地下煤火分维—分级探测预警技术研究

为实现煤自燃隐蔽火源的高效监测预警和防治,本文对地下煤火分维—分级探测预警技术展开研究。首先,揭示煤自燃受热升温及燃烧过程的煤自燃的电磁—声波—温—气变化特征,形成电磁—声波—氡气—红外多维探测方法,实现井上井下联合多维探测应用;其次,根据大尺寸煤自燃指标气体产生规律,建立基于正压输运的煤自燃监测预警体系,实现地下煤自燃发火的分级精准预警;最后,依据复杂区域综放面煤层自燃发火特点、影响因素及变化规律,提出复杂开采煤自燃灾害综合防治技术体系。研究成果可为井下煤自燃灾害预警防治提供依据与参考。

Cross-scale characteristics of backfill material using NMR and fractal theory

This paper analyzed the pore structure, quantified the pore fractal dimension, calculated the grading index(GI) of mixed aggregate, and studied the relationship among GI, pore structure, and strength to describe the cross-scale characteristics of backfill, which is made from stone powder and cemented tailing. A series of experiments were conducted on stone powder cement tailings backfill(SPCTB). The GI formulas for mixed aggregates, containing stone powder and tailings, were derived based on the Füller theory. The nuclear magnetic resonance(NMR) fractal dimensions of backfills were derived using fractal geometry principles. Compared to the mesopore and macropore fractal dimensions, the correlation between micropore fractal dimension and macro-properties in terms of NMR porosity, pore structure complexity, uniaxial compression strength(UCS), and GI is the most significant. Macropore fractal dimension is generally correlated with UCS and GI and the other properties such as the shape of mixed aggregates also have an impact on fractal dimension. However, mesopore fractal dimension has no obvious relationship with macro-properties. Finally, the relationship between GI and UCS was studied, which contributed to improving backfill’s strength and optimizing gradation.