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.

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.

Net energy yield and carbon footprint of summer corn under different N fertilizer rates in the North China Plain

Excessive use of N fertilizer in intensive agriculture can increase crop yield and at the same time cause high carbon（C） emissions.This study was conducted to determine optimized N fertilizer application for high grain yield and lower C emissions in summer corn（Zea mays L.）.A field experiment, including 0（N0）, 75（N75）, 150（N150）, 225（N225）, and 300（N300） kg N ha–1 treatments, was carried out during 2010–2012 in the North China Plain（NCP）.The results showed that grain yield, input energy, greenhouse gas（GHG） emissions, and carbon footprint（CF） were all increased with the increase of N rate, except net energy yield（NEY）.The treatment of N225 had the highest grain yield（10 364.7 kg ha–1） and NEY（6.8%）, but the CF（0.25） was lower than that of N300, which indicates that a rate of 225 kg N ha–1 can be optimal for summer corn in NCP.Comparing GHG emision compontents, N fertilizer（0–51.1%） was the highest and followed by electricity for irrigation（19.73–49.35%）.We conclude that optimazing N fertilizer application rate and reducing electricity for irrigation are the two key measures to increase crop yield, improve energy efficiency and decrease GHG emissions in corn production.

Cardiovascular System Response to Chronic Exposure to Emissions from Gas Turbines Power Plants

Objectives: Heart Rate and Blood Pressure have been reported to be susceptible to the effects of air pollution. Effect of emissions from gas turbine power station among workers is relatively unexplored. Therefore, this study aimed at assessing the effect of emissions from gas turbines on cardiovascular functions and C-reactive protein level in workers of power generating stations. Methodology: 440 individuals made up of 228 workers of gas-fired power plant stations and 212 non-power generating station workers volunteered for this study. A detailed questionnaire was carefully filled by volunteers and anthropometric data measured and recorded. Blood pressure, heart rate and C-reactive protein level (CRP) were measured using standard laboratory techniques in all subjects. Results were presented as Mean ± Standard Error of Mean (SEM) and appropriate analysis was done using Independent student’s t-test and Analysis of Variance (ANOVA). P-value Results: Result showed increased systolic blood pressure (SBP), 142.45 ± 8.87 mmHg of test subjects compared to that of the control (P 0.05) which was 121.32 ± 2.25 mmHg. However, there was no statistically significant difference in the Diastolic Blood Pressure (DBP) of the test and control groups. The Pulse Rate (PR) increased significantly in the test group (89.00 ± 0.02 bpm) when compared to that of the control group (74.34 ± 1.23 bpm). There was also an elevated plasma level of CRP, 6.69 ± 0.03 mg/L in the test group compared to the control (P Conclusion: Exposure to gas emission from natural gas-fired power plants on workers of power generating stations increased SBP, PR and C-reactive Protein level.

Characteristics of Atmospheric Pressure Rotating Gliding Arc Plasmas

In this work, a novel direct current （DC） atmospheric pressure rotating gliding arc （RGA） plasma reactor has been developed for plasma-assisted chemical reactions. The influence of the gas composition and the gas flow rate on the arc dynamic behaviour and the formation of reactive species in the N2 and air gliding arc plasmas has been investigated by means of electrical signals, high speed photography, and optical emission spectroscopic diagnostics. Compared to conventional gliding arc reactors with knife-shaped electrodes which generally require a high flow rate （e.g., 10-20 L/min） to maintain a long arc length and reasonable plasma discharge zone, in this RGA system, a lower gas flow rate （e.g., 2 L/min） can also generate a larger effective plasma reaction zone with a longer arc length for chemical reactions. Two different motion patterns can be clearly observed in the N2 and air RGA plasmas. The time-resolved arc voltage signals show that three different arc dynamic modes, the arc restrike mode, takeover mode, and combined modes, can be clearly identified in the RGA plasmas. The occurrence of different motion and arc dynamic modes is strongly dependent on the composition of the working gas and gas flow rate.

旧街煤矿瓦斯赋存及涌出规律分析

通过实测数据确定了旧街煤矿的瓦斯赋存规律,并分析了开采8号煤层时的瓦斯涌出规律,明确了瓦斯治理的重点。结果表明:旧街煤矿瓦斯含量具有随煤层埋深增加而增大的总体趋势,增长梯度为0.916m3/t/100m;邻近层和采空区瓦斯是矿井瓦斯治理的重点。

基于风险与声发射检测数据分析的储罐底板腐蚀剩余寿命预测方法研究

基于振幅分布理论、统计理论,引入风险概念,综合分析储罐底板腐蚀的声发射检测和漏磁检测数据,得到基于漏磁检测数据的声发射参量对储罐底板腐蚀的量化方法。通过对10台样本储罐的分析,得到某一地区介质为原油的一类储罐的声发射典型参量对储罐底板腐蚀的量化关系,即声发射活度与风险腐蚀速率(CRP)的关系为y=13514.65x,该模型的评价准确率为90.22%。建立实际腐蚀速率(CTP)与CRP的关系模型,为z=12.14x,得到CRP的12倍约为储罐底板腐蚀最严重部位的腐蚀速率的结论。这样对待评估储罐进行声发射检测,利用声发射量化评价模型,可得到CRP,进而得到管理腐蚀速率(CMP),参照API 653—2009,可对储罐底板的剩余寿命进行预测。

胶结充填体应变率与声发射特征响应规律

应变率是影响胶结充填体力学特性和声发射特征的重要因素.为研究胶结充填体应变率与声发射特征响应规律,文中进行了单轴压缩条件下胶结充填体的声发射试验,研究了应变率、声发射在加载过程中的时序特征,探讨了应变率与声发射时序特征之间的相关性,以及两者变化趋势之间的内在联系,进一步揭示了荷载作用下胶结充填体的裂纹演化规律.结果表明:胶结充填体应变率、振铃计数率在变形全过程均表现出“升→降→稳→升”的变化趋势和阶段性特征,两者相关性较高;胶结充填体内部破裂是由骨料之间的摩擦以及胶结材料的断裂演化造成,应变率对声发射特征在一定程度上起到了支配作用;基于应变率在塑性屈服和失稳破坏阶段表现出明显的突变性,可为胶结充填体破裂预测提供有效的前兆信息;结合声发射振铃计数率、平静期以及扩容现象可更加全面的反映胶结充填体破裂情况.研究结果可为胶结充填体力学特性研究奠定理论基础.

安顺煤矿煤与瓦斯突出预测敏感指标及其临界值的探讨

为了更好地提高生产的安全性与突出预测的准确性,对安顺煤矿的煤与瓦斯突出预测指标进行现场跟踪,确定了突出敏感指标及其临界值的选定标准以及测试方案,在对测试数据进行统计分析的基础上,最终确定钻孔瓦斯涌出初速度q为安顺煤矿的突出敏感指标,并根据发生突出的力学条件以及q指标的表达式确定了其临界值q=5 L/min,为该矿的突出预测提供了理论上的数据和参数,并在实践中得到了初步验证.

综掘工作面瓦斯预测技术的研究

通过对平顶山八矿、十矿的综掘工作面的实测分析 ,找出了综掘工作面的瓦斯涌出规律 ,建立了煤壁瓦斯、落煤瓦斯涌出数学模型 ,并通过实测结果进行验证 。

矿井瓦斯涌出量预测的GM(1,1)模型研究

利用灰色系统理论,根据不同时期的瓦斯涌出量数据,建立了瓦斯涌出量灰色预测模型,并通过对发耳矿井的瓦斯涌出资料统计分析,采用了GM(1,1)模型进行预测,选择了合理的误差检验方法对预测结果进行判断,结果表明,该模型的计算结果符合工程实际需要,预测程度较高。

唐山矿南五区瓦斯涌出量预测及其特征分析

南五区为唐山矿新采区,缺少可靠性瓦斯地质资料,而该矿为高瓦斯矿井,因此需要对其瓦斯地质特征进行研究。本文结合矿井地质特征,运用邻近区已开采工作面实测瓦斯涌出量资料,采用分源预测法和地质统计法对南五区5#、8#和9#煤层瓦斯涌出量分别做了预测。预测结果表明:煤层瓦斯涌出量随着埋深的增加明显增大,5#煤层瓦斯涌出量由3m3/t增加到6m3/t,8#和9#煤层瓦斯涌出量由5m3/t增加到7m3/t。

基于分源预测法的华阳煤矿9~#、15~#煤层瓦斯涌出量预测

采用钻屑解吸指标法对华阳煤矿9#煤层和15#煤层的瓦斯含量和残存瓦斯含量进行了测定,并分析了瓦斯含量分布特征;利用线性回归方法研究获得9#煤层和15#煤层瓦斯含量与埋藏深度的关系,得出了9#煤层和15#煤层原始瓦斯含量增长梯度;最后采用分源预测法对华阳煤矿开采前期、中期和后期的瓦斯涌出量进行了预测,确定华阳煤矿在9#煤层和15#煤层开采时属于低瓦斯矿井,为矿井通风设计和瓦斯治理提供了依据。

上部邻近层开采对本煤层瓦斯涌出影响研究

传统的瓦斯涌出量分源预测法未考虑到上部邻近层开采对本煤层煤体瓦斯涌出的影响,本文提出在计算回采工作面瓦斯涌出量时,在传统公式q1=K1K2K3·mM(W0-Wc)中加入了系数K4,K4为上部邻近层开采对工作面煤体瓦斯涌出影响系数,取K4=1-ηi,ηi为开采层向上部邻近层排放瓦斯的瓦斯排放率,从而回采工作面瓦斯涌出量计算公式改为q1=K1K2K3K4·mM(W0-Wc)。加入了上部邻近层开采对工作面煤体瓦斯涌出影响系数的计算瓦斯涌出量的方法,比起传统的瓦斯涌出量分源预测方法提高了矿井瓦斯涌出量计算的准确性。

上部邻近层开采对本煤层瓦斯涌出的影响分析

传统的瓦斯涌出量分源预测法未考虑到上部邻近层开采对本煤层煤体瓦斯涌出的影响,其计算结果误差较大。为此,提出在计算回采工作面瓦斯涌出量时,在传统计算公式中加入上部邻近层开采对工作面煤体瓦斯涌出影响系数K4,将回采工作面瓦斯涌出量计算公式改为q1=K1K2K3K4mM(W0-Wc)。加入上部邻近层开采瓦斯涌出影响系数后,瓦斯涌出量计算方法更加合理,计算结果更加准确。