CN116927856A - Sensor-based spontaneous combustion monitoring and management system for goaf of super-thick coal seam - Google Patents
Sensor-based spontaneous combustion monitoring and management system for goaf of super-thick coal seam Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 259
- 238000002485 combustion reaction Methods 0.000 title claims abstract description 226
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- 239000003245 coal Substances 0.000 title claims abstract description 178
- 230000007613 environmental effect Effects 0.000 claims description 30
- 239000007789 gas Substances 0.000 claims description 18
- 238000012545 processing Methods 0.000 claims description 16
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- 238000012502 risk assessment Methods 0.000 claims description 13
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 12
- 239000001301 oxygen Substances 0.000 claims description 12
- 229910052760 oxygen Inorganic materials 0.000 claims description 12
- 239000002817 coal dust Substances 0.000 claims description 11
- 230000006978 adaptation Effects 0.000 claims description 10
- 238000013459 approach Methods 0.000 claims description 9
- 230000000694 effects Effects 0.000 claims description 7
- 238000012216 screening Methods 0.000 claims description 3
- 230000008030 elimination Effects 0.000 description 3
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- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
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- E—FIXED CONSTRUCTIONS
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- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
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Abstract
The invention belongs to the technical field of spontaneous combustion supervision of a super-thick coal seam goaf, and particularly discloses a spontaneous combustion monitoring management system of the super-thick coal seam goaf based on a sensor.
Description
Technical Field
The invention belongs to the technical field of spontaneous combustion supervision of a super-thick coal seam goaf, and particularly relates to a sensor-based spontaneous combustion monitoring and management system of the super-thick coal seam goaf.
Background
In coal mining, it is sometimes necessary to mine multiple coal seams in order to increase coal yield and efficiency. This simultaneous mining approach may result in a large mining space being formed longitudinally across multiple coal seams, creating extra thick seam goaf. The extra-thick coal seam goaf is high in spontaneous combustion risk due to the fact that the thickness is large, the stacking time is long, and under the condition, in order to find out and master the spontaneous combustion risk in time, serious consequences caused by spontaneous combustion of the extra-thick coal seam goaf are avoided, and effective spontaneous combustion monitoring management is needed to be conducted on the extra-thick coal seam goaf.
However, in the prior art, the spontaneous combustion monitoring management of the goaf of the extra-thick coal seam is generally focused on the discovery of spontaneous combustion risks, namely, the spontaneous combustion risk analysis early warning is carried out through monitoring of the coal volatilization state and the environmental conditions of the goaf of the extra-thick coal seam, so that a manager can be timely reminded of processing, but the determination of the spontaneous combustion risk area and the spontaneous combustion environmental effect direction is ignored, the spontaneous combustion processing lacks targets and directions, the spontaneous combustion processing measures are not targeted, the adaptation degree is low, the processing effect is weakened to a certain extent, even invalid processing is caused, and the rapid and effective elimination of the spontaneous combustion risks is unfavorable.
In addition, as the spontaneous combustion of the goaf of the super-thick coal seam can generate certain danger, the prior art only considers spontaneous combustion per se when analyzing the spontaneous combustion danger of the goaf of the super-thick coal seam, but does not consider the danger generated by the goaf structure of the super-thick coal seam, a large number of gaps and holes are formed after the coal in the goaf is stripped, so that the geological structure of the goaf is damaged and weakened. The goaf stability is weakened, collapse is easy to occur, the oxidation speed of coal and the accumulation of coal dust and the diffusion of heat are difficult to increase when the goaf collapses, so that the risk of spontaneous combustion is increased, if the risk analysis is carried out by spontaneous combustion alone, the analysis is incomplete, the hidden danger of risk underestimation exists, and the scientific, reasonable and reliable data support is difficult to provide for subsequent spontaneous combustion risk rescue.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, an object of the present invention is to provide a sensor-based spontaneous combustion monitoring and management system for a goaf of an ultra-thick coal seam, which is used for solving the problems existing in the prior art.
The aim of the invention can be achieved by the following technical scheme: an ultra-thick coal seam goaf spontaneous combustion monitoring management system based on a sensor, comprising: and the goaf monitoring point layout module is used for laying monitoring points in the goaf of the ultra-thick coal seam.
And the monitoring point space state parameter monitoring module is used for installing monitoring terminals at all the laid monitoring points and carrying out space state parameter monitoring by the monitoring terminals, wherein the space state parameters comprise space coal quality parameters and space environment parameters.
And the deformation monitoring module is used for carrying out deformation monitoring on each laid monitoring point to obtain deformation indications corresponding to each monitoring point, and processing the deformation indications to obtain effective deformation indications corresponding to each horizon of the goaf.
And the spontaneous combustion risk analysis module is used for analyzing spontaneous combustion risk coefficients corresponding to all monitoring points of the super-thick coal seam goaf based on the space state parameters corresponding to all monitoring points monitored in real time.
And the spontaneous combustion early warning module is used for comparing the spontaneous combustion risk coefficient corresponding to each monitoring point of the super-thick coal seam goaf with the set spontaneous combustion allowed risk coefficient, and if the spontaneous combustion risk coefficient corresponding to a monitoring point of the super-thick coal seam goaf is greater than the spontaneous combustion allowed risk coefficient, spontaneous combustion early warning is carried out.
And the spontaneous combustion risk area dynamic demarcation module is used for identifying the risk monitoring points from a plurality of laid monitoring points in real time while carrying out spontaneous combustion early warning, and dynamically demarcating the spontaneous combustion risk area according to the risk monitoring points.
The cloud management library is used for storing minimum environmental acting force coefficients capable of generating spontaneous combustion under various coal volatilization degree coefficients, storing coal granularity and coal stacking density corresponding to various coal types and storing safe stress and safe sinking distances corresponding to various layers of the super-thick coal seam goaf.
And the risk force analysis module of the spontaneous combustion risk area is used for analyzing the risk force coefficient of the defined spontaneous combustion risk area based on the effective deformation indication corresponding to each layer of the goaf.
And the spontaneous combustion processing terminal is used for determining the corresponding environmental action direction of the spontaneous combustion risk area, so that the spontaneous combustion adaptation processing mode is selected, and simultaneously, spontaneous combustion rescue is carried out according to the risk coefficient of the spontaneous combustion risk area.
As a further innovation of the invention, the arrangement of monitoring points in the super-thick coal seam goaf specifically comprises the following steps: and counting the number of the existing horizons in the goaf space of the ultra-thick coal seam, uniformly distributing monitoring points on each horizon, and numbering the distributed monitoring points.
As a further innovation of the invention, the spatial coal quality parameters comprise gas concentration and coal dust content, and the spatial environment parameters comprise temperature, humidity, oxygen concentration and air flow rate.
As a further innovation of the invention, the deformation indicators comprise stress and sinking distances, wherein the specific acquisition process of the effective deformation indicators corresponding to each horizon of the goaf is as follows: and arranging monitoring points existing on each horizon of the goaf according to a set sequence.
And sequentially taking all monitoring points existing on all the layers of the goaf as main body monitoring points according to the arrangement sequence of the monitoring points, and further carrying out approach degree calculation on deformation indications of the main body monitoring points on all the layers and deformation indications of other monitoring points to obtain the approach degree of the deformation indications of all the monitoring points on all the layers of the goaf as the main body monitoring points.
And selecting the deformation indication corresponding to the monitoring point to which the maximum deformation indication approach belongs from the deformation indication approaches of each monitoring point serving as the main body monitoring point on each layer of the goaf as the effective deformation indication corresponding to each layer of the goaf.
As a further innovation of the invention, the analysis of the spontaneous combustion risk coefficient corresponding to the super-thick coal seam goaf specifically comprises the following steps: extracting space coal quality parameters from the space state parameters, and further importing the space coal quality parameters of each monitoring point into a formulaCalculating the coal volatility coefficient eta corresponding to each monitoring point i Where i denotes a monitoring point number, i=1, 2, &.. i 、p i Respectively expressed as the gas concentration and the coal dust content corresponding to the ith monitoring point, q 0 、p 0 Respectively expressed as a pre-configured safe gas concentration, safe coal dust content,>and the coal quality influence factors corresponding to the goaf of the ultra-thick coal seam are expressed.
And matching the coal volatilization degree coefficient corresponding to each monitoring point with the minimum environment acting force coefficient which is stored in the cloud management library and can generate spontaneous combustion under various coal volatilization degree coefficients, and screening the minimum environment acting force coefficient which can generate spontaneous combustion at each monitoring point.
Extracting space environment parameters from the space state parameters, and substituting the space environment parameters of each monitoring point into the expressionCalculating environmental acting force coefficient corresponding to each monitoring point>T i 、W i 、O i 、V i Respectively expressed as the temperature, humidity, oxygen concentration and air flow rate of the ith monitoring point, T 0 、W 0 、O 0 、V 0 Expressed as reference temperature, reference humidity, reference oxygen concentration, reference air flow rate, respectively, e expressed as self-containedBut constant.
Comparing the environmental acting force coefficient corresponding to each monitoring point with the minimum environmental acting force coefficient capable of generating spontaneous combustion of the corresponding monitoring point, calculating the spontaneous combustion risk coefficient phi corresponding to each monitoring point of the super-thick coal seam goaf, wherein the calculation expression is as followsIn->Denoted as the minimum ambient force coefficient at which the ith monitoring point is capable of producing autoignition.
As a further innovation of the invention, the concrete calculation mode of the coal quality influence factors corresponding to the super-thick coal seam goaf is as follows: and acquiring the coal type corresponding to the super-thick coal seam goaf, and further extracting the coal granularity and the coal bulk density corresponding to the coal type of the super-thick coal seam goaf from the cloud management warehouse.
Using expressionsCalculating coal quality influence factors corresponding to goafs of ultra-thick coal seam>Epsilon and rho are respectively expressed as the coal granularity and the coal bulk density corresponding to the coal type of the super-thick coal seam goaf, and epsilon 'and rho' are respectively expressed as the set reference coal granularity and the set reference coal bulk density.
As a further innovation of the method, the specific identification process of the risk monitoring points is that after spontaneous combustion early warning is carried out, spontaneous combustion risk coefficients corresponding to all monitoring points of the super-thick coal seam goaf are compared with set spontaneous combustion allowed risk coefficients in sequence according to set time intervals, and monitoring points larger than the allowed spontaneous combustion risk coefficients are selected from the spontaneous combustion allowed risk coefficients to serve as risk monitoring points corresponding to all moments.
As a further innovation of the present invention, the specific implementation manners of the dynamic demarcation of the spontaneous combustion risk area are as follows: and identifying risk monitoring points belonging to the same horizon from the risk monitoring points corresponding to each moment, and taking the identified horizon as a risk horizon.
And extracting a risk monitoring point distribution profile from a plurality of risk monitoring point distribution states corresponding to each risk horizon, and obtaining a risk monitoring point distribution area corresponding to each risk horizon.
And constructing all risk layers existing in each moment into spontaneous combustion risk areas corresponding to each moment.
As a further innovation of the present invention, the risk coefficient analysis of the delimited autoignition risk region includes the following procedures: and selecting the effective deformation indication corresponding to each risk layer in each moment from the effective deformation indications corresponding to each layer based on the risk layer related to the spontaneous combustion risk area corresponding to each moment.
Calculating the collapse hidden trouble delta of each risk layer at each moment by using the effective deformation indication corresponding to each risk layer at each moment t j, the calculation expression isWhere j is denoted as the number of risk horizons, j=1, 2,.. t j、l t j is respectively expressed as effective stress and effective sinking distance of the jth risk horizon in the t moment, f j Safety, l j The safety is respectively expressed as the safety stress and the safety sinking distance corresponding to the jth risk horizon, U is expressed as a set constant, and U>1。
And selecting the maximum spontaneous combustion risk coefficient from the spontaneous combustion risk coefficients corresponding to each risk monitoring point on each risk layer.
Calculating spontaneous combustion risk presentation indexes corresponding to all risk layers by combining the distribution areas of risk monitoring points corresponding to all risk layers and the maximum spontaneous combustion risk coefficientsS in j 、φ j Respectively expressed as the distribution area of risk monitoring points corresponding to the jth risk layer and the maximum spontaneous combustion risk coefficient, s j The table is expressed as the surface area, phi, corresponding to the j-th risk horizon 0 Expressed as a set allowable auto-ignition risk factor.
And comparing the spontaneous combustion risk presentation indexes corresponding to the risk layers, and selecting the risk layer corresponding to the maximum spontaneous combustion risk presentation index from the risk layers as the specific layer.
Substituting the collapse hidden danger degree and spontaneous combustion risk presentation index corresponding to the specific layer in each moment into the expressionAnd calculating a dangerous force coefficient corresponding to the spontaneous combustion risk area at each moment.
As a further innovation of the present invention, the environmental effect corresponding to the determination of the autoignition risk region is directed to a specific implementation manner: and comparing the space environment parameters corresponding to the risk monitoring points in the specific horizon corresponding to the spontaneous combustion risk areas at each moment with the reference space environment parameters, and calculating the deviation amount corresponding to the space environment parameters in the risk monitoring points in the specific horizon corresponding to each moment.
And comparing the deviation amounts corresponding to the space environment parameters in the risk monitoring points in the specific positions corresponding to the moments, and selecting the space environment parameter corresponding to the maximum deviation amount from the deviation amounts as the trend space environment parameter corresponding to the risk monitoring points in the specific positions corresponding to the moments.
Comparing trend space environment parameters corresponding to the risk monitoring points in the specific horizon at each moment, classifying the risk monitoring points corresponding to the same trend space environment parameters, counting the occupation ratio corresponding to the various trend space environment parameters at each moment, and pointing the trend space environment parameters corresponding to the maximum occupation ratio as the environment effect corresponding to the spontaneous combustion risk area at each moment.
Compared with the prior art, the invention has the following beneficial effects: 1. according to the method, the monitoring points are divided according to the horizon and the coal volatilization state and the environmental condition are monitored at each monitoring point, and the spontaneous combustion risk analysis of each monitoring point is carried out, so that the spontaneous combustion risk area is dynamically defined, the environmental action direction corresponding to the spontaneous combustion risk area is determined, a reliable target and a reliable direction are provided for spontaneous combustion treatment, the spontaneous combustion monitoring management function of the extremely thick coal seam goaf is greatly enriched, the pertinence and the adaptation degree of spontaneous combustion treatment measures are greatly improved, the treatment effect is better, invalid treatment is avoided, and the rapid and effective elimination of spontaneous combustion risks is facilitated.
2. According to the method, after the spontaneous combustion risk area in the super-thick coal seam goaf is determined, deformation monitoring is carried out on the spontaneous combustion risk area, so that the collapse hidden danger state of the spontaneous combustion risk area is obtained, and further, the dangerous force analysis is carried out by combining the spontaneous combustion risk presentation state and the collapse hidden danger state of the spontaneous combustion risk area, so that the comprehensive analysis of the danger of the super-thick coal seam goaf is realized, the analysis result is more accurate and reliable, the occurrence rate of underestimated danger is greatly reduced, scientific, reasonable and reliable data support can be provided for subsequent spontaneous combustion dangerous rescue, unnecessary casualties are avoided to a certain extent, and the guarantee strength of spontaneous combustion rescue safety is facilitated to be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the connection of the modules of the system of the present invention.
Fig. 2 is a schematic diagram of a risk monitoring point distribution profile corresponding to a risk horizon in the present invention.
Reference numerals: 1-risk horizon, 2-risk monitoring points, 3-risk monitoring point distribution profile.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a sensor-based extra-thick coal seam goaf spontaneous combustion monitoring management system, which comprises a goaf monitoring point layout module, a monitoring point space state parameter monitoring module, a coal seam goaf deformation monitoring module, a spontaneous combustion risk analysis module, a spontaneous combustion early warning module, a spontaneous combustion risk area dynamic demarcation module, a cloud management library, a spontaneous combustion risk area dangerous force analysis module and a spontaneous combustion processing terminal, wherein the goaf monitoring point layout module is respectively connected with the monitoring point space state parameter monitoring module and the coal seam goaf deformation monitoring module, the monitoring point space state parameter monitoring module is connected with the spontaneous combustion risk analysis module, the spontaneous combustion risk analysis module is respectively connected with the spontaneous combustion early warning module and the spontaneous combustion risk area dynamic demarcation module, the coal seam goaf deformation monitoring module and the spontaneous combustion risk analysis module are all connected with the spontaneous combustion risk area dangerous force analysis module, the spontaneous combustion risk analysis module and the spontaneous combustion risk area dangerous force analysis module are connected with the spontaneous combustion processing terminal, and the cloud management library is respectively connected with the spontaneous combustion risk analysis module and the spontaneous combustion risk area dangerous force analysis module.
The goaf monitoring point layout module is used for laying monitoring points in the super-thick coal seam goaf, and the concrete layout mode is as follows: and counting the number of the existing horizons in the goaf space of the ultra-thick coal seam, uniformly distributing monitoring points on each horizon, and numbering the distributed monitoring points.
It should be noted that, because the goaf of the super-thick coal seam is a three-dimensional structure formed by multiple layers and multiple holes, different plane positions of the goaf need to be considered when the monitoring points are arranged so as to ensure the monitoring of the whole space of the goaf, the invention ensures the comprehensiveness of coverage by uniformly arranging the monitoring points on each layer of the goaf when the goaf is arranged, and ensures the arrangement of the monitoring points to be more reasonable and effective.
It should be further noted that each layer of the goaf comprises a top plate and a bottom plate of the goaf.
The method is characterized in that the step of numbering the monitoring points refers to uniformly numbering all the monitoring points distributed in the goaf space of the ultra-thick coal seam.
The monitoring point space state parameter monitoring module is used for installing monitoring terminals at all the laid monitoring points, and space state parameter monitoring is carried out by the monitoring terminals in real time, wherein the space state parameters comprise space coal quality parameters and space environment parameters, the space coal quality parameters comprise gas concentration and coal dust content, and the space environment parameters comprise temperature, humidity, oxygen concentration and air flow rate.
It can be appreciated that the monitoring terminal mentioned in the above technical scheme is composed of various sensors, and by way of example, the monitoring terminal is composed of a gas detector, a particulate matter monitor, a temperature and humidity sensor, a gas sensor and a hot wire anemometer, wherein the gas detector is used for detecting gas concentration, the particulate matter monitor is used for detecting pulverized coal content, the temperature and humidity sensor is used for detecting temperature and humidity, the gas sensor is used for detecting oxygen concentration, and the hot wire anemometer is used for detecting air flow rate.
The deformation monitoring module is used for performing deformation monitoring on each laid monitoring point to obtain deformation indicators corresponding to each monitoring point, wherein the deformation indicators comprise stress and sinking distances, and effective deformation indicators corresponding to each horizon of the goaf are obtained through processing.
The stress in the deformation indication can be detected by a pressure sensor, the sinking distance can be obtained by setting a reference surface in the goaf, measuring the separation distance between each monitoring point and the reference surface by using an infrared range finder, and comparing the separation distance with the original separation distance between the layer to which each monitoring point belongs and the reference surface to obtain the sinking distance of each monitoring point.
Based on the scheme, the specific acquisition process of the effective deformation indication corresponding to each layer of the goaf is as follows: and arranging monitoring points existing on each horizon of the goaf according to a set sequence.
Sequentially taking all monitoring points existing on all layers of the goaf as main body monitoring points according to the arrangement sequence of the monitoring points, and further performing proximity calculation on deformation indications of the main body monitoring points on all layers and deformation indications of other monitoring points, wherein the calculation expression of the proximity is shownAndObtaining deformation indication proximity of each monitoring point on each horizon of the goaf as a main body monitoring point, wherein F Other monitoring points 、F Main body monitoring point The stress of the main body monitoring points is respectively expressed as the stress of other monitoring points, L Other monitoring points 、L Main body monitoring point Respectively expressed as the sinking distance of other monitoring points and the sinking distance of the main body monitoring point.
And selecting the deformation indication corresponding to the monitoring point to which the maximum deformation indication approach belongs from the deformation indication approaches of each monitoring point serving as the main body monitoring point on each layer of the goaf as the effective deformation indication corresponding to each layer of the goaf.
The spontaneous combustion risk analysis module is used for analyzing spontaneous combustion risk coefficients corresponding to all monitoring points of the super-thick coal seam goaf based on space state parameters corresponding to all monitoring points monitored in real time, and specifically comprises the following steps: extracting space coal quality parameters from the space state parameters, and further importing the space coal quality parameters of each monitoring point into a formulaCalculating the coal volatility coefficient eta corresponding to each monitoring point i Where i denotes a monitoring point number, i=1, 2, &.. i 、p i Respectively expressed as the gas concentration and the coal dust content corresponding to the ith monitoring point, q 0 、p 0 Respectively expressed as a pre-configured safe gas concentration, safe coal dust content,>the coal quality influence factor is expressed as the coal quality influence factor corresponding to the goaf of the ultra-thick coal seam, wherein the higher the gas concentration is, the higher the coal dust content is, the higher the coal quality influence factor is, and the higher the coal volatilization degree coefficient is.
Further, the specific calculation mode of the coal quality influence factors corresponding to the super-thick coal seam goaf is as follows: and acquiring the coal type corresponding to the super-thick coal seam goaf, and further extracting the coal granularity and the coal bulk density corresponding to the coal type of the super-thick coal seam goaf from the cloud management warehouse.
Using expressionsCalculating coal quality influence factors corresponding to goafs of ultra-thick coal seam>Epsilon and rho are respectively expressed as the coal granularity and the coal stacking density corresponding to the coal type of the super-thick coal seam goaf, epsilon 'and rho' are respectively expressed as the set reference coal granularity and the set reference coal stacking density, wherein the smaller the coal granularity is, the larger the coal stacking density is, and the larger the coal quality influence factor is.
It should be explained that different coal types have different coal quality characteristics, and different coal quality affects the spontaneous combustion, wherein the particle size of the coal has a certain effect on the spontaneous combustion, smaller coal particles are easier to spontaneous combustion because of larger specific surface area and easier loss of moisture, and the bulk density of the coal reflects the porosity and air permeability of the coal to a certain extent. Higher bulk densities may limit gas flow inside the coal seam, resulting in gas accumulation, increasing the risk of spontaneous combustion.
And matching the coal volatilization degree coefficient corresponding to each monitoring point with the minimum environment acting force coefficient which is stored in the cloud management library and can generate spontaneous combustion under various coal volatilization degree coefficients, and screening the minimum environment acting force coefficient which can generate spontaneous combustion at each monitoring point.
Extracting space environment parameters from the space state parameters, and substituting the space environment parameters of each monitoring point into the expressionCalculating environmental acting force coefficient corresponding to each monitoring point>T i 、W i 、O i 、V i Respectively expressed as the temperature, humidity, oxygen concentration and air flow rate of the ith monitoring point, T 0 、W 0 、O 0 、V 0 Respectively expressed as reference temperature, reference humidity and reference oxygenThe air concentration and the reference air flow rate, e, are expressed as natural constants, wherein the higher the temperature of a certain monitoring point is, the higher the oxygen concentration is, the lower the humidity is, the closer the air flow rate is to the reference air flow rate, and the larger the environmental force coefficient is, the larger the environmental force is indicated.
Comparing the environmental acting force coefficient corresponding to each monitoring point with the minimum environmental acting force coefficient capable of generating spontaneous combustion of the corresponding monitoring point, calculating the spontaneous combustion risk coefficient phi corresponding to each monitoring point of the super-thick coal seam goaf, wherein the calculation expression is as followsIn->Denoted as the minimum ambient force coefficient at which the ith monitoring point is capable of producing autoignition.
The spontaneous combustion early warning module is used for comparing spontaneous combustion risk coefficients corresponding to all monitoring points of the super-thick coal seam goaf with the set spontaneous combustion risk coefficients allowed, and if the spontaneous combustion risk coefficient corresponding to a monitoring point of the super-thick coal seam goaf is greater than the spontaneous combustion risk coefficients allowed, spontaneous combustion early warning is carried out, so that relevant management staff of the super-thick coal seam goaf can know the spontaneous combustion risk coefficients in time.
The automatic spontaneous combustion risk area dynamic demarcation module is used for identifying risk monitoring points in real time from a plurality of laid monitoring points in real time while carrying out automatic spontaneous combustion early warning, and dynamically demarcating the automatic spontaneous combustion risk area according to the risk monitoring points.
In one embodiment of the scheme, the specific identification process of the risk monitoring points is as follows, after spontaneous combustion early warning is carried out, the spontaneous combustion risk coefficients corresponding to the monitoring points of the super-thick coal seam goaf are compared with the set spontaneous combustion allowable risk coefficients in sequence according to set time intervals, and monitoring points larger than the spontaneous combustion allowable risk coefficients are selected from the spontaneous combustion allowable risk coefficients to serve as risk monitoring points corresponding to all moments.
In a further embodiment, the dynamic delineation of the auto-ignition risk zone is as follows: and identifying risk monitoring points belonging to the same horizon from the risk monitoring points corresponding to each moment, and taking the identified horizon as a risk horizon.
And extracting a risk monitoring point distribution profile from a plurality of risk monitoring point distribution states corresponding to each risk horizon, wherein the risk monitoring point distribution profile is shown in fig. 2, and the risk monitoring point distribution area corresponding to each risk horizon is obtained.
And constructing all risk layers existing in each moment into spontaneous combustion risk areas corresponding to each moment.
The cloud management library is used for storing minimum environmental acting force coefficients capable of generating spontaneous combustion under various coal volatilization degree coefficients, storing coal granularity and coal stacking density corresponding to various coal types, and storing safe stress and safe sinking distances corresponding to various layers of the super-thick coal seam goaf.
The spontaneous combustion risk area dangerous force analysis module is used for analyzing dangerous force coefficients of the defined spontaneous combustion risk areas based on effective deformation indexes corresponding to all layers of the goaf, and comprises the following steps: and selecting the effective deformation indication corresponding to each risk layer in each moment from the effective deformation indications corresponding to each layer based on the risk layer related to the spontaneous combustion risk area corresponding to each moment.
Calculating the collapse hidden trouble delta of each risk layer at each moment by using the effective deformation indication corresponding to each risk layer at each moment t j, the calculation expression isWhere j is denoted as the number of risk horizons, j=1, 2,.. t j、l t j is respectively expressed as effective stress and effective sinking distance of the jth risk horizon in the t moment, f j Safety, l j The safety is respectively expressed as the safety stress and the safety sinking distance corresponding to the jth risk horizon, U is expressed as a set constant, and U>1。
And selecting the maximum spontaneous combustion risk coefficient from the spontaneous combustion risk coefficients corresponding to each risk monitoring point on each risk layer.
Calculating spontaneous combustion risk presentation indexes corresponding to all risk layers by combining the distribution areas of risk monitoring points corresponding to all risk layers and the maximum spontaneous combustion risk coefficientsS in j 、φ j Respectively expressed as the distribution area of risk monitoring points corresponding to the jth risk layer and the maximum spontaneous combustion risk coefficient, s j The table is expressed as the surface area, phi, corresponding to the j-th risk horizon 0 Expressed as a set allowable auto-ignition risk factor.
And comparing the spontaneous combustion risk presentation indexes corresponding to the risk layers, and selecting the risk layer corresponding to the maximum spontaneous combustion risk presentation index from the risk layers as the specific layer.
Substituting the collapse hidden danger degree and spontaneous combustion risk presentation index corresponding to the specific layer in each moment into the expressionAnd calculating a dangerous force coefficient corresponding to the spontaneous combustion risk area at each moment.
According to the method, after the spontaneous combustion risk area in the super-thick coal seam goaf is determined, deformation monitoring is carried out on the spontaneous combustion risk area, so that the collapse hidden danger state of the spontaneous combustion risk area is obtained, and further, the dangerous force analysis is carried out by combining the spontaneous combustion risk presentation state and the collapse hidden danger state of the spontaneous combustion risk area, so that the comprehensive analysis of the danger of the super-thick coal seam goaf is realized, the analysis result is more accurate and reliable, the occurrence rate of underestimated danger is greatly reduced, scientific, reasonable and reliable data support can be provided for subsequent spontaneous combustion dangerous rescue, unnecessary casualties are avoided to a certain extent, and the guarantee strength of spontaneous combustion rescue safety is facilitated to be improved.
The spontaneous combustion processing terminal is used for determining the corresponding environmental action direction of the spontaneous combustion risk area, so that the spontaneous combustion adaptation processing mode is selected, and simultaneously spontaneous combustion rescue is carried out according to the risk coefficient of the spontaneous combustion risk area.
In a preferred embodiment of the above solution, determining the environmental effect corresponding to the autoignition risk region is directed to a specific implementation manner: comparing the space environment parameters corresponding to the risk monitoring points in the specific layer corresponding to the spontaneous combustion risk areas at each moment with the reference space environment parameters, wherein the reference space environment parameters are reference temperature, reference humidity and reference oxygenCalculating the deviation amount corresponding to each space environment parameter in each risk monitoring point in each specific horizon at each moment by gas concentration and reference air flow velocity, wherein Air flow rate deviation amount= |air flow rate-reference air flow rate|.
And comparing the deviation amounts corresponding to the space environment parameters in the risk monitoring points in the specific positions corresponding to the moments, and selecting the space environment parameter corresponding to the maximum deviation amount from the deviation amounts as the trend space environment parameter corresponding to the risk monitoring points in the specific positions corresponding to the moments.
Comparing trend space environment parameters corresponding to the risk monitoring points in the specific horizon at each moment, classifying the risk monitoring points corresponding to the same trend space environment parameters, counting the occupation ratio corresponding to the various trend space environment parameters at each moment, and pointing the trend space environment parameters corresponding to the maximum occupation ratio as the environment effect corresponding to the spontaneous combustion risk area at each moment.
Specifically, when the environmental effect corresponding to the spontaneous combustion risk area is directed to the oxygen concentration, the spontaneous combustion adaptation treatment mode is to control the oxygen concentration, when the environmental effect corresponding to the spontaneous combustion risk area is directed to the temperature, the spontaneous combustion adaptation treatment mode is to cool, when the environmental effect corresponding to the spontaneous combustion risk area is directed to the humidity, the spontaneous combustion adaptation treatment mode is to take a humidification measure, such as spray water mist, and when the environmental effect corresponding to the spontaneous combustion risk area is directed to the air flow rate, the spontaneous combustion adaptation treatment mode is to control ventilation.
And further, carrying out spontaneous combustion rescue according to the risk coefficient of the spontaneous combustion risk area, wherein the specific operation mode is to compare the risk coefficient of the spontaneous combustion risk area with a preset threshold value, and if the risk coefficient of the spontaneous combustion risk area is greater than the preset threshold value, prohibiting a rescue worker from approaching the spontaneous combustion risk area.
According to the method, the monitoring points are divided according to the horizon and the coal volatilization state and the environmental condition are monitored at each monitoring point, and the spontaneous combustion risk analysis of each monitoring point is carried out, so that the spontaneous combustion risk area is dynamically defined, the environmental action direction corresponding to the spontaneous combustion risk area is determined, a reliable target and a reliable direction are provided for spontaneous combustion treatment, the spontaneous combustion monitoring management function of the extremely thick coal seam goaf is greatly enriched, the pertinence and the adaptation degree of spontaneous combustion treatment measures are greatly improved, the treatment effect is better, invalid treatment is avoided, and the rapid and effective elimination of spontaneous combustion risks is facilitated.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (10)
1. Sensor-based spontaneous combustion monitoring and management system for a goaf of an ultra-thick coal seam, which is characterized by comprising the following components:
the goaf monitoring point layout module is used for laying monitoring points in the goaf of the super-thick coal seam;
the monitoring system comprises a monitoring point space state parameter monitoring module, a monitoring terminal and a monitoring module, wherein the monitoring terminal is installed at each monitoring point which is arranged, and the space state parameter monitoring is carried out by the monitoring terminal, wherein the space state parameter comprises a space coal quality parameter and a space environment parameter;
the deformation monitoring module is used for performing deformation monitoring on each laid monitoring point to obtain deformation indications corresponding to each monitoring point, and processing the deformation indications to obtain effective deformation indications corresponding to each horizon of the goaf;
the spontaneous combustion risk analysis module is used for analyzing spontaneous combustion risk coefficients corresponding to all monitoring points of the super-thick coal seam goaf based on space state parameters corresponding to all monitoring points monitored in real time;
the spontaneous combustion early warning module is used for comparing the spontaneous combustion risk coefficient corresponding to each monitoring point of the super-thick coal seam goaf with the set spontaneous combustion allowed risk coefficient, and if the spontaneous combustion risk coefficient corresponding to a monitoring point of the super-thick coal seam goaf is greater than the spontaneous combustion allowed risk coefficient, spontaneous combustion early warning is carried out;
the spontaneous combustion risk area dynamic demarcation module is used for carrying out spontaneous combustion early warning and identifying risk monitoring points from a plurality of laid monitoring points in real time at the same time, and dynamically demarcating the spontaneous combustion risk area according to the risk monitoring points;
the cloud management library is used for storing minimum environmental acting force coefficients capable of generating spontaneous combustion under various coal volatilization degree coefficients, storing coal granularity and coal stacking density corresponding to various coal types and storing safe stress and safe sinking distances corresponding to various layers of the super-thick coal seam goaf;
the spontaneous combustion risk region dangerous force analysis module is used for analyzing dangerous force coefficients of the defined spontaneous combustion risk region based on the effective deformation indexes corresponding to each layer of the goaf;
and the spontaneous combustion processing terminal is used for determining the corresponding environmental action direction of the spontaneous combustion risk area, so that the spontaneous combustion adaptation processing mode is selected, and simultaneously, spontaneous combustion rescue is carried out according to the risk coefficient of the spontaneous combustion risk area.
2. The sensor-based ultra-thick coal seam goaf spontaneous combustion monitoring and management system as claimed in claim 1, wherein: the method for arranging the monitoring points in the super-thick coal seam goaf specifically comprises the following steps:
and counting the number of the existing horizons in the goaf space of the ultra-thick coal seam, uniformly distributing monitoring points on each horizon, and numbering the distributed monitoring points.
3. The sensor-based ultra-thick coal seam goaf spontaneous combustion monitoring and management system as claimed in claim 1, wherein: the space coal quality parameters comprise gas concentration and coal dust content, and the space environment parameters comprise temperature, humidity, oxygen concentration and air flow rate.
4. A sensor-based ultra-thick coal seam goaf spontaneous combustion monitoring and management system as claimed in claim 2, wherein: the deformation indication comprises stress and sinking distance, wherein the specific acquisition process of the effective deformation indication corresponding to each layer of the goaf is as follows:
arranging monitoring points existing on each layer of the goaf according to a set sequence;
sequentially taking all monitoring points existing on all layers of the goaf as main body monitoring points according to the arrangement sequence of the monitoring points, and further carrying out proximity calculation on deformation indications of the main body monitoring points on all layers and deformation indications of other monitoring points to obtain proximity of deformation indications taking all the monitoring points as main body monitoring points on all the layers of the goaf;
and selecting the deformation indication corresponding to the monitoring point to which the maximum deformation indication approach belongs from the deformation indication approaches of each monitoring point serving as the main body monitoring point on each layer of the goaf as the effective deformation indication corresponding to each layer of the goaf.
5. A sensor-based ultra-thick coal seam goaf spontaneous combustion monitoring and management system as claimed in claim 3, wherein: the analysis of the spontaneous combustion risk coefficient corresponding to the super-thick coal seam goaf specifically comprises the following steps:
extracting space coal quality parameters from the space state parameters, and further importing the space coal quality parameters of each monitoring point into a formulaCalculating the coal volatility coefficient eta corresponding to each monitoring point i Where i denotes a monitoring point number, i=1, 2, &.. i 、p i Respectively expressed as the gas concentration and the coal dust content corresponding to the ith monitoring point, q 0 、p 0 Respectively expressed as a pre-configured safe gas concentration, safe coal dust content,>the coal quality influence factors corresponding to the goaf of the ultra-thick coal seam are expressed;
matching the coal volatilization degree coefficient corresponding to each monitoring point with the minimum environment acting force coefficient which can generate spontaneous combustion under various coal volatilization degree coefficients and is stored in the cloud management library, and screening the minimum environment acting force coefficient which can generate spontaneous combustion of each monitoring point;
extracting space environment parameters from the space state parameters, and substituting the space environment parameters of each monitoring point into the expressionCalculating environmental acting force coefficient corresponding to each monitoring point>T i 、W i 、O i 、V i Respectively expressed as the temperature, humidity, oxygen concentration and air flow rate of the ith monitoring point, T 0 、W 0 、O 0 、V 0 Expressed as a reference temperature, a reference humidity, a reference oxygen concentration, a reference air flow rate, respectively, e expressed as a natural constant;
comparing the environmental acting force coefficient corresponding to each monitoring point with the minimum environmental acting force coefficient capable of generating spontaneous combustion of the corresponding monitoring point, calculating the spontaneous combustion risk coefficient phi corresponding to each monitoring point of the super-thick coal seam goaf, wherein the calculation expression is as followsIn->Denoted as the minimum ambient force coefficient at which the ith monitoring point is capable of producing autoignition.
6. The sensor-based ultra-thick coal seam goaf spontaneous combustion monitoring and management system as claimed in claim 5, wherein: the concrete calculation mode of the coal quality influence factor corresponding to the super-thick coal seam goaf is as follows:
acquiring a coal type corresponding to the super-thick coal seam goaf, and further extracting the coal granularity and the coal bulk density corresponding to the coal type of the super-thick coal seam goaf from the cloud management library;
using expressionsCalculating coal quality influence factors corresponding to the goaf of the ultra-thick coal seamEpsilon and rho are respectively expressed as the coal granularity and the coal bulk density corresponding to the coal type of the super-thick coal seam goaf, and epsilon 'and rho' are respectively expressed as the set reference coal granularity and the set reference coal bulk density.
7. The sensor-based ultra-thick coal seam goaf spontaneous combustion monitoring and management system as claimed in claim 4, wherein: the specific identification process of the risk monitoring points is as follows:
and after spontaneous combustion early warning is carried out, comparing the spontaneous combustion risk coefficient corresponding to each monitoring point of the super-thick coal seam goaf with the set spontaneous combustion allowed risk coefficient in sequence according to a set time interval, and selecting the monitoring point which is larger than the spontaneous combustion allowed risk coefficient as a risk monitoring point corresponding to each moment.
8. The sensor-based ultra-thick coal seam goaf spontaneous combustion monitoring and management system as claimed in claim 7, wherein: the specific implementation mode of the dynamic demarcation of the spontaneous combustion risk area is as follows:
identifying risk monitoring points belonging to the same horizon from the risk monitoring points corresponding to each moment, and taking the identified horizon as a risk horizon;
extracting a risk monitoring point distribution profile from a plurality of risk monitoring point distribution states corresponding to each risk horizon to obtain a risk monitoring point distribution area corresponding to each risk horizon;
and constructing all risk layers existing in each moment into spontaneous combustion risk areas corresponding to each moment.
9. The sensor-based ultra-thick coal seam goaf spontaneous combustion monitoring and management system as claimed in claim 8, wherein: the risk coefficient analysis of the delimited spontaneous combustion risk area comprises the following steps:
selecting effective deformation indicators corresponding to the risk layers in each moment from the effective deformation indicators corresponding to the risk layers based on the risk layers related to the spontaneous combustion risk areas corresponding to each moment;
calculating the collapse hidden trouble delta of each risk layer at each moment by using the effective deformation indication corresponding to each risk layer at each moment t j, the calculation expression isWhere j is denoted as the number of risk horizons, j=1, 2, … …, m, t is denoted as the time number, t=1, 2 t j、l t j is respectively expressed as effective stress and effective sinking distance of the jth risk horizon in the t moment, f j Safety, l j The safety is respectively expressed as the safety stress and the safety sinking distance corresponding to the jth risk horizon, U is expressed as a set constant, and U>1；
Selecting a maximum spontaneous combustion risk coefficient from spontaneous combustion risk coefficients corresponding to each risk monitoring point on each risk layer;
calculating spontaneous combustion risk presentation indexes corresponding to all risk layers by combining the distribution areas of risk monitoring points corresponding to all risk layers and the maximum spontaneous combustion risk coefficientsS in j 、φ j Respectively expressed as the distribution area of risk monitoring points corresponding to the jth risk layer and the maximum spontaneous combustion risk coefficient, s j The table is expressed as the surface area, phi, corresponding to the j-th risk horizon 0 Expressed as a set allowable auto-ignition risk factor;
comparing the spontaneous combustion risk presentation indexes corresponding to the risk layers, and selecting the risk layer corresponding to the maximum spontaneous combustion risk presentation index from the risk layers as a specific layer;
substituting the collapse hidden danger degree and spontaneous combustion risk presentation index corresponding to the specific layer in each moment into the expressionAnd calculating a dangerous force coefficient corresponding to the spontaneous combustion risk area at each moment.
10. A sensor-based ultra-thick coal seam goaf spontaneous combustion monitoring and management system as claimed in claim 9, wherein: the environmental effect corresponding to the self-ignition risk area is determined by the specific implementation mode:
comparing the space environment parameters corresponding to the risk monitoring points in the specific horizon corresponding to the spontaneous combustion risk areas in each moment with the reference space environment parameters, and calculating the deviation amount corresponding to the space environment parameters in the risk monitoring points in the specific horizon corresponding to each moment;
comparing the deviation amounts corresponding to the space environment parameters in the risk monitoring points in the specific horizon corresponding to each moment, and selecting the space environment parameter corresponding to the maximum deviation amount from the deviation amounts as the trend space environment parameter corresponding to the risk monitoring points in the specific horizon corresponding to each moment;
comparing trend space environment parameters corresponding to the risk monitoring points in the specific horizon at each moment, classifying the risk monitoring points corresponding to the same trend space environment parameters, counting the occupation ratio corresponding to the various trend space environment parameters at each moment, and pointing the trend space environment parameters corresponding to the maximum occupation ratio as the environment effect corresponding to the spontaneous combustion risk area at each moment.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117591988A (en) * | 2024-01-18 | 2024-02-23 | 中国矿业大学 | Risk data processing method and system for underground coal mine safety double prevention management |
| CN117993559A (en) * | 2024-02-05 | 2024-05-07 | 兖矿能源集团股份有限公司 | Goaf coal spontaneous combustion prediction method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105070015A (en) * | 2015-08-10 | 2015-11-18 | 中国矿业大学 | Wireless sensor device and method used for coal field fire zone parameter dynamic monitoring |
| WO2021253280A1 (en) * | 2020-06-17 | 2021-12-23 | 山东科技大学 | Test method for simulating spontaneous combustion in goaf region |
| CN113863989A (en) * | 2021-10-29 | 2021-12-31 | 安徽理工大学 | Extremely-close coal seam group mining closed goaf coal spontaneous combustion monitoring and early warning method |
-
2023
- 2023-09-05 CN CN202311141464.XA patent/CN116927856B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105070015A (en) * | 2015-08-10 | 2015-11-18 | 中国矿业大学 | Wireless sensor device and method used for coal field fire zone parameter dynamic monitoring |
| WO2021253280A1 (en) * | 2020-06-17 | 2021-12-23 | 山东科技大学 | Test method for simulating spontaneous combustion in goaf region |
| CN113863989A (en) * | 2021-10-29 | 2021-12-31 | 安徽理工大学 | Extremely-close coal seam group mining closed goaf coal spontaneous combustion monitoring and early warning method |
Non-Patent Citations (2)
| Title |
|---|
| 邢震: "特厚煤层自燃关键参数现场观测及动态数值模拟研究", 《煤炭工程》, vol. 52, no. 1, 31 December 2020 (2020-12-31), pages 111 - 115 * |
| 闫虎君等: "特厚易燃煤层下分层综放面防灭火预测预报技术研究", 内蒙古煤炭经济, 31 December 2020 (2020-12-31), pages 117 - 118 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117591988A (en) * | 2024-01-18 | 2024-02-23 | 中国矿业大学 | Risk data processing method and system for underground coal mine safety double prevention management |
| CN117591988B (en) * | 2024-01-18 | 2024-04-09 | 中国矿业大学 | Risk data processing method and system for underground coal mine safety double prevention management |
| CN117993559A (en) * | 2024-02-05 | 2024-05-07 | 兖矿能源集团股份有限公司 | Goaf coal spontaneous combustion prediction method |
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