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煤矿掘进工作面瓦斯浓度预测

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
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1m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.
Prediction of gas concentration in coal mine excavation working face
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 facegas concentration predictionLaida criteriaLagrange interpolationempirical mode decompositionleast squares support vector machine

陈鲜展、沈易成、洪飞扬、石绅

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安徽建筑大学 土木工程学院,安徽 合肥 230011

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

安徽建筑大学引进人才及博士启动基金安徽省高等学校省级自然科学研究重点项目

2021QDZ062022AH050253

2024

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

工矿自动化
中煤科工集团常州研究院有限公司

工矿自动化

CSTPCD北大核心
影响因子：0.867
ISSN：1671-251X
年,卷(期)：2024.50(4)
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