Grey Smoothing Model for Predicting Mine Gas Emission

Jun.2003Journal of China University of Mining TechnologVol 13 No IGrey Smoothing Model forPredictingMine gas emissionPAN Jie-nan(潘结南), MENG Z/ la0-ping(孟召平), LIU Ya-chuan刘亚川)(1. School of Resources and Safey Engineering, CUMT Beijing 100083, China2. Jiaozuo Institute of Technology Henan Jiaozuo, Henan 454000, ChinaAbstract: A grey smoothing model for predicting mine gas emission was presented by combining the grey system theorywith the smoothing prediction technique. First of all according to the variable sequence, gm 1 1 )model was set upto predict the general development trend of variable as first fitted values then the smoothing prediction technique wasused to revise the fitted values so as to improve the accuracy of prediction The results of application in the No 6 CoalMine in Pingdingshan mining area show that the grey smoothing model has higher accuracy than that of GM 1, 1)inpredicting the variable sequence with strong fluctuation. The research provides antific method for predictingmine gas emissionKey words: mine gas emission grey system smoothing prediction i grey smoothing modelCLC number: TD712.5 Document code: A Article ID: 1006-1266( 2003 01-0076-03Introductionrock surrounding, coal thickness, coal structureFor a long time, mine gas has been the impor- tion and so on. Moreover we cannot neglect the intant factor that threatens mine safety in China. Gasflethods and man-made facaccidents not only result in the lass of the nation and In a word mine gas emission is a synthesizing ettectles life and property but also seriously affect of every kind of factor. In addition because theproduction of coal. Scientific prediction ofneeded parameters to models with a certain causalitygas emission is the key to establishing the precau- are not easy to obtain in this paper the model basedtionary and control measures of gas to insure the on the grey system theory is used to predict thesafe production of coal. Many scholars have madetrend of mine gas emission and te then smoothinglarge quantity work and brought up some gas predicprediction technique is adopted to revise those er-tion methods such as the mine statistical method rors. At last a grey smoothing model for predictingbased on coal seam mining depthfmine gas emission is presented and used to predictguishing source prediction measure i which predicts gas emission in No. 6 Coal Mine in Pingdingshanmine gas emission from gas content 4, the gas geol. mining area. The result of application in the sixthmine is satisfactorogy mathematics method and so on. All the researches have greatly promoted the development of 2 GM 1 ,1 )Grey System Modelscientific prediction of mine gas emission. HoweverThe grey system theory has been developed forthere are many complex geological factors that influence in distribution occurrence movement and emission coal seams gas such as geology structure叫YH中国煤化工 ght up and widely apCNMHG economy biology,aReceived date : 2002-04-01Foundation item: National Natural Science Foundation of China( No, 40172059)Biography: PAN Jie-nard 1972), male, from Anhui Province, lecturer engage in the research on geology engineering and associated re万芳数据Pan Jie-nan et alGrey Smoothing Model for Predicting Mine Gas Emissiongriculture, control and so orf5-7. The GM( 1, 1) ment of the former. In this paper, from the practigrey system model as a one-rank and one-variable cal situation was the first power exponential smootdifferential equation model is able to describe the ing model model adopted to predict the trend of dainterior character and the developing trend of discrete data serials. It is unlike the regression analyThe first power exponential smoothing modelsis which needs accurate statistical distribution expressed by the folloand the determined prediction that needs many pa(1-a)1(4)rameters.So the GM( 1,1)model can be used to where s 1) is the first power exponential smoothingrightly analyze the trend of those data serials which mean value of the t period, x, is the data serial ofare influenced by many ambiguous factors. In this the t period and a is the weight coefficient whichpaper, the single serial modeling method of GM 1, ranges generally form 0.01 to 0. 30. The formula1)is adopted to predict the trend of mine gas emis-(4) can be recursively processed as followssion and the mathematic modef 8]is established1-a0(t)￥t=12灬,,n) is supposed to theax2+(1-aIax-1+(1-a)(11]=original data serials of time series t, so the gm( 1ax2+a(1-a)x1-1+(1-a)s12=1 )model is expressed by the following formulas()={x(1)x(0(2)x(0(n)}ax1+a(1-a)x2-1+(1-a)x1-2+d(1-ayso'x(1(i)=∑(0;;=12…,m),(1)It is obvious that all the data z, are made use ofx1)={x1(1)x(1(2)灬n(1(n)}when we compute S 1). But each x, has a differentdx(1)D), i.e., short-term data have bigger(1)=L(2)weight coefficient while the weight coefficient ofwhere a and u are undetermined parameters. We forward data astringes by 1-a geometric progres-can use the least squares theory to resolve a and uSIonthen according to the initial condition the predictiveWhen there are enough data( more than 50)toformula can be expressed bypredict the initial value s 1) may be assigned as xIx((t+1)=[x0(1)-u/aI1-eor(3)4 The Prediction Example of Mine Gas Emission4. 1 Geological conditions in the mining area3 The Smoothing Prediction TechniqueNo 6 Coal Mine in Pingdingshan mining areaThe smoothing prediction technique is a method lies in the south west limb of Likou syncline. Theto predict time serial data and can be divided mto spreading direction of main geological structure linesthree levels. When data have not some persistent in- in this mining area is approximately parallel to thecreasing or decreasing trend and make a random direction of the Likou synclinal axis. The main geo-variation around some fixed value, we can adopt the logical structures contain Likou syncline in the eastfirst power smoothing prediction model to predict ern part Guodishan fault and Jiulishan fault in thethe trend of data. When data have some persistent western part and Haotang syncline in the middlelinear increasing or decreasing trend we can use the part in this mining area. The structural condition isquadratic smoothing prediction model to predict the relative complex and small structures are very intentrend. And we can make use of the cubic smoothing sive中国煤化工 t districts,minprediction model to predict the trend when data have ingCNMHGngth in the process ofsome persistent curved increasing or decreasing mining D and E coal groups in the mine, mine gas e-trend. The smoothing prediction technique contains mission has a rather large variance. If we use thethe moving average method and the exponential conventional methods to predict mine gas emissionmoothing万布教提, and thelatter is the improve-many parameters are not easy to obtain. So the greyJournal of China University of mining technologyVol 13 No. 1smoothing model is adopted to predict mine gas e3)Testing the model. We can test the greyMiSsiOsmoothing model by using the second errors, the av4.2 Grey Smoothing prediction of mine gaserage value of absolute values of relative errors bei5.98%, which shows that the established greyWe take No 6 Coal Mine in Pinadingshan min- smoothing model has a good accuracy. The greying area as an example and use the model mentioned smoothing prediction curve is more accurate than theabove to predict the trend of its gas emission from grey prediction curve( Fig. 1)1979 to 1991 And the predicting steps are as follows1)Establishing the GM( 1 1 model and predicting the general developing trend of the serialsediction valueBased on the formulas from(1)to(4), we can es- 8B2nothing prediction valuetablish the Gm 1 1)model by using original actual19781980198219841986198819901992ly-measuring mine gas emission serials xo(t I t1 2 p. ,n )and figure out the first fitted values andFig. 1 Grey smoothing prediction of minetheir relative errorsgas emission for the No 6 Coal Mine2 Figuring out the second fitted values. After5 Conclusionshaving acquired the trend of variance serials by usingGM 1,1), we can revise their relative errors and1) By combining the grey system theory withobtain the second fitted values by using the first the first power exponential smoothing predictionpower exponential smoothing prediction technique, we establish the grey smoothing model predictingthen figure out their relative errors. The results are mine gas emission. The newly established combinashown in table 1torial model has the advantages of the grey systemTable 1 Grey smoothing prediction of minemodel, and the first power exponential smoothinggas emission for the No 6 Mineprediction model. Though there are not many variGM 1 1)mdel GM 1 I )smoothing modelance serials the model is easily established to predictActualthe general trend and adapted to the variable seFittedRelativeRelativeerror/quence with strong fluctuation. The combined modelhas higher accuracy than that of gm 1 ,119804.394,6619814,704.6274.602,12 )Based on the grey system therory the new4,571.6ly-combined model makes use of the advantage of the444smoothing prediction technique and widens the ap4.488.39864.064.3910.33) By using the combined model, the predict19874.224.353.14.39ing accuracy of grey model has been improved and19884.384.301.84.370.219904.514.22444the rsearch nuovides a new scientific method to pre-dict mine gas emissionNote: Unit of actual value, fitted values is m t d)H中国煤化工( Transferred to page 8)CNMHG