Abstract
In recent years, Fires and explosion in coal mines imposes number of life threats for mine workers along with a rapid increase in environmental air pollution. By using various risk assessment methodologies, coal miners can easily predict the potential risks of forthcoming hazards in advance. In this work, a novel approach is proposed for monitoring the fire-resistant hydraulic fluids (HFA) contamination level. Fire resistance property of HFA fluids varies with the viscosity. Water content. By monitoring the water content in HFA fluids, fire resistance can be easily predicted. Fire resistance hydraulic fluid properties are trained in Ensemble Boosted Regression Tree (EBRT) to predict the potential risk in coal mines. EBRT is the supervised training algorithm which is proposed for leveraging an efficacious coal mine monitoring into existence. EBRT model estimates stronger prediction by linearly integrating the weaker estimations. Threshold rule-based decision making is adopted for the effective mitigation of risks. EBRT is optimized to minimize the cross-validation loss. Furthermore, Bayesian optimizer is used to minimize the objective function to 7.81 with regularized parameter lambda is chosen as 0.34 to minimize the ensemble trees. The root Mean square error is optimized to 31.68.
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References
Bühlmann, P., Hothorn, T.: Boosting algorithms: regularization, prediction and model fitting. Stat. Sci. 22(4), 477–505 (2007). https://doi.org/10.1214/07-STS242
Cheng, B., Cheng, X., Chen, J.: Lightweight monitoring and control system for coal mine safety using REST style. ISA Trans. 54, 229–239 (2015). https://doi.org/10.1016/j.isatra.2014.07.004
Cheng, J., Yang, S.: Data mining applications in evaluating mine ventilation system. Saf. Sci. 50(4), 18–22 (2012). https://doi.org/10.1016/j.ssci.2011.08.003
Dwuletzki, H., Pfaender, B., Niemczyk, K.: New fire-resistant hydraulic fluids type HFA for mining use – critical analysis. In: Dyczko, A.T., Jerzy Kicki, A., Myszkowski, M., Stopa, Z. (eds.) New Techniques and Technologies in Thin Coal Seam Exploitation, pp 201–209. CRC Press, BOGDANKA (2010)
Helwig, N., Schütze, A.: Data-based condition monitoring of a fluid power system with varying oil parameters. In: 10th International Fluid Power Conference (IFK2016), pp. 425–436 (2016)
Jo, Byungwan, Muhammad, Rana, Khan, Asad: An ınternet of things system for underground mine air quality pollutant prediction based on Azure machine learning. Sensors 18(4), 930 (2018). https://doi.org/10.3390/s18040930
Kozielski, Stanisaw, Dariusz Mrozek, Pawe Kasprowski, Boena Maysiak-Mrozek, and Daniel Kostrzewa. 2015. “Regression Rule Learning for Methane Forecasting in Coal Mines.” In Beyond Databases, Architectures and Structures: 11th International Conference, BDAS 2015 Ustro, Poland, May 26 –29, 2015 Proceedings Communications in Computer and Information Science, 521:495–504. https://doi.org/10.1007/978-3-319-18422-7
Kuenzer, Claudia, Zhang, J., Li, J., Voigt, S., Mehl, H., Wagner, W.: Detecting unknown coal fires: synergy of automated coal fire risk area delineation and improved thermal anomaly extraction. Int. J. Remote Sens. 28(20), 4561–4585 (2007). https://doi.org/10.1080/01431160701250432
Kurnia, J.C., Sasmito, A.P., Wong, W.Y., Mujumdar, A.S.: Prediction and innovative control strategies for oxygen and hazardous gases from diesel emission in underground mines. Sci. Total Environ. 481(1), 317–334 (2014). https://doi.org/10.1016/j.scitotenv.2014.02.058
Li, H., Xu, H., Wang, J., Fu, X., Bai, Z.: Design of automatic control system of coal sampling robot hydraulic system oil temperature. In: Proceedings - 9th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2017, vol. 1, pp. 38–42 (2017). https://doi.org/10.1109/IHMSC.2017.16
Mahdevari, S., Shahriar, K., Esfahanipour, A.: Human health and safety risks management in underground coal mines using fuzzy TOPSIS. Sci. Total Environ. 488-489(1), 85–99 (2014). https://doi.org/10.1016/j.scitotenv.2014.04.076
Mark, C., Gauna, M.: Evaluating the risk of coal bursts in underground coal mines. Int. J. Min. Sci. Technol. 26(1), 47–52 (2016). https://doi.org/10.1016/j.ijmst.2015.11.009
Mellors, Robert, Yang, X., White, J.A., Ramirez, A., Wagoner, J., Camp, D.W.: Advanced geophysical underground coal gasification monitoring. Mitig. Adapt. Strat. Glob. Change 21(4), 487–500 (2016). https://doi.org/10.1007/s11027-014-9584-1
Mishra, R.K., Bahuguna, P.P., Singh, V.K.: Detection of coal mine fire in Jharia coal field using Landsat-7 ETM+ data. Int. J. Coal Geol. 86(1), 73–78 (2011). https://doi.org/10.1016/j.coal.2010.12.010
Natekin, A., Knoll, A.: Gradient boosting machines, a tutorial. Front. Neurorobot. 7, 21 (2013). https://doi.org/10.3389/fnbot.2013.00021
Peter, Hodges: Hydraulic Fluids, 1st edn. Wiley, New York (2004)
Prevention of Fres in Underground Mines — Guideline: Resources Safety. Western Australia (2013). https://doi.org/ISBN978192116316 9
SAFETY AT A Glance (2017). https://www.coalindia.in/DesktopModules/DocumentList/documents/Safety_at_a_Glance_03042018.pdf
Sanmiquel, Lluís, Rossell, Josep M., Vintró, Carla: Study of Spanish mining accidents using data mining techniques. Saf. Sci. 75, 49–55 (2015). https://doi.org/10.1016/j.ssci.2015.01.016
Shen, Z., Dong, H., Yao, N., Li, X.: Condition Monitoring and Fault Diagnosis System of Fully Hydraulic Drilling in Coal Mine (2016)
Suh, J., Kim, S.M., Yi, H., Choi, Y.: An overview of GIS-based modeling and assessment of mining-induced hazards: soil, water, and forest. Int. J. Environ. Res. Public Health 14(12), 1463 (2017). https://doi.org/10.3390/ijerph14121463
Givens, W.A., Michael, P.W.: Hydraulic fluids. In: Totten, G.E., Westbrook, S.R., Shah, R.J. (eds.) Fuels and Lubricants Handbook: Technology, Properties, Performance, and Testing 225. ASTM International (2003). https://doi.org/10.1520/MNL37WCD-EB
Zhu, Q., Yu, F., Cai, M., Liu, J., Wang, H.: Interpretation of the extent of hydraulic fracturing for rockburst prevention using microseismic monitoring data. J. Nat. Gas Sci. Eng. 38, 107–119 (2017). https://doi.org/10.1016/j.jngse.2016.12.034
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Uma Maheswari, R., Rajalingam, S., Senthilkumar, T.K. (2020). Condition Monitoring of Coal Mine Using Ensemble Boosted Tree Regression Model. In: Balaji, S., Rocha, Á., Chung, YN. (eds) Intelligent Communication Technologies and Virtual Mobile Networks. ICICV 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-030-28364-3_2
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