沿空留巷采空区注氮防灭火参数优化

胡海峰; 陈明浩; 李晨光; 张柏林; 张兴华

doi:10.13637/j.issn.1009-6094.2025.077610.13637/j.issn.1009-6094.2025.0776

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沿空留巷采空区注氮防灭火参数优化

安全评价安全工程 | 更新时间：2026-04-17

- 沿空留巷采空区注氮防灭火参数优化
- 沿空留巷采空区注氮防灭火参数优化
- 安全与环境学报 2026年26卷第4期页码：1282-1293
- 作者机构：
  
  1. 国能神东煤炭集团地测公司
  2. 太原理工大学安全与应急管理工程学院
- 作者简介：
- 基金信息：
- DOI：10.13637/j.issn.1009-6094.2025.077610.13637/j.issn.1009-6094.2025.0776
  中图分类号： TD752.2
- 纸质出版：2026
- 稿件说明：
移动端阅览
[1]胡海峰,陈明浩,李晨光,等.沿空留巷采空区注氮防灭火参数优化[J].安全与环境学报,2026,26(04):1282-1293.
[1]胡海峰,陈明浩,李晨光,等.沿空留巷采空区注氮防灭火参数优化[J].安全与环境学报,2026,26(04):1282-1293. DOI： 10.13637/j.issn.1009-6094.2025.077610.13637/j.issn.1009-6094.2025.0776.

DOI：10.13637/j.issn.1009-6094.2025.0776.

摘要

针对柔模墙沿空留巷采空区自然发火防治难题，基于神东上湾煤矿实践，创新性构建了“理论建模—试验模拟—智能优化—现场验证”四维研究体系。通过建立考虑采空区垮落特征的“C形圈”理论模型，结合自主研发的PLC控制相似模拟平台，实现采空区流场分布高精度可视化。基于48组高密度传感器采集的O

体积分数数据，采用Kriging空间插值算法重构三维浓度场，数据精度提升22%。利用机器学习和随机森林算法对1 500组注氮试验数据进行训练，建立了注氮量-氧气(一氧化碳)体积分数分布预测模型，确定最优注氮量为600 m

/h。现场试验表明，注氮优化后采空区窒息带(O

体积分数5%)占比从52.6%增至78.3%

CO峰值降幅达0.204%

数值模拟与实测误差小于7%。相比传统经验法，该方案节省注氮能耗15%。

Abstract

To address the challenge of preventing spontaneous combustion in the goaf adjacent to the gob-side entry retaining with flexible formwork walls

we have innovatively established a four-dimensional research framework encompassing “theoretical modeling-experimental simulation-intelligent optimization-field validation”

based on practices at Shendong Shangwan Coal Mine. By developing a “C” ring theory model that accounts for the caving characteristics of the goaf and integrating a self-developed PLC-controlled similarity simulation platform

we achieved high-precision visualization of the flow field distribution within the goaf. Utilizing O

concentration data gathered from 48 groups of high-density sensors

we reconstructed a three-dimensional concentration fiel

d using the Kriging spatial interpolation algorithm

which resulted in a 22% improvement in data accuracy. Additionally

we developed a predictive model for the distribution of nitrogen injection volume and O

(CO) concentrations by training on 1 500 sets of nitrogen injection experimental data with machine learning techniques

specifically employing random forest algorithms. By comparing the changes in O

concentration within the goaf at nitrogen injection rates of 200 m

400 m

600 m

800 m

1 000 m

and 1 200 m

we found that continuously increasing the nitrogen injection volume can lead to a maximum reduction in O

concentration of up to 21.2 percentage points. The magnitude and rate of O

concentration decline increased as the nitrogen injection rate rose from 200 m

/h to 600 m

/h. However

when the nitrogen injection rate was further increased from 600 m

/h to 1 200 m

the reduction in O

concentration only improved by 0.4 percentage points

with negligible differences observed in the rate of decline. Field trials have established that the optimal nitrogen injection rate is 600 m

/h. Following the optimization of nitrogen injection

the proportion of the asphyxiation zone(where the O

volume fraction is less than 5%) in the goaf increased from 52.6% to 78.3%. The peak CO concentration decreased by 0.204%

and the error between numerical simulations and field measurements was found to be less than 7%. Compared to traditional empirical methods

this approach reduces nitrogen injection energy consumption by 15%.

关键词

Keywords

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