煤矿掘进工作面瓦斯浓度预测

陈鲜展; 沈易成; 洪飞扬; 石绅

doi:10.13272/j.issn.1671-251x.18122

煤矿掘进工作面瓦斯浓度预测

doi: 10.13272/j.issn.1671-251x.18122

安徽建筑大学土木工程学院，安徽合肥　230011

基金项目: 安徽建筑大学引进人才及博士启动基金项目（2021QDZ06）；安徽省高校省级自然科学研究重点项目（2022AH050253）。

详细信息

作者简介:
陈鲜展（1983—），男，安徽宿州人，高级工程师，博士，研究方向为煤矿瓦斯综合治理技术，E-mail：76482670@qq.com

中图分类号: TD712
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- 被引次数: 0
出版历程
- 收稿日期: 2023-05-15
- 修回日期: 2024-03-22
- 网络出版日期: 2024-05-10

Prediction of gas concentration in coal mine excavation working face

College of Civil Engineering, Anhui Jianzhu University, Hefei 230011, China

摘要

摘要: 针对目前瓦斯浓度预测方法存在数据处理不确定性、特征提取局限性及受主观性因素影响产生预测偏差等问题，提出了一种用于煤矿掘进工作面的瓦斯浓度预测方法。首先，在煤矿掘进工作面回风巷内每隔1 m布设激光瓦斯传感器，形成传感器网络，实时采集瓦斯浓度数据。然后，根据拉依达准则搜索并剔除瓦斯浓度数据中的异常值，并利用Lagrange插值多项式填补瓦斯浓度数据中的缺失值。最后，以剔除异常值及填补缺失值的瓦斯浓度数据为基础，采用经验模态分解算法将瓦斯浓度数据分解成本征模态函数和趋势项，再利用Hilbert变换对本征模态函数进行处理以获取数据的高频项和低频项，并将其输入最小二乘支持向量机中进行加权处理，输出瓦斯浓度预测结果。通过掘进工作面模拟装置进行瓦斯浓度预测模拟试验，并在某煤矿掘进工作面进行现场试验，结果表明：该方法预测的瓦斯浓度与实际测量值非常接近，均方误差小，表明预测结果准确率高；均方误差波动幅度小，表明适应性好，预测结果的稳定性强；预测用时短，表明预测效率高。
- 掘进工作面 /
- 瓦斯浓度预测 /
- 拉依达准则 /
- Lagrange插值 /
- 经验模态分解 /
- 最小二乘支持向量机
Abstract: In current gas concentration prediction methods, there are problems of data processing uncertainty, feature extraction limitations, and prediction bias caused by subjective factors. In order to solve the above problems, a gas concentration prediction method for coal mine excavation working face is proposed. Firstly, laser gas sensors are installed every 1 meter in the return airway of the coal mine excavation working face, forming a sensor network to collect real-time gas concentration data. Secondly, the method searches and removes outliers in the gas concentration data according to the Laida criterion, and uses the Lagrange interpolation polynomial to fill in the missing values in the gas concentration data. Finally, based on removing outliers and filling in missing values in the gas concentration data, the empirical mode decomposition algorithm is used to decompose the gas concentration data into intrinsic mode functions and trend terms. The Hilbert transform is then used to process the intrinsic mode functions to obtain the high-frequency and low-frequency terms of the data, which are then input into the least squares support vector machine for weighted processing to output the gas concentration prediction results. The gas concentration prediction simulation experiment is conducted using a simulation device for the excavation working face, and an on-site test is conducted on a certain coal mine excavation working face. The results show that the predicted gas concentration by this method is very close to the actual measurement value, with a small mean square error, indicating a high accuracy of the prediction results. The small fluctuation of mean square error indicates good adaptability and strong stability of prediction results. Short prediction time indicates high prediction efficiency.
- excavation working face /
- gas concentration prediction /
- Laida criteria /
- Lagrange interpolation /
- empirical mode decomposition /
- least squares support vector machine

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图 1 不同方法瓦斯浓度预测结果对比

Figure 1. Comparison of gas concentration prediction results of different methods

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图 2 不同方法均方误差对比

Figure 2. Comparison of mean square error of different methods

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表 1 不同方法瓦斯浓度预测时间对比

Table 1. Comparison of gas concentration prediction time of different methods s

方法	预测时间
本文方法	3.2
文献[3]方法	5.8
文献[4]方法	6.4

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表 2 传感器布置

Table 2. Sensors layout

传感器位置编号	传感器位置	瓦斯体积分数实际测量值
位置1	距离掘进工作面150 m	瓦斯体积分数相对较低，为0.25%～0.30%
位置2	距离掘进工作面120 m	瓦斯体积分数相对较低，为0.25%～0.30%
位置3	距离掘进工作面90 m	瓦斯体积分数相对较低，为0.25%～0.30%
位置4	距离掘进工作面60 m	瓦斯体积分数逐渐上升，为0.30%～0.35%
位置5	距离掘进工作面30 m	瓦斯体积分数逐渐上升，为0.35%～0.40%
位置6	距离掘进工作面3 m	瓦斯体积分数高峰区域，为0.40%～0.43%

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表 3 本文方法的瓦斯浓度预测结果

Table 3. Gas concentration prediction results of the proposed method %

时刻	瓦斯体积分数预测值
时刻	位置1	位置2	位置3	位置4	位置5	位置6
08:00	0.25	0.25	0.24	0.35	0.35	0.40
08:30	0.27	0.24	0.25	0.33	0.37	0.42
09:00	0.28	0.30	0.32	0.31	0.36	0.39
09:30	0.30	0.31	0.33	0.34	0.37	0.40
10:00	0.30	0.32	0.30	0.33	0.39	0.42
10:30	0.31	0.32	0.31	0.35	0.39	0.44

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