Mine Gas Emission Research Articles

Coal mine gas emission prediction based on multifactor time series method

The prediction of coal mine gas emission is an important indicator for ventilation systems reliability and a data basis for mine gas extraction design. The traditional gas emission prediction methods have weak universal applicability, and the existing prediction models are mostly based on single-factor time series prediction. To solve this problem, the gas emission prediction method based on Recursive Feature Elimination with cross-validation (RFECV) and Bidirectional Long and Short-Term Memory (Bi-LSTM) was proposed. Aiming at the problems of numerous influencing factors, strong nonlinear characteristics and time correlation, feature selection methods based on RFECV were applied. The RFECV method embedding of two base models, Ridge Regression (Ridge) and Random Forest (RF), obtained four gas emission prediction multifactor combinations. The predictive accuracy of different models was compared with multifactor combinations when the training set accounted for 60, 70 and 80 % of the total sample. The RMSE, MAE, R2, model stability, and running time of the RF-RFECV-Bi-LSTM model were 0.2455,0.1914,0.9897,0.9431 and 12.20 s, respectively. The result indicated that the constructed prediction model had high accuracy and reliability, which can be used as a basis for the accurate prediction of gas emission in multifactor time series.

A software for calculating coal mine gas emission quantity based on the different-source forecast method

The ability to predict gas emissions accurately is pivotal in managing gas control and ensuring safe mining operations. Existing internationally acknowledged gas control and prediction software does not cater to the specific conditions in Chinese coal mines. Hence, this paper introduces an object-oriented programming method to design a software tool for calculating the total gas emission quantity using the MATLAB application program designer runtime environment. The software incorporates an algorithm, data structure, framework, and module functions, all of which enable seamless integration and visualization of gas emission calculation software. This software tool mitigates the inefficiencies and inaccuracies associated with manual, different-source forecast methods. Based on the field data of the Hulonggou Coal Mine in Shanxi province, this technical software was used to predict the gas emission of the mine. The research results show that the predicted value of the technical software is close to the actual measured value. The differing estimates of the working face and coal mine output primarily account for the deviation between the tool's predicted gas emission value and the field-measured value. The underlying design logic of this technical software determines that it has good adaptability to mines with clear mining technology parameters and gas geological parameters. This study provides a valuable method for researchers and engineers seeking to improve gas emission calculation efficiency.

A new IPCA-CPSO-BP Model for Predicting Gas Emission in Underground Coal Mines

Gas has become the primary factor restricting the safe and efficient production of coal mines. Gas emission prediction model is very important for mine gas emission prediction and gas disaster prevention. The improved principal component analysis (IPCA) was used to reduce the dimension of 13 influencing factors of gas emission, and the CPSO-BP neural network prediction model was built with MATLAB software to predict the absolute gas emission of Zhongtai Mining.The results show that the cumulative contribution rate of principal components is not less than 95%, and the number of principal components after improvement is reduced from the original 6 to 3, which effectively solves the problem of excessive principal components and data redundancy caused by the correlation difference between various influencing factors and gas emission amount. At the same time, the optimized particle swarm optimization algorithm alleviates the trouble that the particle swarm optimization algorithm is prone to fall into the local optimal solution. By coupling the improved particle swarm optimization algorithm with BP neural network, the problem of BP neural network's over-dependence on the initial value of weights and thresholds is solved, and the optimal initial weights and thresholds are provided for BP neural network, improving the accuracy of model prediction results. The average relative error of the original BP neural network is 1.6638%, the normalized mean square error is 0.3155, and the regression correlation index is 0.8157. The IPCA-CPSO-BP prediction model decreased to 0.5749%, 0.1143 and 0.9758, respectively. IPCA improves the data dimensionality reduction ability of principal component analysis, IPCA-CPSO-BP enhances the stability of particle swarm optimization algorithm, and significantly improves the prediction accuracy of BP model. The prediction trend is highly consistent with the actual value, which verifies the reliability of the model and provides strong data support for mine gas prevention and control.

Research on gas emission quantity prediction model based on EDA-IGA

In order to accurately predict the possible gas emission quantity in coal mines, it is proposed to use the multi-thread calculation of the Immune Genetic Algorithm (IGA) and injection of vaccines to improve the accuracy of prediction and combine the Estimation of Distribution Algorithm (EDA) to the distribution probability of excellent populations. Calculating, and selecting excellent populations for iteration, optimize the population generation process of the Immune Genetic Algorithm, so that the population quality is continuously optimized and improved, and the optimal solution is obtained, thereby establishing a gas emission quantity prediction model based on the Immune Genetic Algorithm and Estimation of Distribution Algorithm. Using the 9136 mining face with gas emission hazards in a coal mine from Shandong Province in China as the prediction object, the absolute gas emission quantity is used to scale the gas emission quantity, and it is found that the model can accurately predict the gas emission quantity, which is consistent with the on-site emission unanimous. In the prediction comparison with IGA, it is found that the accuracy of the prediction results has increased by 9.51%, and the number of iterations to achieve the required goal has been reduced by 67%, indicating that the EDA has a better role in optimizing the population update process such as genetic selection of the IGA. Comparing the prediction results of other models, it is found that the prediction accuracy of the EDA-IGA is 94.93%, which is the highest prediction accuracy, indicating that this prediction model can be used as a new method for the prediction of coal mine gas emission. Accurately predicting the gas emission quantity can provide guidance for safe mining in coal mines. The gas emission quantity can also be used as a safety indicator to reduce the possibility of coal mine accidents, ensure the personal safety of coal miners and reduce economic losses in coal mines.

Prediction of coal mine gas emission based on hybrid machine learning model

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.

Analysis of the Characteristics of CH4 Emissions in China’s Coal Mining Industry and Research on Emission Reduction Measures

CH4 is the second-largest greenhouse gas and has a significant impact on global warming. China has the largest amount of anthropogenic coal mine methane (CMM) emissions in the world, with coal mining emissions (or gas emissions) accounting for 90% of total energy industry emissions. The results of CH4 emission inventories from previous studies vary widely, with differences in the spatial and temporal dimensions of gas emission factors of belowground mining being the main points of disagreement. Affected by the policies of “eliminating backward production capacity” and “transferring energy base to the northwest”, China’s coal production layout has changed greatly in the past ten years, but the closely related CH4 emission factors have not been dynamically adjusted. This paper investigated 23 major coal producing provinces in China, obtained CH4 emission data from coal mining, calculated CH4 emission factors in line with current production conditions, and studied the reduction measures of coal mine gas emission. According to the CH4 emission data of China’s coal mines in 2018, 15.8 Tg of methane is released per year in the coal mining industry in China, and 11.8 Tg after deducting recycling. Shanxi Province’s CH4 emissions are much higher than those of other provinces, accounting for 35.5% of the country’s total emissions. The weighted CH4 emission factor of coal mining in China is 6.77 m3/t, of which Chongqing is the highest at approximately 60.9 m3/t. Compared with the predicted value of the IPCC, the growth trend of CCM has slowed significantly, and the CH4 utilization rate has gradually increased. This change may be aided by China’s coal industry’s policy to resolve excess capacity by closing many high-gas and gas outburst coal mines. In addition, the improvement of coal mine gas extraction and utilization technology has also produced a relatively significant effect. This paper determines the distribution of methane emissions and emission sources in China’s coal mining industry, which is useful in formulating CCM emission reduction targets and adopting more efficient measures.

A calculation method of gas emission zone in a coal mine considering main controlling factors

The gas emission zone is an important parameter for the space–time effect of coal excavation and gas emission. In this paper, according to the effect of roadway excavation, a numerical model of gas emission zone based on the evolution of stress and permeability was established to obtain the width of gas emission zone with different pressure and permeability coefficient. Then the numerical simulation results were verified by measuring the gas content at different depths. Through numerical simulation and field measured data, the theoretical calculation formula is established on the basis of comprehensive consideration of the influencing factors of gas emission zone. The results showed that the gas emission zone increases with the increase of coal seam gas pressure and permeability coefficient when the roadway section and exposure time are the same. The measured gas emission zone, when taking gas content as the index with the same logistic function growth curve, matches the measured results with a relative error of 1.3 to 6%. The validity of the model is also verified by field experiments. The results can provide guidance for mine gas emission and gas drainage design.

Retraction Note: Structural strength analysis of loess based on genetic algorithm and the prediction and optimization of coal mine gas emission

Retraction Note: Structural strength analysis of loess based on genetic algorithm and the prediction and optimization of coal mine gas emission

Coal mine gas emission prediction method based on random forest regression

Coal mine gas emission prediction method based on random forest regression

RETRACTED ARTICLE:Structural strength analysis of loess based on genetic algorithm and the prediction and optimization of coal mine gas emission

RETRACTED ARTICLE:Structural strength analysis of loess based on genetic algorithm and the prediction and optimization of coal mine gas emission

Research on Prediction Accuracy of Coal Mine Gas Emission Based on Grey Prediction Model

In order to achieve the accuracy of gas emission prediction for different workplaces in coal mines, three coal mining workings and four intake and return air roadway of working face in Nantun coal mine were selected for the study. A prediction model of gas emission volume based on the grey prediction model GM (1,1) was established. By comparing the predicted and actual values of gas emission rate at different working face locations, the prediction error of the gray prediction model was calculated, and the applicability and accuracy of the gray prediction method in the prediction of gas gushing out from working faces in coal mines were determined. The results show that the maximum error between the predicted and actual measured values of the gray model is 2.41%, and the minimum value is only 0.07%. There is no significant prediction error over a larger time scale; the overall prediction accuracy is high. It achieves the purpose of accurately predicting the amount of gas gushing from the working face within a short period of time. Consequently, the grey prediction model is of great significance in ensuring the safety production of coal mine working face and promote the safety management of coal mine.

Groundwater Risk Assessment in the Context of an Underground Coal Mine Closure and an Economic Evaluation of Proposed Treatments: A Case Study

Mining companies are responsible for the impacts that result from their mining activities even after the mining period has ended. At the same time, at the European and international levels, there is a lack of a detailed operational methodology comprising environmental risks during and after closure of underground coal mines. The environmental risk aspects that need to be considered when planning the closure of an underground coal mine and post closure in the broader environmental context are the following: modification of water flow scheme, surface instability, mine gas emission on the surface, and water and soil pollution. In this study, we focus on assessing groundwater risk in the context of an underground coal mine closure and evaluating the selected risk mitigation strategies in terms of performance and cost. The results from this study could be used for developing a final closure groundwater assessment plan by selecting the most feasible treatment alternatives for different environmental impacts, together with the transitional monitoring that could guarantee a hazard level in compliance with land reuse and the use of natural resources. Finally, the cost-efficient monitoring and treatment programs are used to estimate the financial provisions needed to mitigate groundwater risks during underground coal mine closure contexts.

Research on Computer Algorithm of Mine Gas Release Source Location Based on Multi-source Sensor Fusion

Aiming at the problem of gas leakage source difficult to locate or inaccurately located due to the air leakage phenomenon in the goaf of coal mines, this paper proposes a mine gas emission source location algorithm based on multi-source sensor fusion. Firstly, by analyzing the gas distribution law of the goaf in the fully mechanized caving face, the sensor observation model and the gas release source diffusion model of the mine goaf are established, and then the machine learning positioning algorithm is used to estimate the parameters of the gas release source in the goaf and according to the iterative calculation The coordinate positions of the estimated parameters are obtained. Finally, the data fusion of the wireless sensor target source sensing node and the cluster head node is used to achieve the precise positioning of the gas release source. The results show that: compared with other algorithms, machine learning algorithms have obvious advantages in positioning accuracy. This method can effectively solve the problem of difficulty in locating the gas release source due to the air leakage phenomenon, and then provide a reference basis for gas prominent warning and gas extraction in the goaf.

Prediction of gas emission zone width in laneway’s coal seam based on slippage effect of gas seepage

ABSTRACT The width of gas emission zone in laneway’s coal seam is an important parameter to predict mine gas emission amount and gas content in the fully mechanized mining face. Based on slippage effect of gas seepage in the coal seam, the width of gas emission zone in laneway’s coal seam is theoretically predicted with a combination of experimental test and numerical simulation. The research results show that gas seepage in coal seam with lower pressure will have a slippage effect, and the slippage factor is about 0.24 in test coal sample. Slippage effect has a significant effect for gas emission from coal seam to coal roadway. With increase of exposure time of coal wall, gas pressure in coal seam decreases obviously with considering slippage effect, which is in good agreement with the experimental results. In the meantime with considering the slippage effect, the calculation results of gas emission zone width in laneway’s coal seam is basically consistent with the test results, and the relative errors are less than 7%. With exposure time of coal wall increasing, the increment of gas emission zone width decreases gradually, and the power function can be used to well fit the relation of gas emission zone width and exposure time.

Prediction of Coal Mine Gas Emission Quantity Based on Grey-Gas Geologic Method

To improve the accuracy and reliability of gas emission prediction, the various factors affecting the amount of gas emission were researched and the main factor determining the amount of gas emission was determined by the gas geology theory. In this paper, we adopted grey-gas geologic method and grey relevancy analysis separately to estimate forecast accuracy and to establish the grey systematic forecasting model; meanwhile, two residual tests were carried out. Combined with the concurrent in situ data, the result of the grey systematic prediction model is verified. The later residual test results indicated that the model is of a high accuracy and the prediction result is reliable, manifesting the method of grey-gas geologic method is a better way to forecast the gas emission.

Carbon emissions accounting for China’s coal mining sector: invisible sources of climate change

Coal is the primary source of China’s carbon emissions due to the energy structure and its resource endowment. This reality creates enormous pressure and impetus for low-carbon pathways of coal production and consumption. Based on a literature review on carbon emissions accounting methods, this paper builds a source-driven CO2 emissions accounting model for the coal development sector using the emissions factor method. Scenario analysis is employed to predict future carbon emission equivalents and to indicate possible implications for climate change mitigation in this sector. Carbon emissions from coal development are mainly derived from coal mine gas emissions, which yield 62% of the sector’s total carbon emissions, followed by energy consumption. The recent decline in coal mining-driven CO2 emissions is mainly due to the strict deployment of coal mine gas and the changing structure of coal mines. The results from the scenarios suggest that the carbon emissions reduction potential will largely be determined by technology innovation in the coal mine gas industry. Policy implications for further addressing carbon emissions from the supply side of the coal industry include improvements in energy efficiency and coal mine gas extraction and utilization.

Prediction method of mine gas emission based on complex neural work optimized by Wolf pack algorithm

ABSTRACTIn view of the local extreme problem of the gradient descent algorithm, which makes the working face of mine gas emission prediction uncertainly, this paper combined Wolf pack algorithm (WPA) with complex neural network nonlinear prediction method to the established new prediction model. The WPA shows good global convergence and computational robustness in the solving process of complex high-dimensional functions. Working face in a coal mine as a case, this paper selects seven factors as input variables of the mine gas emission prediction, uses training data to mature prediction model and adopted it to predict six group gas emission data. Research results show that the mean absolute percentage value of the complex neural network model which has been optimized by WPA is 0.06%, the root mean square error value is 0.0191, the mean absolute error value is 0.0175 and the equal coefficient value is 0.9979. The prediction results are very close to the real value, and the change trend is highly consistent with the actual situation.

Mine Gas Emission Prediction Based on Grey Markov Prediction Model

This article puts forward the gray markov prediction model to predict mine gas emission by combining grey system theory and markov chain theory. And GM (1, 1) model is established in the first place for grey data by the model. To eliminate the error, and improve the prediction accuracy of the model, secondary parameters fitting was done on the basis of GM (1, 1) model. And we get second parameter fitting for trend prediction. Then using Markov state transfer probability matrix prediction method to do quadratic fitting for its predictive value, which can improve the prediction precision of the volatile random variables. It proves the prediction results of the model are satisfactory by analyzing history data of gas emission prediction. This conclusion broadens the application scope of grey forecast model and provides a new method for mine gas emission scientific forecast.

Coal seam drainage enhancement using borehole presplitting basting technology – A case study in Huainan

Xinji No. 2 underground coal mine extracts the coal seams #4 and #5. These two seams are highly gassy and gas drainage is required to control mine gas emission and reduce outburst risk. Because the seam permeability coefficient is very low and around 0.1 m2/(MPa2·d), a number of technologies have been trialled to enhance the seam permeability prior to gas drainage. Of these technologies trialled, the deep borehole presplitting blasting technology has been proven to be quite effective in increasing permeability. In Xinji No. 2mine it doubled or sometimes tripled gas drainage volume. This paper describes the technology, its application in the enhancement of seam permeability in Xinji No. 2 coal mine, and its effect on gas drainage performance.

Gas Emission Prediction Model of Coal Mine Based on CSBP Algorithm

In view of the nonlinear characteristics of gas emission in a coal working face, a prediction method is proposed based on cuckoo search algorithm optimized BP neural network (CSBP). In the CSBP algorithm, the cuckoo search is adopted to optimize weight and threshold parameters of BP network, and obtains the global optimal solutions. Furthermore, the twelve main affecting factors of the gas emission in the coal working face are taken as input vectors of CSBP algorithm, the gas emission is acted as output vector, and then the prediction model of BP neural network with optimal parameters is established. The results show that the CSBP algorithm has batter generalization ability and higher prediction accuracy, and can be utilized effectively in the prediction of coal mine gas emission.