CN112036609B - Dynamic prediction method for water inflow of coal mine working face based on multi-order power system model - Google Patents
Dynamic prediction method for water inflow of coal mine working face based on multi-order power system model Download PDFInfo
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Abstract
The invention relates to a mine water inflow prediction method, belongs to the field of mine roof water damage prevention and water resource protection, and particularly relates to a coal mine working face water inflow dynamic prediction method based on a multi-order power system model. The method mainly comprises the following steps: the method comprises the steps of constructing a mathematical model of the actual hydrophobic condition water inrush process of a roof head of a mining face in a mining area, solving the mathematical model of the water inrush process of the roof head of the mining face without hydrophobic condition of the mining area, analyzing main control factors of the water inrush process of a roof pushing and mining face, developing a multi-stage power system model for predicting water inflow of the face pushing and mining process, developing and predicting working face water inflow prediction software based on the multi-stage power system model, realizing accurate and dynamic prediction of water inflow of a coal mine face, and obtaining the maximum water inflow, normal water inflow and occurrence position which are most concerned in the field of coal mine water damage prevention.
Description
Technical Field
The invention relates to a mine water inflow prediction method, belongs to the field of mine roof water damage prevention and water resource protection, and particularly relates to a coal mine working face water inflow dynamic prediction method based on a multi-order power system model.
Background
The mining of the coal bed causes roof aquifer water to enter the goaf, firstly brings water damage threat, and secondly causes the loss of groundwater resources. The prediction of the water inflow of the roof of the coal seam exploitation can provide basic data for reasonable design of a mine water prevention and drainage system and fine formulation of a water damage prevention and control technical scheme, and can provide reference for calculation of the loss of underground water resources.
At present, in the field of coal mine roof water damage prevention and control, a method for calculating a water gushing process of a working face roof mainly comprises eight types, namely a water balancing method, an analysis method, a numerical method, a deep curve equation method, a hydrogeological comparison method, a correlation analysis method, a time sequence analysis method, an artificial neural network and the like. The first four types are based on classical seepage theory and are influenced by the well diameter application range, exploration hydrogeologic parameter errors and boundary condition dynamic change factors, the analysis method and the numerical method have larger difference between the prediction result and the actual value, the mine with the water inflow error value less than 30 percent calculated by the method only has 10 percent, and the mine number with the prediction error less than 50 percent exceeds 80 percent. And the water inflow calculated by the methods is static, and the continuous dynamic change characteristics of the water inflow process along with the mining engineering cannot be reflected, which is not consistent with the actual water inflow law. The latter four methods are mainly based on the statistical characteristics of measured data of water inflow, and the comparison calculation result can reflect the dynamics of water inflow, but can only be used for mines with similar geological and mining engineering conditions, the hydrogeology and rock mechanics meaning indicated by the water inflow change process is ignored, meanwhile, the comparison process has higher requirements on the accumulation degree of measured data, the condition similarity is a relative concept, once the analogue elements are changed, the calculation process of the method lacks theoretical basis, and the prediction result is not reliable any more.
The prediction of the water inflow of the roof of the working face of the coal mine is a hot spot problem, a plurality of students research the problem, but the problems of poor applicability of the existing disclosed water inflow prediction technical documents and patents generally exist, the problems are caused by limitation of understanding complex geological and hydrogeological conditions, the dynamic change of underground water caused by mining activities, the complex movement form of the underground water in space and time in the process of moving to a roadway are difficult to give a correct physical model. Therefore, it is necessary to develop a roof water inflow dynamic prediction method which not only can reflect the dynamic property of the water inflow of the working face, but also can highlight the water inflow prediction precision.
Disclosure of Invention
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
The invention mainly aims to solve the technical problems in the prior art and provides a coal mine working face water inflow dynamic prediction method based on a multi-order power system model.
In order to solve the problems, the scheme of the invention is as follows:
A coal mine working face water inflow dynamic prediction method based on a multi-order power system model comprises the following steps:
The construction step of the actual dredging and descending conditional water gushing model is used for constructing a mathematical model representing the actual dredging and descending conditional water gushing process of the roof head of the adopted working face in the mining area;
A non-sparse-drop condition water-flushing model solving step, namely solving a mathematical model of a water-flushing process without sparse-drop condition of a roof head of a mined working surface of a mining area based on an actual water-flushing quantity of a roof before mining and a working surface pushing water-flushing quantity process equation of the actual water-flushing condition;
Analyzing the main control factors in the roof water gushing process, namely excavating the main control factors in the roof water gushing process by the working face, and establishing mathematical relations between model parameters and control elements by utilizing a multiple linear regression method;
And a step of constructing a multi-order power system prediction model, wherein the multi-order power system prediction model is constructed based on the pre-mining prediction model and the in-mining secondary correction model in the working face pushing and mining process.
Preferably, in the method for dynamically predicting the water inflow of the coal mine working face based on the multi-order power system model, in the step of constructing the roof water inflow change model, a first-order power system model is used for describing a roof static release (Qj) process model and a lateral dynamic replenishment (Qd) process model respectively, the roof static release (Qj) process model and the lateral dynamic replenishment (Qd) process model are overlapped to form a second-order power system model, and the second-order power system model is used as the second-order power system model.
Preferably, in the method for dynamically predicting the water inflow of the coal mine working face based on the multi-order power system model, the step of constructing the roof water inflow change model is based on the following formula to construct a mathematical model of the water inflow process of the actual sparse and descending condition of the roof water head of the mined working face in the mining area:
Qs＝W1sexp(-k1sx)-W2sexp(-k2sx)+Cs
In the formula, Q s is a process equation of pushing water inflow of the working face under the actual drainage condition; x represents the ratio of the trend length to the trend width of the stoped working face at the moment t in the working face pushing and mining process, k 1s represents the static water release attenuation coefficient of the actual dredging condition (m 3/h·1),k2s represents the lateral dynamic replenishment growth coefficient of the actual dredging condition (m 3/h·1),Cs represents the dynamic stable water inflow of the actual dredging condition (m 3/h),W1s and W 2s represent the initial values of the dynamic water inflow and the dynamic water inflow of the working face under the actual dredging condition respectively).
Preferably, in the method for dynamically predicting the water inflow of the coal mine working face based on the multi-order power system model, in the step of solving the water inflow model without the sparse and descending condition, the mathematical model of the water inflow process without the sparse and descending condition of the roof head of the adopted working face of the mining area is solved based on the following equation:
In the formula, Q T is a process equation of the water inflow of the pushing of the working surface without the drainage condition of the head of the top plate; q S is a process equation of the water inflow of the pushing of the working face under the actual drainage condition; Δq is the pre-harvest pre-drain water amount; x is the working face pushing and picking distance; x m、xe is the pushing and mining distance of the working face corresponding to the maximum water inflow and the initial stable water inflow respectively; and t e is the moment when the water inflow amount reaches initial stability in the working face pushing and mining process.
Preferably, in the method for dynamically predicting water inflow of a coal mine working face based on a multi-stage power system model, the working face pushing and mining roof water inflow process main control factors determined in the roof water inflow process main control factor analysis step include natural factors and human factors, wherein the natural factors include: geological information, water filling aquifer head, hydrogeological parameters; the human factors include: mining information, drilling construction parameters and development height of the water guide fracture zone.
A coal mine face water inflow dynamic prediction system based on a multi-order power system model, comprising:
The actual dredging and descending conditional water gushing model construction module is used for constructing a mathematical model for representing the actual dredging and descending conditional water gushing process of the roof head of the adopted working face in the mining area;
The non-sparse-drop condition water-flushing model solving module is used for solving a mathematical model of the non-sparse-drop condition water-flushing process of the roof head of the mined working surface of the mining area based on the actual water drainage quantity of the roof before mining and the working surface pushing water-flushing quantity process equation of the actual water drainage condition;
the roof water gushing process main control factor analysis module is used for excavating main control factors of the working face pushing roof water gushing process, and a multiple linear regression method is used for establishing mathematical relations between model parameters and control elements;
the multi-order power system prediction model building module is used for building a water inflow multi-order power system prediction model in the working face pushing and mining process based on the pre-mining prediction model and the in-mining secondary correction model.
Preferably, in the above system for dynamically predicting water inflow of a coal mine working face based on a multi-stage power system model, in the module for constructing a roof water inflow variation model, a first-stage power system model is used to describe a roof static release (Qj) process model and a lateral dynamic replenishment (Qd) process model respectively, and the roof static release (Qj) process model and the lateral dynamic replenishment (Qd) process model are superimposed to form a second-stage power system model, and the second-stage power system model is used as the second-stage power system model.
Preferably, in the above system for dynamically predicting water inflow of a coal mine working face based on a multi-stage power system model, the roof water inflow change model building module builds a mathematical model of water inflow process under actual dredging and descending conditions of roof water head of a mined working face in a mining area based on the following formula:
Qs＝W1sexp(-k1sx)-W2sexp(-k2sx)+Cs
In the formula, Q s is a process equation of pushing water inflow of the working face under the actual drainage condition; x represents the ratio of the trend length to the trend width of the stoped working face at the moment t in the working face pushing and mining process, k 1s represents the static water release attenuation coefficient of the actual dredging condition (m 3/h·1),k2s represents the lateral dynamic replenishment growth coefficient of the actual dredging condition (m 3/h·1),Cs represents the dynamic stable water inflow of the actual dredging condition (m 3/h),W1s and W 2s represent the initial values of the dynamic water inflow and the dynamic water inflow of the working face under the actual dredging condition respectively).
Preferably, in the above system for dynamically predicting water inflow of a coal mine working face based on a multi-order power system model, in the solution module for the water inflow model without sparse and descending conditions, the mathematical model for the water inflow process without sparse and descending conditions of the roof head of the mined working face in the mining area is solved based on the following equation:
In the formula, Q T is a process equation of the water inflow of the pushing of the working surface without the drainage condition of the head of the top plate; q S is a process equation of the water inflow of the pushing of the working face under the actual drainage condition; Δq is the pre-harvest pre-drain water amount; x is the working face pushing and picking distance; x m、xe is the pushing and mining distance of the working face corresponding to the maximum water inflow and the initial stable water inflow respectively; and t e is the moment when the water inflow amount reaches initial stability in the working face pushing and mining process.
Preferably, in the above system for dynamically predicting water inflow of a coal mine working face based on a multi-stage power system model, the working face pushing and mining roof water inflow process main control factors determined in the roof water inflow process main control factor analysis module include natural factors and human factors, wherein the natural factors include: geological information, water filling aquifer head, hydrogeological parameters; the human factors include: mining information, drilling construction parameters and development height of the water guide fracture zone.
From the above, the invention has the following advantages:
1) The theoretical basis established by the prediction model is system dynamics, and the first-order attenuation model and the first-order recovery model based on the system dynamics can accurately describe the vertical release and lateral dynamic replenishment process of the roof static reserve in the working face pushing and mining process, so that the bottleneck that the applicable condition of the water inflow prediction analysis method formed on the basis of the classical seepage theory is infinitely small, but not the calculation of the oversized well diameter of the goaf is broken through, and the dynamic prediction of the water inflow along with the pushing and mining process is realized.
2) The prediction of the maximum water inflow and the occurrence position of the goaf, the normal water inflow and the occurrence position in the working face mining pushing process can be realized through the first derivative solving of the prediction model, which cannot be realized by the existing water inflow prediction method.
3) The coal mine working face water inflow prediction method based on the multi-order power system model jointly comprises the primary prediction based on water inflow process influence factors and the secondary correction and twice prediction based on the actual water inflow measured data in the pushing and mining process, and can remarkably improve the water inflow prediction precision
Drawings
The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments of the present invention and, together with the description, further serve to explain the principles of the invention and to enable a person skilled in the pertinent art to make and use the disclosure.
FIG. 1 is a flow chart of the dynamic prediction of water inflow of the present invention;
FIG. 2 is a dynamic water inflow prediction software interface based on a multi-stage powertrain model;
FIG. 3 is a flow of dynamic water inflow prediction software workflow based on a multi-stage powertrain model;
FIG. 4 is a graph of a prediction of face water inflow for a 204 th order powertrain model.
Embodiments of the present invention will be described with reference to the accompanying drawings.
Detailed Description
Examples
The following describes the present invention in detail with reference to specific examples: the present embodiment is implemented on the premise of the technical scheme of the present invention, and detailed embodiments and steps are given, but the protection scope of the present invention is not limited to the following embodiments.
The method is mainly used for predicting the push-mining water inflow of a coal mine working face, a power system model in the water inflow superposition releasing process is built through a system analysis of two water inflow modes of movement of underground water to a large goaf well, the hydraulic system model is reversely calculated on the basis of pre-drainage of drilling holes before mining, a main control factor of key parameters is analyzed through a multivariate statistical method, a mathematical relation between the key parameters and the main control factor is built, a water inflow process mathematical model is combined to form a water inflow primary prediction model, on the basis, the water inflow actual measurement data of the initial water inflow of the working face is utilized to build a water inflow correction model in the water inflow mining, the two models are combined to form a water inflow prediction model based on a multi-order power system model, and prediction software of the water inflow multi-order power system of the working face is developed and combined with the two prediction models, so that prediction of the water inflow curve, the maximum water inflow and normal water inflow of the whole working face in the working face and corresponding occurrence positions are realized.
The embodiment specifically comprises the following steps:
firstly, constructing a mathematical model of the water flushing process of the actual dredging and descending condition of the roof head of the mined working surface in the mining area;
The first-order power system model is used for describing the processes of top plate static release (Q j) release and lateral dynamic replenishment (Q d), and the two models are overlapped to form a second-order power system model, which describes the water inflow change process of the top plate pushed and collected along with a working surface.
Qs＝Qj+Qd＝W1sexp(-k1sx)-W2sexp(-k2sx)+Cs ①
Wherein, the physical meaning represented by the parameters is respectively as follows: x represents the ratio of the trend length to the trend width of the stoped face at the time t of the face pushing and mining process, Q j represents the roof static release water quantity (m 3/h),Qd represents the lateral dynamic supply water quantity (m 3/h),k1s represents the static release water attenuation coefficient (m 3/h·1),k2s represents the lateral dynamic supply growth coefficient (m 3/h·1),Cs represents the stable water inflow quantity (m 3/h)).
Secondly, solving a mathematical model of the water flushing process without the sparse drop condition of the roof head of the mined working surface in the mining area;
The mathematical equation of the water inflow change process of the roof water head without the dredging and lowering condition can be calculated by utilizing the mathematical quantitative relation between the water inflow equation of the non-dredging and lowering condition and the actual dredging and lowering condition.
In the formula, Q T is a process equation of the water inflow of the pushing of the working surface without the drainage condition of the head of the top plate; q S is a process equation of the water inflow of the pushing of the working face under the actual drainage condition; Δq is the pre-harvest pre-drain water amount; x is the working face pushing and picking distance; x m、xe is the pushing and mining distance of the working face corresponding to the maximum water inflow and the initial stable water inflow respectively; and t e is the moment when the water inflow amount reaches initial stability in the working face pushing and mining process.
The expression of Q T is as follows:
QT＝W1Texp(-k1Tx)-W2Texp(-k2Tx)+CT ③
Thirdly, analyzing main control factors in the water gushing process of the roof pushing and collecting by the working face;
main control elements of the water gushing process of the goaf of the working face are excavated by adopting a statistical basic principle, and the main control elements comprise: natural factors (geological information, water filling aquifer head, hydrogeological parameters), artificial factors (mining information, drilling construction parameters and water guiding fracture zone development height).
And establishing a mathematical relationship between the model parameters and the control elements by using a multiple linear regression method.
Fourthly, developing a prediction model of the water inflow multi-order power system in the working face pushing and mining process;
the prediction model consists of a pre-sampling primary prediction model and a mid-sampling secondary correction model.
Step 1: working face water inflow amount pre-production prediction model construction based on water inflow process main control factors
On the basis of a water inflow curve equation of the working surface without the sparse drop condition of the top plate, the mathematical relationship between the model parameters and the main influencing factors is combined to form a water inflow pre-mining prediction model suitable for a research area. The primary prediction model of water inflow is formed as follows:
Wherein: q t respectively represents the pushing and mining water inflow of the working face at the position of the moment t calculated by the model, H, H f, Q, M, V, mu and D respectively represent the residual water head of the water-bearing stratum, the maximum development height of the water-guiding fracture zone, the unit water inflow, the thickness of the water-bearing stratum, the pushing and mining speed of the working face, the water release coefficient and the maximum height initial pushing and mining distance of the water-guiding fracture zone.
The calculation step of the maximum water inflow of the working face is as follows:
sub-step 1: first-order derivation of water inflow curve equation in working face pushing and mining process
According to ⑤, the maximum water inflow generation position x m of the working surface is obtained.
Sub-step 2: calculation of maximum water inflow in working face pushing and mining process
Qm＝W1texp(-k1txm)-W2texp(-k2txm)+Ct ⑥
According to ⑥, the maximum water inflow Q m of the working surface is obtained.
Step 2: construction of secondary correction model in working face water inflow mining based on actual measurement data in initial mining stage
Based on the pre-mining prediction model and based on initial mining observation dataThe correction power system predicts W 1,W2,k1,k2, C in model Q (x i,W1,W2,k1,k2, C).
Wherein x i is the working face sampling width ratio, and Q i is the corresponding measured water inflow.
We build the following solution model:
Where λ is the co-ordination coefficient, when λ=0, meaning that the parameters found are all determined from the observed data; lambda → infinity means that the last parameter is determined only by the initial value.
J (W 1,W2,k1,k2, C) is convex with respect to the parameters W 1,W2, C when solving for the optimal solution, so W 1, W, C can be solved for by the least squares method when fixing k 1,k2. However, the least squares method cannot guarantee that the parameters are non-negative, so that the non-negative least squares method is used to guarantee that W 1>0,W2 >0.
Therefore, the solving steps of the correction model are as follows:
step a, discretizing the interval for a range k1∈[k1-interval,k1+interval],k2∈[k2-itnerval,k2+itnerval], of the selected k 1,k2 over a large range.
And B, for the (k 1,k2) pair in a large range, calling a non-negative least square method to obtain a non-negative optimal solution W 1,W2 and C.
Step C, comparing the loss of each pair (W, W 2,k1,k2, C) in the range, and selecting the smallest pair (W 1,W2,k1,k2, C).
And D, changing the selected range of the optimal data pair in the previous step into k1∈[k1-interval/10,k1+interval/10],k2∈[k2-interval/10,k2+interval/10]. to discretize the interval, and repeating the step B. Until the loss of the data pair (W 1,W2,k1,k2, C) does not change, the data pair (W, W 2,k1,k2, C) is the optimal solution.
Fifthly, developing and predicting dynamic water inflow prediction software of a working face based on a multi-order power system model;
On the basis of a multi-order power system model for water inflow prediction, a Python language is adopted to write a water inflow prediction program, and one-time quick prediction of the water inflow in the whole process of working face pushing and mining is realized through input of a model parameter main control factor; and the secondary piecewise correction of the water inflow prediction result is realized through the input of the actual water inflow observation data section by section in the pushing and mining process.
The specific effects of the embodiment are verified as follows.
Firstly, fitting a dynamic system model of a water gushing process of a working face under an actual dredging condition;
The actual observation data of the water inflow of 9 mined working surfaces (No. 1-9) in the Jurassic coal field Ningdong mining area in the northwest is collected, and a working surface push mining water inflow second-order power system equation (Q 1S-Q9S) under the actual dredging condition is obtained through fitting by using 1stopt software.
Q1S＝135.73exp(-0.405x)-158.73exp(-0.877x)+17.289
Q2S＝433.88exp(-0.158x)-544.50exp(-0.610x)+45.730
Q3S＝1308.24exp(-1.615x)-1452.85exp(-2.362x)+6.588
Q4S＝1008.67exp(-0.975x)-1668.16exp(-4.064x)+10.141
Q5S＝435.70exp(-0.973x)-471.41exp(-1.976x)+9.296
Q6S＝262.50exp(-0.660x)-254.40exp(-1.330x)+12.348
Q7S＝311.08exp(-1.016x)-390.22exp(-2.339x)+8.610
Q8S＝1653.54exp(-1.403x)-1820.97exp(-2.475x)+7.650
Q9S＝1408.74exp(-1.708x)-1376.26exp(-2.693x)+6.311
Secondly, solving a dynamic system equation of the water gushing pushing and mining process of the working face under the condition of no dredging;
Based on the pre-drainage of the underground drilling and the water inflow change curve equation (Q 1S-Q9S) under the actual drainage condition fitted in the first step, the water inflow equation (Q 1T-Q9T) of the working face pushing and mining process under the non-drainage condition is reversely calculated by using 1stopt software and a fixed integral model (formula ②).
Q1T＝3190.00exp(-0.473x)-3212.88exp(-0.680x)+17.289
Q2T＝2992.50exp(-0.178x)-3103.12exp(-0.465x)+45.730
Q3T＝3277.50exp(-0.496x)-3422.11exp(-3.627x)+6.588
Q4T＝3135.00exp(-0.375x)-3794.49exp(-4.738x)+10.141
Q5T＝3306.00exp(-0.591x)-3341.71exp(-2.379x)+9.296
Q6T＝1575.00exp(-0.338x)-1566.90exp(-2.216x)+12.348
Q7T＝1601.25exp(-0.543x)-1680.39exp(-2.513x)+8.610
Q8T＝1806.00exp(-0.442x)-1973.43exp(-4.232x)+7.650
Q9T＝2520.00exp(-0.390x)-2487.52exp(-6.905x)+6.311
Thirdly, analyzing main control factors in the water gushing process of the roof pushing and collecting by the working face;
Step 1: and analyzing the influence factors of the water inflow equation parameters in the water inflow process of the working face. And (3) carrying out single-factor linear correlation analysis by utilizing SPSS software, and finding out the correlation between the model parameters K 1、k2、W1、W2 and C and the influence factors of the pre-mining roof head height (H), the aquifer permeability coefficient (K), the aquifer unit water inflow (q), the pre-mining roof pre-drainage water amount (W), the aquifer thickness (M), the maximum development height of the water guide crack zone (H f), the initial pushing and mining distance (D) of the maximum development height of the water guide crack zone, the working face pushing and mining speed (V) and the aquifer water release coefficient (mu), wherein the results are shown in Table 1.
TABLE 1 analysis of correlation of Water inflow dynamic System model parameters and influencing factors
Step 2: and constructing a mathematical relation model of the working face water inflow equation parameters and main influence factors. Based on the main parameter values of the top plate water inflow equation without the sparse drop condition of the 9 working surfaces and the actual observed values (or simulation values) of the 9 influence factors, establishing mathematical relation formulas between k 1、k2、W1、W2 and C and the respective main influence factors by using Excel software.
W1＝101H+75Hf+8V
W2＝84H+33Hf+11V
k1＝5Hf+3D+0.8q
k2＝4M+2μ+0.7W
C＝15q+12μ+6D
Fourthly, constructing a coal mine working face water inflow prediction model suitable for Ningdong mining areas;
step 1: constructing a water inflow primary prediction model;
And obtaining a primary water inflow prediction model suitable for the working face pushing and mining process of the Ningdong mining area according to the model parameter-influence factor mathematical relation model and the water inflow second-order power system model.
Step 2: building a water inflow secondary correction model;
The actual measurement data (table 2) of the "water inflow-collecting width ratio" of the working face of the Ningdong mining area 204 is collected, and the calculation solution is difficult to be carried out by combining the fifth step software because the calculation amount of the second correction approximate solution is large.
TABLE 2 actual measurement data of "water inflow-mining Width ratio" at initial working face mining push-out period 204
Fifthly, the method is suitable for development and prediction of water inflow prediction software of a coal mine working face in Ningdong mining areas;
step 1, working face water inflow prediction software development based on a multi-order power system model;
On the basis of a water inflow prediction multi-stage power system model, a Python language is adopted to write a water inflow prediction program, and the water inflow prediction program comprises two calculation modules, wherein the first is to predict the water inflow of a working face for the first time by inputting 9 influencing factor values such as hydrogeology, mining and the like; secondly, the actual measurement data of water inflow-mining width ratio in the early stage of mining pushing of the working face is imported to carry out secondary correction on the water inflow prediction model.
Step 2, predicting the water inflow of the working face before mining at step 204;
by inputting the influence factors such as the hydrogeology of the working face A, mining and the like, the working face water inflow change curve (figure 4), the maximum water inflow, the normal water inflow and the corresponding positions (table 3) are obtained through software calculation.
Step 3: performing secondary correction on a water inflow prediction model of 300m and 600m before pushing and mining on a working surface 204;
According to the actual measurement data of table 2, respectively constructing 204 front 300m and front 600m 'water inflow-sampling width ratio' dbf files of the working face, importing and operating a calculation function through software data, respectively calculating corrected water inflow change curves (figure 4) under the conditions of the actual measurement data of 300m and the actual measurement data of 600m, and correcting the corrected water inflow value, dynamic stable water inflow and corresponding positions (table 3).
TABLE 3 calculation errors for water inflow prediction software based on a multistage powertrain model
In summary, the above examples are only examples of embodiments of the method of the present invention and are not intended to be limiting of the embodiments. It will be apparent to those skilled in the art from this description that calculations may be performed using functionally similar software based on similar statistical rationales, and need not be exhaustive. Various modifications or alternatives to the complementary forms are within the scope of the invention.
Note that references in the specification to "one embodiment," "an embodiment," "example embodiments," "some embodiments," etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Furthermore, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (1)
1. A coal mine working face water inflow dynamic prediction method based on a multi-order power system model is characterized by comprising the following steps of:
The construction step of the actual dredging and descending conditional water gushing model is used for constructing a mathematical model representing the actual dredging and descending conditional water gushing process of the roof head of the adopted working face in the mining area;
A non-sparse-drop condition water-flushing model solving step, namely solving a mathematical model of a water-flushing process without sparse-drop condition of a roof head of a mined working surface of a mining area based on an actual water-flushing quantity of a roof before mining and a working surface pushing water-flushing quantity process equation of the actual water-flushing condition;
A roof water inflow process main control factor analysis step, namely establishing a model parameter and an influence factor mathematical relation model reflecting the influence factors of the water inflow equation parameters of the working face water inflow process and the water inflow equation parameters of the working face water inflow process by using a multiple linear regression method based on the observation value or the simulation value of the water inflow equation parameters of the working face water inflow process, which are obtained by solving a mathematical model of the water inflow process without sparse drop condition of the roof water head of the working face of a mining area;
Constructing a multi-order power system prediction model, namely constructing a pre-harvest prediction model; correcting the prediction model before mining based on observation data in the initial mining period; the pre-mining prediction model is obtained based on the model parameter and the influence factor mathematical relation model and the water inflow second-order power system model;
in the step of constructing the actual dredging and descending conditional water surging model, an actual dredging and descending conditional working face water surging pushing process equation Q s is constructed:
Q s＝Qj+Qd＝W1sexp(-k1sx)-W2sexp(-k2sx)+Cs type ①
In the formula ①, x represents the ratio of the trend length to the trend width of the stoped working face at the time t of the working face pushing and mining process, Q j represents the static water release quantity of the top plate, Q d represents the lateral dynamic water supply quantity, k 1s represents the static water release attenuation coefficient, k 2s represents the lateral dynamic water supply growth coefficient, and C s represents the stable water inflow quantity; w 1s and W 2s respectively represent initial values of dynamic and static water release and dynamic water inflow supply of the working surface under actual relief conditions;
In the solving step of the water gushing model without the sparse drop condition, a mathematical model of the water gushing process without the sparse drop condition of the roof head of the mined working face of the mining area is constructed by the following steps:
In ②, Q T is a roof head non-drainage condition working face pushing water inflow process equation; Δq is the pre-harvest pre-drain water amount; x is the working face pushing and picking distance; x m、xe is the pushing and mining distance of the working face corresponding to the maximum water inflow and the initial stable water inflow respectively; t e is the moment when the water inflow amount reaches initial stability in the working face pushing and mining process;
the water inrush process main control factors in the top plate water inrush process main control factor analysis step comprise natural factors and human factors, wherein the natural factors comprise: geological information, water filling aquifer head, hydrogeological parameters; the human factors include: mining information, drilling construction parameters and development height of the water guide fracture zone.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103924975A (en) * | 2014-04-16 | 2014-07-16 | 河北煤炭科学研究院 | Water retaining method for coal mining process |
| CN108915667A (en) * | 2018-06-27 | 2018-11-30 | 中煤科工集团西安研究院有限公司 | Based on infiltration coefficient with the method for porecasting water upwelling amount of mine well of separate zone production dynamic change |
| WO2019223357A1 (en) * | 2018-05-21 | 2019-11-28 | 中国矿业大学 | Method for predicting process water inflow of coal mine working face roof |
| CN110580655A (en) * | 2019-09-17 | 2019-12-17 | 山东浪潮通软信息科技有限公司 | Method and system for predicting coal mine water inflow based on LSTM algorithm |
| CN111191849A (en) * | 2020-01-03 | 2020-05-22 | 中煤科工集团西安研究院有限公司 | Method for predicting water inflow of deep-buried working face in western mining area |
-
2020
- 2020-08-07 CN CN202010786826.0A patent/CN112036609B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103924975A (en) * | 2014-04-16 | 2014-07-16 | 河北煤炭科学研究院 | Water retaining method for coal mining process |
| WO2019223357A1 (en) * | 2018-05-21 | 2019-11-28 | 中国矿业大学 | Method for predicting process water inflow of coal mine working face roof |
| CN108915667A (en) * | 2018-06-27 | 2018-11-30 | 中煤科工集团西安研究院有限公司 | Based on infiltration coefficient with the method for porecasting water upwelling amount of mine well of separate zone production dynamic change |
| CN110580655A (en) * | 2019-09-17 | 2019-12-17 | 山东浪潮通软信息科技有限公司 | Method and system for predicting coal mine water inflow based on LSTM algorithm |
| CN111191849A (en) * | 2020-01-03 | 2020-05-22 | 中煤科工集团西安研究院有限公司 | Method for predicting water inflow of deep-buried working face in western mining area |
Non-Patent Citations (5)
| Title |
|---|
| Numerical modeling of the transient hydrogeological response produced by tunnel construction in fractured bedrocks;Molinero等;Engineering Geology;第64卷(第4期);369-386 * |
| 侏罗系煤田顶板砂岩水疏放后采空区涌水规律及预测方法;李德彬;煤矿安全(第05期);全文 * |
| 侏罗纪煤田顶板疏放水钻探设计方法探讨;邵红旗;王建文;徐拴海;谷拴成;矿业工程(第06期);全文 * |
| 富水条件下软岩巷道变形特征与过程控制研究;许兴亮;张农;中国矿业大学学报(第03期);全文 * |
| 煤矿工作面推采采空区涌水双指数动态衰减动力学研究;周振方;靳德武;虎维岳;董阳;曹海东;赵春虎;彭江军;赵宝峰;董兴玲;煤炭学报(第09期);全文 * |
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