Gas emission quantity prediction and drainage technology of steeply inclined and extremely thick coal seams

Gas emissions of workfaces in steeply inclined and extremely thick coal seams differ from those under normal geological conditions, which usually feature a high gas concentration and a large emission quantity. This study took the Wudong coal mine in Xinjiang province of China as a typical case. The gas occurrence of the coal seam and the pressure-relief range of the surrounding rock(coal) were studied by experiments and numerical simulations. Then, a new method to calculate the gas emission quantity for this special geological condition was provided. Based on the calculated quantity, a further gas drainage plan, as well as the evaluation of it with field drainage data, was finally given. The results are important for engineers to reasonably plan the gas drainage boreholes of steeply inclined and extremely thick coal seams.

Spatial context in the calculation of gas emissions for underground coal mines

The prediction of gas emissions arising from underground coal mining has been the subject of extensive research for several decades, however calculation techniques remain empirically based and are hence limited to the origin of calculation in both application and resolution. Quantification and management of risk associated with sudden gas release during mining(outbursts) and accumulation of noxious or combustible gases within the mining environment is reliant on such predictions, and unexplained variation correctly requires conservative management practices in response to risk. Over 2500 gas core samples from two southern Sydney basin mines producing metallurgical coal from the Bulli seam have been analysed in various geospatial context including relationships to hydrological features and geological structures. The results suggest variability and limitations associated with the present traditional approaches to gas emission prediction and design of gas management practices may be addressed using predictions derived from improved spatial datasets, and analysis techniques incorporating fundamental physical and energy related principles.

Dynamic prediction of gas emission based on wavelet neural network toolbox

This paper presents a method for dynamically predicting gas emission quantity based on the wavelet neural network （WNN） toolbox. Such a method is able to predict the gas emission quantity in adjacent subsequent time intervals through training the WNN with even time-interval samples. The method builds successive new model with the width of sliding window remaining invariable so as to obtain a dynamic prediction method for gas emission quantity. Furthermore, the method performs prediction by a self-developed WNN toolbox. Experiments indicate that such a model can overcome the deficiencies of the traditional static prediction model and can fully make use of the feature extraction capability of wavelet base function to reflect the geological feature of gas emission quantity dynamically. The method is characterized by simplicity, flexibility, small data scale, fast convergence rate and high prediction precision. In addition, the method is also characterized by certainty and repeatability of the predicted results. The effectiveness of this method is confirmed by simulation results. Therefore, this method will exert practical significance on promoting the application of WNN.

Cooperative prediction method of gas emission from mining face based on feature selection and machine learning

Collaborative prediction model of gas emission quantity was built by feature selection and supervised machine learning algorithm to improve the scientifc and accurate prediction of gas emission quantity in the mining face.The collaborative prediction model was screened by precision evaluation index.Samples were pretreated by data standardization,and 20 characteristic parameter combinations for gas emission quantity prediction were determined through 4 kinds of feature selection methods.A total of 160 collaborative prediction models of gas emission quantity were constructed by using 8 kinds of classical supervised machine learning algorithm and 20 characteristic parameter combinations.Determination coefcient,normalized mean square error,mean absolute percentage error range,Hill coefcient,mean absolute error,and the mean relative error indicators were used to verify and evaluate the performance of the collaborative forecasting model.As such,the high prediction accuracy of three kinds of machine learning algorithms and seven kinds of characteristic parameter combinations were screened out,and seven optimized collaborative forecasting models were fnally determined.Results show that the judgement coefcients,normalized mean square error,mean absolute percentage error,and Hill inequality coefcient of the 7 optimized collaborative prediction models are 0.969–0.999,0.001–0.050,0.004–0.057,and 0.002–0.037,respectively.The determination coefcient of the fnal prediction sequence,the normalized mean square error,the mean absolute percentage error,the Hill inequality coefcient,the absolute error,and the mean relative error are 0.998%,0.003%,0.022%,0.010%,0.080%,and 2.200%,respectively.The multi-parameter,multi-algorithm,multi-combination,and multijudgement index prediction model has high accuracy and certain universality that can provide a new idea for the accurate prediction of gas emission quantity.

Hybrid optimization model and its application in prediction of gas emission

According to the complex nonlinear relationship between gas emission and its effect factors, and the shortcomings that basic colony algorithm is slow, prone to early maturity and stagnation during the search, we introduced a hybrid optimization strategy into a max-rain ant colony algorithm, then use this improved ant colony algorithm to estimate the scope of RBF network parameters. According to the amount of pheromone of discrete points, the authors obtained from the interval of net- work parameters, ants optimize network parameters. Finally, local spatial expansion is introduced to get further optimization of the network. Therefore, we obtain a better time efficiency and solution efficiency optimization model called hybrid improved max-min ant system （H1-MMAS）. Simulation experiments, using these theory to predict the gas emission from the working face, show that the proposed method have high prediction feasibility and it is an effective method to predict gas emission.

Numerical simulation to determine the gas explosion risk in longwall goaf areas:A case study of Xutuan Colliery

Underground gassy longwall mining goafs may suffer potential gas explosions during the mining process because of the irregularity of gas emissions in the goaf and poor ventilation of the working face,which are risks difficult to control.In this work,the 3235 working face of the Xutuan Colliery in Suzhou City,China,was researched as a case study.The effects of air quantity and gas emission on the three-dimensional distribution of oxygen and methane concentration in the longwall goaf were studied.Based on the revised Coward’s triangle and linear coupling region formula,the coupled methane-oxygen explosive hazard zones(CEHZs)were drawn.Furthermore,a simple practical index was proposed to quantitatively determine the gas explosion risk in the longwall goaf.The results showed that the CEHZs mainly focus on the intake side where the risk of gas explosion is greatest.The CEHZ is reduced with increasing air quantity.Moreover,the higher the gas emission,the larger the CEHZ,which moves towards the intake side at low goaf heights and shifts to the deeper parts of the goaf at high heights.In addition,the risk of gas explosion is reduced as air quantities increase,but when gas emissions increase to a higher level(greater than 50 m3/min),the volume of the CEHZ does not decrease with the increase of air quantity,and the risk of gas explosion no longer shows a linear downward trend.This study is of significance as it seeks to reduce gas explosion accidents and improve mine production safety.

Study on primal CO gas generation and emission of coal seam

The main method of casting coal spontaneous combustion is prediction of index gases, with carbon monoxide(CO) commonly used as an index gas. However, coal spontaneous combustion is not the sole source of CO evolution; primal CO is generated through coalification, which can lead to forecasting mistakes. Through theoretical analysis, primal CO generation and emission from coal seams was determined.In this study, six coal samples were analyzed under six different experimental conditions. The results demonstrated the change in coal seam primal gas and concentration as functions of time, different coal samples, occurrence, various gas types and composition concentration, which are in agreement with the previous study on primal CO generation. Air charging impacts on primal gas emission. Analysis of the experimental data with SPSS demonstrates that the relationship between primal CO concentration and time shows a power exponent distribution.

Prediction and control of rock burst of coal seam contacting gas in deep mining

By analyzing the characteristics and the production mechanism of rock burstthat goes with abnormal gas emission in deep coal seams,the essential method of eliminatingabnormal gas emission by eliminating the occurrence of rock burst or depressingthe magnitude of rock burst was considered.The No.237 working face was selected asthe typical working face contacting gas in deep mining;aimed at this working face,a systemof rock burst prediction and control for coal seam contacting gas in deep mining wasestablished.This system includes three parts:① regional prediction of rock burst hazardbefore mining,② local prediction of rock burst hazard during mining,and ③ rock burstcontrol.

Grey Smoothing Model for Predicting Mine Gas Emission

A grey smoothing model for predicting mine gas emission was presented by combining the grey system theory with the smoothing prediction technique. First of all, according to the variable sequence, GM(1,1) model was set up to predict the general development trend of variable as first fitted values, then the smoothing prediction technique was used to revise the fitted values so as to improve the accuracy of prediction. The results of application in the No.6 Coal Mine in Pingdingshan mining area show that the grey smoothing model has higher accuracy than that of GM(1,1) in predicting the variable sequence with strong fluctuation. The research provides a new scientific method for predicting mine gas emission.

基于KPCA-LSSVM的回采工作面瓦斯涌出量的预测

为了提高瓦斯涌出量预测精度,针对瓦斯涌出量影响因素具有线性重叠、高维非线性等问题,提出使用核主成分分析法(KPCA)对影响因素进行非线性降维。选取沈阳某矿30组样本数据,以前24组数据作为训练集,后6组数据作为测试集,将确定后的核主成分作为最小二乘支持向量机(LSSVM)的输入变量,建立KPCA-LSSVM预测模型,将预测结果与PCA-LSSVM、LSSVM、多元非线性回归、KPCA-BP神经网络、PCA-BP神经网络以及BP神经网络预测结果进行对比。以最大相对误差绝对值作为模型预测精度的评价指标。研究结果表明:当选取前4个核主成分时,即达到模型训练要求。KPCA-LSSVM模型的预测最大相对误差绝对值为5.89%,预测精度均优于其他6种对比模型。研究结果可为实现瓦斯涌出量高精度预测提供参考。

基于CNN_BiLSTM的矿井瓦斯涌出量预测模型

为了实现对瓦斯涌出量准确预测,从而有效预防瓦斯灾害。提出1种结合卷积神经网络(convolutional neural network,CNN)和双向长短期记忆网络(bidirectional long short-term memory,BiLSTM)的瓦斯涌出量预测模型,采用CNN在时间序列上提取瓦斯涌出量及其影响因素的局部关键特征,有效捕捉数据的局部时序相关性;BiLSTM模型利用这些特征,通过其前向和后向处理能力,全面捕捉时间序列中长期依赖性和复杂模式。研究结果表明:该模型预测准确率达93.6%,均方误差显著低于CNN、BPNN、LSTM、BiLSTM、CNN_LSTM、CNN_BiLSTM 6个模型,决定系数接近1,表明其出色的预测能力和解释力。研究结果可有效预测瓦斯涌出量波动,有助于提高矿井瓦斯风险预警能力,提升矿井安全管理水平。

大通煤矿3^(#)煤层瓦斯赋存特征及涌出量预测

开采深度和开采速度的增加,瓦斯动力突出灾害频发,瓦斯预测以及全矿区评判成为开采任务的重要研究方向。为进一步揭示大通矿区范围内瓦斯赋存以及突出可能性,采用采区实测数据对矿井范围内瓦斯含量以及涌出量进行预测。研究结果,4个采区的瓦斯赋存量和埋深进行拟合,3^(#)煤层的埋藏深度与瓦斯含量拟合关系为W=0.0057H+2.1618(R^(2)=0.88),分源预测法分别计算了回采工作面、掘进工作面和生产采区的瓦斯涌出量,井田内3#煤层瓦斯含量具有北高南低的特征,矿井最大绝对瓦斯涌出量达14 m^(3)/min,相对瓦斯涌出量约为5.6 m^(3)/t。

基于深度神经网络的回采工作面瓦斯涌出量预测

为了提高煤矿安全性,设计了一种基于深度神经网络的回采工作面瓦斯涌出量预测方法,并在某煤矿回采工作面瓦斯涌出实际量进行了测试分析。研究结果表明:预测结果和实际参数发生了轻微变化,而总体预测结果与瓦斯涌出量变化特点相符,可以较准确反馈涌出量变化幅度。预测误差位于接近0的部位,在-3~3之间,沿两边呈现逐渐下降特点,超过75%的参数预报误差在1.75以内,预测得到误差处于允许范围之内。该研究有助于提高煤矿节能减排的效果,具有很好的实际意义。

矿井瓦斯涌出量预测技术

介绍了我国矿井瓦斯涌出量预测技术研究现状,分析了我国主要预测方法的原理与技术、适用条件及存在的不足等,重点介绍了目前普遍应用的矿井瓦斯涌出量预测方法——分源预测法,最后对我国煤矿瓦斯涌出量预测研究发展作了展望。

回采工作面瓦斯涌出BP神经网络分源预测模型及应用

基于回采工作面瓦斯涌出分源涌出,利用人工神经网络分别预测开采煤层、邻近煤层、采空区3种来源的瓦斯涌出量;因3种来源瓦斯涌出量的影响因素不同,为了避免不相关因素的干扰,提高预测精度,确定整个预测体系由开采层、邻近层、采空区等3个瓦斯涌出量预测神经网络组成,对每个涌出源分别建立神经网络预测模型;最后采用Matlab中BP神经网络算法,针对实际矿井进行应用,预测误差小.

基于ACC-ENN算法的煤矿瓦斯涌出量动态预测模型研究

为了对煤矿瓦斯监测数据进行有效分析,以实现准确、可靠的回采工作面绝对瓦斯涌出量预测,提出了蚁群聚类算法优化Elman神经网络的绝对瓦斯涌出量动态预测方法。算法通过对Elman神经网络的权值、阈值寻优,建立了基于ACC-ENN算法的绝对瓦斯涌出量预测模型,并结合矿井监测到的历史数据进行实例分析。试验结果表明:经蚁群聚类优化后的Elman神经网络绝对瓦斯涌出量预测模型较其他预测模型具有更好的泛化能力和更高的预测精度,有效地实现了煤矿绝对瓦斯涌出量动态预测。

基于小波神经网络的瓦斯涌出量预测研究

准确地预测瓦斯涌出量对于指导矿井设计和安全生产有重要意义,而瓦斯涌出量是一个与自然因素及开采技术等多因素有关的非线性建模问题。鉴于传统神经网络方法解决非线性问题收敛速度慢,易陷入局部最优解的缺陷,笔者提出一种既充分利用小波变换的时频局部化性质,又能结合神经网络的自学习能力的小波神经网络预测瓦斯涌出量的方法,并建立了预测模型。在此基础上,采用Delphi语言,设计了小波/BP神经网络仿真器。通过实例分析表明该方法较传统神经网络收敛迅速,预测精度高。

遗传算法的BP网络模型进行瓦斯涌出量预测

从提高采煤工作面瓦斯涌出量预测的速度和精度入手,将遗传算法与神经网络2种非线性最优化算法的优势加以融合,提出了一种利用遗传算法同时优化BP网络的连接权和拓扑结构的网络模型,并以韩城下峪口煤矿为例,进行了实际应用。结果表明:改进后的BP网络模型预测精度较高,具有良好的应用前景。

矿井瓦斯涌出量建模预测

针对矿井瓦斯涌出量影响因素复杂,数据序列波动性较大,灰色GM(1,1)预测模型精度低,本身存在一定缺陷的特点,将自记忆性原理引人灰色系统理论,建立了矿井瓦斯涌出量预测的灰色自记忆预测模型。经在韩城下峪口煤矿应用表明,该模型具有预测精度高,稳定性好的特点。

矿井瓦斯涌出量预测的模糊分形神经网络研究

将模糊控制技术、分形理论中的时间序列分析方法与神经网络技术有机地结合起来 ,并运用于矿井瓦斯涌出量的预测中。通过对矿井瓦斯涌出量时间序列的模糊分形处理 ,用BP神经网络对影响因素间的非线性关系进行拟合。检验结果表明 ,模型可靠 ,预测精度高。