A study on source identification of gas explosion in coal mines based on gas concentration. (15th April 2021)

• Record Type: Journal Article
• Authors: Lei, Baiwei; Zhao, Chenguang; He, Binbin; Wu, Bing
• Is Part Of: Fuel. Volume 290(2021)
• Publication Date: 2021-04-15
• Subjects: Gas explosion -- Source identification -- Catastrophic gas concentration -- Particle swarm optimization -- Inversion; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2020.120053
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: The migration rule of catastrophic gas in complex pipe network is analyzed. The inversion model with PSO algorithm of mine gas explosion is established. The accuracy of the inversion model is verified by large scale roadway experiment. Abstract: When the gas explosion occurs in the mine, it is essential to determine in time whether the ventilation system is damaged and trace the explosion location and time to develop an emergency rescue plan and conduct rescue work. In this study, a method to trace the source of explosion by monitoring catastrophic gas concentrations, based on the one-dimensional unsteady flow differential equation, an unsteady flow model for the transport of catastrophic gas in the ventilation network was established. Further, the change laws of catastrophic gas concentrations at roadway intersections were analyzed in combination with the nodal air volume balance law in the ventilation network theory. Finally, we used the particle swarm optimization (PSO) algorithm to invert the gas explosion source point and time in conjunction with the catastrophic gas concentration monitoring data at the monitoring point. Herein, an instantaneous release tracer gas was used instead of a catastrophic gas after a gas explosion, and a concentration monitoring experiment was carried out in a full-size experimental roadway. The inversion results show that the PSO can invert the location and time of the explosion source and simultaneously assess the damage status of dampers within the ventilation network. Note that the root mean square error between the inversion and measured results was less than 1, proving the reliability of the inversion results.