Gas Outburst Prediction Research Articles

Scale-span feature of surface analysis, macromolecular and pore structure in coal: Implications for inherent evolutionary mechanism of morphological microstructure

Morphological microstructural evolution of tectonic coal determines CBM storage and transportation mechanism as well as the occurrence of coal and gas outburst. Scale-span morphological feature of original and tectonic coals was characterized by AFM, Raman spectrum and physisorption method. The results demonstrate that metamorphism could remold the surface property of coal from rugged band to flat porous structure with pore form factor from 0.572 to 0.831, promoting the formation of regular and round pores. Tectonism facilitates ability of gas sorption in coal, breaking the basic structural integrity. Metamorphism could facilitate the ordered macromolecular structural evolution on recombination and aromatization as well as condensation; besides, tectonism may promote the evolution and development of microcrystalline structure in advance. The aforementioned results have revealed essential differences in morphological microstructure between original and tectonic coals, which are of great guiding significance to safe mining of CBM and prediction of coal and gas outburst.

Dynamic gas emission during coal seam drilling under the thermo-hydro-mechanical coupling effect: A theoretical model and numerical simulations

The potential risk of coal and gas outbursts has increased with the depth of coal mining, posing the threat of heavy casualties, vast economic losses, and harm to the ecological environment. Considering the limitations of existing coal and gas outburst prediction methods, novel indicators or methods are needed. In this study, a fully coupled dynamic drilling gas emission THM model of gas diffusion, seepage, and flow is established, and the reliability of the model was verified by the measured data. The validated model was applied to single-factor and multi-factor impact analysis of dynamic drilling gas emission. The simulation results showed that (1) During the drilling process, the increase in permeability is mainly attributed to the coal seam damage and disturbance caused by the borehole and the coupling effects of the THM model, which increase the porosity around the borehole. Driven by the pressure gradient and temperature gradient, the gas pressure and temperature of the coal seam around the borehole show a conical distribution. (2) In the single-factor analysis, increasing the values of coal seam parameters (initial gas pressure, initial permeability, and initial coal temperature) and drilling parameters (borehole radius) will promote gas emissions in the drilling process to different degrees. The most significant promoting effect is the initial permeability, and the weakest is the initial coal temperature. (3) The response surface analysis results of the four factors show that the interaction between initial coal temperature C and borehole radius D is the least significant, and the interaction between initial gas pressure A and initial permeability B is the most significant. Meanwhile, the established multiple regression model characterizes the relationship between borehole gas emissions and initial coal seam gas pressure, and the idea of using drilling gas emission volume to predict coal and gas outbursts was proposed.

Study on the Influence of Geological Structure on Gas Occurrence and Gas Outburst

Gas is a gaseous geological body and a product of geological genesis. The geological structure and the historical evolution of coal bearing strata control the generation conditions of coal-bed gas and the preservation conditions of coal-bed gas subjected to depression, uplift, compression and tension in previous tectonic movements. The geological conditions that control the occurrence of gas disasters determine the state of gas occurrence, gas content, gas pressure, gas emission and the risk of coal and gas outburst in coal seams. There are obvious characteristics of zoning and zoning. Gas occurrence and gas outburst are controlled by various geological factors, and geological structure plays a decisive role in coal and gas outburst. Studying the influence of geological structure on gas occurrence law and gas outburst and mastering gas geological law can achieve the purpose of gas prediction and gas control. This paper discusses the influence of fold, fault and sliding structure on gas occurrence, and discusses the influence of geological structure on gas outburst from three aspects of coal seam gas pressure, in-situ stress and coal body structure. It can provide a basis for the prediction of gas outburst and has important significance for the prevention and control of gas accidents in coal mines.

Experimental study of coal and gas outburst processes influenced by gas pressure, ground stress and coal properties

With the continuous increase of mining depth, coal and gas outburst poses a significant threat to mining safety. Conducting research on the mechanisms of coal and gas outbursts contributes to understanding the evolutionary process of such incidents, thus enabling accurate prediction and prevention of coal and gas outbursts during mining operations. This paper has developed a comprehensive visual experimental system that is specifically tailored to simulate diverse coal body conditions, ground stress and gas pressures. By monitoring and analyzing the real-time progression of coal fissures during the outburst process, we can obtain valuable insights into the evolution and mechanisms of coal and gas outbursts. Additionally, this study introduces a method to determine the critical threshold for predicting coal and gas outbursts, and the critical gas pressure threshold for Jiulishan Coal Mine (Jiaozuo City, Henan Province, China) is established at 0.6 MPa.

Elimination mechanism of coal and gas outburst based on geo-dynamic system with stress–damage–seepage interactions

Coal and gas outburst is a complex dynamic disaster during coal underground mining. Revealing the disaster mechanism is of great significance for accurate prediction and prevention of coal and gas outburst. The geo-dynamic system of coal and gas outburst is proposed. The framework of geo-dynamic system is composed of gassy coal mass, geological dynamic environment and mining disturbance. Equations of stress–damage–seepage interaction for gassy coal mass is constructed to resolve the outburst elimination process by gas extraction with boreholes through layer in floor roadway. The results show the occurrence of outburst is divided into the evolution process of gestation, formation, development and termination of geo-dynamic system. The scale range of outburst occurrence is determined, which provides a spatial basis for the prevention and control of outburst. The formation criterion and instability criterion of coal and gas outburst are established. The formation criterion F1 is defined as the scale of the geo-dynamic system, and the instability criterion F2 is defined as the scale of the outburst geo-body. According to the geo-dynamic system, the elimination mechanism of coal and gas outburst—‘unloading + depressurization’ is established, and the gas extraction by boreholes through layer in floor roadway for outburst elimination is given. For the research case, when the gas extraction is 120 days, the gas pressure of the coal seam is reduced to below 0.4 MPa, and the outburst danger is eliminated effectively.

Coal and gas outburst prediction model based on principal component analysis and improved support vector machine

In order to predict the coal outburst risk quickly and accurately, a PCA-FA-SVM based coal and gas outburst risk prediction model was designed. Principal component analysis (PCA) was used to pre-process the original data samples, extract the principal components of the samples, use firefly algorithm (FA) to improve the support vector machine model, and compare and analyze the prediction results of PCA-FA-SVM model with BP model, FA-SVM model, FA-BP model and SVM model. Accuracy rate, recall rate, Macro-F1 and model prediction time were used as evaluation indexes. The results show that: Principal component analysis improves the prediction efficiency and accuracy of FA-SVM model. The accuracy rate of PCA-FA-SVM model predicting coal and gas outburst risk is 0.962, recall rate is 0.955, Macro-F1 is 0.957, and model prediction time is 0.312s. Compared with other models, The comprehensive performance of PCA-FA-SVM model is better.

Research on Coal and Gas Outburst Risk Warning Based on Multiple Algorithm Fusion

To improve the accuracy of gas outburst early warning, this paper proposes a gas outburst risk warning model based on XGBoost–GR–stacking. The statistic is based on gas outburst data from 26 mines and establishes a data generation model based on XGBoost. The obtained virtual datasets are analyzed through visualization analysis and ROC curve analysis with respect to the original data. If the augmented data has an ROC area under the curve of 1, it indicates good predictive performance of the augmented data. Grey correlation analysis is used to calculate the grey correlation degrees between each indicator and the “gas emission”. The indicator groups with correlation degrees greater than 0.670 are selected as the main control factor groups based on the sorting of correlation degrees. In this study, SVM, RF, XGBoost, and GBDT are selected as the original models for stacking. The original data and virtual data with correlation degrees greater than 0.670 are used as inputs for SVM, RF, XGBoost, GBDT, and stacking fusion models. The results show that the stacking fusion model has an MAE, MSE, and R2 of 0.031, 0.031, and 0.981. Comparing the actual and predicted values for each model, the stacking fusion model achieves the highest accuracy in gas outburst prediction and the best model fitting effect.

A novel combined intelligent algorithm prediction model for the risk of the coal and gas outburst

The mechanism of coal and gas outburst disasters is perplexing, and the evaluation methods of outburst disasters based on various sensitive indicators often have some imprecision and fuzziness. With the concept of accurate and intelligent mining in coal mines proposed in China, selecting quantifiable parameters for machine learning risk prediction can avoid the deviation caused by human subjectivity, and improve the accuracy of coal and gas outburst prediction. Aiming at the shortcomings of the support vector machine (SVM) such as low noise resistance and being prone to be influenced by parameters easily, this research proposed a prediction method based on a grey wolf optimizer to optimize the support vector machine (GWO-SVM). To coordinate the global and local optimization ability of the GWO, Tent Chaotic Mapping and DLH strategies were introduced to improve the optimization ability of the GWO and reduce the local optimal probability. The improved prediction model IGWO-SVM was used to predict the coal and gas outburst. The results showed that this model has faster training speed and higher classification prediction accuracy than the SVM and GWO-SVM models, which the accuracy rate reaching 100%. Finally, to obtain the correlation between the parameters of the coal and gas outburst prediction parameters, the random forest algorithm was used for training, and the three parameters with the highest feature importance were selected to rebuild the data set for machine learning. The accuracy of the IGWO-SVM outburst prediction model based on Random Forest was still 100%. Therefore, even if some prediction parameters are missing, the outburst can still be effectively predicted by using the RF-IGWO-SVM model, which is beneficial for the model application and underground safety management.

Coal and Gas Outburst Prediction Model Based on Miceforest Filling and PHHO–KELM

Coal and gas outbursts are some of the most serious coal mine disasters, and effective prediction of coal and gas outbursts can reduce the likelihood of accidents and fatalities. Previously conducted studies have established that machine learning has achieved results in the prediction of coal and gas outbursts, but there is a problem that the available accident data of coal and gas outbursts are diminished or missing. This paper proposes a prediction model based on multiple filling of chained equations for random forests (miceforest) and the Harris Hawk optimization algorithm with Piecewise chaos mapping (PHHO) to optimize the kernel extreme learning machine (KELM) to solve the problem of missing data in coal and gas outburst prediction and to improve prediction accuracy in the case of missing data. Firstly, the miceforest algorithm was adopted to fill missing values in the salient samples, and then the PHHO algorithm was used to optimize the parameters of KELM. Finally, the datasets before and after filling were input into the PHHO–KELM model for experimentation and comparison with other models. The results show that miceforest filling is effective in improving the salient sample accuracy and overall accuracy of predictions, but the improvement is not significant for non-salient samples. The use of the PHHO–KELM model can effectively avoid falling into a local optimum and further improve the prediction accuracy of the KELM algorithm. The salient sample accuracy and overall accuracy of the miceforest–PHHO–KELM model prediction are 96.77% and 98.50%. And an effective coal and gas outburst model has been proposed, which is the miceforest–PHHO–KELM model.

Integrated framework for feature extraction and weighting in coal and gas outburst classification

The prediction of coal and gas outburst is very necessary for the prevention of gas disaster, so an outburst prediction model coupled with feature extraction and feature weighting using optimized classifier is proposed. First, Pearson correlation coefficient(PCC) and symmetric uncertainty(SU) are employed to measure the effective information in outburst sample data. Second, Kernel principal component analysis(KPCA) and linear discriminant analysis(LDA) methods are used to extract the exiting discriminate information, and the extracted linear and nonlinear feature information can effectively reflect significant information of outburst influencing factors. Third, the combination of gradient boost decision tree(GBDT) and grey relation analysis(GRA) is used to weight and fuse the extracted linear and nonlinear feature components, then form a new feature set as important discriminant information. Forth, the weighted and fused features of the coal and gas outburst influencing factors are used as the input of support vector machine(SVM) classifier with optimized parameters, it can classify outburst states, and the achieved classification accuracy can obtain 95%. Finally, the proposed model and the existing outburst classification models in literatures are used to predict outburst, then the experiment results verify the effectiveness of the proposed model and conclude that the performance of the proposed predication model are significant than present outburst prediction models.

Study on the Prediction of Low-Index Coal and Gas Outburst Based on PSO-SVM

Low-index coal and gas outburst (LI-CGO) is difficult to predict, which seriously threatens the efficient mining of coal. To predict the LI-CGO, the Support Vector Machine (SVM) algorithm was used in this study. The Particle Swarm Optimization (PSO) algorithm was used to optimize the parameters of the SVM algorithm. The results show that based on the training sets and test set in this study, the prediction accuracy of SVM is higher than that of Back Propagation Neural Network and Distance Discriminant Analysis. The prediction accuracy of the SVM model trained by the training set T2 with LI-CGO cases is higher than that of the SVM model trained by the training set T1 without LI-CGO cases. The prediction accuracy gets better when the SVM model is trained by the training set T3, made by adding the data of the other two coal mines (EH and SH) to the training set T2, that only contains the data of XP and PJ. Furthermore, the PSO-SVM model achieves a better predictive effect than the SVM model, with an accuracy rate of 90%. The research results can provide a method reference for the prediction of LI-CGO.

Data Mining Technology and Its Applications in Coal and Gas Outburst Prediction

Coal and gas outburst accidents seriously threaten mine production safety. To further improve the scientific accuracy of coal and gas outburst risk prediction, a system software (V1.2.0) was developed based on the C/S architecture, Visual Basic development language, and SQL Server 2000 database. The statistical process control (SPC) method and logistic regression analyses were used to assess and develop the critical value of outburst risk for a single index, such as the S value of drill cuttings and the K1 value of the desorption index. A multivariate information coupling analysis was performed to explore the interrelation of the outburst warning, and the prediction equation of the outburst risk was obtained on this basis. Finally, the SPC and logistic regression analysis methods were used for typical mines. The results showed that the SPC method accurately determined the sensitivity value of a single index for each borehole depth, and the accuracy of the logistic regression method was 94.7%. These methods are therefore useful for the timely detection of outburst hazards during the mining process.

Machine learning in coal and gas outburst prediction

Artificial intelligence is flourishing, and its research achievements are being extensively applied across various industries. In the field of predicting coal and gas outbursts, methods such as machine learning and deep learning have been widely explored, resulting in accurate prediction accuracy and excellent predictive effects. This has significantly improved the safety of coal mine underground operations.

Study on the law of initial gas expansion energy and its feasibility in coal and gas outburst prediction

In order to explore the relationship between IEERG and outburst intensity and verify the feasibility of the former in predicting coal and gas outburst, a series tests with different gases and different gas pressures were conducted on the basis of self-developed coal and gas outburst simulation system and IEERG measuring instrument. The results show that with the increase of gas pressure, the IEERG increases gradually. Under the same gas pressure, the coal has the strongest adsorption capacity for CO2, followed by CH4 and N2. When the IEERG is less than 24.40mJ·g-1, no outburst will occur. When the IEERG is greater than 24.40mJ·g-1, weak outburst will occur. When the IEERG is greater than 34.72mJ·g-1, strong outburst will occur. This shows that the magnitude of IEERG is closely related to the outburst. The larger the IEERG, the greater the possibility of outburst and the greater the intensity of outburst. It is feasible to predict the risk of outburst using IEERG, and it can be quantified.

Determination of initial velocity of gas diffusion under temperature control

Introduction: The initial velocity of gas diffusion (ΔP) of coal is an important index to characterize the outburst risk of coal, and temperature is an important factor affecting the determination of initial velocity of gas diffusion. However, how to control the temperature during ΔP determination remains a significant challenge. Methods: In this paper, an experimental system for ΔP testing under temperature-controlled conditions was constructed, and the effect of temperature on the ΔP determination results was further explored by accurately controlling the temperature of the testing system. Results: The results show that the temperature change will affect the determination result of ΔP in the range of 13°C–40°C, and the determination results do not show obvious regularity. Discussion: The main reasons for the analysis are as follows: during the determination of ΔP, although the higher the temperature is, the faster the speed of gas diffusion and the greater the amount of gas diffusion and the pressure produced in the first 60 s, but the smaller the amount of gas adsorption before release. There is a situation of mutual offset between the two, resulting in the fluctuation of ΔP determination results. It is concluded that the temperature change will affect the determination of the initial velocity of gas diffusion, and when ΔP is measured, the temperature of the experimental system should be consistent with the actual temperature of the sampled coal body as far as possible, so as to increase the reliability of the measured results for the prediction of coal and gas outburst. This paper provides an experimental basis for clarifying the industry standard of temperature related to ΔP.

Hazard prediction of coal and gas outburst based on the Hamming distance artificial intelligence algorithm (HDAIA)

Currently, coal mining faces the uncertainty of the risk of coal and gas outbursts and inaccurate prediction results. Owing to this, an artificial immune algorithm (AIA) was developed for coal and gas outburst prediction based on the Hamming distance (HD) calculation method of antibody and antigen affinity called the Hamming distance artificial intelligence algorithm (HDAIA). The correlation matrix of coal and gas outburst indicators was constructed using the interpolation function in the algorithm. The HD algorithm was used to obtain the affinity between the antibody and antigen, and the minimum HD was screened to obtain the prediction result. The collected dynamic data of the drilling cuttings gas desorption index K1 and the drilling cuttings weight S during the excavation process of the 11,192-working face of a coal mine in Guizhou Province, China, were used as prediction indices. The results indicate that the prediction result of the HDAIA for the risk of coal and gas outbursts is consistent with the actual risk of outbursts, and it has a good prediction of the risk of coal and gas outbursts. The HDAIA can be used as a novel method for predicting the risk of coal and gas outbursts.

Quantitative evaluation of the indexes contribution to coal and gas outburst prediction based on machine learning

The coal and gas outburst is a most destructive coal mine power disaster, which seriously threatens the safety of coal mine production. Accurate prediction of gas outbursts can effectively reduce its harm, but the mechanism of outburst is still unclear. We propose a gas outburst prediction method based on comprehensive index and machine learning. XGBoost parameter optimization is studied based on grid search and meta-heuristic algorithm optimization to obtain the best parameter combination for outburst prediction. The prediction accuracy of XGBoost with parameters optimization for the outbursts in the test set was 100%. Finally, we have studied the contribution rate of each index in outburst prediction based on XGBoost interpretability, and the gas content has the highest prediction decision-making contribution, which is 28.61%. This study is of great significance to the safety production of coal mines, and enriches the theory of coal and gas outburst.

Regional Prediction of Coal and Gas Outburst Under Uncertain Conditions Based on the Spatial Distribution of Risk Index

Regional Prediction of Coal and Gas Outburst Under Uncertain Conditions Based on the Spatial Distribution of Risk Index

Risk Prediction of Coal and Gas Outburst in Deep Coal Mines Based on the SAPSO-ELM Algorithm.

Effective risk prevention and management in deep coal mines can reduce the occurrences of outburst accidents and casualties. To address the low accuracy and inefficiency of coal–gas outburst prediction in deep coal mines, this study proposes a deep coal–gas outburst risk prediction method based on kernal principal component analysis (KPCA) and an improved extreme learning machine (SAPSO-ELM) algorithm. Firstly, high-dimensional nonlinear raw data were processed by KPCA. Secondly, the extracted sequence of outburst-causing indicator principal components were used as the input variables for the simulated annealing particle swarm algorithm (SAPSO), which was proposed to optimize the input layer weights and implied layer thresholds of the ELM. Finally, a coal and gas outburst risk prediction model for a deep coal mine based on the SAPSO-ELM algorithm was developed. The research results show that, compared with the ELM and PSO-ELM algorithms, the SAPSO-ELM optimization algorithm significantly improved the accuracy of risk prediction for coal–gas outbursts in deep coal mines, and the accuracy rate was as high as 100%. This study enriches the theory and methods of safety management in deep coal mines, and effectively helps coal mine enterprises in improving their ability to manage coal–gas outburst risks.

Comparative Study on the Seepage Characteristics of Gas-Containing Briquette and Raw Coal in Complete Stress-Strain Process.

In this study, triaxial compression and seepage tests were conducted on briquette and raw coal samples using a coal rock mechanics-seepage triaxial test system (TAWD-2000) to obtain the complete stress–strain curves of the two samples under certain conditions. On this basis, the different damage forms of the two coal samples and the effect of their deformation and damage on their permeability were analyzed from the perspective of fine-scale damage mechanics. Moreover, the sensitivity of permeability to external variables and the suddenness of coal and gas outbursts were discussed. The results show that the compressive strength of raw coal is 27.1 MPa and the compressive strength of briquette is 17.3 MPa, the complete stress–strain curves of the two coal samples can be divided into four stages and show a good correspondence to the permeability–axial strain curves. Since briquette and raw coal have different structural properties, they present different damage mechanisms under load, thus showing great diversity in the permeability-axial strain curve, especially in the damage stage. The deformation affects the seepage characteristics of briquette mainly in the latter two stages, while it affects raw coal throughout the test. The four stages of the complete stress–strain seepage test of raw coal can well explain the four stages of coal and gas outburst process, i.e., preparation, initiation, development, and termination. Hence, the law of coal permeability to gas variation can be utilized for the coal and gas outburst prediction and forecast. The research results are valuable for exploring the real law of gas migration in coal seams.