Coal Based on Its Proximate Analysis

References

• Arisoy, A., and B. Beamish. 2015. Reaction kinetics of coal oxidation at low temperatures. Fuel 159:412–17. doi:10.1016/j.fuel.2015.06.054.
   Web of Science ®Google Scholar
• ASTM, D5142. 2013. Standard test methods for proximate analysis of the analysis sample of coal and coke by instrumental procedures. West Conshohocken, PA: ASTM International. www.astm.org.
   Google Scholar
• Banerjee, S. C. 2000. Prevention and combating mine fires, 33. New Delhi, India: Oxford and IBH Publishing Co. Pvt Ltd.
   Google Scholar
• Dutta, S., and J. P. Gupta. 2010. PVT correlations for Indian crude using artificial neural networks. J. Pet. Sci. Eng. 72:93–109.
   Web of Science ®Google Scholar
• Eroglu, H. N., 1992. Factors affecting spontaneous combustion liability index. Ph.D. Thesis, University of the Witwatersrand Johannesburg, South Africa.
   Google Scholar
• Feng, K. K., R. N. Chakravorty, and T. S. Cochrane. 1973. Spontaneous combustion - a coal mining hazard. IM Bull. 66 (783):75–84. October.
   Google Scholar
• Genc, B., and A. Cook. 2015. Spontaneous combustion risk in South African coalfields. J. South Afr. Inst. Min. Metall. 115:563–68.
   Web of Science ®Google Scholar
• Genc, B., M. Onifade, and A. Cook. 2018. Spontaneous combustion risk on South African coalfields: Part 2. Proceedings of the 21st International Coal Congress of Turkey ‘ICCET’, 13–25. Zonguldak, Turkey, April 11–13.
   Google Scholar
• Gevrey, M., I. Dimopoulos, and S. Lek. 2003. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Modell 160 (3):249–64. doi:10.1016/S0304-3800(02)00257-0.
   Web of Science ®Google Scholar
• Gouws, M. J., and H. N. Eroglu. 1993. A spontaneous combustion liability index. The 13th Mining Congress of Turkey, 59–68. Istanbul.
   Google Scholar
• Gouws, M. J., and L. Wade. 1989. The self-heating liability of coal: Predictions based on composites indices. Min. Sci. Technol. 9:81–85. doi:10.1016/S0167-9031(89)90797-4.
   Google Scholar
• Kaymakci, E., and V. Didari. 2002. Relations between coal properties and spontaneous combustion parameters. Turk. J. Eng. Environ. Sci. 26 (1):59–64.
   Google Scholar
• Lawal, A. I., and M. A. Idris. 2019. An artificial neural network-based mathematical model for the prediction of blast-induced ground vibrations. Int. J. Environ. Stud. 1–17. doi:10.1080/00207233.2019.1662186.
   Google Scholar
• Mahadevan, V., and M. A. Ramlu. 1985. Fire risk rating of coal mines due to spontaneous heating. J. Mines Metal Fuels 33 (8):357–62.
   Google Scholar
• Moghtaderi, B., B. Z. Dlugogorski, and E. M. Kennedy. 2000. Effects of wind flow on self-heating characteristics of coal stockpiles. Process Saf. Environ. Prot. 78 (6):445–53. doi:10.1205/095758200530998.
   Web of Science ®Google Scholar
• Mohalik, N. K., D. C. Panigrahi, and V. K. Singh. 2010. An investigation to optimise the experimental parameters of differential scanning calorimetry method to predict the susceptibility of coal to spontaneous. Arch. Min. Sci. 55:669–89.
   Web of Science ®Google Scholar
• Nguyen, H., X.-N. Bui, and H. Moayedi. 2019. A comparson of advanced computational models and experimental techniques in predicting blast-induced ground vibration in open-pit coal mine. Acta Geophys. 67 (4):1025–37. doi:10.1007/s11600-019-00304-3.
   Web of Science ®Google Scholar
• Nimaje, D. S., and D. P. Tripathy. 2016. Characterization of some Indian coals to assess their liability to spontaneous combustion. Fuel 163:139–47. doi:10.1016/j.fuel.2015.09.041.
   Web of Science ®Google Scholar
• Nourani, V., and M. S. Fard. 2012. Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes. Adv. Eng. Software 47 (1):127–46. doi:10.1016/j.advengsoft.2011.12.014.
   Web of Science ®Google Scholar
• Onifade, M. 2018. Spontaneous combustion liability of coals and coal-shales in the South African coalfields. A PhD Thesis, University of the Witwatersrand, Johannesburg, South Africa.
   Google Scholar
• Onifade, M., A. I. Lawal, E. A. Aladejare, S. Bada, and M. A. Idris. 2019. Prediction of gross calorific value of solid fuels from their proximate analysis using soft computing and regression analysis. Int. J. Coal Pre. Util. 1–15. doi:10.1080/19392699.2019.1695605.
   Web of Science ®Google Scholar
• Onifade, M., and B. Genc. 2018a. A review of spontaneous combustion studies - South African context. Int. J. Min. Reclam. Environ. 33 (8):527–47. doi:10.1080/17480930.2018.1466402.
   Web of Science ®Google Scholar
• Onifade, M., and B. Genc. 2018b. Prediction of the spontaneous combustion liability of coal and coal-shale using statistical analysis. J. South Afr. Inst. Min. Metall. 118:799–808.
   Web of Science ®Google Scholar
• Onifade, M., and B. Genc. 2018c. Modelling spontaneous combustion liability of carbonaceous materials. Int. J. Coal Sci. Technol. 5 (2):191–212. doi:10.1007/s40789-018-0209-2.
   Google Scholar
• Onifade, M., and B. Genc. 2018d. Comparative analysis of coal and coal-shale intrinsic factors affecting spontaneous combustion. Int. J. Coal Sci. Technol. 5 (3):282–94. doi:10.1007/s40789-018-0222-5.
   Google Scholar
• Onifade, M., and B. Genc. 2019. Spontaneous combustion liability of coal and coal-shale- A review of prediction methods. Int. J. Coal Sci. Technol. 6 (2):151–68. doi:10.1007/s40789-019-0242-9.
   Google Scholar
• Onifade, M., B. Genc, and A. Carpede. 2018. A new apparatus to establish the spontaneous combustion propensity of coals and coal-shales. Int. J. Min. Sci. Technol. 28 (4):649–55. doi:10.1016/j.ijmst.2018.05.012.
   Web of Science ®Google Scholar
• Onifade, M., B. Genc, and N. Wagner. 2019. Influence of organic and inorganic properties of coal-shale on spontaneous combustion liability. Int. J. Min. Sci. Technol. 29 (6):851–57. doi:10.1016/j.ijmst.2019.02.006.
   Web of Science ®Google Scholar
• Panigrahi, D. C., A. Ojha, N. C. Saxena, and B. K. Kejriwal, 1997. A study of coal oxygen interaction by using Russian U-Index and its correlation with basic constituents of coal with particular reference of Jharia coal field, International conference of safety in mines research institutes, New Delhi, India, pp. 134–41.
   Google Scholar
• Panigrahi, D. C., and S. K. Ray. 2014. Assessment of self-heating susceptibility of indian coal seams - A neural network approach’. Arch. Min. Sci. 59 (4):1061–76. doi:10.2478/amsc-2014-0073.
   Web of Science ®Google Scholar
• Ren, T., J. Edwards, and D. Clarke. 1999. Adiabatic oxidation study on the propensity of pulverised coals to spontaneous combustion. Fuel 78 (14):1611–20. doi:10.1016/S0016-2361(99)00107-6.
   Web of Science ®Google Scholar
• Rosema, A., H. Guan, and H. Veld. 2001. Simulation of spontaneous combustion to study the causes of coal fires in the Rujigou Basin. Fuel 80 (1):7–16. doi:10.1016/S0016-2361(00)00065-X.
   Web of Science ®Google Scholar
• Roy, D. H., and T. N. Singh. 2019. Predicting deformational properties of Indian coal: Soft computing and regression analysis approach. Measurement. doi:10.1016/j.measure-ment.2019.106975.
   PubMed Web of Science ®Google Scholar
• Sahu, H. B., D. C. Panigrahi, and N. M. Mishra, 2005. Assessment of spontaneous heating susceptibility of coal seams by experimental techniques: A comparative study. International symposium on advances in mining technology and management, 459–65. Kharagpur, India, November 29–December 2.
   Google Scholar
• Singh, R. N., and S. Demirbilek. 1987. Statistical appraisal of intrinsic factors affecting spontaneous combustion of coal. Min. Sci. Technol. 4 (2):155–65. doi:10.1016/S0167-9031(87)90266-0.
   Google Scholar
• Singh, R. V. K. 2013. Spontaneous heating and fire in coal mines’, in procedia engineering. doi:10.1016/j.proeng.2013.08.046.
   Google Scholar
• Tang, Y., and S. Xue. 2015. Laboratory study on the spontaneous combustion propensity of lignite undergone heating treatment at low temperature in inert and low-oxygen environments. Energy and Fuels 29 (8):4683–89. doi:10.1021/acs.energyfuels.5b00217.
   Web of Science ®Google Scholar
• Wade, L., M. J. Gouws, and H. R. Phillips, 1987. An apparatus to establish the spontaneous combustion propensity of South African coals. Proceedings of the symposium on Safety in Coal Mines, CSIR, 7.1–7.2. Pretoria.
   Google Scholar
• Yuan, L., and A. Smith. 2008. Numerical study on effects of coal properties on spontaneous heating in longwall gob areas. Fuel 87 (15–16):3409–19. doi:10.1016/j.fuel.2008.05.015.
   Web of Science ®Google Scholar
• Zhang, J., C. Wonyoung, I. Takamasa, T. Katsumi, and F. Masahiro. 2016. Modelling and parametric investigations on spontaneous heating in coal pile. Fuel 179:181–89. doi:10.1016/j.fuel.2016.02.059.
   Web of Science ®Google Scholar
• Zhang, Y. J., G. G. Wu, H. F. Xu, X. L. Meng, and G. Wang. 2009. Prediction of oxygen concentration and temperature distribution in loose coal based on BP neural network. Min. Sci. Technol. 19:0216–19.
   Google Scholar