Dynamic Analytical Platform for Mine Ventilation Networks

Abstract:

Mine disasters frequently occur with serious consequences, so additional research is needed to effectively deal with mine disasters especially the secondary catastrophes. Modularization modeling method was used to develop the dynamic analytical platform for modeling mine ventilation networks. The platform includes models of the ventilation network and the control systems. A small experimental ventilation network was also constructed to validate the platform. The results show that the platform provides accurate real-time simulations of normal operations and accident conditions in the ventilation network. The platform accurately models dynamic spreading of the disaster in the lab with errors of around 5%. Thus, the platform can be used to analyze the mine ventilation systems during accidents and develop methods to prevent the secondary catastrophes and the deterioration of mine disasters.

• Periodical: Applied Mechanics and Materials (Volumes 295-298)
• Pages: 2940-2949
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.295-298.2940
• Online since: February 2013
• Authors: Kun Li*, Cui Ding, Chang Fu You
• Keywords: Dynamic Analytical Platform, Mine Disaster, Modularization Modeling Method, Secondary Catastrophes, Ventilation Network

References

[1] Zhang, X.C., Qian, M.G., 2003. China's Coal Mining Science and Technology. Beijing, China Coal Industry Press, Beijing, p.7.

[2] LI, K, et al., 2008. Analysis of Secondary Catastrophes from Mine Fires in Tunnels. Progress in Safety Science and Technology, Vol VII, Part (B), 1362~1365.

[3] Scott, D.R., Hinsley F.B., 1952. Ventilation Network Thoery, Coll. Engng, pp.324-334.

[4] Hu, Y. N., et al., 2003. Nonlinear control of mine ventilation network, System and Control Letters, 49 (4), 239~254.

[5] Wang, Y. J., 1990. Solving mine ventilation networks with fixed and non-fixed branches. Mining Engineering, 42 (9), 1091~1095.

[6] Wang, Y. J., Mutmansky, M., 1997. Modeling mine ventilation networks using five basic network elements, Mining Engineering, 49 (3), 65~ 69.

[7] Cheng, L. H., et al., Simulation of ventilation and fire in the underground facilities, Fire Safety Journal, 36 (6), 597~619.

DOI: 10.1016/s0379-7112(01)00013-3

[8] Wu, Q., et al., 2008. The prediction of size-limited structures in a coal mine using Artificial Neural Networks, International Journal of Rock Mechanics & Mining Science, 45 (6), 999~1006.

DOI: 10.1016/j.ijrmms.2007.08.015

[9] Karacan, C. Ö., 2007. Development and application of reservoir models and artificial neural networks for optimizing ventilation air requirements in development mining of coal seams, International Journal of Coal Geology, 72 (3-4) , 221~239.

DOI: 10.1016/j.coal.2007.02.003

[10] Karacan, C. Ö., 2008. Modeling and prediction of ventilation methane emissions of U.S. longwall mines using supervised artificial neural networks, International Journal of Coal Geology, 73 (3-4), 371~387.

DOI: 10.1016/j.coal.2007.09.003

[11] Zhao, Q. L., Gao, Q., 2006. Study of application of optimal reconstruction of mine ventilation system by mine ventilation simulation system MVSS, Proceedings of International Colloquium on Safety Science and Technology, Part (B), 1823~1828.

[12] Lv, C. D. et al., 2002. Simuation and Modeling of Modern Thermal Power System,. Tsinghua University Press, Beijing.

[13] Incropera, F. P., DeWitt, D. P., 2006. Fundamentals of Heat and Mass Transfer, John Wiley&Sons, NewYork.