CN114925586A - Parameter calculation method for long-pressure short-pumping air-control dust removal equipment of fully mechanized coal mining face - Google Patents
Parameter calculation method for long-pressure short-pumping air-control dust removal equipment of fully mechanized coal mining face Download PDFInfo
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Abstract
The invention relates to a parameter calculation method for long-pressing short-pumping air-control dust removal equipment of a fully mechanized excavation face of a coal mine, and belongs to the technical field of coal mines. By automatically calculating the parameters of the long-pressing short-pumping air-control dust removal equipment on the fully mechanized excavation face of the coal mine, the defect that parameter regulation and control are needed manually according to experience in the prior art is overcome, the operation efficiency of the fully mechanized excavation face air-control dust removal system of the coal mine is greatly improved, and the labor intensity of coal mine operators is reduced. The regulation and control parameter value of the comprehensive digging surface dust-settling system is determined without manual work according to experience or by referring to historical data, and the automatic calculation of the parameter of the comprehensive digging surface dust-settling system can be realized; the method can realize quick calculation of the regulation and control parameters which are not available in experience, and avoid the condition that the regulation and control parameters cannot be determined due to the change of working conditions. The self-adaptive dust removal system is conveniently applied to the long-pressing short-pumping self-adaptive dust removal system of the fully-mechanized excavating face, and the intelligent level of the system is improved.
Description
Technical Field
The invention belongs to the technical field of coal mines, and relates to a parameter calculation method for long-pressure short-pumping air-control dust removal equipment of a fully mechanized excavation working face of a coal mine.
Background
The reduction of dust concentration of the fully mechanized excavation face by a dust reduction and removal technology is an important way for preventing pneumoconiosis. Long-pressure short-suction air-controlled dust removal is the most effective dust removal mode for the fully-mechanized excavating face, but the problem of dust pollution is not effectively solved after a lot of mines use the technology and the system. In order to ensure high-efficiency dust control and suppression of the long-pressure short-suction air-control dust removal system, matched technical parameters need to be reasonably selected. The thick tree macros and the like take a bay coal mine of a sheep farm as an engineering background, the influence of the axial-radial air compression ratio, the position of the wall-attached air duct and the like on the dust migration rule is contrastively analyzed, and the axial-radial air compression ratio and the distance between the wall-attached air duct and the head-on are determined; aiming at the caragana coal mine tunneling working face, a wind current regulation finite element model with changeable air outlet parameters is established by Gongxian swallow and the like, and the optimal wind current regulation parameters of the air outlet at the nearest distance of 5m and the farthest distance of 10m from the tunneling section are obtained.
But with the progress of production and tunneling, parameters such as the distance between the dust extraction opening of the dust removal system and the head-on distance, the air supply quantity and the like also change at any time. The parameters of the dust remover such as air quantity, air control quantity and the like need to be correspondingly adjusted to achieve the optimal dust settling effect. The existing technology only analyzes the influence of partial parameters on the air flow distribution and the dust-settling effect of the tunneling working face singly, does not consider the interaction influence among the parameters, can not realize the optimal calculation of the parameters, has unstable dust-settling effect when used on site, has the dust-settling efficiency fluctuating between 75 percent and 90 percent, and has difficult effective guarantee of dust-control effect.
Disclosure of Invention
In view of the above, the invention aims to provide a parameter calculation method for long-pressure short-pumping air-control dust removal equipment of a fully mechanized coal mining face. By automatically calculating the parameters of the long-pressing short-pumping air-control dust removal equipment on the fully mechanized excavation face of the coal mine, the defect that parameter regulation and control are needed manually according to experience in the prior art is overcome, the operation efficiency of the fully mechanized excavation face air-control dust removal system of the coal mine is greatly improved, and the labor intensity of coal mine operators is reduced.
In order to achieve the purpose, the invention provides the following technical scheme:
a parameter calculation method for long-pressure short-pumping air-control dust removal equipment of a fully mechanized coal mining face comprises the following steps:
s1: the operation characteristics of the fully-mechanized excavation face of the coal mine are investigated, the cross-sectional area S of the fully-mechanized excavation face of the roadway is determined, and the pressure air-extraction rate R of the dust-removing system is reduced yc Radial air quantity ratio R of air supply shaft zj Distance L between outlet of dust control device and head of development machine ky Length L of dust control device k The distance L between the suction inlet of the dust hood and the head of the development machine cy The value ranges of 6 index factors;
s2: adopting an equivalence class division method for 6 index factors in S1, dividing each index factor into a high level, a middle level and a low level, and determining a test scheme according to an optimization test design method, wherein the optimization test design method comprises an orthogonal test and a response surface method;
s3: establishing a three-dimensional model of the fully-mechanized excavation face normal-pressure short-pumping air-control dust removal system according to the test scheme in S2, and carrying out discretization treatment on the model;
s4: taking the wind flow as a continuous phase and the dust as a dispersed phase; simulating dust generation and dust particle generation at the head of the development machine by adopting a group injection speed incidence modeThe numerator is marked as N 1i Analyzing the dust track in the flow field based on the Lagrange method, calculating the number of particles removed by the long-pressure short-pumping system under the i test schemes, and recording the number as N 2i (ii) a The number of particles at the target position f i Represents;
f i ＝N 1i -N 2i (1)
s5: using the above i test protocols as sample data set, where i is the number of individuals in the sample data set, and each individual is S, R yc 、R zj 、L ky 、L k 、L cy 6 characteristics, and simultaneously calculating the number of particles at the target position of each individual according to the step S4;
s6: the quantity of the particles at the target position after the long-pressure short-pumping method is regulated and controlled is used as a wind-control dust-removing target, and a constraint multi-target optimization problem is established
In the formula: x ═ S, R yc 、R zj 、L ky 、L k 、L cy ) Is a 6-dimensional real vector; (x) is an objective function; g is a radical of formula j (X) represents a jth inequality constraint;
a and b are evaluation weights, a is 0.7 at the driver, and b is 0.3 at the pedestrian behind the driver;
s7: and predicting technical parameters of long-pressure short-pumping by adopting a particle swarm algorithm.
Optionally, in S4, the target position includes a driving driver position and a pedestrian position behind the driver position.
Optionally, the S7 specifically includes:
s71: isolating abnormal values in the sample data set in the step S5 by adopting a box-type formula four-bit distance method, uniformly mapping data of different dimensions to an interval [0,1] through maximum and minimum normalization to obtain normalized data, and normalizing the wind control and dust removal target;
s72: generating initialization particles and particle speeds by adopting a random method, and recording the population scale as m;
s73: calculating the fitness of each particle by adopting a formula (2);
s74: updating the individual optimal fitness of the particles by comparing the current fitness of the particles with the past optimal fitness of the particles, and storing the current optimal particles to an external file;
s75: judging whether the number of particles in the external archive exceeds the solution set capacity, if so, deleting redundant non-inferior solutions, and optimizing the solution set by using a formula (3);
in the formula: p is a radical of d The probability that a non-inferior solution is deleted; i, j are serial numbers of non-inferior solutions; n is the number of non-inferior solutions in the unified grid with the non-inferior solution; k is the number of current non-inferior solutions;
s76: updating the position and the speed of the particles by adopting a formula (4);
in the formula: i is the particle number; v is the particle velocity; t is the number of iterations; a is 1 ，a 2 Is the inertial weight; p gi A local optimal position vector for the individual; p is zi A global optimal position vector for the population;
s77: judging whether the iteration times reach the population scale iteration times m or not, and if not, turning to the step S73; if the iteration number m is reached, terminating;
s78: and (4) calculating the trust of each particle in the external archive by combining the wind-control dust-removal target, sorting the particles according to the trust from large to small, and performing inverse normalization on the sorted particles according to the normalization parameters in the step S71 to obtain corresponding index parameters and model values.
The invention has the beneficial effects that:
(1) the regulation and control parameter value of the comprehensive digging surface dust-settling system is determined without manual work according to experience or by referring to historical data, and the automatic calculation of the parameter of the comprehensive digging surface dust-settling system can be realized;
(2) the method can realize quick calculation of the regulation and control parameters which are not available in experience, and avoid the condition that the regulation and control parameters cannot be determined due to the change of working conditions;
(3) obviously help improving and combine a face dust pelletizing system operating efficiency, be favorable to improving dust fall efficiency, stabilize dust fall effect.
(4) The self-adaptive dust removal system is conveniently applied to the long-pressing short-pumping self-adaptive dust removal system of the fully-mechanized excavating face, and the intelligent level of the system is improved.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of the present invention;
fig. 2 is a flowchart of step S7 in the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustration only and not for the purpose of limiting the invention, shown in the drawings are schematic representations and not in the form of actual drawings; for a better explanation of the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limiting the present invention, and the specific meaning of the terms described above will be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, a method for calculating parameters of a long-pressure short-pumping air-control dust-removing device of a fully mechanized coal mining face is provided.
S1: the operation characteristics of the fully-mechanized excavation face of the coal mine are investigated, the cross-sectional area S of the fully-mechanized excavation face of the roadway is determined, and the pressure air-extraction rate R of the dust-removing system is reduced yc Radial air quantity ratio R of air supply shaft zj Distance L between outlet of dust control device and head of development machine ky Length L of dust control device k The distance L between the suction inlet of the dust hood and the head of the development machine cy The value ranges of 6 index factors.
S2: and (3) adopting an equivalence class division method for 6 index factors in S1, dividing each index factor into a high level, a medium level and a low level, and determining a test scheme according to an optimized test design method, wherein the test method comprises but is not limited to an orthogonal test and a response surface method (for example, for the level of 6 factors 3 in the embodiment, the orthogonal test method needs 65 tests which are far less than 729 times of a full factor test).
S3: and (5) establishing a three-dimensional model of the fully-mechanized excavating face normal-pressure short-pumping air-control dust removal system according to the test scheme in S2, and carrying out discretization treatment on the model.
S4: taking wind flow as continuous phase, powderDust as the dispersed phase. Simulating dust generation at the head of the development machine by adopting a group injection speed incidence mode, wherein the number of dust generation particles is recorded as N 1i Analyzing the dust track in the flow field based on a Lagrange method, calculating the number of particles removed by a long-pressure short-pumping system under the i test schemes, and recording the number as N 2i . The number of particles at the target location f i And (4) showing.
f i ＝N 1i -N 2i (1)
Preferably, the target position in S4 includes a tunneling driver position and a pedestrian position behind the driver position.
S5: using the above i test protocols as a sample data set, wherein i is the number of individuals in the data set, and each individual is S, R yc 、R zj 、L ky 、L k 、L cy And 6 features, and simultaneously calculating the number of the target position particles of each individual according to the step S4.
S6: the number of particles at the target position after the long-pressure short-pumping method is regulated and controlled is used as a wind-control dust-removal target, and a constraint multi-target optimization problem is established
In the formula: x ═ X (S, R) yc 、R zj 、L ky 、L k 、L cy ) Is a 6-dimensional real vector; (x) is a target (fitness value) function; g j (X) represents the jth inequality constraint.
The weights a and b are evaluated, with a being 0.7 at the driver and b being 0.3 at the pedestrians behind the driver.
S7: and predicting technical parameters of long-pressure short-pumping by adopting a particle swarm algorithm.
The method comprises the following specific steps:
isolating abnormal values in the sample data set in the step S5 by adopting a box-type formula four-bit distance method, uniformly mapping data of different dimensions to an interval [0,1] through maximum and minimum normalization to obtain normalized data, and normalizing the wind control and dust removal target.
Secondly, generating initialization particles and particle speeds by adopting a random method, and recording the population size as m.
And thirdly, calculating the fitness of each particle by adopting a formula (2).
Updating the individual optimal fitness of the particles by comparing the current fitness of the particles with the past optimal fitness of the particles, and storing the current optimal particles to an external file;
judging whether the number of particles in the external archive exceeds the solution set capacity, if so, deleting redundant non-inferior solutions, and optimizing the solution set by using a formula (3).
In the formula: p is a radical of d The probability that a non-inferior solution is deleted; i, j are serial numbers of non-inferior solutions; n is the number of non-inferior solutions in the unified grid with the non-inferior solution; k is the number of current non-inferior solutions;
sixthly, updating the position and the speed of the particles by adopting a formula (4);
in the formula: i is the particle number; v is the particle velocity; t is the number of iterations; a is 1 ，a 2 Is the inertial weight; p is gi A local optimal position vector for the individual; p zi A global optimal position vector for the population;
seventhly, judging whether the iteration times reach the population scale (iteration times) m, and if not, turning to the step (c); and if the iteration number m is reached, terminating.
And combining the wind control and dust removal targets, calculating the trust of each particle in an external archive, sequencing each particle according to the trust from large to small, and performing inverse normalization on the sequenced particles according to the normalization parameters in the step I to obtain corresponding index parameters and model values.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (3)
1. A parameter calculation method for long-pressure short-pumping air-control dust removal equipment of a fully mechanized coal mining face is characterized by comprising the following steps of: the method comprises the following steps:
s1: the operation characteristics of the fully-mechanized excavation face of the coal mine are investigated, the cross-sectional area S of the fully-mechanized excavation face of the roadway is determined, and the pressure air-extraction rate R of the dust-removing system is reduced yc Radial air quantity ratio R of air supply shaft zj And the distance L between the outlet of the dust control device and the head of the development machine ky Length L of dust control device k And the distance L between the suction inlet of the dust hood and the head of the development machine cy The value ranges of 6 index factors;
s2: adopting an equivalence class division method for 6 index factors in S1, dividing each index factor into a high level, a middle level and a low level, and determining a test scheme according to an optimization test design method, wherein the optimization test design method comprises an orthogonal test and a response surface method;
s3: establishing a three-dimensional model of the fully-mechanized excavation face normal-pressure short-pumping air-control dust removal system according to the test scheme in S2, and carrying out discretization treatment on the model;
s4: taking the wind flow as a continuous phase and the dust as a dispersed phase; simulating dust generation at the head of the development machine by adopting a group injection speed incidence mode, wherein the number of dust generation particles is recorded as N 1i Analyzing the dust track in the flow field based on the Lagrange method, calculating the number of particles removed by the long-pressure short-pumping system under the i test schemes, and recording the number as N 2i (ii) a The number of particles at the target location f i Represents;
f i ＝N 1i -N 2i (1)
s5: using the above i test protocols as sample data set, where i is the number of individuals in the sample data set, and each individual is S, R yc 、R zj 、L ky 、L k 、L cy 6 features, and simultaneously calculating the number of particles at the target position of each individual according to the step S4；
S6: the quantity of the particles at the target position after the long-pressure short-pumping method is regulated and controlled is used as a wind-control dust-removing target, and a constraint multi-target optimization problem is established
In the formula: x ═ S, R yc 、R zj 、L ky 、L k 、L cy ) Is a 6-dimensional real vector; (x) is an objective function; g j (X) represents a jth inequality constraint;
a and b are evaluation weights, a is equal to 0.7 at the driver, and b is equal to 0.3 at the pedestrian behind the driver;
s7: and predicting the technical parameters of long-pressure short-pumping by adopting a particle swarm algorithm.
2. The method for calculating the parameters of the long-pressure short-pumping air-control dust-removal equipment of the fully mechanized coal mining face according to claim 1, wherein the method comprises the following steps: in S4, the target position includes a tunneling driver position and a pedestrian position behind the driver.
3. The method for calculating the parameters of the long-pressure short-pumping air-control dust-removal equipment of the fully mechanized coal mining face according to claim 1, characterized by comprising the following steps: the S7 specifically includes:
s71: isolating abnormal values in the sample data set in the step S5 by adopting a box-type formula four-bit distance method, uniformly mapping data with different dimensions to an interval [0,1] through maximum and minimum normalization to obtain normalized data, and normalizing the wind control and dust removal target;
s72: generating initialization particles and particle speeds by adopting a random method, and recording the population scale as m;
s73: calculating the fitness of each particle by adopting a formula (2);
s74: updating the individual optimal fitness of the particles by comparing the current fitness of the particles with the current optimal fitness of the particles, and storing the current optimal particles to an external file;
s75: judging whether the number of particles in the external archive exceeds the solution set capacity, if so, deleting redundant non-inferior solutions, and optimizing the solution set by using a formula (3);
in the formula: p is a radical of d The probability that a non-inferior solution is deleted; i, j are serial numbers of non-inferior solutions; n is the number of non-inferior solutions in the unified grid with the non-inferior solution; k is the number of current non-inferior solutions;
s76: updating the position and the speed of the particles by adopting a formula (4);
in the formula: i is the particle number; v is the particle velocity; t is the number of iterations; a is 1 ，a 2 Is the inertial weight; p is gi A local optimal position vector for the individual; p zi A global optimal position vector for the population;
s77: judging whether the iteration times reach the population scale iteration times m or not, and if not, turning to the step S73; if the iteration number m is reached, terminating;
s78: and (4) calculating the trust of each particle in the external archive by combining the wind-control dust-removal target, sorting the particles according to the trust from large to small, and performing inverse normalization on the sorted particles according to the normalization parameters in the step S71 to obtain corresponding index parameters and model values.
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