Coal sorting method and system based on coal pan instrument

Abstract

The present invention provides a coal sorting method and system based on a coal pan meter, relating to the technical field of coal sorting. The method comprises obtaining parameter information of the coal to be sorted; establishing a coal sorting numerical model based on the parameter information of the coal to be sorted, wherein the optimization parameters of the coal sorting numerical model include rotation speed, tilt angle, feed rate, and airflow velocity; performing multi-objective optimization on the coal sorting numerical model using a multi-objective differential evolution algorithm and an ε-constraint method to obtain an optimal solution set, wherein the multi-objective optimization aims to maximize the clean coal recovery rate and the gangue removal rate; selecting the optimal solution from the optimal solution set based on a trade-off compromise method, using the optimal solution as an operating parameter, and controlling the coal pan meter to sort the coal to be sorted according to the operating parameter. The present invention solves the problem that traditional coal pan meters cannot efficiently adapt to changing working conditions and coal characteristics, resulting in uncertainty and low precision in coal sorting results.

Description

Technical Field

[0001] The present invention relates to the technical field of coal sorting, and in particular to a coal sorting method and system based on a coal pan meter. Background Art

[0002] Coal sorting removes impurities, reduces environmental pollution, and improves combustion efficiency. Existing coal sorting machines face numerous challenges during the coal sorting process, impacting both efficiency and accuracy. First, when dealing with unevenly sized or wet coal, traditional coal sorting machines are unable to effectively address the differences between different coal types. Optimizing the balance between clean coal recovery and gangue removal is difficult, and improving one often results in a decrease in the other. Second, existing equipment lacks adaptability to varying coal characteristics and is unable to adjust operating parameters in real time to account for variations in coal particle size and other factors. Uneven airflow distribution is also a common problem, leading to uneven distribution of coal and gangue, impacting sorting effectiveness. Furthermore, wet and sticky coal particles tend to adhere, reducing sorting effectiveness, making traditional coal sorting machines less capable of handling these specialized coal types. Equipment operation is also susceptible to particle abrasion and environmental changes, resulting in unstable sorting accuracy.

[0003] Furthermore, traditional coal panning machines rely heavily on manual experience and lack dynamic optimization capabilities based on real-time data analysis. Consequently, they are unable to effectively adapt to changing operating conditions and coal characteristics, resulting in uncertainty and fluctuations in sorting results. Therefore, traditional coal panning machines urgently need to incorporate modern optimization technologies to improve their adaptability and sorting accuracy. Summary of the Invention

[0004] The purpose of the present invention is to provide a coal sorting method and system based on a coal pan meter to improve the above problems. In order to achieve the above purpose, the technical solutions adopted by the present invention are as follows:

[0005] In a first aspect, the present application provides a coal sorting method based on a coal pan meter, comprising:

[0006] Acquiring parameter information of the coal to be sorted, wherein the parameter information includes particle size distribution information, particle size information, and moisture information of the coal particles;

[0007] A coal sorting numerical model is established based on parameter information of the coal to be sorted, wherein the optimization parameters of the coal sorting numerical model include rotation speed, tilt angle, feed rate and airflow velocity;

[0008] The coal sorting numerical model is multi-objective optimized by a multi-objective differential evolution algorithm and an ε-constraint method to obtain an optimal solution set, wherein the multi-objective optimization aims to maximize the clean coal recovery rate and the gangue removal rate;

[0009] The optimal solution in the optimal solution set is selected based on a trade-off method, the optimal solution is used as an operating parameter, and the coal pan meter is controlled according to the operating parameter to sort the coal to be sorted.

[0010] In a second aspect, the present application also provides a coal sorting system based on a coal pan meter, comprising:

[0011] an acquisition unit, configured to acquire parameter information of the coal to be sorted, wherein the parameter information includes particle size distribution information, particle size information, and moisture information of the coal particles;

[0012] A construction unit, configured to establish a coal sorting numerical model based on parameter information of the coal to be sorted, wherein the optimization parameters of the coal sorting numerical model include rotation speed, tilt angle, feed rate and airflow velocity;

[0013] an optimization unit, configured to perform multi-objective optimization on the coal sorting numerical model by using a multi-objective differential evolution algorithm and an ε-constraint method to obtain an optimal solution set, wherein the multi-objective optimization aims to maximize the clean coal recovery rate and the gangue removal rate;

[0014] The sorting unit is used to select the optimal solution in the optimal solution set based on a trade-off method, use the optimal solution as an operating parameter, and control the coal pan meter to sort the coal to be sorted according to the operating parameter.

[0015] The beneficial effects of the present invention are as follows: by establishing a numerical model for coal sorting, the present invention combines information such as the particle size distribution and humidity of coal particles, as well as the flow characteristics of the fluid, to accurately simulate the movement trajectory and sorting behavior of coal particles in the coal pan meter. And by combining the multi-objective differential evolution algorithm with the ε-constraint method, it can effectively optimize the operating parameters of the coal pan meter, while maximizing the clean coal recovery rate and gangue removal rate, and can find the best compromise between multiple goals to improve the overall sorting efficiency. At the same time, it can adapt to different types of coal and working conditions, avoiding the problems of unstable performance and low efficiency of traditional coal pan meters when processing different coals.

[0016] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 Schematic diagram of the process of coal sorting based on the coal pan instrument in an embodiment of the present invention;

[0019] Figure 2 Schematic diagram of the structure of the coal sorting system based on the coal pan meter described in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are used only to distinguish the description and should not be understood as indicating or implying relative importance.

[0022] Example 1:

[0023] This embodiment provides a coal sorting method based on a coal pan meter.

[0024] See also Figure 1 , the figure shows that the method includes step S100, step S200, step S300, and step S400.

[0025] Step S100: obtaining parameter information of the coal to be sorted, wherein the parameter information includes particle size distribution information, particle size information and humidity information of the coal particles;

[0026] In this embodiment, parameter information of the coal to be sorted is obtained through laboratory analysis and real-time online monitoring before sorting.

[0027] The particle size distribution of coal particles indicates the distribution of the number or mass of coal particles within different particle size ranges. This distribution has a significant impact on the physical properties, sorting efficiency, and subsequent processing of coal. Particle size information indicates the specific particle size of each particle or the particle size distribution characteristics, including size range and uniformity. Coal moisture affects the adhesion of coal particles. Excessive humidity causes particles to adhere to each other, thus affecting coal sorting efficiency.

[0028] Step S200: establishing a coal sorting numerical model based on parameter information of the coal to be sorted, wherein the optimization parameters of the coal sorting numerical model include rotation speed, tilt angle, feed rate and airflow speed;

[0029] In this embodiment, the coal sorting machine usually uses centrifugal force, gravity, air flow and other effects to separate coal into different components according to different physical properties to obtain coal and gangue. Therefore, through fluid dynamics, particle dynamics and centrifugal separation principles, a multi-physical field coupled mathematical model is constructed, that is, a coal sorting numerical model is established to simulate the process of coal sorting by the coal sorting machine.

[0030] The step S200 includes:

[0031] Step S201: setting boundary conditions of the coal pan meter, wherein the boundary conditions include the geometric shape of the coal pan meter, the air flow channel, the inlet boundary, the outlet boundary and the wall boundary;

[0032] Step S202: establishing a discrete element numerical model of the coal to be sorted based on the parameter information and the boundary conditions of the coal pan analyzer;

[0033] In this example, the motion trajectory of coal particles is simulated using the discrete element method. During the discrete element simulation, the coal particles are discretized, treating them as a series of discrete small units. The discrete element method simulates the interactions between coal particles by calculating the contact forces and other interactions between them. The discrete element numerical model can reveal the forces acting on and the motion of the coal particles.

[0034] The step S202 includes:

[0035] Step A100: setting initial conditions of the coal particles, wherein the initial conditions include an initial position, an initial velocity, and an initial angular velocity of the coal particles;

[0036] Step A200: establishing an initial discrete element numerical model based on the initial conditions and the parameter information, and using the boundary conditions of the coal pan meter as the boundary conditions of the initial discrete element numerical model;

[0037] Step A300: Based on the force conditions of the coal particles in the coal pan, establish the force equation and motion equation of the coal particles;

[0038] In this embodiment, the force equation of the coal particles is expressed as follows:

[0039] F c1 =mω 2 r

[0040] F c2 =F n +F t

[0041] F g =mg

[0042]

[0043] Where, F c1 Indicates centrifugal force, F c2 Represents the contact force, F n represents the normal force, F t represents the tangential force, m represents the mass of the coal particle, ω represents the angular velocity of the coal pan, r represents the distance from the coal particle to the rotation axis of the coal pan, F g represents gravity, g represents gravitational acceleration, F d represents air resistance, C d represents the drag coefficient of coal particles, ρ air represents the air density, A represents the cross-sectional area of ​​the coal particles, and v represents the relative velocity of the coal particles.

[0044] In this embodiment, the contact force is the actual force generated when coal particles come into contact with other coal particles or surfaces, including elastic force, damping force, and friction force, etc. The contact force is divided into two main components, namely normal force and tangential force. Among them, the normal force is the force perpendicular to the contact surface, which usually reflects the restoring force and damping force generated by the deformation of the particles. The tangential force is the force along the contact surface, which mainly includes friction force and viscous damping force.

[0045] Normal force F n and tangential force F t The expression is:

[0046] F n =k n δ n -γ n v n

[0047] F t =k t δ t -γ t v t

[0048] Where k n and k t denote the normal stiffness and tangential stiffness respectively, δ n and δ t denote the normal displacement and tangential displacement respectively, γ n and γ t are the normal damping coefficient and the tangential damping coefficient, v n and v t represent the normal relative velocity and tangential relative velocity respectively.

[0049] The equation of motion of the coal particles is expressed as:

[0050]

[0051] Where R represents the spatial position of coal particles, represents the acceleration of coal particles, F a Indicates airflow force, F c1 Indicates centrifugal force, F c2 Represents the contact force, F n represents the normal force, F t Represents tangential force.

[0052] Step A400: Inputting the force equation and the motion equation into an initial discrete element numerical model to simulate the motion trajectory of coal particles, thereby obtaining a discrete element numerical model of the coal to be sorted.

[0053] Step S203: using the Navier-Stokes equation to simulate the flow characteristics of the fluid and establish a fluid dynamic model of the coal to be sorted;

[0054] In this embodiment, the fluid dynamics model can solve the velocity field and pressure field of the fluid, simulate the flow of the fluid, including turbulence, airflow and other effects, and can describe the fluid flow and the air resistance of the coal particles.

[0055] The step S203 includes:

[0056] Step B100: setting the initial conditions and properties of the fluid, wherein the initial conditions include the initial velocity field and initial pressure field of the fluid, and the properties include the density and viscosity of the fluid;

[0057] Step B200: establishing an initial fluid dynamic model based on the fluid initial conditions, the fluid properties and the boundary conditions of the coal pan instrument;

[0058] Step B300: using the Navier-Stokes equation to simulate the flow characteristics of the fluid, inputting the Navier-Stokes equation into the initial fluid dynamics model, and performing meshing to obtain a fluid dynamics model of the coal to be sorted.

[0059] In this embodiment, the fluid calculation domain is divided into fine grids to ensure that the coal particles can interact with the fluid grids, that is, the size of the coal particles should be much smaller than the fluid grid units.

[0060] Step S204: Based on the interaction between coal particles and fluid, the discrete element numerical model and the fluid dynamics model are coupled to obtain a coal separation numerical model.

[0061] In this embodiment, force calculation and transmission are performed based on the interaction between the coal particles and the fluid, including the force exerted by the fluid on the coal particles and the force exerted by the coal particles on the fluid.

[0062] The forces exerted by the fluid on the coal particles include drag and lift. The drag force is the air resistance, which represents the resistance of the fluid to the movement of the coal particles. The lift force represents the force of the fluid caused by the flow velocity gradient. The calculation formula is:

[0063]

[0064] Where, F d ′ represents the drag force, C d represents the drag coefficient of the coal particle, ρ represents the fluid density, A represents the cross-sectional area of ​​the coal particle, that is, the cross-sectional area of ​​the coal particle perpendicular to the flow direction, v′ represents the velocity of the coal particle relative to the fluid, |v′| represents the modulus of v′, and F l represents lift, C l represents the lift coefficient, represents the curl of the fluid velocity field.

[0065] The force of coal particles on the fluid is added to the Navier-Stokes equation to correct the velocity field and pressure field of the fluid.

[0066] When coupling the discrete element numerical model and the fluid dynamics model, the fluid velocity field acts on the coal particles through the drag force, affecting the movement of the coal particles. At the same time, the presence of coal particles changes the local velocity and pressure distribution of the fluid, and the feedback affects the flow of the fluid, thus obtaining the final coal sorting numerical model.

[0067] In this embodiment, during the coal sorting simulation process of the coal pan meter, the rotation speed, the tilt angle, the feed rate and the air flow velocity are four important control parameters, namely, the optimization parameters of the coal sorting numerical model.

[0068] Among them, the rotation speed refers to the rotation rate of the coal pan meter, which affects the airflow field and the motion trajectory of the particles. In the coal sorting numerical model, it is represented by setting boundary conditions. The rotation speed is the product of the angular velocity of the disk and the distance to the rotation center.

[0069] The tilt angle refers to the angle between the coal pan meter's surface and the horizontal plane. The tilt of the pan affects the gravity distribution of coal particles, the direction of airflow, and the coal particle sorting effect. In the coal sorting numerical model, the tilt of the pan changes the geometry of the airflow channel, thereby affecting the direction and velocity of fluid flow. The tilt angle also affects the fluid's pressure gradient and flow velocity distribution. At the same time, the distribution of gravity on the particles on the tilted pan changes, and the movement of particles is affected by gravity in different directions. Therefore, the tilt angle can be reflected by changing the geometry of the pan and adjusting the direction of the fluid inlet velocity.

[0070] Feed rate refers to the amount of coal particles entering the coal pan, typically controlled by a feeding system. The feed rate directly affects the distribution of coal particles on the pan and the airflow's effect on the particles. In coal sorting numerical models, this is represented by setting the inlet velocity and number of coal particles, as well as their initial position in the pan.

[0071] The air flow velocity affects the interaction between coal particles and air flow. Appropriate air flow velocity helps improve the efficiency of gangue removal.

[0072] Step S300: performing multi-objective optimization on the coal sorting numerical model by using a multi-objective differential evolution algorithm and an ε-constraint method to obtain an optimal solution set, wherein the multi-objective optimization aims to maximize the clean coal recovery rate and the gangue removal rate;

[0073] In this embodiment, the calculation formulas for the clean coal recovery rate and the gangue removal rate are:

[0074]

[0075] Where C1 represents the clean coal recovery rate, C2 represents the gangue removal rate, M1 represents the weight of clean coal after sorting, M2 represents the weight of gangue removed after sorting, M3 represents the total weight of coal before sorting, and M4 represents the weight of gangue in coal before sorting.

[0076] In this example, the two objectives of maximizing the clean coal recovery rate and the gangue removal rate are conflicting. Generally, increasing the clean coal recovery rate may lead to a decrease in the gangue removal rate, and vice versa. Therefore, an optimal solution set is obtained through the optimization algorithm, which represents a compromise between the different objectives.

[0077] In this embodiment, step S300 includes:

[0078] Step C100: Initializing a population to obtain an initial population, wherein the population includes a plurality of individuals, wherein each individual is a set of optimized parameters;

[0079] In this embodiment, a larger population is selected to increase the global search capability and avoid falling into a local optimal solution.

[0080] Step C200: inputting each individual into the coal sorting numerical model to simulate sorting, and obtaining the distribution of coal and gangue corresponding to each individual after simulated sorting;

[0081] Step C300: Calculate the clean coal recovery rate and gangue removal rate of each individual in the initial population based on the distribution of coal and gangue;

[0082] Step C400: Based on the clean coal recovery rate and gangue removal rate of each individual in the initial population, select the current individual to perform a differential mutation operation to generate a mutant individual;

[0083] In this embodiment, for each individual in the initial population, three random individuals are selected to perform differential mutation operations to generate new mutant individuals. The specific formula is:

[0084]

[0085] Where a i represents the i-th new individual generated after the differential mutation operation, F represents the mutation factor, and Represent three random individuals in the initial population.

[0086] Step C500: exchange information between the current individual and the variant individual to obtain a cross population;

[0087] Step C600: performing a selection operation on the crossover population based on the ε-constraint method to obtain a new population;

[0088] The step C600 includes:

[0089] Step C601: Use Pareto non-dominated sorting to sort and divide the individuals in the crossover population to obtain the Pareto frontier;

[0090] Step C602: maximizing the clean coal recovery rate is set as the main goal, and a constraint condition is set by the gangue removal rate, wherein the constraint condition is that the gangue removal rate is greater than a preset threshold;

[0091] Step C603: In the Pareto frontier layers other than the first layer of the Pareto frontier, individuals that meet the constraint conditions are selected as target individuals;

[0092] Step C604: taking the target individual and all individuals in the first layer of the Pareto front as the selection population;

[0093] Step C605 calculates the clean coal recovery rate of each individual in the selection population, and assigns a selection probability to each individual based on the size of the clean coal recovery rate of each individual;

[0094] Step C606 performs a selection operation on individuals in the selection population based on the selection probability to obtain a new population.

[0095] In this embodiment, the original multi-objective optimization problem is transformed into a single-objective optimization problem through the ε-constraint method.

[0096] Step C700: Determine whether the number of iterations reaches a preset threshold. If so, stop the iteration and use the new population as the optimal solution set. Otherwise, use the new population as the initial population for the next iteration and perform the next iteration.

[0097] Step S400: selecting the optimal solution in the optimal solution set based on a trade-off method, taking the optimal solution as an operating parameter, and controlling the coal pan meter to sort the coal to be sorted according to the operating parameter.

[0098] In this embodiment, the weights of the clean coal recovery rate and the gangue removal rate are set according to the actual sorting requirements, and the weighted sum of the clean coal recovery rate and the gangue removal rate corresponding to each solution in the optimal solution set is calculated based on the weights, and the solution with the largest weighted sum is selected as the optimal solution.

[0099] In this embodiment, the comparison results of the operation parameters before and after optimization when sorting a certain weight of 1000 kg of coal are also given, as shown in Table 1, which is a sorting result comparison table.

[0100] Table 1

[0101] Operational parameters Before optimization After optimization Rotation speed (rpm) 30 35 Tilt angle (°) 10 12 Air flow velocity (m / s) 3.0 3.5 Feeding rate (kg / min) 50 55 Raw coal weight (kg) 1000 1000 Clean coal recovery rate (%) 85.2 92.4 Gangue removal rate (%) 87.4 90.7

[0102] As can be seen from Table 1, by optimizing the operating parameters of the coal sorting machine, the clean coal recovery rate and the gangue removal rate are improved. This optimization scheme not only improves the utilization efficiency of coal resources and has high economic benefits, but also ensures high efficiency and balance in the sorting process.

[0103] In summary, the numerical model of coal sorting established in the present invention accurately simulates the movement trajectory and sorting behavior of coal particles in the coal pan meter, and can better predict the distribution of coal and gangue. At the same time, the multi-objective differential evolution algorithm and ε-constraint method are used to find the best compromise solution between multiple objectives through global search. It is suitable for irregular, nonlinear and large-scale optimization problems, and has strong adaptability. It is suitable for complex industrial optimization problems that need to balance multiple objectives. It can provide effective operating parameter selection during coal sorting, meet production needs and improve efficiency.

[0104] Example 2:

[0105] This embodiment provides a coal sorting system based on a coal pan meter, the system comprising:

[0106] an acquisition unit, configured to acquire parameter information of the coal to be sorted, wherein the parameter information includes particle size distribution information, particle size information, and moisture information of the coal particles;

[0107] A construction unit, configured to establish a coal sorting numerical model based on parameter information of the coal to be sorted, wherein the optimization parameters of the coal sorting numerical model include rotation speed, tilt angle, feed rate and airflow velocity;

[0108] an optimization unit, configured to perform multi-objective optimization on the coal sorting numerical model by using a multi-objective differential evolution algorithm and an ε-constraint method to obtain an optimal solution set, wherein the multi-objective optimization aims to maximize the clean coal recovery rate and the gangue removal rate;

[0109] The sorting unit is used to select the optimal solution in the optimal solution set based on a trade-off method, use the optimal solution as an operating parameter, and control the coal pan meter to sort the coal to be sorted according to the operating parameter.

[0110] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0111] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

[0112] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A coal sorting method based on a coal pan meter, characterized in that: include: Acquiring parameter information of the coal to be sorted, wherein the parameter information includes particle size distribution information, particle size information, and moisture information of the coal particles; A coal sorting numerical model is established based on parameter information of the coal to be sorted, wherein the optimization parameters of the coal sorting numerical model include rotation speed, tilt angle, feed rate and airflow velocity; The coal sorting numerical model is multi-objective optimized by a multi-objective differential evolution algorithm and an ε-constraint method to obtain an optimal solution set, wherein the multi-objective optimization aims to maximize the clean coal recovery rate and the gangue removal rate; Selecting an optimal solution from the optimal solution set based on a trade-off method, using the optimal solution as an operating parameter, and controlling the coal pan instrument to sort the coal to be sorted according to the operating parameter; The method of establishing a coal sorting numerical model based on parameter information of the coal to be sorted includes: Setting boundary conditions of the coal pan analyzer, wherein the boundary conditions include the geometric shape of the coal pan analyzer, the air flow channel, the inlet boundary, the outlet boundary and the wall boundary; Establishing a discrete element numerical model of the coal to be sorted based on the parameter information and the boundary conditions of the coal pan instrument; The Navier-Stokes equations are used to simulate the flow characteristics of the fluid and establish a fluid dynamic model of the coal to be sorted; Based on the interaction between coal particles and fluid, the discrete element numerical model and the fluid dynamic model are coupled to obtain a coal separation numerical model; The discrete element numerical model of the coal to be sorted is established based on the parameter information and the boundary conditions of the coal pan meter, including: Setting initial conditions of the coal particles, wherein the initial conditions include an initial position, an initial velocity, and an initial angular velocity of the coal particles; An initial discrete element numerical model is established based on the initial conditions and the parameter information, and the boundary conditions of the coal pan meter are used as the boundary conditions of the initial discrete element numerical model; Based on the force conditions of coal particles in the coal pan instrument, the force equation and motion equation of coal particles are established; Inputting the force equation and the motion equation into an initial discrete element numerical model to simulate the motion trajectory of coal particles, thereby obtaining a discrete element numerical model of the coal to be sorted; The Navier-Stokes equation is used to simulate the flow characteristics of the fluid and establish a fluid dynamic model of the coal to be sorted, including: Setting fluid initial conditions and fluid properties, wherein the fluid initial conditions include the initial velocity field and initial pressure field of the fluid, and the fluid properties include the density and viscosity of the fluid; Establishing an initial fluid dynamic model based on the fluid initial conditions, the fluid properties and the boundary conditions of the coal pan instrument; The flow characteristics of the fluid are simulated using the Navier-Stokes equation, the Navier-Stokes equation is input into the initial fluid dynamic model, and meshing is performed to obtain a fluid dynamic model of the coal to be sorted.

2. The coal sorting method based on the coal pan meter according to claim 1 is characterized in that , the formula of the force equation of the coal particles is: F c1 =mω 2 r F c2 =F n +F t F g =mg Where, F c1 Indicates centrifugal force, F c2 Represents the contact force, F n represents the normal force, F t represents the tangential force, m represents the mass of the coal particle, ω represents the angular velocity of the coal pan, r represents the distance from the coal particle to the rotation axis of the coal pan, F g represents gravity, g represents gravitational acceleration, F d represents air resistance, C d represents the drag coefficient of coal particles, ρ air represents the air density, A represents the cross-sectional area of ​​the coal particles, and v represents the relative velocity of the coal particles.

3. The coal sorting method based on the coal pan meter according to claim 1 is characterized in that , the motion equation of the coal particles is expressed as: Where R represents the spatial position of coal particles, represents the acceleration of coal particles, F a Indicates airflow force, F c1 Indicates centrifugal force, F c2 Represents the contact force, F n represents the normal force, F t Represents tangential force.

4. The coal sorting method based on the coal pan meter according to claim 1 is characterized in that ,The calculation formulas for the clean coal recovery rate and gangue removal rate are: Where C1 represents the clean coal recovery rate, C2 represents the gangue removal rate, M1 represents the weight of clean coal after sorting, M2 represents the weight of gangue removed after sorting, M3 represents the total weight of coal before sorting, and M4 represents the weight of gangue in coal before sorting.

5. The coal sorting method based on the coal pan meter according to claim 1 is characterized in that The multi-objective differential evolution algorithm and the ε-constraint method are used to perform multi-objective optimization on the coal sorting numerical model to obtain the optimal solution set, including: Initializing a population to obtain an initial population, wherein the population includes a plurality of individuals, wherein each individual is a set of optimization parameters; Input each individual into the coal sorting numerical model to simulate sorting, and obtain the distribution of coal and gangue corresponding to each individual after simulated sorting; Based on the distribution of coal and gangue, the clean coal recovery rate and gangue removal rate of each individual in the initial population are calculated; Based on the clean coal recovery rate and gangue removal rate of each individual in the initial population, the current individual is selected for differential mutation operation to generate mutant individuals; Exchange information between the current individual and the mutant individual to obtain a cross population; Perform selection operation on the crossover population based on the ε-constraint method to obtain a new population; Determine whether the number of iterations reaches the preset threshold. If so, stop the iteration and use the new population as the optimal solution set. Otherwise, use the new population as the initial population for the next round of iteration and perform the next iteration.

6. The coal sorting method based on the coal pan meter according to claim 5 is characterized in that ,The ε-constraint method is used to select the cross population to obtain a new population, including: Use Pareto non-dominated sorting to sort and divide the individuals in the crossover population to obtain the Pareto frontier; Maximizing the clean coal recovery rate is taken as the main objective, and a constraint condition is set by the gangue removal rate, wherein the constraint condition is that the gangue removal rate is greater than a preset threshold; In the Pareto frontier layers other than the first layer, individuals that meet the constraints are selected as target individuals. Taking the target individual and all individuals in the first layer of the Pareto front as the selection population; Calculate the clean coal recovery rate of each individual in the selection population, and assign the selection probability of each individual based on the size of the clean coal recovery rate of each individual; Based on the selection probability, the individuals in the selection population are selected to obtain a new population.

7. A coal sorting system based on a coal pan meter, characterized in that: include: an acquisition unit, configured to acquire parameter information of the coal to be sorted, wherein the parameter information includes particle size distribution information, particle size information, and moisture information of the coal particles; A construction unit, configured to establish a coal sorting numerical model based on parameter information of the coal to be sorted, wherein the optimization parameters of the coal sorting numerical model include rotation speed, tilt angle, feed rate and airflow velocity; an optimization unit, configured to perform multi-objective optimization on the coal sorting numerical model by using a multi-objective differential evolution algorithm and an ε-constraint method to obtain an optimal solution set, wherein the multi-objective optimization aims to maximize the clean coal recovery rate and the gangue removal rate; A sorting unit is configured to select an optimal solution from the optimal solution set based on a trade-off method, use the optimal solution as an operating parameter, and control the coal pan meter to sort the coal to be sorted according to the operating parameter; The method of establishing a coal sorting numerical model based on parameter information of the coal to be sorted includes: Setting boundary conditions of the coal pan analyzer, wherein the boundary conditions include the geometric shape of the coal pan analyzer, the air flow channel, the inlet boundary, the outlet boundary and the wall boundary; Establishing a discrete element numerical model of the coal to be sorted based on the parameter information and the boundary conditions of the coal pan instrument; The Navier-Stokes equations are used to simulate the flow characteristics of the fluid and establish a fluid dynamic model of the coal to be sorted; Based on the interaction between coal particles and fluid, the discrete element numerical model and the fluid dynamic model are coupled to obtain a coal separation numerical model; The discrete element numerical model of the coal to be sorted is established based on the parameter information and the boundary conditions of the coal pan meter, including: Setting initial conditions of the coal particles, wherein the initial conditions include an initial position, an initial velocity, and an initial angular velocity of the coal particles; An initial discrete element numerical model is established based on the initial conditions and the parameter information, and the boundary conditions of the coal pan meter are used as the boundary conditions of the initial discrete element numerical model; Based on the force conditions of coal particles in the coal pan instrument, the force equation and motion equation of coal particles are established; Inputting the force equation and the motion equation into an initial discrete element numerical model to simulate the motion trajectory of coal particles, thereby obtaining a discrete element numerical model of the coal to be sorted; The Navier-Stokes equation is used to simulate the flow characteristics of the fluid and establish a fluid dynamic model of the coal to be sorted, including: Setting fluid initial conditions and fluid properties, wherein the fluid initial conditions include the initial velocity field and initial pressure field of the fluid, and the fluid properties include the density and viscosity of the fluid; Establishing an initial fluid dynamic model based on the fluid initial conditions, the fluid properties and the boundary conditions of the coal pan instrument; The flow characteristics of the fluid are simulated using the Navier-Stokes equation, the Navier-Stokes equation is input into the initial fluid dynamic model, and meshing is performed to obtain a fluid dynamic model of the coal to be sorted.

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