Hydraulic support column descending and support moving dust production and migration rule simulation method and device

Abstract

The invention relates to a simulation method and device for a hydraulic support column descending and support moving dust production migration rule, and belongs to the technical field of coal mine dust control. In order to solve the problems that the prediction error of an existing model exceeds 40%, the migration path deviation exceeds 50%, the side seam adjusting precision of the device is + / -20 mm, and the dust falling efficiency fluctuates by 35%, a multi-factor coupling modeling and intelligent dynamic regulation and control technology is provided. The method comprises the following steps: constructing a dust production prediction model of coal rock hardness, water content, roof pressure bearing and side seam spacing coupling, monitoring dust distribution in real time by adopting a laser scattering sensor and a particle size spectrometer array, designing a high-precision electric push rod to adjust the side seam spacing to + / -0.5 mm, and dynamically correcting an error in combination with Kalman filtering. The device comprises an adjustable support assembly, a triaxial pressure loading module and an intelligent verification system. The dust production prediction error is 9.8%, the migration deviation is 14.6%, the adjusting efficiency is improved by 3.2 times, the dust falling fluctuation is compressed to 7.9%, and the respiratory dust inhibition rate reaches 85.4%.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of coal mine dust control, and relates to a hydraulic support column lowering and moving frame dust production and migration law simulation method and device. BACKGROUND

[0002] In a fully mechanized coal mining face, hydraulic support column lowering and moving frame operation is the second largest dust source next to coal cutting by a coal mining machine. The respirable dust concentration produced by hydraulic support column lowering and moving frame operation can reach 80 to 150 milligrams per cubic meter, accounting for 30% to 40% of the total dust source of the working face. Due to the coupling effect of multiple factors such as coal and rock body crushing, roof stress release, and inter-frame air leakage during repeated lifting and lowering of the support, the dust diffusion presents the characteristics of instantaneous high concentration and multidirectional diffusion, which seriously threatens the health of underground workers. However, the current research on the dust production law of hydraulic supports still has the following technical bottlenecks:

[0003] Existing models are mostly based on laboratory drop hammer impact tests, and there is an essential difference between the energy loading mode and the gradual crushing process of coal and rock in the actual working condition of the hydraulic support. The traditional model only considers single factors such as coal hardness and moisture content, without introducing key parameters such as roof pressure and adjacent support side seam spacing, resulting in a prediction error of dust production generally exceeding 40%.

[0004] Existing numerical simulation methods mostly use the steady flow field assumption, which cannot depict the coupling effect of instantaneous coal dust injection and inter-frame turbulence during column lowering and moving frame operation. The measured data shows that the dust concentration at the side seam of the support increases by 200% within 3 seconds after moving the frame, but due to the lack of high spatiotemporal resolution dynamic monitoring means, the deviation between the predicted and measured migration paths is more than 50%.

[0005] The traditional simulation device uses a fixed support model, which cannot reproduce the dynamic changes of the lifting height of 2.5 to 7 meters and the moving speed of 0.1 to 0.5 meters per second in the actual working condition. Laboratory tests show that when the side seam spacing increases from 500 millimeters to 1500 millimeters, the dust escape amount between the frames increases by 80%, but the existing device has a side seam adjustment accuracy of only ±20 millimeters, which cannot quantify the influence of structural parameters.

[0006] Existing dust control technologies still use fixed time or position triggering mode, without forming a closed-loop feedback with real-time dust concentration, moving speed and other working condition parameters. Underground application shows that the dust suppression efficiency of the traditional method fluctuates by more than 35% when the moving speed changes, and the inhibition rate for respirable dust with a particle size of less than 7 microns is less than 50%. SUMMARY

[0007] Therefore, the purpose of the present application is to provide a hydraulic support column lowering and moving frame dust production and migration law simulation method and device.

[0008] To achieve the above purpose, the present application provides the following technical solutions:

[0009] A hydraulic support column lowering and moving frame dust production and migration simulation method, comprising the following steps:

[0010] S1. Construct a hydraulic support column lowering and moving frame dust production characteristic similarity simulation test platform, the platform comprising an adjustable simulation hydraulic support assembly, a coal rock sample loading device, and a multidimensional dust monitoring array;

[0011] S2. Set a coal rock physicochemical parameter module, a roof bearing pressure parameter module, and a support structure parameter module, wherein the support structure parameters include the adjacent support side seam spacing L;

[0012] S3. Drive the simulation hydraulic support assembly to perform a column lowering and moving frame action, and simultaneously activate the coal rock sample loading device to apply confining pressure;

[0013] S4. Real-time collection of dust production M and dust production performance index R by the multidimensional dust monitoring array, wherein λ is the coal rock crushing degree index, N is the coal rock crushing index, d p is the dust particle size, with units of μm;

[0014] S5. Establish a multi-factor coupled dust production characteristic prediction model, taking coal rock hardness f, moisture content n, roof bearing pressure p, and side seam spacing L as input variables, and outputting dust production and particle size distribution parameters.

[0015] Further, the multidimensional dust monitoring array in S1 comprises:

[0016] A laser scattering type dust concentration sensor 101 arranged at the side seam of the simulation support, installed at a height of 1.2-1.8 m from the bottom plate;

[0017] An aerosol particle size spectrometer 102 arranged at the coal rock crushing point, with a constant sampling flow of 2.83 L / min±3%;

[0018] Airflow velocity sensors 103 distributed at the boundaries of the simulation area, forming a three-dimensional flow field monitoring network.

[0019] Further, the column lowering and moving frame action in S3 comprises:

[0020] The hydraulic cylinder driving speed is controlled at a programmable displacement curve of 0.1 m / s-0.5 m / s;

[0021] The confining pressure p applied by the coal rock sample loading device is dynamically related to the hydraulic support roof bearing pressure, satisfying:

[0022] p=K·σ v

[0023] In the formula, K is the lateral pressure coefficient, and σ v is the vertical stress.

[0024] Furthermore, the dust generation characteristic prediction model in S5 is established through multivariate nonlinear regression, satisfying:

[0025] M=α·f β ·n γ ·p δ ·L ε

[0026] In the formula, α is the proportionality coefficient, and β, γ, δ, and ε are the exponential weights of each parameter, respectively.

[0027] The formula for calculating the proportion of respirable dust η is:

[0028]

[0029] In the formula, θ and φ are material property coefficients.

[0030] A device for simulating the dust transport pattern of a hydraulic support column lowering and moving mechanism includes:

[0031] The simulated hydraulic support assembly 201 includes a programmable hydraulic drive system 202 and a side gap adjustment mechanism 203;

[0032] The coal and rock loading module 204 integrates the confining pressure loading unit 205 and the coal sample moisture content control system 206;

[0033] The multi-source data acquisition system 207 includes the dust concentration sensor 101, particle size analyzer 102, and airflow sensor 103 as described in claim 2.

[0034] The data processing terminal 208 is equipped with a dust generation characteristic prediction algorithm module 209 and a three-dimensional dust cloud visualization interface 210.

[0035] Furthermore, the side seam spacing adjustment mechanism 203 includes:

[0036] The dual-rail sliding device 301 has a stroke range of 500mm to 1500mm.

[0037] Electric linear actuator 302, positioning accuracy ±0.5mm;

[0038] The gap measurement sensor 303 uses a laser rangefinder to provide real-time feedback on the actual gap L of the side gap.

[0039] Furthermore, the coal and rock loading module 204 further includes:

[0040] Triaxial pressure chamber 401, maximum confining pressure loading capacity 10MPa;

[0041] Microwave moisture control unit 402, moisture content control range 2%~15%±0.5%;

[0042] The 403 coal and rock hardness test probe uses a cone-shaped indenter hardness tester to measure the f-value online.

[0043] Furthermore, the data processing terminal 208 has the following built-in features:

[0044] Dust transport coupling calculation module 501 calculates the superposition effect of multiple dust sources based on the discrete phase model DPM.

[0045] The real-time source analysis unit 502 determines the contribution rate of the dominant dust source through a particle reverse tracking algorithm.

[0046] The adaptive learning database 503 stores a comparison table of dust generation characteristic parameters under different geological conditions.

[0047] Furthermore, the real-time source tracing analysis unit 502 performs the following operations:

[0048] Extract the spatiotemporal distribution data of dust concentration and establish the concentration gradient matrix C(x,y,z,t);

[0049] Calculate the influence weight ω of each dust source i ,satisfy:

[0050]

[0051] In the formula, C i For the concentration contributed by a single dust source, C total Where V is the total dust concentration, and V is the volume of the integration region;

[0052] Generate a dynamic heatmap of dust source contributions and mark key dust source areas with a contribution rate exceeding 30%.

[0053] Furthermore, it also includes a verification module 601, which includes:

[0054] The on-site measured data interface 602 is connected to the downhole hydraulic support condition monitoring system.

[0055] The model error compensation unit 603 uses a Kalman filter to correct prediction bias.

[0056] The 604 visualization calibration interface overlays and displays the simulation results and measured data curves.

[0057] The beneficial effects of this invention are as follows:

[0058] (1) Based on a multi-factor coupled model considering coal and rock hardness, moisture content, roof bearing capacity, and side joint spacing, and taking into account the interaction mechanism between coal and rock fracturing and dust diffusion during the hydraulic support column lowering and moving process, a dust generation characteristic prediction model was established through multivariate nonlinear regression. Experimental verification shows that the average error of the model in predicting the total dust generation was reduced from more than 40% in traditional methods to 9.8%, and the prediction accuracy of the proportion of respirable dust reached 97.2%.

[0059] (2) A multi-dimensional dust monitoring array was constructed using a laser scattering dust concentration sensor, an aerosol particle size analyzer, and a three-dimensional flow field monitoring network. Combined with a discrete phase model and a particle inverse tracking algorithm, millisecond-level dynamic analysis of the spatiotemporal distribution of dust concentration was achieved. Actual measurement data showed that the deviation between the predicted dust transport path and the actual downhole measurement results was reduced from 52.3% to 14.6%, achieving a spatiotemporal resolution of 1 ms time accuracy and 1 cm accuracy. 3 Spatial precision.

[0060] (3) The side gap spacing adjustment mechanism, consisting of a double-rail sliding device and an electric push rod, enables continuous adjustment of the side gap spacing within the range of 500mm to 1500mm, with an adjustment accuracy of ±0.5mm. Combined with a triaxial pressure chamber and a microwave moisture control unit, it can reproduce the actual working conditions of 10MPa confining pressure and 2% to 15% coal and rock moisture content. Operational tests of the device show that the side gap spacing adjustment efficiency is increased to 3.2 times that of traditional devices, and the working condition parameter reproduction rate reaches 96.7%.

[0061] (4) The model error compensation unit based on the Kalman filter corrects the deviation between the dust generation prediction model and the measured data in real time, reducing the fluctuation range of dust reduction efficiency from 35.6% in the traditional method to 7.9%. Through the setting of respirable dust concentration management limit and multi-parameter coupling control strategy, the respirable dust suppression rate is increased to 85.4%, and the system response time is shortened to 1.8 seconds.

[0062] (5) The fully wireless battery-powered spray control system reduces the line maintenance requirements of traditional wired deployments. Combined with non-contact automatic spraying devices, it reduces sensor maintenance costs by 31.5%. The overall energy consumption of the device is reduced by 42.3% compared to traditional systems. On-site installation and commissioning time is reduced from 15 person-days to 3 person-days, and operation and maintenance efficiency is improved by 400%.

[0063] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0064] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0065] Figure 1 A structural block diagram of a device for simulating the dust transport law of hydraulic supports;

[0066] Figure 2 This is a block diagram of an intelligent dust control closed-loop system.

[0067] Figure label:

[0068] 101-Laser Scattering Dust Concentration Sensor

[0069] 102-Aerosol Particle Size Spectrometer

[0070] 103-Airflow velocity sensor

[0071] 201-Simulated Hydraulic Support Assembly

[0072] 202-Programmable Hydraulic Drive System

[0073] 203-Side seam spacing adjustment mechanism

[0074] 204-Coal and Rock Loading Module

[0075] 205-Confining Pressure Loading Element

[0076] 206-Coal Sample Moisture Content Control System

[0077] 207-Multi-source data acquisition system

[0078] 208-Data Processing Terminal

[0079] 209-Dust Generation Characteristic Prediction Algorithm Module

[0080] 210-3D Dust Cloud Visualization Interface

[0081] 301-Double guide rail sliding device

[0082] 302-Electric Actuator

[0083] 303-Gap Measurement Sensor

[0084] 401-Triaxial Pressure Chamber

[0085] 402-Microwave Moisture Control Unit

[0086] 403-Coal and Rock Hardness Testing Probe

[0087] 501-Dust Transport Coupling Calculation Module

[0088] 502 - Real-time Source Tracing Analysis Unit

[0089] 503 - Adaptive Learning Database

[0090] 601 - Verification Module

[0091] 602 - Field Measurement Data Interface

[0092] 603-Model Error Compensation Unit

[0093] 604 - Visual Calibration Interface Detailed Implementation

[0094] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0095] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0096] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0097] like Figure 1 As shown, this embodiment provides a device for simulating the dust transport law of hydraulic support column lowering and moving, comprising the following components:

[0098] 1. Simulated hydraulic support assembly 201

[0099] The simulated hydraulic support assembly 201 includes a programmable hydraulic drive system 202 and a side gap adjustment mechanism 203.

[0100] The programmable hydraulic drive system 202 provides power to the support assembly through hydraulic pipelines, simulating the lowering and moving action of the downhole hydraulic support, with a hydraulic pressure range of 0-20MPa.

[0101] The side gap spacing adjustment mechanism 203 includes a double guide rail sliding device 301, an electric push rod 302, and a laser rangefinder 303. It achieves precise adjustment of the side gap spacing d within the range of 500 to 1500 mm through gear and rack transmission, with an accuracy of ±0.5 mm.

[0102] 2. Coal and Rock Loading Module 204

[0103] The coal and rock loading module 204 integrates a confining pressure loading unit 205, a coal sample moisture content control system 206, and a hardness testing probe 403.

[0104] The confining pressure loading unit 205 applies axial pressure through the triaxial pressure chamber 401, with a maximum confining pressure of 10 MPa, to simulate the stress state of coal and rock underground.

[0105] The coal sample moisture content control system 206 uses microwave control technology to control the coal sample moisture content n within 2%-15% ± 0.5%.

[0106] The hardness test probe 403 measures the hardness f value of coal and rock in real time using the conical indentation method, and the data is transmitted to the data processing terminal 208 via the orange dashed arrow.

[0107] 3. Multi-source data acquisition system 207

[0108] The system includes a laser scattering dust concentration sensor 101, an aerosol particle size analyzer 102, and a three-dimensional airflow sensor 103.

[0109] The dust concentration sensor 101 is installed at the side seam of the simulation bracket (height 1.2-1.8m) and transmits the dust concentration data to the data acquisition system in real time via a solid line (RS-485 protocol).

[0110] The sampling port of the particle size analyzer 102 was aligned with the coal and rock fracture point, and the sampling flow rate was kept constant at 2.83 L / min ± 3%.

[0111] 4. Data processing terminal 208

[0112] The terminal has a built-in dust transport coupling calculation module 501, a real-time source tracing analysis unit 502, and an adaptive learning database 503.

[0113] The dust transport coupling calculation module 501 generates a dust concentration field based on the discrete phase model DPM, and the calculation results are transmitted to the three-dimensional visualization interface 210 through a purple optical fiber symbol.

[0114] The real-time source tracing analysis unit 502 uses a particle backtracking algorithm to calculate the contribution rate weight ω under the coupling effect of multiple dust sources. i .

[0115] 5. Verification Module 601

[0116] The module is connected to the downhole hydraulic support condition monitoring system via the field measurement data interface 602 to collect parameters such as dust concentration and wind speed.

[0117] Model error compensation unit 603 utilizes the Kalman filter formula: The prediction bias is corrected, and the corrected data is transmitted to the visual calibration interface 604 via a cyan double-dotted line.

[0118] like Figure 2 As shown in the figure, this embodiment provides an intelligent dust control closed-loop system, the operation logic of which is as follows:

[0119] 1. Data Input and Processing

[0120] Downhole measured data (including dust concentration and support operating parameters) are input into the system through the field measured data interface 602.

[0121] The model error compensation unit 603 compares the measured data with the simulated predicted values ​​and uses the Kalman filter algorithm to dynamically adjust the model parameters so that the prediction deviation Δ≤5%.

[0122] 2. Closed-loop control process

[0123] Positive control chain:

[0124] Measured data → Error compensation → Visual calibration

[0125] The visualization calibration interface 604 displays the simulation results and measured data in the form of superimposed curves, and marks key deviation points.

[0126] Feedback correction chain:

[0127] Calibration interface → Feedback deviation signal to error compensation unit

[0128] Feedback signals trigger adaptive updates to model parameters, ensuring real-time prediction accuracy.

[0129] 3. Multi-source coupling analysis

[0130] The real-time source tracing analysis unit 502, based on the formula Calculate the contribution rate weights of dust sources from coal cutting by the coal mining machine and dust sources from hydraulic support movement.

[0131] When the contribution rate of a certain dust source is ω iWhen the dust concentration is greater than 30%, the system automatically marks it as a high-contribution area and links the spray controller to adjust the dust suppression intensity of the corresponding area.

[0132] Example 1: Construction and Validation of a Multi-Factor Coupled Dust Generation Prediction Model

[0133] Implementation steps:

[0134] 1. Experimental System Setup

[0135] A 1:3 scale hydraulic support simulation platform was built in the laboratory, configured with the following modules:

[0136] Triaxial pressure chamber: maximum loading pressure 10MPa, pressure sensor accuracy ±0.1MPa

[0137] Microwave moisture control unit: Moisture content control range 2%-15%, fluctuation ≤ ±0.5%

[0138] Electric push rod type side seam adjustment mechanism: stroke 500~1500mm, positioning accuracy ±0.5mm

[0139] Conical indentation hardness tester: measuring range 0-50MPa, resolution 0.01MPa

[0140] 2. Parameter calibration and sample preparation

[0141] Three types of typical coal and rock samples were selected (hard coal f=2.5, medium-hard coal f=1.8, and soft coal f=1.2).

[0142] Ten groups of coal samples with moisture contents n=5%, 10%, and 15% were prepared by microwave drying and steam humidification.

[0143] The coal sample was cut into standard test blocks of 300mm×300mm×300mm and installed in the pressure chamber.

[0144] 3. Dynamic dust generation simulation test

[0145] Set the top plate bearing pressure p = 3MPa and the side joint spacing L = 800mm, and start the hydraulic drive system to perform the column lowering and frame moving action at a speed of 0.3m / s.

[0146] The following monitoring devices will be activated simultaneously:

[0147] Aerosol particle size analyzer: Collects dust particle size distribution data every 0.5 seconds (d). p =0.5~75μm)

[0148] Laser scattering sensor array: monitors dust generation M (g / s) at a frequency of 1000Hz.

[0149] High-speed camera: records the coal and rock crushing process and analyzes the trajectory of the crushed particles.

[0150] 4. Model Training and Validation

[0151] The collected f, n, p, and L data are input into a multivariate nonlinear regression model, and after iterative optimization, the following results are obtained:

[0152] M = 1.32·f 0.87 ·n -0.65 ·p 0.43 ·L 0.21

[0153] Formula for the proportion of respirable dust:

[0154]

[0155] Verification results: Based on 36 sets of test data, the average error in dust generation prediction was 9.8%, and the error in the proportion of respirable dust was 2.8%.

[0156] Technical effects:

[0157] The model can quantify the impact of changes in the side joint spacing L on dust generation (when L increases from 500 to 1500 mm, the escaped dust increases by 81.7%) and analyze the coupling effect of roof bearing pressure p and coal and rock hardness f (for every 1 MPa increase in p, the proportion of respirable dust increases by 12.3%).

[0158] Example 2: Verification of High-Precision Side Seam Adjustment Device and Dust Migration Pattern

[0159] Implementation steps:

[0160] 1. Equipment commissioning

[0161] Install a dual-rail sliding device and configure the following components:

[0162] Servo electric linear actuator: 5000N thrust, repeatability ±0.5mm

[0163] Laser rangefinder: measuring range 2000mm, resolution 0.01mm

[0164] Programmable hydraulic controller: Frame movement speed range 0.1~0.5m / s

[0165] 2. Dynamic Adjustment and Data Acquisition

[0166] Set the frame moving speed to 0.2 m / s, and gradually increase the side seam spacing L from 600 mm to 1200 mm (in 200 mm increments). Deploy monitoring points at the following locations:

[0167] Side seam exit of the bracket: 3 sets of light scattering sensors (heights of 1.5m, 2.0m, and 2.5m) are installed.

[0168] Work area: A breathing zone monitoring station (1.6m high) will be set up 5m behind the hydraulic support.

[0169] Simultaneous collection of dust concentration, wind speed, and temperature data at a sampling frequency of 100Hz.

[0170] 3. Transport path modeling

[0171] A three-dimensional transport simulation was established based on the discrete phase model (DPM), and boundary conditions were set:

[0172] Inlet wind speed: 1.2 m / s; turbulence intensity: 10%.

[0173] The initial velocity of the dust jet was 8 m / s, and the particle size distribution conformed to the Rosin-Rammler distribution.

[0174] The measured data were imported into the model, and the dust source contribution rate ω was calculated using the particle backtracking algorithm. i

[0175] 4. Results Analysis

[0176] When L = 600 mm, the peak dust concentration at the side seam is 152 mg / m³. 3 The concentration in the breathing zone of the work area was 38 mg / m³. 3 When L increases to 1200mm, the proportion of escaped dust rises from 21.3% to 47.6%, and the prediction deviation of the transport path is ≤14.6%. Technical effect:

[0177] This study reveals the quantitative relationship between the side gap spacing L and the dust escape rate (escape rate increases by 8.6% for every 100mm increase in L), validating the spatiotemporal resolution (1ms time accuracy, 1cm) of the dynamic transport model. 3 Spatial accuracy)

[0178] Example 3: Verification of Closed-Loop Control of Intelligent Dust Control System

[0179] Implementation steps:

[0180] 1. System Integration

[0181] Deploy the following hardware:

[0182] Dust concentration sensor: measuring range 0~500mg / m³ 3 Accuracy ±1%

[0183] Hydraulic support operating condition acquisition module: Real-time acquisition of support movement speed v and top plate bearing pressure p

[0184] Dust suppression spray system: pressure adjustment range 0.5~4.0MPa, nozzle flow rate 0~10L / min

[0185] 2. Control Logic Implementation

[0186] Set the respirable dust concentration threshold C threshold =5mg / m 3

[0187] Establish control rules:

[0188] When C real ≥C threshold At that time, the spray pressure P = 3.0 MPa, and the number of nozzles N = 8 groups are open.

[0189] When C real <C threshold At that time, P = 1.5 MPa, N = 4 groups

[0190] Kalman filter parameter settings: process noise Q = 0.01, observation noise R = 0.1

[0191] 3. Dynamic control test

[0192] Simulated sudden change in frame movement speed: v increases from 0.1 m / s to 0.4 m / s.

[0193] Monitoring system response:

[0194] High-pressure spray will be activated within 1.8 seconds after the dust concentration exceeds the limit.

[0195] The concentration of respirable dust was 7.2 mg / m³. 3 Reduced to 3.5 mg / m³ 3 Fluctuation range ≤7.9%

[0196] Data recording: During 72 hours of continuous operation, the system experienced 0 false triggers, and energy consumption decreased by 42.3%.

[0197] Technical effects:

[0198] Achieve rapid response (≤2 seconds) to dust concentration exceeding the standard.

[0199] The respirable dust suppression rate was 85.4%, an improvement of 35 percentage points compared to traditional methods.

[0200] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for simulating the dust transport law of hydraulic support column lowering and moving, characterized in that: Includes the following steps: S1. Construct a similar simulation test platform for the dust generation characteristics of hydraulic support column lowering and moving, the platform including an adjustable simulated hydraulic support component, a coal and rock sample loading device, and a multi-dimensional dust monitoring array; S2. Set up the coal and rock physical and chemical parameters module, the roof pressure bearing parameters module, and the support structure parameters module, where the support structure parameters include the spacing L of the side seams between adjacent supports; S3. Drive the simulated hydraulic support assembly to perform column lowering and support shifting actions, and simultaneously activate the coal and rock sample loading device to apply confining pressure; S4. Real-time collection of dust generation M and dust generation performance index R is achieved through a multi-dimensional dust monitoring array. λ is the coal and rock fragmentation index, N is the coal and rock fragmentation index, and d p The particle size is expressed in μm. S5. Establish a multi-factor coupled dust generation characteristic prediction model, taking coal and rock hardness f, moisture content n, roof bearing pressure p, and side joint spacing L as input variables, and output dust generation amount and particle size distribution parameters.

2. The method for simulating the dust transport law of hydraulic support column lowering and moving according to claim 1, characterized in that: The multi-dimensional dust monitoring array in S1 includes: A laser scattering dust concentration sensor (101) is installed at a height of 1.2 to 1.8 m from the base plate, located at the side seam of the simulation support. An aerosol particle size analyzer (102) was installed at the coal and rock fracture point, with a sampling flow rate of 2.83 L / min ± 3%. Airflow velocity sensors (103) distributed along the boundary of the simulated region form a three-dimensional flow field monitoring network.

3. The method for simulating the dust transport law of hydraulic support column lowering and moving according to claim 1, characterized in that: The column lowering and frame shifting action in S3 includes: The hydraulic cylinder drive speed is controlled within a programmable displacement curve of 0.1m / s to 0.5m / s; The confining pressure p applied by the coal and rock sample loading device is dynamically correlated with the bearing pressure of the hydraulic support roof, satisfying the following: p=K·σ v In the formula, K is the lateral pressure coefficient, σ v It is a vertical stress.

4. The method for simulating the dust transport law of hydraulic support column lowering and moving according to claim 1, characterized in that: The dust generation characteristic prediction model in S5 is established through multivariate nonlinear regression, satisfying the following: M=α·f β ·n γ ·p δ ·L ε In the formula, α is the proportionality coefficient, and β, γ, δ, and ε are the exponential weights of each parameter, respectively. The formula for calculating the proportion of respirable dust η is: In the formula, θ and φ are material property coefficients.

5. A device for simulating the dust transport law of hydraulic support column lowering and moving, characterized in that: include: The simulated hydraulic support assembly (201) includes a programmable hydraulic drive system (202) and a side gap adjustment mechanism (203); The coal and rock loading module (204) integrates the confining pressure loading unit (205) and the coal sample moisture content control system (206); The multi-source data acquisition system (207) includes the dust concentration sensor (101), particle size analyzer (102), and airflow sensor (103) as described in claim 2; The data processing terminal (208) is equipped with a dust generation characteristic prediction algorithm module (209) and a three-dimensional dust cloud visualization interface (210).

6. The device for simulating the dust transport law of hydraulic support column lowering and moving according to claim 5, characterized in that: The side seam spacing adjustment mechanism (203) includes: Dual-rail sliding device (301), stroke range 500mm~1500mm; Electric linear actuator (302), positioning accuracy ±0.5mm; The gap measurement sensor (303) uses a laser rangefinder to provide real-time feedback on the actual gap L of the side gap.

7. The device for simulating the dust transport law of hydraulic support column lowering and moving according to claim 5, characterized in that: The coal and rock loading module (204) further includes: Triaxial pressure chamber (401), maximum confining pressure loading capacity 10MPa; Microwave moisture control unit (402), moisture content control range 2%~15%±0.5%; The coal and rock hardness test probe (403) uses a cone-shaped indenter hardness tester to measure the f value online.

8. The device for simulating the dust transport law of hydraulic support column lowering and moving according to claim 5, characterized in that: The data processing terminal (208) has the following built-in features: The dust transport coupling calculation module (501) calculates the superposition effect of multiple dust sources based on the discrete phase model (DPM); The real-time source analysis unit (502) determines the contribution rate of the dominant dust source through a particle reverse tracking algorithm; An adaptive learning database (503) stores a comparison table of dust generation characteristic parameters under different geological conditions.

9. The device for simulating the dust transport law of hydraulic support column lowering and moving according to claim 8, characterized in that: The real-time source tracing analysis unit (502) performs the following operations: Extract the spatiotemporal distribution data of dust concentration and establish the concentration gradient matrix C(x,y,z,t); Calculate the influence weight ω of each dust source i ,satisfy: In the formula, C i For the concentration contributed by a single dust source, C total Where V is the total dust concentration, and V is the volume of the integration region; Generate a dynamic heatmap of dust source contributions and mark key dust source areas with a contribution rate exceeding 30%.

10. The device for simulating the dust transport law of hydraulic support column lowering and moving according to claim 5, characterized in that: It also includes a verification module (601), which contains: The on-site measured data interface (602) is connected to the downhole hydraulic support condition monitoring system; The model error compensation unit (603) uses a Kalman filter to correct prediction bias; A visual calibration interface (604) overlays simulation results and measured data curves.

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