摘要
Spontaneous combustion of the coal mine goaf is one of the main disasters in the coal mine. The detection technology based on symbolic gas is the main means to realize the spontaneous combustion prediction of the coal mine goaf, and ethylene gas is an important symbol gas of spontaneous combustion in the coal accelerated oxidation stage. In order to overcome the problem of current coal ethylene detection, the paper presents a mine optical fiber multi-point ethylene concentration sensor based on the tunable diode laser absorption spectroscopy. Based on the experiments and analysis of the near-infrared spectrum of ethylene, the system employed the 1.62 μm （DFB） wavelength fiber coupled distributed feedback laser as the light source. By using the wavelength scanning technique and developing a stable fiber coupled Herriot type long path gas absorption cell, a ppm-level high sensitivity detecting system for the concentration of ethylene gas was realized, which could meet the needs of coal mine fire prevention goaf prediction.
Spontaneous combustion of the coal mine goaf is one of the main disasters in the coal mine. The detection technology based on symbolic gas is the main means to realize the spontaneous combustion prediction of the coal mine goaf, and ethylene gas is an important symbol gas of spontaneous combustion in the coal accelerated oxidation stage. In order to overcome the problem of current coal ethylene detection, the paper presents a mine optical fiber multi-point ethylene concentration sensor based on the tunable diode laser absorption spectroscopy. Based on the experiments and analysis of the near-infrared spectrum of ethylene, the system employed the 1.62 μm （DFB） wavelength fiber coupled distributed feedback laser as the light source. By using the wavelength scanning technique and developing a stable fiber coupled Herriot type long path gas absorption cell, a ppm-level high sensitivity detecting system for the concentration of ethylene gas was realized, which could meet the needs of coal mine fire prevention goaf prediction.
基金
This work was supported by the National Natural Science Foundation of China （60977058 ＆ 61475085） and Fundamental Research Funds of Shandong University （2014YQ011）.
