TD752.2
国家自然科学基金资助项目（51774170，51574143）；国家重点研发计划项目（2018YFC0807901）。
为了解决超长双煤巷自燃发热的早期预报问题，在自燃标志气体分析法失效的情况下，运用井下监测系统大数据的CO趋势分析法。为了筛除井下柴油机动车尾气产生CO的波动干扰，创新提出能够仅反映煤相对缓慢氧化的背景CO体积分数的概念；对矿井某一独立通风的考察区域，在某一足够长的时间段内，总能找到所有柴油车都不工作的极端时刻（或不受尾气干扰的情况），且CO体积分数值被监测系统记录到，从而建立了背景CO体积分数的筛查方法；经时间单元周期为0.125 d和0.5 d时的筛查结果对比，随着考察时间单元的取大，CO体积分数曲线越来越低，波动减小，背景CO体积分数曲线越来越清晰，证明其客观存在性。以最短自然发火期的一部分为考查期，依据背景CO体积分数的趋势走势来预判煤柱自然发火，结合红庆梁煤矿经验，得到CO趋势递增率k1*=0.607×10-6 d-1，以此作为自燃危险预判的临界指标，将自燃危险预警分三级，即当k1 ≥ k1*，一级预警，启动重点巡查，将超长距离巷道每天全面巡查，改升级到为有针对性加大人力物力的重点巡查；当k1＜k1*，二级预警，查明原因；当k1≤0，无自燃危险。预测结果满足工程要求。
To realize early prediction of spontaneous combustion of overlong double coal roadway, this paper proposes a CO trend analysis method based on the big data of the underground monitoring system when the spontaneous combustion mark-gas analysis method is invalid. Meanwhile, to screen out and remove fluctuation disturbance of CO caused by underground diesel vehicle exhaust, the concept of background CO volume fraction which can only reflect the relatively slow oxidation of coal is put forward. The background CO volume fraction screening method is established on the basis that for an independent ventilated investigation area of a mine, there is always an extreme moment that no diesel vehicle works (or exhaust gas does not interfere all diesel car) in a long enough time, and the CO volume fraction value is recorded by the monitoring system. Through the comparison of the screening results of a 0.125-day time unit cycle and a 0.5-day time unit cycle, it is found that as the time unit increases, the CO volume fraction curve becomes lower, the fluctuation decreases, and the background CO volume fraction curve becomes clearer, suggesting the objective existence of background CO volume fraction. Taking a part of the shortest spontaneous combustion period as the examination period, the spontaneous combustion of coal pillars is predicted based on the trend development of the background CO volume fraction. Combined with the experience of Hongqingliang coal mine, the CO increasing trend(increasing rate k1*=0.607×10-6 d-1) is obtained as the critical index of spontaneous combustion risk prejudgment. The spontaneous combustion risk warning is divided into three levels. When k1≥k1*, it is first-level warning, which triggers key inspections, and upgrades the daily comprehensive inspection to the targeted inspection with more manpower and material resources. When k11<k1*, it is second-level alert, which requires to find out the causes. When k1≤0, there is no spontaneous combustion risk. The predicted results meet the engineering requirements.
李腾,李宗翔,王继仁,杨富强,张春华,贾进章.超长煤巷监测数据背景CO筛查及自燃预判研究[J].重庆大学学报,2022,45(2):94-102.
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