CN111859288B - Goaf spontaneous combustion risk prediction method - Google Patents
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- 238000002485 combustion reaction Methods 0.000 title claims abstract description 82
- 230000002269 spontaneous effect Effects 0.000 title claims abstract description 82
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000011156 evaluation Methods 0.000 claims abstract description 66
- 239000003245 coal Substances 0.000 claims abstract description 33
- 239000011159 matrix material Substances 0.000 claims description 68
- 238000012360 testing method Methods 0.000 claims description 11
- 230000008878 coupling Effects 0.000 claims description 8
- 238000010168 coupling process Methods 0.000 claims description 8
- 238000005859 coupling reaction Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 6
- 230000002087 whitening effect Effects 0.000 claims description 6
- 239000011435 rock Substances 0.000 claims description 4
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 3
- 238000009933 burial Methods 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 238000013278 delphi method Methods 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 239000001301 oxygen Substances 0.000 claims description 3
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 2
- 230000003449 preventive effect Effects 0.000 abstract description 2
- 230000008569 process Effects 0.000 abstract description 2
- 238000011002 quantification Methods 0.000 abstract description 2
- 238000011835 investigation Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 238000013210 evaluation model Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 239000000779 smoke Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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Abstract
The invention relates to the technical field of coal mine safety engineering, in particular to a goaf spontaneous combustion risk prediction method, which is characterized in that according to a fuzzy statistics principle, an AHP-entropy combined weight method is improved to simulate a human decision thinking process, and expert scoring evaluation quantification is carried out on uncertainty multi-factors causing spontaneous combustion risk of coal in a working face goaf, so that a numerical value capable of describing spontaneous combustion risk of the goaf is obtained, and further, the spontaneous combustion risk of the goaf is objectively judged, so that scientific and reasonable goaf spontaneous combustion preventive measures are adopted. According to the method, the spontaneous combustion risk of the goaf is researched by using a fuzzy mathematical prediction model, the importance of each index is judged by using an improved combined weighting method, the weight of each index is calculated, and the spontaneous combustion risk of the goaf is comprehensively evaluated by combining a gray theory method, so that the weight ranking of each factor of the spontaneous combustion risk level of the goaf can be rapidly and accurately performed, and the estimated problem of objectively evaluating the spontaneous combustion risk by using less information can be solved.
Description
Technical Field
The invention relates to the technical field of coal mine safety engineering, in particular to a goaf spontaneous combustion risk prediction method.
Background
Spontaneous combustion in goaf is an internal factor fire influenced by multiple factors, and due to the fact that mining depth is increased in recent years, surrounding rock temperature is increased and other factors influence, the danger of spontaneous combustion in goaf is more and more serious, spontaneous combustion in goaf is usually caused by deep coal, only smoke is generated without naked flame, and the position of a fire source is difficult to determine. The spontaneous combustion fire of the goaf not only causes the waste of a large number of coal resources and the damage of material equipment, but also often accompanies the secondary damage of dust and gas explosion, the spontaneous combustion of the coal of the goaf at present causes the fire accounting for about 90% of the total fire, 46% -49% of key coal mines in China have spontaneous combustion dangers of the coal of the goaf, so that the prediction and evaluation of whether the goaf is influenced by the spontaneous combustion dangers of the coal is a urgent task, how to efficiently predict the spontaneous combustion dangers of the goaf is already the working key and the difficulty of scientific researchers, and the research on the spontaneous combustion dangers of the goaf has important significance.
Since spontaneous combustion fire in a goaf of a coal mine is a dynamic complex system with burstiness and uncertainty, the probability of occurrence of the spontaneous combustion fire is difficult to evaluate by linear change. When the system is evaluated, the primary and secondary relations of different factors need to be comprehensively considered, and the weight coefficient of the accident occurrence is determined according to the importance ranking of the factors. The existing research has important significance for prediction and prevention of spontaneous combustion danger of goaf, but the built model has the problems of difficult weight determination, incomplete index factor consideration, ambiguous primary and secondary relationship of influencing factors and the like in actual operation.
Disclosure of Invention
In order to solve the problems, the invention provides a goaf spontaneous combustion risk prediction method, which is characterized in that according to a fuzzy statistics principle, an AHP-entropy combined weight method is improved to simulate a human decision thinking process, and expert scoring evaluation quantification is carried out on uncertainty multi-factors causing spontaneous combustion risk of coal in a working face goaf, so that a numerical value capable of describing spontaneous combustion risk of the goaf is obtained, and further, the spontaneous combustion risk of the goaf is objectively judged, so that scientific and reasonable goaf spontaneous combustion preventive measures are adopted.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a goaf spontaneous combustion risk prediction method comprises the following steps,
step 1: determining an evaluation index and determining an evaluation index weight: taking the oxygen uptake, CO temperature rise rate, coal sample ignition temperature difference, air leakage rate, coal layer thickness, coal layer inclination angle, air leakage duration, air leakage intensity, coal loss thickness, surrounding rock temperature and coal layer burial depth of the test point spontaneous combustion dangerous single index coal as evaluation indexes, and determining each index weight of the goaf spontaneous combustion danger by utilizing an improved AHP-entropy combination weight-gray theory method;
step 2: establishing a hierarchical analysis model for spontaneous combustion risk evaluation of the goaf: analyzing the relation and influence of each basic element in the goaf spontaneous combustion risk evaluation system, and dividing each index into an index layer, a criterion layer and a target layer three-level hierarchical structure; comparing the importance of each layer of index factors relative to a certain criterion of the previous layer according to a pairwise comparison scale method to construct a judgment matrix A= (a) ij ) n×n ：
Wherein a is ij Representing the ratio of index i to index j importance;
determining index weight by using an improved AHP weighting method, solving n times of square roots for each row of element products of the matrix, and solving a weight formula by using a geometric average method;
β＝{β 1 ，β 2 …β n } T
wherein: n is the index number;
representing the product of each row of elements of the matrix; beta i Representing the i-th index weight; beta is an index weight vector;
normalization processing
w A ＝{w A1 ，w A2 …w An } T To approximate the normalized eigenvector, where w Ai The weight corresponding to the fourth index factor is subjective weight, and
step 3: consistency test of the judgment matrix;
step 4: constructing decision matrix B 0 : according to the principle of qualitative and quantitative combination, evaluating m samples of spontaneous combustion risk levels, and an original data matrix B= (B) of n spontaneous combustion risk level evaluation indexes ij ) m×n Constructing decision matrix B by dimensionless and standardized processing 0 ＝(r ij ) m×n ：
Wherein b is ij Is an initial value; r is (r) ij Is a standardized index; b j max ，b j min Respectively representing the maximum value and the minimum value in index values of different samples under the index j;
step 5: determining an evaluation index entropy value: in order to avoid zero index feature specific gravity value, ensure that the evaluation index entropy has mathematical meaning, and r is ij ·ln r ij The influence on the index entropy is controlled in a reasonable range, and then the evaluation index entropy value is determined:
wherein: x is x ij Is a corrected evaluation index; psi is a correction coefficient; e, e j The entropy value is the j index entropy value; m is the number of samples;
step 6: calculating the information weight of the j-th index:
w E ＝(w E1 ，w E2 …w En )
wherein: w (w) Ej The weight corresponding to the j index in the entropy method is the objective weight, and
step 7: obtaining a comprehensive weight vector: coupling subjective weight and objective weight of each index factor, namely:
Q＝(q 1 ，q 2 …q n )
wherein: q i Coupling weight for the ith index factor; q is a comprehensive weight vector;
step 8: establishing a comprehensive evaluation index system: screening all indexes by combining a Delphi method to construct a 3-level hierarchical structure comprehensive evaluation index system;
step 9: establishing a comment matrix M;
step 10: according to a combination weighting principle, determining the weight of each index, and constructing a judgment matrix R based on an improved AHP-entropy combination weight-gray theory method;
step 11: according to the comprehensive weight vector Q, the judgment matrix R and the judgment matrix M, the spontaneous combustion risk evaluation result of the goaf can be obtained, namely:
L＝Q·R·M
wherein: l is an evaluation result; q is a comprehensive weight vector coupling subjective weight and objective weight; r is a comment matrix, and M is a comment matrix.
Preferably, in step 3, consistency test is performed on the judgment matrix to determine whether the weight distribution of each layer is reasonable;
wherein lambda is max Judging the maximum eigenvalue of the matrix; CI is a consistency index; IR is a random consistency index; CR is a consistency ratio.
Preferably, for the CR test: when CR is less than 0.1, the constructed judgment matrix meets the consistency requirement, otherwise, the judgment matrix needs to be corrected.
Preferably, in step 9, the spontaneous combustion risk level of the goaf is established, and the spontaneous combustion risk of the goaf is divided into a plurality of levels of comment matrixes M according to the safety production characteristics of the coal mine.
Preferably, the spontaneous combustion risk of the goaf is divided into a multi-level arithmetic progression comment matrix M.
Preferably, the goaf spontaneous combustion risk is classified into 5 grades of comment matrix M.
Preferably, in step 10, a whitening weight function of multiple evaluation levels of spontaneous combustion risk of the goaf is constructed according to a gray system theory;
evaluation index U ij Having a plurality of scores d ijt Indicating the t-th pair index U ij Giving a score, substituting the score into a whitening weight function, f k (d ijt ) Represents the t expert pair index U in the k-th gray ij And then sum to obtain U ij The kth gray evaluation coefficient X of (2) ijk Further obtain the total gray evaluation coefficient X of each index ij The method comprises the following steps:
X ijk and X is ij The ratio of the two is used to obtain an index U ij Belongs to the kth gray evaluation weight vector eta ijk Thereby obtaining the second level index U ij Gray evaluation weight vector η ij Comprehensive weight W obtained by combining improved AHP method and entropy value method ij A gray weight judging matrix R of each index is obtained, and a first-level index U is calculated according to the gray weight judging matrix R i Is the evaluation matrix of (1), namely:
η ij ＝(η ij1 ，η ij2 …η ijn )
to obtain a judgment matrix R.
The beneficial effects of using the invention are as follows:
according to the method, the spontaneous combustion risk of the goaf is researched by using a fuzzy mathematical prediction model, the importance of each index is judged by using an improved combined weighting method, the weight of each index is calculated, and the spontaneous combustion risk of the goaf is comprehensively evaluated by combining a gray theory method, so that the weight ranking of each factor of the spontaneous combustion risk level of the goaf can be rapidly and accurately performed, and the estimated problem of objectively evaluating the spontaneous combustion risk by using less information can be solved.
Drawings
FIG. 1 is a goaf spontaneous combustion risk prediction evaluation system hierarchical model;
fig. 2 is a modified AHP-entropy combination weight-gray theory method evaluation model flow.
Detailed Description
In order to make the objects, technical solutions and advantages of the present technical solution more apparent, the present technical solution is further described in detail below in conjunction with the specific embodiments. It should be understood that the description is only illustrative and is not intended to limit the scope of the present technical solution.
In combination with the summary of the invention, as shown in fig. 1, the goaf spontaneous combustion risk prediction method provided in this embodiment includes the following steps:
step 1: taking the oxygen uptake, CO temperature rise rate, coal sample ignition temperature difference, air leakage rate, coal layer thickness, coal layer inclination angle, air leakage duration, air leakage intensity, coal loss thickness, surrounding rock temperature and coal layer burial depth of the test point spontaneous combustion dangerous single index coal as evaluation indexes, and determining each index weight of the goaf spontaneous combustion danger by utilizing an improved AHP-entropy combination weight-gray theory method;
step 2: and establishing a hierarchical analysis model for spontaneous combustion risk evaluation of the goaf. And analyzing the relation and influence of each basic element in the goaf spontaneous combustion risk evaluation system, and dividing each index into an index layer, a criterion layer and a target layer three-level hierarchical structure by combining expert opinion. Comparing the importance of each layer of index factors relative to a certain criterion of the previous layer according to a pairwise comparison scale method to construct a judgment matrix A= (a) ij ) n×n Wherein a is ij The ratio of index i to index j importance is represented.
Determining index weight by using an improved AHP weighting method, solving n times of square roots for each row of element products of the matrix, and solving a weight formula by using a geometric average method;
β＝{β 1 ，β 2 …β n } T
wherein: n is the index number;
representing the product of each row of elements of the matrix; beta i Representing the i-th index weight; beta is an index weight vector.
Normalization processing
Thereby obtaining w A ＝{w A1 ，w A2 …w An } T I.e. an approximation of the normalized feature vector, where w Ai Is the weight corresponding to the ith index factor, i.e. subjective weight, and
step 3: and (5) consistency test of the judgment matrix. The judgment matrix which is usually constructed is difficult to meet the requirement of complete consistency, a certain standard needs to be established, and when the judgment matrix meets the standard, the judgment matrix is regarded as having complete consistency, so that consistency test needs to be carried out on the judgment matrix to determine whether the weight distribution of each layer is reasonable or not.
Wherein lambda is max Judging the maximum eigenvalue of the matrix; CI is a consistency index; IR is a random consistency index, which can be determined by looking up a table. CR is a consistency ratio, and when CR is less than 0.1, the constructed judgment matrix is considered to meet the consistency requirement, otherwise, the judgment matrix needs to be corrected.
Step 4: constructing decision matrix B 0 Raw data matrix b= (B) of n spontaneous combustion risk level evaluation indexes of m samples for spontaneous combustion risk level evaluation according to qualitative and quantitative combination principle ij ) m×n Constructing decision matrix B by dimensionless and standardized processing 0 ＝(r ij ) m×n ：
Wherein b is ij Is an initial value; r is (r) ij Is a standardized index; b j max ，b j min Respectively represent the maximum value and the minimum value in the index values of different samples under the index j.
Step 5: determining an evaluation index entropy value, ensuring that the evaluation index entropy has mathematical significance in order to avoid zero index characteristic specific gravity value, and adding r ij ·ln r ij The influence on the index entropy is controlled in a reasonable range, and partial factors in the decision matrix need to be corrected, so that the evaluation index entropy value is determined:
wherein: x is x ij Is a corrected evaluation index; psi is a correction coefficient; e, e j The entropy value is the j index entropy value; m is the number of samples.
Step 6: calculating the information weight of the j-th index:
w E ＝(w E1 ，w E2 …w En )
wherein: w (w) Ej The weight corresponding to the j index in the entropy method is the objective weight, and
step 7: in order to ensure that subjective knowledge and objective investigation data of decision makers on spontaneous combustion influence factors of the goaf can truly reflect rules, subjective weights and objective weights of all index factors are coupled to obtain a comprehensive weight vector, and the comprehensive weight vector is marked as Q.
Q＝(q 1 ，q 2 …q n )
Wherein: q i Coupling weight for the ith index factor; q is the integrated weight vector.
Step 8: due to complexity and uncertainty of spontaneous combustion danger of a coal mine goaf, various indexes are screened by combining a Delphi method through field investigation and reference of related documents, and a 3-level hierarchical structure comprehensive evaluation index system is constructed, as shown in table 1.
Table 1 goaf spontaneous combustion risk evaluation index and expert scoring average value
Step 9: the spontaneous combustion danger grade of the goaf is established, the spontaneous combustion danger of the goaf is divided into 5 grades reasonably according to the safety production characteristics of the coal mine, and the goaf is respectively endowed with grade I (safe-basically impossible), grade II (safer-less likely), grade III (medium safe-likely), grade IV (less safe-more likely) and grade V (unsafe-highly likely) to form a comment matrix M, wherein the higher the index is, the smaller the spontaneous combustion danger of the goaf is caused by the index, and the smaller the spontaneous combustion probability of the goaf is caused by the index is.
Step 10: and constructing whitening weight functions of 5 evaluation grades of spontaneous combustion risks of the goaf according to a gray system theory.
Evaluation index U ij There are n experts giving scores, d ijt Representing the t expert pair index U ij Giving a score, substituting the score into a whitening weight function, f k (d ijt ) Represents the t expert pair index U in the k-th gray ij And then sum to obtain U ij The kth gray evaluation coefficient X of (2) ijk Further obtain the total gray evaluation coefficient X of each index ij 。
X ijk And X is ij The ratio of the two is used to obtain an index U ij Belongs to the kth gray evaluation weight vector eta ijk Thereby obtaining the second level index U ij Gray evaluation weight vector η ij Comprehensive weight W obtained by combining the improved AHP method and entropy value method ij A gray weight judging matrix R of each index is obtained, and a first-level index U is calculated according to the gray weight judging matrix R i Is a matrix of evaluation of (a).
η ij ＝(η ij1 ，η ij2 …η ijn )
Step 11: according to the combination weighting principle, determining the weight of each index, and constructing a judging matrix R based on an improved AHP-entropy combination weight-gray theory method to obtain the spontaneous combustion risk evaluation result of the goaf.
L＝Q·R·M
Wherein: l is an evaluation result; q is a comprehensive weight vector coupling subjective weight and objective weight; r is a comment matrix, and M is a comment matrix.
The foregoing is merely exemplary of the present invention, and those skilled in the art can make many variations in the specific embodiments and application scope according to the spirit of the present invention, as long as the variations do not depart from the spirit of the invention.
Claims (7)
1. A goaf spontaneous combustion risk prediction method is characterized by comprising the following steps: comprises the steps of,
step 1: determining an evaluation index and determining an evaluation index weight: taking the oxygen uptake, CO temperature rise rate, coal sample ignition temperature difference, air leakage rate, coal layer thickness, coal layer inclination angle, air leakage duration, air leakage intensity, coal loss thickness, surrounding rock temperature and coal layer burial depth of the test point spontaneous combustion dangerous single index coal as evaluation indexes, and determining each index weight of the goaf spontaneous combustion danger by utilizing an improved AHP-entropy combination weight-gray theory method;
step 2: establishing a hierarchical analysis model for spontaneous combustion risk evaluation of the goaf: analyzing the relation and influence of each basic element in the goaf spontaneous combustion risk evaluation system, and dividing each index into an index layer, a criterion layer and a target layer three-level hierarchical structure; comparing the importance of each layer of index factors relative to a certain criterion of the previous layer according to a pairwise comparison scale method to construct a judgment matrix A= (a) ij ) n×n ：
Wherein a is ij Representing the ratio of index i to index j importance;
determining index weight by using an improved AHP weighting method, solving n times of square roots for each row of element products of the matrix, and solving a weight formula by using a geometric average method;
β＝{β 1 ，β 2 …β n } T
wherein: n is the index number;
representing the product of each row of elements of the matrix; beta i Representing the i-th index weight; beta is an index weight vector;
normalization processing
w A ＝{w A1 ，w A2 …w An } T To approximate the normalized eigenvector, where w Ai Is the weight corresponding to the ith index factor, i.e. subjective weight, and
step 3: consistency test of the judgment matrix;
step 4: constructing decision matrix B 0 : according to the principle of qualitative and quantitative combination, evaluating m samples of spontaneous combustion risk levels, and an original data matrix B= (B) of n spontaneous combustion risk level evaluation indexes ij ) m×n Constructing decision matrix B by dimensionless and standardized processing 0 ＝(r ij ) m×n ：
Wherein b is ij Is an initial value; r is (r) ij Is a standardized index; b jmax ，b jmin Respectively representing the maximum value and the minimum value in index values of different samples under the index j;
step 5: determining an evaluation index entropy value: in order to avoid zero index feature specific gravity value, ensure that the evaluation index entropy has mathematical meaning, and r is ij ·lnr ij The influence on the index entropy is controlled in a reasonable range, and then the evaluation index entropy value is determined:
wherein: x is x ij Is a corrected evaluation index; psi is a correction coefficient; e, e j The entropy value is the j index entropy value; m is the number of samples;
step 6: calculating the information weight of the j-th index:
w E ＝(w E1 ，w E2 …w En )
wherein: w (w) Ej The weight corresponding to the j index in the entropy method is the objective weight, and
step 7: obtaining a comprehensive weight vector: coupling subjective weight and objective weight of each index factor, namely:
Q＝(q 1 ，q 2 …q n )
wherein: q i Coupling weight for the ith index factor; q is a comprehensive weight vector;
step 8: establishing a comprehensive evaluation index system: screening all indexes by combining a Delphi method to construct a 3-level hierarchical structure comprehensive evaluation index system;
step 9: establishing a comment matrix M;
step 10: according to a combination weighting principle, determining the weight of each index, and constructing a judgment matrix R based on an improved AHP-entropy combination weight-gray theory method;
step 11: according to the comprehensive weight vector Q, the judgment matrix R and the judgment matrix M, the spontaneous combustion risk evaluation result of the goaf can be obtained, namely:
L＝Q·R·M
wherein: l is an evaluation result; q is a comprehensive weight vector coupling subjective weight and objective weight; r is a comment matrix, and M is a comment matrix.
2. The goaf spontaneous combustion risk prediction method according to claim 1, wherein:
in step 3, consistency test is carried out on the judgment matrix to determine whether the weight distribution of each layer is reasonable or not;
wherein lambda is max Judging the maximum eigenvalue of the matrix; CI is a consistency index; IR is a random consistency index; CR is a consistency ratio.
3. The goaf spontaneous combustion risk prediction method according to claim 2, wherein:
for CR test: when CR is less than 0.1, the constructed judgment matrix meets the consistency requirement, otherwise, the judgment matrix needs to be corrected.
4. The goaf spontaneous combustion risk prediction method according to claim 1, wherein:
in step 9, the spontaneous combustion risk level of the goaf is established, and the spontaneous combustion risk of the goaf is divided into a multi-level comment matrix M according to the safety production characteristics of the coal mine.
5. The goaf spontaneous combustion risk prediction method according to claim 4, wherein: the spontaneous combustion danger of the goaf is divided into a multi-level arithmetic array comment matrix M.
6. The goaf spontaneous combustion risk prediction method according to claim 4, wherein: the spontaneous combustion risk of the goaf is classified into 5 grades of comment matrixes M.
7. The goaf spontaneous combustion risk prediction method according to claim 4, wherein:
in step 10, according to gray system theory, constructing whitening weight functions of multiple evaluation grades of spontaneous combustion risks of the goaf;
evaluation index U ij Having a plurality of scores d ijt Indicating the t-th pair index U ij Giving a score, substituting the score into a whitening weight function, f k (d ijt ) Represents the t expert pair index U in the k-th gray ij And then sum to obtain U ij The kth gray evaluation coefficient X of (2) ijk Further obtain the total gray evaluation coefficient X of each index ij The method comprises the following steps:
X ijk and X is ij The ratio of the two is used to obtain an index U ij Belongs to the kth gray evaluation weight vector eta ijk Thereby obtaining the second level index U ij Gray evaluation weight vector η ij Comprehensive weight w obtained by combining improved AHP method and entropy value method ij A gray weight judging matrix R of each index is obtained, and a first-level index U is calculated according to the gray weight judging matrix R i Is the evaluation matrix of (1), namely:
η ij ＝(η ij1 ，η ij2 …η ijn )
to obtain a judgment matrix R.
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