Bayesian discriminant analysis for prediction of coal and gas outbursts and application

Based on the principle of Bayesian discriminant analysis, we established a model of Bayesian discriminant analysis for predicting coal and gas outbursts. We selected five major indices which affect outbursts, i.e., initial speed of methane diffusion, a consistent coal coefficient, gas pressure, destructive style of coal and mining depth, as discriminating factors of the model. In our model, we divided the type of coal and gas outbursts into four grades regarded as four normal populations. We then obtained the corresponding discriminant functions through training a set of data from engineering examples as learning samples and evaluated their criteria by a back substitution method to verify the optimal properties of the model. Finally, we applied the model to the prediction of coal and gas outbursts in the Yunnan Enhong Mine. Our results coincided completely with the actual situation. These results show that a model of Bayesian discriminant analysis has excellent recognition performance, high prediction accuracy and a low error rate and is an effective method to predict coal and gas outbursts.

Development of regional prediction information system of coal and gas outburst

Coal and gas outburst information system is based on-Geographic Information System(GIS), with which the relation among mine geological structure, coal features, stress field and coal and gas outburst were researched, and also the relation between gas distributed condition and dangerous degrees. Various prediction method, index and technique were applied to realize the data visualization; the accuracy of region prediction was increased. The system has successfully applied in Huainan minging area and Pingdingshan minging area.

Application of dynamic set-pair analysis in coal and gas outburst prediction

Analyzed the factors which affected the coal and gas outburst,then established the corresponding indicator system.Built a dynamic set-pair analysis prediction model which combined of Markov model and set-pair analysis model,and then it applied to coal and gas outburst prediction.Finally,compared the prediction results with the actual results As provided a reference to the coalmine in safety decision-making.The research results indicate that there are four districts in high dangerous level,two districts in middle level and one district in low level,which consistent with the actual situation;the dynamic set-pair analysis model has a good effect in predicting coal and gas outburst.Especially in the continuous time intervals,according to the data of mined exploration and the connec- tion degree analysis,we can deduce the dangerous levels of unexplored districts from the historical data.In different districts,the relevant indicators can be adjusted accordingly,so as to enhance the accuracy of the prediction.

Prediction method for risks of coal and gas outbursts based on spatial chaos theory using gas desorption index of drill cuttings

Based on the evolution of geological dynamics and spatial chaos theory, we proposed the advanced prediction an advanced prediction method of a gas desorption index of drill cuttings to predict coal and gas outbursts. We investigated and verified the prediction method by a spatial series data of a gas desorption index of drill cuttings obtained from the 113112 coal roadway at the Shitai Mine. Our experimental results show that the spatial distribution of the gas desorption index of drill cuttings has some chaotic charac- teristics, which implies that the risk of coal and gas outbursts can be predicted by spatial chaos theory. We also found that a proper amount of sample data needs to be chosen in order to ensure the accuracy and practical maneuverability of prediction. The relative prediction error is small when the prediction pace is chosen carefully. In our experiments, it turned out that the optimum number of sample points is 80 and the optimum prediction pace 30. The corresponding advanced prediction pace basically meets the requirements of engineering applications.

Pattern recognition prediction of coal and gas outburst hazard in the sixth mine of Hebi

Based on the systematical analysis influence factors of coal and gas outburst, the main factors and their magnitude was determined by the corresponding methods.With the research region divided into finite predicting units,the internal relation between the factors and the hazard of coal and gas outburst,that was combination model of influence factors,was ascertained through multi-factor pattern recognition method.On the basis of contrastive analysis the pattern of coal and gas outburst between prediction region and mined region,the hazard of every predication unit was determined.The mining area was then divided into coal and gas outburst dangerous area,threaten area and safe area re- spectively according to the hazard of every predication unit.Accordingly the hazard of mining area is assessed.

Matter-elements model and application for prediction of ccoal and gas outburst

The theory and method of extenics were applied to establish classical field matterelements and segment field matter elements for coal and gas outburst.A matter-element model for prediction was established based on five matter-elements,which includedgas pressure,types of coal damage,coal rigidity,initial speed of methane diffusionand in-situ stress.Each index weight was given fairly and quickly through the improvedanalytic hierarchy process,which need not carry on consistency checks,so accuracy ofassessment can be improved.

Application of computational intelligence platform in coal and gas outburst prediction

The present situation of lacking fast and effective coal and gas outburst prediction techniques will lead to long out- burst prevention cycles and poor accurate prediction effects and slows down coal roadway drive speed seriously. Also, due to historical and economic reasons, some coal mines in China are equipped with poor safety equipment, and the staff professional capability is low. What＇s worse, artificial and mine geological conditions have great influences on the traditional technologies of coal and gas outburst prediction. Therefore, seeking a new fast and efficient coal and gas outburst prediction method is nec- essary. By using system engineering theory, combined with the current mine production conditions and based on the coal and gas outburst composite hypothesis, a coal and gas outburst spatiotemporal forecasting system was established. This system can guide forecasting work schedule, optimize prediction technologies, carry out step-by-step prediction and eliminate hazard hier- archically. From the point of view of application, the proposed system improves the prediction efficiency and accuracy. On this basis, computational intelligence methods to construct disaster information analysis platform were used. Feed-back results pro- vide decision support to mine safety supervisors.

Research on prediction for coal and gas outburst based on Matlab neural network toolbox and its application

In order to predict the danger of coal and gas outburst in mine coal layer correctly, on the basis of the VLBP and LMBP algorithm in Matlab neural network toolbox, one kind of modified BP neural network was put forth to speed up the network convergence speed in this paper. Firstly, according to the characteristics of coal and gas outburst, five key influencing factors such as excavation depth, pressure of gas, and geologic destroy degree were selected as the judging indexes of coal and gas outburst. Secondly, the prediction model for coal and gas outburst was built. Finally, it was verified by practical examples. Practical application demonstrates that, on the one hand, the modified BP prediction model based on the Matlab neural network toolbox can overcome the disadvantages of constringency and, on the other hand, it has fast convergence speed and good prediction accuracy. The analysis and computing results show that the computing speed by LMBP algorithm is faster than by VLBP algorithm but needs more memory. And the resuits show that the prediction results are identical with actual results and this model is a very efficient prediction method for mine coal and gas outburst, and has an important practical meaning for the mine production safety. So we conclude that it can be used to predict coal and gas outburst precisely in actual engineering.

Research on coal structure indices to coal and gas outbursts in Pingdingshan Mine Area,China

According to the feature that coal and gas outbursts is controlled by coal structure in Pingdingshan mine area, based on the study of the distribution law of disturbed coal in Mine Area and the macroscopic characteristics of coal structure, the characteristics and genesis to micro-pore of disturbed coal, the relationship between the type of coal structure and gas parameter, and the structural feature of coal at outbursts sites are mainly explored in this paper. Further, the steps and methods are put forward that coal structure indices applied to forecast coal and gas outbursts.

Research on Geological Structure Mark of Coal and Gas Outbursts in Pingdingshan Mining Area

Based on the study of regional displaying rules of coal and gas outburst controlled by geological structure in Pingdingshan mining area, the geological structure features in outburst sites were investigated emphatically. The combination type, orientation and least seam thickness in outburst sites were put forward. This research provides a geological mark for forecasting gas outbursts in deep mining.

Pattern Recognition and Forecast of Coal and Gas Outburst

Coal and gas outburst is a complicated dynamic phenomenon in coal mines, Multi-factor Pattern Recognition is based on the relevant data obtained from research achievements of Geo-dynamic Division, With the help of spatial data management, the Neuron Network and Cluster algorithm are applied to predict the danger probability of coal and gas outburst in each cell of coal mining district. So a coal-mining district can be divided into three areas： dangerous area, minatory area, and safe area. This achievement has been successfully applied for regional prediction of coal and gas outburst in Hualnan mining area in China.

STUDY ON THE METHOD FOR FORECASTING DANGEROUSNESS OF COAL FACE AND HEADING FACE OUTBURST BY TWO TEMPERATURE INDEXES

In line with the sensitivity of coal drillings temperature and coalbed temperature to the dangerous zone of coal and gas outburst, two temperature sensitive indexes (△Tm, △tm) for forecasting dangerousness of coal face and heading face outburst are defined, and deal with the foundation on drillings and coalbed temperatures used as sensitive indexes and the principle and method of determining drillings and coalbed temperatures. On the basis of this, we put forward the method for forecasting dangerousness of coal face and heading face outburst by two temperature sensitive indexes and determine the critical values of two temperature sensitive indexes (△Tm= 5.5℃, △tm = 4.5℃) by in-situ observation and requirement for determining sensitive index.

△P index with different gas compositions for instantaneous outburst prediction in coal mines

In this study we measured the △P(initial speed of gas emission) index with different gas concentrations of carbon dioxide(pure CO2,90% CO2+10% CH4,67% CO2+33% CH4,50% CO2+50% CH4,30% CO2+10% CH4 and pure CH4) of coal samples from the No.2 coal seam in the Yaojie Coal Mine,Gansu province,China.The effect of carbon dioxide concentration,gas composition,coal strength and particle size of coal samples on the △P index was investigated.The experimental results show that with gas of various compositions,the △P value of three samples were clearly different.The △P index of coal samples A,B and C(0.2～0.25 mm) were 4,6 and 7 with pure CH4 and 22,30 and 21 when pure CH4 was used.Carbon dioxide concentration affects the △P index markedly.The △P index increases with an increase in carbon dioxide concentration,especially for coal B.Hence,the △P index and K(another outburst index) values tested only with pure CH4 for prediction of the danger of outburst is not accurate.It is important to determine the initial speed of gas emission given the gas composition of the coal seam to be tested for exact outburst prediction.

Distribution of Heavy Hydrocarbon in Coal Seams and Its Use in Predicting Outburst of Coal

In order to verify whether any special gas component exists in outburst samples or not, coal samples from both outburst coal seams and non-outburst coal seams were collected. Some gases were extracted from the samples and analyzed qualitatively and quantitatively on chromatogram-mass spectrograph. The qualitative analysis show that there is no special gases in coal seams. And the quantitative analysis indicates that the heavy hydrocarbon content in coal samples from outburst coal seams is apparently higher than that from non-outburst district ones, which reflects the damage of geological tectonic movement to coal body in history. Therefore, the heavy hydrocarbon content of coal sample can be used as an index to predict coal outburst.

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 Ki 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.

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.

Advances in gas content based on outburst control technology in Huainan, China

The sudden and violent nature of coal and gas outbursts continues to pose a serious threat to coal mine safety in China. One of the key issues is to predict the occurrence of outbursts. Current methods that are used for predicting the outbursts in China are considered to be inadequate, inappropriate or impractical in some seam conditions. In recent years, Huainan Mining Industry Group(Huainan) in China and the Commonwealth Scientific and Industrial Research Organisation(CSIRO) in Australia have been jointly developing technology based on gas content in coal seams to predict the occurrence of outbursts in Huainan. Significant progresses in the technology development have been made, including the development of a more rapid and accurate system in determining gas content in coal seams, the invention of a sampling-while-drilling unit for fast and pointed coal sampling, and the coupling of DEM and LBM codes for advanced numerical simulation of outburst initiation and propagation. These advances are described in this paper.

煤瓦斯突出研究现状及其研究方向探讨

本文通过学习总结煤炭行业近200年来对煤瓦斯突出的研究成果和工作经验,了解其具体的研究思路方法、研究历史发展过程、研究现状及存在的关键问题,探讨采用地质力学理论方法进行煤瓦斯突出研究的可能性与可行性。

冲击地压和突出的统一预测及防治技术

对冲击地压、煤与瓦斯突出的形成机理、预测预报方法和工程治理技术等三个方面进行了分析和总结 ,进一步说明了冲击地压和突出二者的本质统一性 ,有利于建立适用于工程指导和应用的统一认识和技术路线。

集对理论聚类分析法综合预测煤与瓦斯突出

基于集对理论和聚类分析相结合的方法,将现行的钻屑指标法、钻孔瓦斯涌出初速度法、电磁辐射法和单项指标法等单因素单数值预测煤与瓦斯突出的方法和指标,通过集对理论综合运用一个具体的集对联系度数学模型表达出来,结合系统聚类分析思想,通过计算预测系统联系度之间的同异反距离,按照同异反模式识别的"择近原则",来辨别待测样本系统所属的类别,从而进行多因素综合预测煤与瓦斯突出。通过以焦作九里山矿巷道掘进工作面为例,应用综合预测模型计算分析煤与瓦斯突出预测指标的实例数据,结合相似权计算法确定各指标因素权重,提高预测模型精度,预测结果符合现场实际情况,取得了良好的预测效果。