Abnormal gas emission in coal mines and a method for its dilution using ventilator control

In the present study, an investigation was carried out on Parvadeh coal mine in Tabas, Iran, to survey the effect of fractures on unusual methane gas emission in coal mines. This coal mine was chosen for investigating because of its high methane gas content in the coal body and available data from sensors in desired locations. Gas concentration monitoring programs were carried out at the mine site and a large amount of data were collected and analyzed. It is revealed that there is a good correlation between excavating fracture-bearing faces and high methane gas emission events at the mine site. High gas emissions have been observed before, during, or after excavating fracture-bearing faces. When gas content is high and all boundary conditions are met, rockbursts, faults movement and also mining activities can trigger unusual gas emission, and sometimes the gas gushes are violent enough to fit into the category of gas outbursts. Since the fracture generation is happening before the increase of gas concentration in the air, a sensitive and highly accurate microseismic monitoring system can be used to detect locations of rock fracturing, thus provide an effective means to issue warnings of high gas emission in the working area.

Research on Prediction of Gas Emission based on Self-organizing Data Mining in Coal Mines

Coal mine gas accident is one of the most serious threats in the process of safe coal mine mining, making it important to accurately predict coal mine gas emission. To improve the accuracy of coal mine gas emission prediction, a hybrid machine learning prediction model combining random forest (RF) algorithm, improved gray wolf optimizer (IGWO) algorithm and support vector regression (SVR) algorithm is proposed, the model prediction effect is validated by using actual measured gas emission data from a coal mine. Firstly, the RF algorithm is used to screen 13 influencing factors of coal mine gas emission, and finally 6 influencing factors are selected as the input variables of the prediction model; Secondly, the GWO algorithm is improved using the nonlinear convergence factor and DLH search strategy to obtain the IGWO algorithm; Finally, the IGWO algorithm is used to optimize the parameters of the SVR algorithm, and the RF-IGWO-SVR model is established. The results show that the mean absolute percentage error, mean absolute error and root mean square error of the RF-GWO-SVR model are 1.55%, 0.0759, and 0.1103, respectively, and this result is better than the other comparative models, which indicates that the model can effectively improve the prediction accuracy of coal mine gas emission and provide a new model for coal mine gas emission prediction.

Earthquake-induced unusual gas emission in coalmines — A km-scale in-situ experimental investigation at Laohutai mine

An automatic approach for the control of the airflow volume and concentrations of hazardous gases in coal mine galleries

According to the actual status of coal mine gas utilization and emission reduction in China, and on the consideration of the policy, technology and results of utilization and emission reduction, we established the comprehensive evaluation method of coal mine gas utilization and emission reduction by Analytic Hierarchy Process. We used this method to assess the capacity of gas utilization and emission reduction in Luling Coal Mine, and the results of evaluation show that the capacity of gas utilization and emission reduction in Luling Coal Mine is outstanding, and this result is consistent with the result of National Coal Mine Gas Control Expert Consultation of China in 2010, which verify the reliability of this evaluation method.

Abnormal gas emission is a common gas phenomenon in coal mining face of gas mine. In order to prevent gas accidents, on the one hand, gas extraction measures are adopted to reduce or avoid abnormal gas emission; on the other hand, when abnormal gas emission is unavoidable, in order to prevent abnormal gas emission from occurring gas explosion accident by electric spark, non-cost cutting is adopted. Emergency measures such as power supply for quality and safety electrical equipment are taken as the second line of defense. Aiming at the abnormal gas emission phenomenon in East 102 coal face of Weijiadi Coal Mine, in order to avoid the gas explosion accident during the abnormal gas gushing period, the emergency measures for gas abnormal emission were expounded from the aspects of gas electric blockade, power cut and evacuation.

Distribution of methane and carbon dioxide concentrations in the near-surface zone, genetic implications, and evaluation of gas flux around abandoned shafts in the Jastrzębie-Pszczyna area (southern part of the Upper Silesian Coal Basin, Poland)

There is a need to characterize gas concentrations and emissions from rabbit production. A study was conducted in order to determine ammonia, nitrous oxide and carbon dioxide concentrations and emissions in three rabbit farms in the Spanish Mediterranean area. Gas emissions were measured for 187 days in two different production stages (reproductive does and fattening rabbits). Gas concentrations were measured every two hours. Indoor temperature, relative humidity and ventilation flow were measured hourly. As a result, indoor temperature and relative humidity varied throughout the year, following a sinusoidal daily variation pattern. Maximum gas concentrations (14.3 mg/m3 of NH3, 7041 mg/m3 of CO2 and 5.10 mg/m3 of N2O) did not exceed the maximum recommended thresholds considering human health and animal welfare. Ammonia emissions were on average 55.9 and 10.2 mg/h per reproductive doe and fattening rabbit, respectively, and they were affected by temperature and relative humidity. The average carbon dioxide emission was 12588 mg/h per animal for does and 3341 mg/h for fattening rabbits. Nitrous oxide emission from does was 10.3 mg/h per animal, whereas for fattening rabbits the emission was negligible. Daily variation patterns of all measured parameters were observed and characterised in this study.

The research on predicting gas outburst hazard on the basis of the unbalanced problem of gas monitoring samples under usual circumstances is given in this paper. Combined with outburst-preventing monitoring parameters, a new kind of method of predicting gas outburst hazard based on v-SVM algorithm through analyzing features of real-time gas monitoring data and extracting parameters of gas concentration real-time variation trend, parameters of gas variation rates and feature parameters of gas emission is put forward in this paper. And the application shows that this algorithm for the prediction of actual gas emission in coal mines is effective.

Gas (coalbed methane) and fissure water often coexist in coal-bearing strata, and gas is sealed in adjacent rock strata by fissure water in some coal mines. After the fissure water is discharged, abnormal gas emission may occur due to accidental release of large amount of sealed gas. In the present work, abnormal gas emission caused by accidental discharge of floor fissure water occurred at Tangjiahe Coal Mine (Guangyuan, China), and its control method was discussed. Sealing technology, uniform pressure ventilation and gas drainage for intercepting gas by cross-boreholes were adopted to solve abnormal gas emission. Firstly, several sealings were constructed to seal gobs full of high-concentration gas, which prevented gas diffusion and spread into mining space. Two gas extraction systems were installed, and uniform pressure ventilation was applied to eliminate fissure gas accumulated in gobs. Subsequently, a comprehensive recovery of ventilation was realized. As a supplementary measure, downward cross-boreholes were drilled to prevent gas from releasing into mining space.

Duanwang Coal Mine is located at north of Qinshui coalfield in Shanxi province, China, which gently inclined and thick seams have been mined. Authorized production capacity of the coal mine is 1.8Mt/a. With the increase of mining depth, the mine gas emission quantity increased. Karst collapse columns are very developed in the minefield, and the phenomenon of abnormal gas emission always occurred at the coal face and driving gateway around the collapse columns, then the mine became high gassy one from low gassy mine. Using field measurement and theoretical analysis methods, the following conclusion can be drawn. Karst collapse columns have significant influences on gas emission of the coal face and driving gateway. Here are large amount of free form gas into and around the collapse columns, the collapse columns were disclosure while driving gateway, a large amount of the free gas into collapse column would be instantly released, which caused abnormal gas emission at driving gateway, even leading to gas density exceeding limitation by Coal Mine Safety Regulation of China. However, during mining area of the collapse columns, gas emission quantity at coal face was relatively smaller.

Investigating the impact of gas emission uncertainty on airflow distribution in an auxiliary ventilation system using CFD and Monte-Carlo simulation

Affection Laws of Atmospheric Pressure to Gas Emission at Fully Mechanized Working Face

Contaminated gases emissions from livestock industry are becoming one of the most significant contributors to the increasingly serious environmental pollution. To find a way to reduce gases emissions, it is essential to reveal the factors that can affect the gases emissions. In this study, the concentrations of typical gases (including ammonia (NH3), carbon dioxide (CO2), hydrogen sulfide (H2S), and sulfur dioxide (SO2)) generated from naturally-ventilated dairy cow barns were detected through the sample-data method in Tianjin, northern China. Indoor environmental conditions, such as temperature (T) and relative humidity (RH), were measured simultaneously. After applying the carbon dioxide mass balance method, ammonia, hydrogen sulfide and sulfur dioxide emissions were determined. The correlation analysis and regression analysis between the climate condition and gas emissions were conducted to assess the data collected in dairy cow barns during the whole study period. There was a significant relationship between environmental conditions and gas emissions. NH3, H2S and SO2 emissions from the building are in the range of 0.98-2.36 g/LU·h, 0-0.034 g/LU·h, and 0-0.069 g/LU·h, respectively. The numerical analysis shows that the NH3 emission is highly correlated with the temperature and relative humidity. The ventilation rate shows a positive correlation with all the three gases. Keywords: gas emissions, environmental conditions, correlation analysis, regression analysis, carbon dioxide, H2S emission, NH3 emission, SO2 emission DOI: 10.25165/j.ijabe.20201302.4802 Citation: Zou B, Shi Z X, Du S H. Gases emissions estimation and analysis by using carbon dioxide balance method in natural-ventilated dairy cow barns. Int J Agric & Biol Eng, 2020; 13(2): 41–47.