Deming Wang

Controlling coal fires using the three-phase foam and water mist techniques in the Anjialing Open Pit Mine, China

Coal fires are a serious environment, health, and safety hazard throughout the world. They damage the environment, threaten the health of people living nearby, burn away non-renewable coal, and result in significant economic losses. In this paper, the characteristics of the ignition and propagation of coal fires are illustrated first. Semi-enclosed environments (loose zones and abandoned roadways) favor the ignition of coal fires. The “upper fire” is pointed out to be prevalent and difficult to be controlled. Furthermore, the advantages and disadvantages of several commonly used techniques for controlling coal fires are analyzed. The three-phase foam and water mist techniques are believed to be effective in controlling coal fires, especially the “upper fires” in loose zones and abandoned roadways, respectively. Then, the three-phase foam coal fire extinguishing system is improved, and the water mist coal fire extinguishing system is developed. Finally, these two techniques are applied to control coal fires in the Anjialing Open Pit Mine. The results show that the three-phase foam and water mist techniques control coal fires efficiently and ensure the safe production of the mine as well as the security of personnel and equipments. Most importantly, this study provides a valuable method for the control of other coal fires.

GPR Simulation for the Fire Detection in Ground Coal Mine Using FDTD Method

As the radar wave is a kind of electro-magnetic wave and the spread of the wave accords with Maxwell equation, the wave field simulation method has been used in forward simulation of ground-penetrating radar (GPR) profile. The finite-difference time-domain (FDTD) scheme for simulation is derived from the Maxwell’s curl equations. The method is applied to simulate the (GPR) profile of fire detection in ground coal mine. The numerical results show that this method is correct and feasible.

Detection, extinguishing, and monitoring of a coal fire in Xinjiang, China

Coal fire is a global catastrophe. Xinjiang suffers the most severe coal fire in China and even in the world. Coal firefighting work has been being conducted for decades in Xinjiang. In this paper, coal fire detection, extinguishing, and monitoring approaches that were derived from coal firefighting experience are introduced in detail by taking the Fifth Fire Area (FFA) of the Heshituoluogai coal fire for instance. We first introduce the geology and fire situation in the FFA. Before developing efficient strategies to extinguish it, magnetic and self-potential methods are adopted to delineate the extent of the fire. A composite index is proposed to better indicate the fire. The comprehensive coal firefighting method is illustrated in detail, which consists of surface cooling, excavation and leveling, borehole drilling, borehole water injection and grouting, and loess backfill. The subsequent temperature and CO monitoring records show that the fire is extinguished successfully without burnback. The methodology presented here provides guidance and reference for putting out other coal fires around the world.

PLS Regression on Wavelet Transformed Infrared Spectra for Prediction of Coal Contents

Study on multivariate calibration for infrared spectrum of coal was presented. The discrete wavelet transformation as pre-processing tool was carried out to decompose the infrared spectrum and compress the data set. The compressed data regression model was applied to simultaneous multi-component determination for coal contents. Compression performance with several wavelet functions at different resolution scales was studied, and prediction ability of the compressed regression model was investigated. Numerical experiment results show that the wavelet transform performs an effective compression preprocessing technique in multivariate calibration and enhances the ability in characteristic extraction of coal infrared spectrum. Using the compressed data regression model, the reconstructing results are almost identical compared to the original spectrum, and the original size of the data set has been reduced to about 5% while the computational time needed decreases significantly.