基于改进灰色预测的瓦斯突出敏感指标分析

doi:10.16265/j.cnki.issn1003-3033.2022.11.0326

摘要/Abstract

摘要：

为理清煤与瓦斯突出敏感指标间的相互关系,首先采用灰色关联分析模型,结合钻孔瓦斯初速度法和钻屑指标法,选取钻孔瓦斯涌出初速度q_m为参考序列,钻屑瓦斯解析指标Δh₂、K₁与钻屑量S为比较序列,分析煤与瓦斯突出敏感指标关联性,判定影响煤与瓦斯突出事故的关键因素;其次引入缓冲弱化算子和自动寻优定权法,用以改进经典GM (1,1)模型,然后建模,并量化分析q_m、 Δh₂、 K₁、 S等4个敏感指标的相互关系;最后以山西某矿井工作面实测瓦斯数据为例,计算各敏感指标参数。结果表明:该矿井瓦斯突出敏感指标对突出危险影响的排序为Δh₂> S >K₁;且Δh₂、K₁、S与q_m间存在交叉关系;改进后的灰色预测模型小误差概率值从0.69增加至0.87,后验差比值从0.500 0降低至0.431 7,预测等级由及格提升至良好级别。

关键词: 瓦斯突出, 敏感指标, 灰色预测, 灰色关联分析, 钻屑瓦斯解析指标

Abstract:

In order to clarify the relationship between the sensitive indexes of coal and gas outburst, firstly, the grey relational model was adopted, combined with the initial velocity method of drilling gas and the drilling cutting index method, the initial gas emission velocity q_m of drilling gas was selected as the reference sequence, and the analysis index of drilling cuttings gas Δh₂, K₁ and the amount of drilling cuttings S were selected as the comparison sequences to carry out the correlation analysis of the sensitive indexes of coal and gas outburst and determine the key factors affecting the coal and gas outburst accidents. Then the classical Gray Prediction model (GM (1,1)) was improved by introducing buffer weakening operators and automatic optimization and weighting method. The model was built, and the correlations among the q_m, Δh₂, K₁ and S were quantitatively analyzed. Finally, each index parameter was calculated using the measured gas data from a coal mining face in Shanxi, China. Results show that in the mentioned coalmine, the order of influence of coal and gas outburst sensitive indexes on outburst risk is followed as Δh₂ > S > K₁. And there is a crossing relation between Δh₂ K₁, S and q_m. The small error probability value of the improved grey prediction model increased from 0.69 to 0.87, the ratio of the posterior error decreased from 0.500 0 to 0.431 7, and the prediction grade was improved from pass to good.

Key words: coal and gas outburst, sensitive indicators, gray prediction, gray correlation analysis, analysis index of drilling cuttings gas

鲁锦涛, 贾小榕, 郭昕曜. 基于改进灰色预测的瓦斯突出敏感指标分析[J]. 中国安全科学学报, 2022, 32(11): 74-81.

LU Jintao, JIA Xiaorong, GUO Xinyao. Analysis on sensitive indicators of gas outburst based on improved gray prediction method[J]. China Safety Science Journal, 2022, 32(11): 74-81.

图/表 7

表1

图1

表2

表3

归一化处理结果

序号	q_m/ (mL·s^-1)	K₁/(mL· g^-1·min^-1/2)	$Δ h 2$ / Pa	S/ (kg·m^-1)
1	0.368	1.000	1.000	0.000
2	1.000	0.538	0.750	0.964
3	0.000	0.692	0.000	0.571
4	1.000	0.846	1.000	1.000
5	0.526	0.308	0.500	0.464
6	0.053	0.615	0.250	0.107
7	0.579	0.000	1.000	0.250
8	0.000	0.077	0.000	0.071
9	0.474	0.769	0.750	0.179
10	0.053	0.692	0.500	0.357

表3

表4

参考序列qm的差序列

序号	$K 1 - q m$	$Δ h 2 - q m$	$S - q m$
1	0.632	0.632	0.368
2	0.170	0.382	0.596
3	0.324	0.368	0.203
4	0.478	0.632	0.632
5	0.061	0.132	0.096
6	0.247	0.118	0.261
7	0.368	0.632	0.118
8	0.291	0.368	0.297
9	0.401	0.382	0.190
10	0.324	0.132	0.011

表4

表5

煤与瓦斯突出各项指标灰色关联度

序号	$q m$	$γ 1$	$γ 2$	$γ 3$
1	1.00	0.60	0.70	0.64

表5

表6

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预测精度等级	p	q
优	>0.95	<0.35
较好	>0.80	<0.50
及格	>0.70	<0.65
不及格	≤0.70	≥0.65

序号	q_m/ (mL·s^-1)	K₁/ (mL·g^-1·min^-1/2))	Δh₂/ Pa	S/ (kg·m^-1)
1	6.00	0.26	80	3.0
2	7.20	0.20	70	5.7
3	5.30	0.22	40	4.6
4	7.20	0.24	80	5.8
5	6.30	0.17	60	4.3
6	5.40	0.21	50	3.3
7	6.40	0.13	80	3.7
8	5.30	0.14	40	3.2
9	6.20	0.23	70	3.5
10	5.40	0.22	60	4.0

类型	1	2	3	4	5	6	7	8	9	10
原始数据	6.00	7.20	5.30	7.20	6.30	5.40	6.40	5.30	6.20	5.40
原始数据优化	6.00	6.92	5.34	7.28	6.00	5.12	6.32	4.91	6.00	5.65
预测数据	5.90	6.94	5.46	7.32	5.94	5.16	6.28	4.77	5.91	5.82