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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.

Gas emission forecast is the important basis for building new mines and new ventilating designs in mining panel and preventing and managing gas; it plays an important role in reducing gas explosion accidents and ensuring the safety in coal mine production [

Combining grey correlation theory with gas geology theory, the basic idea of grey-gas geological forecast method is to analyze the geological factors such as the depth of overlaying rocks, coal seams, the mud rocks, and the geological structure with the foundation of gas geology theory. Then the grey correlation method is used to make a quantitative description and comparison to analyze the dynamic relation between geology and development of gas emission. By this way, the similarities of curve shapes of gas emission and several influential factors are determined to estimate the correlation degree that the higher the correlation, the closer the curve, and vice versa. When determining the gas emission according to the correlation degree, theories factor is removed. This forecast method can either make a qualitative analysis of different influential factors on the gas emission or make a quantitative analysis of the relations between those factors, reducing workload by avoiding unnecessary calculation. Meanwhile, it can also figure out the regularities between those factors to build a forecast model of gas emission, improve the forecast accuracy, and provide reliable basis for managing gas emission [

The calculation procedures of grey-gas geologic method are as follows.

Take the amount of gas emission as dependent variables to compose matrix Y:

In the formulas,

In this formula,

Due to multiple factors affecting gas emission, the correlation degree is large, so this paper takes

Raw data series are

Raw data are irregular, random series with obvious swing. Accumulate

Build differential equation:

This model is GM (1, N) and is a differential equation with N Variables in one order.

In formula (

In the following formulas,

Finally, calculate the differential equation with N Variables in one order by

Residual test is to test model precision and it is a direct arithmetic checking [

The original sequence is

The simulated sequence is

The residual sequence is

The compared residual sequence is

The mean relative error is

The precision can be presented as

Given the value of

The mean and variance of the original sequence

The mean and variance of the residual

If

According to the above two testing model methods, the ratio of the mean simulation relative error and mean square error should be as small as possible, and the error probability should be as large as possible.

The coal face of the twelfth coal mine JI 15-17200 of Ping Mine is used as the experimental area to predict gas emission by computer programming.

^{3}/t, which is comparatively high. Despite little change of absolute gas emission quantity with the fluctuation of gas content, there still exists positive relevance: the absolute gas emission quantity will increase with the increase of gas content. Moreover, the relative gas emission quantity in coal face is more than gas content in coal seam, proving that the source of gas emission quantity is not just from current seam but also from surrounding rocks of coal seam.

Being a single coal seam of Ji 15, the coal face of 17200 has a whole occurrence and the fissure develops well with a partial dirt band. Influenced by the structure, the thickness of regional coal seam and dig angle changes greatly.

According to the geophysical analysis results and downhole data, there are three areas that the thickness of coal seam changes: the first area is 360m to 450m up the terminal mining line of intake airflow roadway, 215m to 300m of which is a coal seam thickness abnormal area; the second is 60m-75m down to the terminal mining line of intake airflow roadway which is a coal seam thinning abnormal area; then around the mining line of intake airflow roadway is a thin coal abnormal area [

According to the data of gas emission quantity, the coal seam thickness abnormal area was mined from May to July, 2012, the coal seam thinning abnormal area was mined from March to July, 2013, and the thinning coal seam area was mined from October to November, 2013. In thickness abnormal area, especially in thinning area, the gas emission quantity became great. Meanwhile, in thickness abnormal area, the stability of coal seam is weak and the variation coefficients of coal seam are great which may cause coal and gas outburst. So it is also important to manage and prevent outburst while managing the gas emission quantity.

According to the comprehensive geographic analysis, there emerges a normal fault (1#) in the intake airflow roadway of the coal face, 11m back of Jin 6; there emerges a thrust fault (6#) in return airway, 22m back of Jin 4; there emerge four normal faults (2#, 3#, 4#, 5#) with 2#, respectively, 330m being away from terminal mining line, 3# 270m away from terminal mining line, 4# away from terminal mining line and 5# away from terminal mining line. Those faults of coal face all belong to minor faults and distribute loosely, influencing little on gas emission quantity.

The coal face of 12th Ping Mine 17200 is taken for example to make grey relational analysis. The factor influencing the gas emission quantity is independent variable

According to the gas emission quantity data of coal face of 17200 after selection, the above grey relational analysis method is adapted to calculate the correlation of gas emission quantity to gas content, the thickness of overlying rock, the thickness of coal seam, the output of coal face, the process of mining, and the thickness of overlying mudstone. The result is shown in Table

The calculation results of correlation degree.

| | | | | |
---|---|---|---|---|---|

correlation degree | 0.70 | 0.75 | 0.73 | 0.65 | 0.69 |

According to the calculation result, the correlation of gas emission quantity to those influencing factor above 0.6 represents a strong relation. Those factors are main factors influencing the gas emission quantity of coal face and the thickness of overlying rock of coal seam is the dominant factor. Meanwhile, the gas content, the thickness of coal seam, the output of coal face, and the thickness of mudstone all play an important part to gas emission quantity which deserves necessary consideration when building gas emission quantity prediction model [

C language programming is adapted to realize the grey-gas geographic method to predict gas emission quantity.

Choose the factors to do the grey relation calculation to get the grey correlation degree of each influencing factor. After calculation, select the dominant factor according to the grey correlation degree. Then analyze the gas emission quantity of coal face of 12th mine 17200, followed by analysis of the thickness of coal seam, the thickness of overlying rock, the gas content, the average of daily output, and the thickness of mudstone, shown in Figure

The grey relation calculation.

The mathematical model is based on the result from the grey relational calculation as shown in Figure

Acquired prediction formula.

The residual test result by grey-gas geographic model and predicted result of gas emission quantity are shown in Figure

Model predicted results.

Based on the comparison between the predicting result of gas emission quantity and measured value shown in Table

Contrast and statistics of the predicted result of gas emission quantity in coal face.

Number | Gas content/m^{3}/t | The thickness of overlying rock/m | The thickness of coal seam/m | The average of daily output | The thickness of mudstone/m | Gas emission quantity predicted value/m^{3}/t | Gas emission quantity measured value/m^{3}/min |
---|---|---|---|---|---|---|---|

1 | 3.79 | 672.00 | 4.03 | 1320.00 | 8.80 | 7.26 | 7.58 |

2 | 4.74 | 670.00 | 4.05 | 1037.00 | 8.90 | 8.58 | 7.44 |

3 | 6.04 | 668.00 | 4.08 | 1182.00 | 9.00 | 9.09 | 8.51 |

4 | 6.04 | 666.00 | 4.10 | 1182.00 | 8.90 | 8.97 | 8.51 |

5 | 5.85 | 663.00 | 4.20 | 1000.00 | 8.40 | 8.85 | 8.42 |

6 | 6.30 | 664.00 | 4.32 | 1130.00 | 7.70 | 8.62 | 8.22 |

7 | 4.50 | 666.00 | 4.36 | 780.00 | 7.40 | 7.94 | 8.58 |

8 | 5.94 | 668.00 | 4.40 | 1201.00 | 7.00 | 8.12 | 7.97 |

9 | 4.90 | 670.00 | 4.41 | 805.00 | 6.60 | 8.33 | 7.58 |

10 | 5.95 | 674.00 | 4.42 | 1529.00 | 6.00 | 7.68 | 6.75 |

11 | 6.05 | 678.00 | 4.45 | 1476.00 | 5.50 | 8.01 | 7.39 |

12 | 6.36 | 680.00 | 4.42 | 1529.00 | 4.80 | 8.48 | 7.82 |

13 | 4.39 | 687.00 | 4.50 | 1052.00 | 4.00 | 8.09 | 7.55 |

14 | 8.29 | 695.00 | 4.40 | 1523.00 | 3.70 | 10.95 | 10.31 |

15 | 4.08 | 702.00 | 4.32 | 1474.00 | 3.30 | 8.20 | 7.90 |

The analysis of predicted results of gas emission quantity in coal face.

The data used to support the findings of this study are available from the corresponding author upon request.

The authors declare that there are no conflicts of interest regarding the publication of this paper.

This study is financially supported by the National Coal Field Engineering Research Center for Gas Geology and Gas Control, the Collaborative Innovation Center of Coal Safety Production of Henan Province, the Collaborative Innovation Center of the Central Plains Economic Zone CBM (Shale Gas) of Henan Province, the Key Science and Technology Program of Henan Province (152102210105), the Foundation of Henan Educational Committee (2010B440004), the Program for Innovative Research Team in University of Ministry of Education of China (IRT_16R22), and the project funded by China Postdoctoral Science Foundation (2017M622343), and this project is supported by Henan Postdoctoral Foundation.