Coal cutter coal cutting dust production characteristic test platform and method based on analog simulation

Abstract

The invention relates to a coal cutter coal cutting dust production characteristic test platform and method based on analog simulation, and belongs to the technical field of coal mining dust prevention and control. The method comprises the steps that S1, a coal cutter coal cutting dust production characteristic test platform is built, and the coal cutter coal cutting dust production characteristic test platform comprises a coal rock simulation unit, a pressure loading module, a cutting power system and a dust monitoring system; s2, preparing a coal rock sample by utilizing the coal rock simulation unit, wherein the coal rock hardness and the water content are simulated; s3, putting the prepared coal rock sample into a test platform, applying pressure to the coal rock sample by using a pressure loading module, and dynamically adjusting the power of a cutting power system according to a mathematical model of hardness, water content, pressure and power; and S4, in combination with the dust concentration, the diffusion trajectory and the particle size distribution monitored by the dust monitoring system, obtaining multiple groups of working condition data through an orthogonal test, and fitting by adopting a least square method to obtain a correlation model of the dust yield and the power, thereby providing a quantitative basis for energy conservation and consumption reduction of the coal mining machine.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of coal mining dust prevention and control, and relates to a coal cutter coal cutting dust production characteristic test platform and method based on similar simulation. BACKGROUND

[0002] In the field of coal mining, the study of dust production characteristics during the coal cutting process of the coal cutter drum is of great significance. Dust not only affects the safety and health of the working environment, but also threatens the operation of equipment and production efficiency, especially in enclosed spaces, where dust problems can cause serious safety hazards. Therefore, accurate prediction and control of dust production characteristics have become a key issue to ensure the safety and efficiency of coal mine production.

[0003] However, current research methods rely heavily on field measurements or simple theoretical calculations, which have significant limitations. Field tests are limited by the complex environment and high-risk operating conditions of the mine, with high data acquisition costs and safety concerns. Simple theoretical models often ignore the coupling effects of multiple factors, resulting in insufficient prediction accuracy and difficulty in meeting actual production needs.

[0004] The core challenge in this field is how to accurately simulate the dust production process during coal cutting by the coal cutter under limited conditions and establish a reliable prediction model. First, dust production characteristics are influenced by multiple factors, such as the hardness and moisture content of coal and rock, which directly determine the amount of dust generated and the degree of dispersion. Due to the complex changes of these factors in actual working conditions, it is difficult to fully understand their action rules through a single theoretical analysis, which leads to insufficient representativeness of experimental data. The deeper challenge is how to build a simulation platform in a laboratory environment that can truly reflect the actual coal cutting process to safely and efficiently obtain dust production data under different working conditions. The difficulty of building such a simulation platform directly affects the accuracy of data analysis and the reliability of subsequent prediction models.

[0005] Therefore, how to accurately test and predict the dust production characteristics during the coal cutting process of the coal cutter drum by building a similar simulation test platform and analyzing multiple influencing factors has become a key problem that needs to be solved in this research. SUMMARY

[0006] Therefore, the purpose of the present application is to provide a coal cutter coal cutting dust production characteristic test method based on similar simulation, design a test platform that can truly simulate coal mining conditions, and simultaneously detect dust production and dispersion characteristics on this basis. At the same time, a prediction model considering the coupling effects of coal and rock hardness (f), moisture content (η), roof pressure (p), and coal cutting power (P) is constructed to accurately predict dust production characteristics under complex working conditions and provide quantitative basis for energy saving and consumption reduction of the coal cutter.

[0007] To achieve the above object, the present application provides the following technical solutions:

[0008] A coal cutter coal cutting dust production characteristic test method based on similar simulation, specifically comprising the following steps:

[0009] S1: build a coal cutter coal cutting dust production characteristic test platform, including a coal rock simulation unit, a pressure loading module, a cutting power system and a dust monitoring system;

[0010] S2: prepare coal rock samples using the coal rock simulation unit, including simulating coal rock hardness f and moisture content η;

[0011] S3: simulate the coal cutting dust production process of the coal cutter: place the prepared coal rock sample into the test platform, apply pressure p to the coal rock sample using the pressure loading module, dynamically adjust the power P of the cutting power system according to the mathematical model of hardness f, moisture content η, pressure p and power P, and realize the dynamic correlation of cutting strength and dust generation;

[0012] S4: combine the dust concentration, diffusion trajectory and particle size distribution monitored by the dust monitoring system, obtain multiple groups of working condition data through orthogonal test, and obtain the correlation model of dust production M and power P by least squares fitting, to provide quantitative basis for energy saving and consumption reduction of the coal cutter.

[0013] Further, in step S1, the coal rock simulation unit includes a hardness adjustable sample box and a humidity control system; the hardness adjustable sample box uses gypsum-sand-coal powder composite material; the humidity control system is used to control the moisture content of coal rock; and an optical fiber pressure sensor is pre-embedded in the sample box.

[0014] Further, in step S1, the pressure loading module includes a top pressure simulation device, which uses a hydraulic servo system to simulate the coal rock pressure p in front of the working face, supporting sinusoidal or step load waveform.

[0015] Further, in step S1, the cutting power system includes a simulation drum, a simulation drum traction track, a variable frequency motor and a motor power monitoring system; the variable frequency motor is used to drive the simulation drum to cut coal; the simulation drum traction track is used to pull the simulation drum forward or backward; and the motor power monitoring system is used to monitor the power of the variable frequency motor in real time.

[0016] Further, in step S1, the dust monitoring system includes a laser scattering type dust concentration sensor, a particle size spectrometer and a high-speed camera system; the laser scattering type dust concentration sensor is used to monitor the dust concentration; the particle size spectrometer is used to analyze the dust particle size distribution; and the high-speed camera system is used to record the dust diffusion trajectory.

[0017] Further, in step S1, the coal cutter coal cutting dust production characteristic test platform further comprises a dust collection system for collecting dust particles less than 75 μm, facilitating the acquisition of dust production.

[0018] Further, in step S3, the mathematical model expression of the hardness f, the moisture content η, the pressure p and the power P power is:

[0019] R(d)=100·exp(-λd N )

[0020] λ=0.68f+0.12η-0.05p+0.03P

[0021] N=1.05-0.15ln(f)

[0022] Wherein, R is the dispersion degree, that is, the cumulative mass percentage above a certain particle size d; d is the dust particle size, λ is the crushing degree index, and N is the crushing index.

[0023] Further, in step S4, the correlation model expression of the dust production M and the power P is:

[0024] lnM=1.32+0.32lnf-0.4lnη+0.17lnp+0.85lnP+ε M

[0025] Wherein, ε M is a residual term.

[0026] Further, the calculation formula of the dust production M is:

[0027]

[0028] Wherein, H(·) is a Heaviside step function for screening dust particles with a particle size less than 75 μm; m i is the mass of the i-th sampling (g); n is the sampling number; t is the total sampling time (min); d i is the dust particle size of the i-th sampling.

[0029] The beneficial effects of the present application are:

[0030] (1) Multi-dimensional working condition simulation capability

[0031] The present application realizes the dynamic coupling regulation and control of the four-dimensional parameters of coal hardness, moisture content, roof pressure and coal cutting power by integrating the coal rock simulation unit, the hydraulic servo loading system and the variable frequency power system, the experimental working condition is close to the actual complex conditions underground, and the working condition matching degree is high.

[0032] (2) Fine monitoring of dust production characteristics

[0033] The application adopts multi-modal monitoring technology (laser scattering sensor, particle size spectrometer, high-speed camera system), realizes the synchronous collection of dust concentration (resolution 1mg / m 3 ), particle size distribution (classification accuracy 0.1-100um) and diffusion trajectory (frame rate 1000fps), greatly improves the data integrity, and supports the whole cycle analysis of dust generation and diffusion.

[0034] (3) Precise prediction model construction

[0035] Based on orthogonal test and least square regression, a dust production prediction equation considering multi-factor coupling is established, the model fitting is high, the prediction error is small, and quantitative basis is provided for the optimization of the power of the coal mining machine, and the energy saving efficiency is greatly improved.

[0036] (4) Innovative materials and dynamic loading technology

[0037] The gypsum-powder-sand standardized formula library (hardness error ±0.2f) is developed, combined with a dynamic hydraulic servo system (waveform response frequency ≥10Hz), the stress fluctuation in the coal rock crushing process is simulated, the nonlinear change characteristics of the cutting resistance are truly restored, and the reliability of the experimental data is improved.

[0038] (5) Safety and standardization upgrade

[0039] The infrared dust concentration early warning and emergency stop interlocking system (response time ≤0.5s) is integrated into the fully enclosed negative pressure experimental cabin, which meets the explosion-proof standard of “Coal Mine Safety Regulations”; the dust collection specification (75um screen + Heaviside function screening) is proposed, and the standardized measurement of dust production quality is realized.

[0040] (6) Significant industrial application value

[0041] Through virtual simulation, the parameter combination of extreme working conditions is optimized, and the risk of entity test is reduced.

[0042] In summary, through multi-parameter dynamic coupling control, standardized material preparation and intelligent monitoring technology, the application breaks through the bottleneck of low working condition restoration and data fragmentation of traditional test methods, and provides a systematic solution for energy saving and dust precise prevention and control of the coal mining machine, and fills the technical gap of dust production characteristic prediction under complex working conditions.

[0043] Other advantages, objects and features of the application will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following or can be learned by practice of the application. The objects and other advantages of the application can be realized and attained by the embodiments particularly pointed out in the written description and claims hereof. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to make the objects, technical solutions and advantages of the present application clearer, the preferred embodiments of the present application will be described in detail below with reference to the drawings, in which:

[0045] Fig. 1 A flow chart of a coal cutter coal cutting dust production characteristic testing method based on similarity simulation of the present application;

[0046] Fig. 2 A logic diagram of a coal cutter drum coal cutting dust production characteristic prediction model of the present application;

[0047] Fig. 3 A structure diagram of a coal cutter coal cutting dust production characteristic testing platform of the present application. DETAILED DESCRIPTION

[0048] The embodiments of the present application will be described below through specific concrete examples, and other advantages and effects of the present application can be easily understood by those skilled in the art from the disclosure of the present specification. The present application can also be implemented or applied through other different concrete embodiments, and various modifications or changes can be made to the details in the present specification based on different views and applications without departing from the spirit of the present application. It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0049] The drawings are only used for exemplary illustration, and the representation is only a schematic diagram, not a physical diagram, and should not be understood as a limitation on the present application; in order to better illustrate the embodiments of the present application, some components in the drawings may be omitted, enlarged or reduced, and do not represent the actual product size; it can be understood by those skilled in the art that some well-known structures and their descriptions in the drawings may be omitted.

[0050] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it should be understood that if the terms "upper", "lower", "left", "right", "front", "back" and the like indicate the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore the terms describing the positional relationship in the drawings are only used for exemplary illustration, and should not be understood as a limitation on the present application, and for those skilled in the art, the specific meanings of the above terms can be understood according to the specific circumstances.

[0051] Referring to Figs. 1-3 The present application provides a coal cutter coal cutting dust production characteristic testing method based on similarity simulation, specifically comprising the following steps:

[0052] S1: Build a coal cutter coal cutting dust production characteristics test platform, including coal rock simulation unit, pressure loading module, cutting power system, dust monitoring system and dust collection system.

[0053] S2: Use the coal rock simulation unit to prepare coal rock samples, including simulating coal rock hardness f and moisture content η.

[0054] S3: Simulate the coal cutting dust production process of the coal cutter: put the prepared coal rock sample into the test platform, use the pressure loading module to apply pressure p to the coal rock sample, according to the mathematical model of hardness f, moisture content η, pressure p and power P, dynamically adjust the power P of the cutting power system, realize the dynamic correlation of cutting strength and dust generation;

[0055] S4: Combine the dust concentration, diffusion trajectory and particle size distribution monitored by the dust monitoring system, obtain multiple groups of working condition data through orthogonal test, and use the least squares method to fit the correlation model of dust production M and power P, and use the improved particle swarm optimization algorithm (PSO) for global optimization, to provide quantitative basis for energy saving and consumption reduction of the coal cutter.

[0056] Example 1:

[0057] As shown in Fig. 2 , the present embodiment provides a coal cutter coal cutting dust production characteristics test platform, wherein,

[0058] Coal rock simulation unit: equipped with a hardness adjustable coal rock sample box (hardness range f = 1-6), integrated with a humidity control system (moisture content η control range 0-15%);

[0059] Pressure loading module: including a top pressure simulation device, using a hydraulic servo system to simulate the coal rock pressure p in front of the working face (0-20MPa), accuracy ±0.5MPa; support sine or step load waveform.

[0060] Cutting power system: including simulation drum, simulation drum traction track, variable frequency motor and motor power monitoring system; variable frequency motor is used to drive the simulation drum to cut coal. The variable frequency motor drives the simulation drum (power P = 50-500kW adjustable), the rotating speed is 0-100rpm. The simulation drum traction track is used to pull the simulation drum forward or backward.

[0061] Dust monitoring system: using a laser scattering type dust concentration sensor (range 0-1000mg / m 3 ); particle size spectrometer (0.1-100μm grading detection); high-speed camera system (1000fps) records the dust diffusion trajectory.

[0062] Dust collection system: cyclone separation dust collection tray (with 75 μm screen) or anti-static dust bag is used to collect dust particles less than 75 μm, which is convenient for obtaining dust yield.

[0063] Example 2:

[0064] Based on the coal cutter coal cutting dust production characteristic test platform provided in example 1, experiments are carried out, which include the following parts:

[0065] 1. Standardized preparation of coal and rock samples

[0066] (1) Similar material ratio optimization

[0067] Gypsum-sand-coal powder composite material is used to simulate coal and rock with different hardness (f = 1-6), and the equivalence of the mechanical parameters of the material needs to be verified by physical simulation. The sample preparation needs to strictly follow the moisture content η control standard (0-15%), to ensure the uniformity of humidity distribution.

[0068] (2) Bedding structure and load simulation

[0069] The pre-embedded pressure sensor is applied with 0-20 MPa axial pressure to simulate the real stress state of coal and rock underground, and the loading accuracy directly affects the spatial distribution characteristics of dust production behavior.

[0070] 2. Multi-parameter coordinated regulation

[0071] (1) Dynamic working condition matching

[0072] According to the actual coal cutting process of the coal cutter (oblique cutting of the knife→cutting of the triangular coal→moving of the scraper conveyor), the simulation drum speed, traction speed and power output (50-500 kW) need to be adjusted synchronously to realize the dynamic correlation of cutting intensity and dust generation.

[0073] (2) Four-dimensional parameter matrix construction

[0074] Based on PLC control, the coupling control model of f (hardness), η (moisture content), p (pressure) and P (power) is established, and the parameter combination is optimized by orthogonal test method to identify the dust production sensitive factor.

[0075] P(d) = 100·exp(-λd N )

[0076] λ = 0.68f + 0.12η - 0.05p + 0.03P

[0077] N = 1.05 - 0.15ln(f)

[0078] Where R is the dispersion, that is, the cumulative mass percentage of the screen (greater than a certain particle size d); d is the particle size of the dust, λ is the crushing degree index, and N is the crushing index.

[0079] 3. Precise capture of dust generation data

[0080] (1) Multi-modal monitoring fusion

[0081] Combining laser scattering method (dust concentration), high-speed camera system (diffusion trajectory) and particle size spectrometer (particle size distribution), realize the whole cycle quantitative analysis of dust generation-diffusion.

[0082] (2) Energy efficiency correlation analysis

[0083] Based on the real-time data of cutting resistance feedback value and power P, establish the mathematical model of dust generation rate (g / kWh), provide quantitative basis for energy saving and consumption reduction of coal mining machine.

[0084] Prediction model construction method:

[0085] 1) Parameter definition

[0086] Dust generation quality M = dust mass passing through 75 μm sieve hole per unit time (mg / min);

[0087] Dispersion R (d) = 100·exp (-λd N ), where λ is the fragmentation index (0.5-2.3), N is the fragmentation index (0.8-1.5).

[0088] 2) Obtain multiple working condition data through orthogonal test;

[0089] L9 orthogonal table design is adopted, and the parameter range and precision control are as shown in Table 1. Sliding window method is used to process particle size analysis data.

[0090] Table 1 L9 orthogonal table design parameters

[0091]

[0092]

[0093] 3) The correlation model expression of dust production M and power P is obtained by least square method fitting as follows:

[0094] lnM = 1.32 + 0.32lnf- 0.4lnη + 0.17lnp + 0.85lnP + ε M

[0095] Where ε M is the residual term.

[0096] 4) Dispersion dynamic model: combine the principle of conservation of energy to obtain the traction speed.

[0097] 4、Safety verification closed loop

[0098] (1)Emergency response test

[0099] Simulate dust concentration over limit (≥500mg / m 3 ) scene, verify the linkage reliability of the spray dust suppression device and the emergency stop system, and ensure that the explosion-proof performance of the experimental platform meets the standards.

[0100] (2) Virtual simulation calibration

[0101] Through the virtual experiment platform, extreme working conditions (such as cutting of high hardness rock stratum) are preformed, the physical experiment parameter configuration is optimized, and the real test risk is reduced.

[0102] 5. Verification experiment:

[0103] Select the simulated coal rock of f=2.5, adjust η=8%;

[0104] Set p=25MPa, P=300kW;

[0105] Collect dust samples for 30min continuously;

[0106] M=248mg / min, d50=18.7μm are measured;

[0107] The model prediction error is less than 7.3%.

[0108] The modeling of the present application realizes the accurate prediction of dust production characteristics under complex working conditions by fusing the dynamic simulation data of the coal mining machine and the measured dust distribution law.

[0109] Finally, it should be pointed out that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the purpose and scope of the present technical solutions, and they should be included in the scope of the claims of the present application.

Claims

1. A method for testing the dust generation characteristics of coal cutting machines based on similarity simulation, characterized in that, The method specifically includes the following steps: S1: Construct a coal cutting dust generation characteristic test platform for coal mining machines, including a coal and rock simulation unit, a pressure loading module, a cutting power system, and a dust monitoring system; S2: Coal and rock samples are prepared using a coal and rock simulation unit, including simulating coal and rock hardness f and moisture content η; S3: Simulate the dust generation process of coal cutting by a coal mining machine: Place the prepared coal and rock sample into the test platform, apply pressure p to the coal and rock sample using the pressure loading module, and dynamically adjust the power P of the cutting power system according to the mathematical model of hardness f, moisture content η, pressure p and power P to realize the dynamic correlation between cutting intensity and dust generation. S4: Combining the dust concentration, diffusion trajectory and particle size distribution monitored by the dust monitoring system, multiple sets of working condition data were obtained through orthogonal experiments, and the correlation model between dust production M and power P was obtained by fitting the least squares method.

2. The method for testing the dust generation characteristics of coal mining machines based on similarity simulation according to claim 1, characterized in that, In step S1, the coal and rock simulation unit includes a hardness-adjustable sample box and a humidity control system; the hardness-adjustable sample box is made of gypsum-sand-coal powder composite material; the humidity control system is used to control the moisture content of the coal and rock; and an optical fiber pressure sensor is pre-embedded in the sample box.

3. The method for testing the dust generation characteristics of coal mining machines based on similarity simulation according to claim 1, characterized in that, In step S1, the pressure loading module includes a top pressure simulation device, which uses a hydraulic servo system to simulate the coal and rock pressure p in front of the working face, and supports sinusoidal or step load waveforms.

4. The method for testing the dust generation characteristics of coal mining machines based on similarity simulation according to claim 1, characterized in that, In step S1, the cutting power system includes a simulated drum, a simulated drum traction track, a variable frequency motor, and a motor power monitoring system; the variable frequency motor is used to drive the simulated drum to cut coal; the simulated drum traction track is used to pull the simulated drum forward or backward; and the motor power monitoring system is used to monitor the power of the variable frequency motor in real time.

5. The method for testing the dust generation characteristics of coal mining machines based on similarity simulation according to claim 1, characterized in that, In step S1, the dust monitoring system includes a laser scattering dust concentration sensor, a particle size analyzer, and a high-speed camera system; the laser scattering dust concentration sensor is used to monitor dust concentration; the particle size analyzer is used to analyze dust particle size distribution; and the high-speed camera system is used to record dust diffusion trajectory.

6. The method for testing the dust generation characteristics of coal mining machines based on similarity simulation according to claim 1, characterized in that, In step S1, the coal mining machine dust generation characteristic test platform also includes a dust collection system for collecting dust particles smaller than 75μm to facilitate obtaining dust output.

7. The method for testing the dust generation characteristics of coal mining machines based on similarity simulation according to claim 1, characterized in that, In step S3, the mathematical model expressions for hardness f, moisture content η, pressure p, and power P are: P(d)=100·exp(-λd N ) λ = 0.68f + 0.12η - 0.05p + 0.03P N = 1.05 - 0.15ln(f) Where R is the dispersion degree; d is the dust particle size; λ is the breakage degree index; and N is the breakability index.

8. The method for testing the dust generation characteristics of coal mining machines based on similarity simulation according to claim 1, characterized in that, In step S4, the correlation model expression between dust production M and power P is: ln M=1.32+0.32ln f-0.4lnη+0.17ln p+0.85ln P+ε M Where, ε M This is the residual term.

9. The method for testing the dust generation characteristics of coal mining machines based on similarity simulation according to claim 6 or 8, characterized in that, The formula for calculating dust production M is: Where H(·) is the Heaviside step function, used to screen dust particles with a diameter less than 75 μm; m i The mass of the i-th sample is denoted as ; n is the number of samples; t is the total sampling time; d i Let be the particle size of the dust sample taken in the i-th sampling.

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