Method for optimizing parameters of rotary kiln based on particle segregation evaluation

Abstract

This invention relates to a rotary kiln parameter optimization method based on particle segregation evaluation, comprising: establishing a quasi-three-dimensional SPH geometric model of the rotary kiln; using the Mohr-Coulomb elastoplastic constitutive model to describe particle flow behavior based on the discrete form of the SPH equations of mass conservation, momentum conservation, and energy conservation; determining boundary conditions according to set operating parameters and material parameters; performing explicit time-integral SPH simulation; calculating local and global segregation indices based on pseudo-particle component concentrations; using a Bayesian optimization algorithm to optimize parameter combinations and determine the optimal parameter combination; identifying high-incidence segregation regions based on the local segregation index; verifying and further optimizing the optimal parameter combination. This invention solves the technical problems of poor real-time performance of existing segregation evaluation methods, large computational load for parameter optimization making it difficult to stably converge to the global optimum, and thus failing to meet the requirements of industrial applications.

Description

Technical Field

[0001] This invention relates to the field of rotary kiln parameter optimization technology, and in particular to a rotary kiln parameter optimization method based on particle segregation evaluation. Background Technology

[0002] Rotary kilns are widely used in particle heat treatment processes in industries such as cement, metallurgy, and chemicals. The uniformity of particle mixing within the kiln directly determines product quality and heat transfer efficiency. Due to differences in particle size, density, and shape, the rotating flow in a rotary kiln can induce significant segregation, characterized by small particles penetrating towards the center of the bed and large particles migrating to the periphery. This leads to uneven mixing, incomplete reactions, and in severe cases, affects process stability. A key prerequisite for optimizing rotary kiln process parameters is accurate evaluation of particle segregation.

[0003] Existing technologies for process parameter optimization based on segregation evaluation suffer from the following problems: Firstly, particle segregation evaluation methods suffer from low efficiency and poor real-time performance. Specifically: Experimental image analysis methods acquire images of the particle bed surface or cross-section through high-speed photography, CT scanning, or particle tracing, and then use image processing techniques to calculate the segregation index. While these methods are intuitive and reliable, they struggle to obtain information on the segregation distribution within the particle bed, and are costly and time-consuming. Furthermore, due to limitations in optical accessibility, they cannot be used in high-temperature, enclosed industrial rotary kilns. Numerical simulation post-processing methods, represented by the Discrete Element Method (DEM), can accurately simulate the microscopic process of segregation by tracking the motion trajectory of each real particle. However, the segregation index calculation is usually performed post-processing after the simulation, resulting in poor real-time feedback of the segregation degree. Moreover, DEM computation is extremely expensive; when the number of particles reaches millions or more, the computation time often takes weeks or even months, making it unsuitable for the simulation and evaluation of full-scale industrial rotary kilns. On the other hand, industrial practices typically rely on empirical trial and error or orthogonal experimental design to determine optimal operating parameters to minimize segregation. The former lacks scientific basis, while the latter requires extensive simulations (full factorial experiments require dozens to hundreds of operating conditions), which is time-consuming, labor-intensive, and difficult to find a globally optimal solution. There is a complex nonlinear relationship and parameter interaction coupling effect between the segregation exponent and operating parameters, making it difficult for traditional methods to handle such high-dimensional optimization problems.

[0004] Therefore, there is an urgent need for a parameter optimization method that can automatically and efficiently find the optimal parameter combination within a limited number of simulations. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a rotary kiln parameter optimization method based on particle segregation evaluation, which solves the technical problems of poor real-time performance, large computational load for parameter optimization, difficulty in stable convergence to the global optimum, and inability to meet the needs of industrial applications.

[0006] The technical solution adopted in this invention is as follows: This invention provides a method for optimizing rotary kiln parameters based on particle segregation evaluation, comprising: A quasi-three-dimensional SPH geometric model of the rotary kiln is established, and the particle bed inside the kiln is discretized into several SPH pseudo-particles. Each pseudo-particle carries information on mass, density, velocity, temperature, and component concentration. Based on the SPH discretization form of the mass conservation, momentum conservation, and energy conservation equations, the Mohr-Coulomb elastoplastic constitutive model is used to describe the particle flow behavior. Boundary conditions are determined according to the set operating parameters and material parameters, and explicit time-integral SPH simulation is performed. At each time step or each output step, the spatial coordinates and component concentrations of all pseudo-particles are recorded. The local segregation index of each pseudoparticle and the global segregation index of the entire particle bed are calculated based on the component concentration. A Bayesian optimization algorithm is used to find the optimal parameter combination: by changing the operating parameters and / or material parameters, the SPH simulation is repeated to obtain the local segregation index and the global segregation index under different parameter combinations; the optimal parameter combination is determined with the goal of minimizing the global segregation index. The local segregation index is used to identify high-incidence segregation areas, and the optimal parameter combination is verified and further optimized.

[0007] The process of changing operating parameters and / or material parameters and repeatedly performing simulation calculations to obtain the local segregation index and global segregation index under different parameter combinations includes: Determine optimization parameters: Select several key parameters that affect segregation from the operation parameters and material parameters and determine their value range, while fixing other parameters as benchmark values; Single-factor preliminary scan: For the aforementioned key parameters, the values ​​of individual parameters are changed sequentially, and SPH simulation is repeated to obtain the corresponding local segregation index and global segregation index in the stable phase. The average value is calculated to obtain the average global segregation index, and the single-factor influence curve is plotted to determine the sensitive interval of each key parameter. Multi-factor orthogonal experiment: Select several values ​​within the sensitive range of each key parameter, design an orthogonal table, perform SPH simulation on multiple parameter combinations in the table, and calculate the final average global segregation index.

[0008] To minimize the global segregation exponent, the optimal parameter combination is determined, including: Substituting the orthogonal experimental results into the quadratic polynomial model:

[0009] The response surface equation of the quadratic polynomial model is obtained by fitting it using the least squares method. The optimal parameter combination is then solved by differentiating the equation and setting the derivative to zero. In the above formula, The final average global segregation index is used as the objective function for optimization. The intercept term represents the baseline segregation index when all parameters are at zero. Let be the coefficient of the first-order term, representing the first term. The linear influence of individual changes in key parameters on the segregation index; The coefficient of the quadratic term represents the th term. The effect of the squared terms of the key parameters on the segregation index. Subscript This indicates a self-interaction of the same parameter; The coefficients of the interaction term, and , indicating the first The and the first The influence of the coupling interaction between key parameters on the segregation index, subscript This represents the cross-effect of different parameters; , The first The and the first The encoded values ​​of key parameters.

[0010] The key parameters include rotational speed, filling rate, and particle size ratio.

[0011] The operating parameters include rotational speed, filling rate, and wall temperature; the material parameters include particle size ratio, density ratio, and internal friction angle.

[0012] Before performing parameter combination optimization, the local segregation index is mapped to the spatial coordinates of the pseudo-particles to generate a segregation index cloud map, identify high-incidence segregation areas, and adjust the parameter value range.

[0013] The verification and further optimization of the optimal parameter combination includes: If the degree of segregation in the high-incidence segregation area identified by the local segregation index exceeds the set range, the optimal parameter combination shall be further adjusted or the rotary kiln structure shall be improved.

[0014] The calculation of the local segregation index includes:

[0015] in, S i pseudo-particles i Local segregation index, For the first i Each pseudo-particle represents the volume fraction or mass fraction of the target component within the region; The average concentration of the entire particle bed represented by all SPH pseudoparticles.

[0016] The calculation of the global segregation index includes:

[0017] in, S global The global segregation index for the entire particle bed. N This represents the total number of pseudo-particles.

[0018] The present invention also provides an apparatus for performing the method.

[0019] The technical solution of the present invention can achieve at least some of the following beneficial effects: This invention combines SPH (Smoothed Particle Dynamics) simulation with Bayesian optimization to automatically optimize key parameters affecting particle segregation in rotary kilns, forming an integrated closed-loop process of "simulation-evaluation-optimization". The SPH method solves the computational efficiency problem of industrial-scale rotary kiln segregation simulation, enabling real-time evaluation at both local and global scales. Bayesian optimization solves the sample efficiency problem of optimization in high-dimensional parameter spaces. The synergistic effect of the two methods can automatically find the optimal combination of operating parameters that minimizes the global segregation index with a limited number of simulations. Compared with the traditional orthogonal experimental method, which requires dozens to hundreds of sets of operating conditions and empirical trial and error, the computational cost is reduced and the optimization efficiency is significantly improved, making real-time optimization of industrial-grade segregation parameters possible.

[0020] The output of this invention consists of operational parameters that can be directly applied to production, such as optimal rotation speed and filling rate, rather than abstract segregation index values, thus possessing clear engineering guidance value. Industrial users can directly input the optimized parameters into the rotary kiln control system to achieve quantitative control of segregation suppression and improve product quality stability.

[0021] This invention, based on Bayesian optimization, can handle the complex nonlinear relationship and parameter interaction coupling effect between the segregation index and multiple operating parameters such as rotational speed, filling rate, particle size ratio, and internal friction angle. It avoids the defect of traditional single-factor analysis methods that ignore the interaction effect and can stably converge to the global optimum rather than the local optimum.

[0022] This invention, based on SPH (Segregation by Phosphorus) continuous medium simulation, can accurately capture the spatial distribution and temporal evolution of component concentrations within a particle bed, thereby precisely calculating the particle segregation index and achieving accurate quantitative evaluation of the degree of segregation. Furthermore, high-segregation areas are visually visualized, such as free surfaces and the boundary between the active and passive layers, overcoming the limitations of traditional experiments where internal information is invisible, DEM methods are computationally expensive, and real-time evaluation is impossible.

[0023] The method of this invention is universal and is not limited by the type of particulate material, particle size distribution, density difference, or operating conditions of the rotary kiln. It is applicable to various materials such as cement raw materials, metal ores, and chemical particles, as well as rotary kiln equipment of different sizes. It can be extended to the segregation optimization problem of various types of rotary drum equipment. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the method of an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of the quasi-three-dimensional SPH geometric model of the rotary kiln constructed using the Ansys LS-DYNA software platform in an embodiment of the present invention.

[0026] Figure 3 This is a cloud map showing the distribution of component concentrations within the particle bed at different times, obtained through simulation in an embodiment of the present invention. Detailed Implementation

[0027] The specific embodiments of the present invention are described below with reference to the accompanying drawings.

[0028] See Figure 1 This embodiment provides a method for optimizing rotary kiln parameters based on particle segregation evaluation, including: S1. Establish a quasi-three-dimensional SPH geometric model of the rotary kiln, discretize the particle bed inside the kiln into several SPH pseudo-particles, and record the mass, density, velocity, temperature and component concentration information of each pseudo-particle; based on the SPH discretization form of the mass conservation, momentum conservation and energy conservation equations, use the Mohr-Coulomb elastoplastic constitutive model to describe the particle flow behavior, determine the boundary conditions according to the set operating parameters and material parameters, and perform explicit time integral SPH simulation; at each time step or each output step, record the spatial coordinates and component concentration of all pseudo-particles.

[0029] As a preferred embodiment, this example uses the Ansys LS-DYNA software platform to construct a quasi-three-dimensional SPH geometric model of the rotary kiln, see [link to documentation]. Figure 2 The rotary kiln has a diameter of 0.1 m, an axial thickness of 0.01 m, and a filling rate of 25%. The modeling process includes: A structured mesh was generated in HyperMesh, with the mesh size determined to be 0.002 m after independence verification. At this point, the number of mesh cells was approximately 4500. After importing the mesh file into LS-DYNA, each mesh cell was converted into an SPH pseudo-particle, located at the center of the mesh cell. Each pseudo-particle... i Carrying mass m i、 density ρ i velocity vector u i Temperature T i and component concentration c i ;where c i ∈[0,1], indicating the first... iEach pseudo-particle represents the volume fraction or mass fraction of the target component within a region; the target component is a small or light particle; in this embodiment, the target component is preferably a small particle with a particle size of 1 mm; initially, the component concentration of all pseudo-particles is set to c. i =0.5, meaning that small particles and large particles (3mm in diameter) are uniformly mixed; the "SPH-Symmetry-Plane" keyword is used to constrain both ends of the model along the axis, ignoring the influence of axial segregation, thus improving efficiency while ensuring computational accuracy; Particulate materials are treated as continuous matter, and their behavior is described by the Mohr-Coulomb elastoplastic model, where the plastic potential function is defined as:

[0030] In the formula, Let be the plastic potential function. For the maximum principal stress, For the minimum principal stress, The expansion angle is taken as ; since the expansion angle of cohesive particulate materials is usually very small, this embodiment takes . =0.1°; the yield criterion adopts the Mohr-Coulomb criterion:

[0031] In the formula, Maximum shear stress; internal friction angle =30°, =0pa means there is no cohesive force; The normal stress on the failure surface; Young's modulus is taken as 10 7 pa, Poisson's ratio is taken as 0.3; Wall boundary conditions: The inner wall temperature of the kiln is constant at 398 K, and the wall friction coefficient is 0.3; the rotary kiln speed is set to 5 rpm (reference value), and the filling rate is 25%; The SPH kernel function uses a Cubic Spline kernel, with the smoothing length set to 1.2 times the mesh size; Initially, each pseudo-particle is assigned an initial concentration value according to a preset mixing ratio; The total simulation time was 30 seconds, with the time step automatically determined by the CFL conditions. The spatial coordinates and component concentrations of all pseudo-particles were output every 0.1 seconds. i Spatial coordinates (x) i ,y i ), component concentration c i .

[0032] Specifically, the explicit time-integrated SPH simulation was performed in LS-DYNA to solve the SPH discrete form of the mass conservation, momentum conservation, and energy conservation equations. During the simulation, the velocity distribution of the particle bed exhibited a typical two-zone structure: in the active zone near the free surface, the particles flowed rapidly with the velocity direction opposite to the rotation direction of the rotary kiln; in the passive zone at the bottom, the particles underwent rigid body rotational motion with the cylinder wall, and the velocity was proportional to the distance from their location to the center of rotation; in between, there was a quasi-static layer where the particle velocity was almost zero. Figure 3 The distribution cloud map of component concentration in the particle bed at different times is shown, which intuitively presents the spatiotemporal evolution law of particle segregation. It can be seen that small particles gradually penetrate into the center of the bed, while large particles migrate to the periphery, forming a typical "core-shell" segregation structure. This can provide a direct visual basis for global segregation index calculation and Bayesian optimization. Figure 3 In this context, L and X represent the chord length of the particle bed and the displacement along the chord direction of the particle bed, respectively.

[0033] S2. Calculate the local segregation index of each pseudoparticle and the global segregation index of the entire particle bed based on the component concentration.

[0034] As a preferred embodiment, the calculation of the local segregation index includes: Calculate the average concentration of the entire particle bed represented by all SPH pseudoparticles. :

[0035] in, N The total number of pseudo-particles in this embodiment N =4500, due to the closed system and initial uniform mixing. =0.5 remained constant throughout the simulation; Then, the local segregation index is calculated for each pseudo-particle using the following formula:

[0036] in, S i pseudo-particles i The local segregation index is used to describe the degree of mixing uniformity at the location of a single SPH pseudoparticle. If the concentration at that point is exactly equal to the average concentration of the system, it indicates that the mixture is completely mixed at that point. If the value is greater, the point deviates from the average concentration. The larger the value, the more severe the segregation at that location (it contains almost only one component). Substitution =0.5 S i =4∣ -0.5∣, therefore we getS i The value range is [0,2]. S i =0 indicates that the mixture at that position is completely mixed. S i =2 indicates that the position is completely occupied by a single component.

[0037] The calculation of the global segregation index includes:

[0038] in, S global It is the global segregation index for the entire particle bed, used to describe the overall degree of segregation of the entire particle bed; This represents the variance of component concentrations within the granular bed. This reflects the degree of dispersion in the concentration distribution; This represents the maximum possible variance, i.e., the variance under a fully segregated state; when the average concentration of the particle bed is... At that time, complete segregation means that some particles have a concentration of 1 and some have a concentration of 0, and the ratio is 1 / 2. The variance at this time is Therefore .

[0039] In this embodiment (1- ) = 0.25, therefore S global =4σ c 2 S global =4σ c 2 . S global The value range is [0,1]. S global =0 indicates complete mixing. S global =1 indicates complete segregation.

[0040] The curve of global segregation index evolution over time: 0–2 s is the quasi-static stage, when the particles are not yet fully mixed. S global The particle slowly rises to 0.05; the 2–10 s period is the mixing and rising phase, during which particles rapidly convection and diffuse within the active layer. S global It rises rapidly to 0.38; the 10–30 s period is a stable phase, during which segregation reaches dynamic equilibrium. S global It fluctuates between 0.38 and 0.40.

[0041] As a preferred method, the curve of the global segregation index changing over time can be obtained through post-processing, which allows for the analysis of the entire process of segregation initiation, development, and stabilization.

[0042] S3. Parameter combination optimization is performed using a Bayesian optimization algorithm: SPH simulations are repeated by changing the operating parameters and / or material parameters to obtain the local segregation index and global segregation index under different parameter combinations; the optimal parameter combination is determined with the goal of minimizing the global segregation index. As a preferred method, the following methods are included: S31. Determine optimization parameters: Select several key parameters that affect segregation from the operating parameters and material parameters and determine their value range, while fixing other parameters as benchmark values.

[0043] Specifically, the operating parameters include rotational speed, filling rate, and wall temperature; the material parameters include particle size ratio, density ratio, and internal friction angle; and the key parameters include rotational speed, filling rate, and particle size ratio.

[0044] Preferably, rotational speed Range 1~20 rpm, fill rate The range is 15%~50%, and the particle size ratio is... The range is 0.33~1.0. These are the small particle diameter and the large particle diameter, respectively; other parameters are baseline values: wall temperature 398 K, density ratio 1.0, and internal friction angle 30°.

[0045] S32. Preliminary Single-Factor Scan: For the aforementioned key parameters, change each parameter sequentially (e.g., rotational speed of 1, 5, 10, and 20 rpm), repeat the SPH simulation, obtain the corresponding local segregation index and global segregation index during the stable phase (15~30 s), calculate the average value to obtain the average global segregation index, plot the single-factor influence curve, and determine the sensitivity range of each key parameter.

[0046] S33. Multi-factor orthogonal experiment: Select several values ​​within the sensitive range of each key parameter (e.g., rotation speed of 2, 5, and 8 rpm; filling rate of 20%, 25%, and 30%; particle size ratio of 0.5, 0.67, and 0.85), design an orthogonal array (e.g., L9 orthogonal array), conduct 9 sets of SPH simulations, and calculate the final average global segregation index. .

[0047] S34. Response Surface Fitting and Optimization: Substituting orthogonal experimental results into a quadratic polynomial model:

[0048] The response surface equation of the quadratic polynomial model was obtained by fitting the equation using the least squares method. The optimal parameter combination was then solved by differentiating the equation and setting the derivative to zero. ; In the above formula, The final average global segregation index is used as the objective function for optimization. The intercept term represents the baseline segregation index when all parameters are at zero. Let be the coefficient of the first-order term, representing the first term. The linear influence of individual changes in key parameters on the segregation index; The coefficient of the quadratic term represents the th term. The effect of the squared terms of the key parameters on the segregation index. Subscript This indicates a self-interaction of the same parameter; The coefficients of the interaction term, and , indicating the first The and the first The influence of the coupling interaction between key parameters on the segregation index, subscript This represents the cross-effect of different parameters; , The first The and the first The encoded values ​​of key parameters.

[0049] S35. Using the optimal parameter combination Re-execute the simulation to obtain If the value is lower than the minimum value in the orthogonal experiment, then the combination is accepted as optimal; otherwise, the verification result is added to the dataset, and step S34 is repeated to update the response surface.

[0050] Final output makes The smallest combination of parameters, such as: rotational speed rpm, fill rate Particle size ratio ,at this time This achieves excellent mixing uniformity.

[0051] S4. Identify high-incidence segregation areas based on the local segregation index, verify and further optimize the optimal parameter combination.

[0052] Specifically, if the degree of segregation in the high-incidence segregation area identified by the local segregation index exceeds the set range, the optimal parameter combination will be further adjusted or the rotary kiln structure will be improved.

[0053] As an improvement, before performing parameter combination optimization in step S3, this embodiment maps the local segregation index to the spatial coordinates of the pseudo-particles, generates a segregation index cloud map, identifies high-incidence segregation areas, and adjusts the parameter value range.

[0054] As an improvement, the method in this embodiment also includes: adding a temperature variable to each pseudo-particle, simultaneously calculating the temperature field distribution, establishing the relationship between the global segregation index and the temperature standard deviation and the comprehensive heat transfer coefficient, realizing the coupled evaluation of segregation and heat transfer, thereby performing multi-objective optimization of segregation and heat transfer efficiency.

[0055] This embodiment also provides an apparatus for performing the rotary kiln parameter optimization method based on particle segregation evaluation.

[0056] This invention overcomes the shortcomings of high DEM calculation cost and inability to obtain internal distribution in experiments, and provides an efficient tool for quantitative evaluation and parameter optimization of segregation in industrial-scale rotary kilns.

[0057] It will be understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing parameters of a rotary kiln based on evaluation of particle segregation, characterized in that, include: A quasi-three-dimensional SPH geometric model of the rotary kiln is established, and the particle bed inside the kiln is discretized into several SPH pseudo-particles. Each pseudo-particle carries information on mass, density, velocity, temperature, and component concentration. Based on the SPH discretization form of the mass conservation, momentum conservation, and energy conservation equations, the Mohr-Coulomb elastoplastic constitutive model is used to describe the particle flow behavior. Boundary conditions are determined according to the set operating parameters and material parameters, and explicit time-integral SPH simulation is performed. At each time step or each output step, the spatial coordinates and component concentrations of all pseudo-particles are recorded. The local segregation index of each pseudoparticle and the global segregation index of the entire particle bed are calculated based on the component concentration. A Bayesian optimization algorithm is used to find the optimal parameter combination: by changing the operating parameters and / or material parameters, SPH simulations are repeated to obtain the local segregation index and global segregation index under different parameter combinations; the optimal parameter combination is determined with the goal of minimizing the global segregation index. The local segregation index is used to identify high-incidence segregation areas, and the optimal parameter combination is verified and further optimized.

2. The method of claim 1, wherein, The process of changing operating parameters and / or material parameters and repeatedly performing simulation calculations to obtain the local segregation index and global segregation index under different parameter combinations includes: Determine optimization parameters: Select several key parameters that affect segregation from the operation parameters and material parameters and determine their value range, while fixing other parameters as benchmark values; Single-factor preliminary scan: For the aforementioned key parameters, the values ​​of individual parameters are changed sequentially, and SPH simulation is repeated to obtain the corresponding local segregation index and global segregation index in the stable phase. The average value is calculated to obtain the average global segregation index, and the single-factor influence curve is plotted to determine the sensitive interval of each key parameter. Multi-factor orthogonal experiment: Select several values ​​within the sensitive range of each key parameter, design an orthogonal table, perform SPH simulation on multiple parameter combinations in the table, and calculate the final average global segregation index.

3. The method of claim 2, wherein, To minimize the global segregation exponent, the optimal parameter combination is determined, including: Substituting the orthogonal experimental results into the quadratic polynomial model: ， The response surface equation of the quadratic polynomial model is obtained by fitting it using the least squares method. The optimal parameter combination is then solved by differentiating the equation and setting the derivative to zero. In the above formula, The final average global segregation index is used as the objective function for optimization. The intercept term represents the baseline segregation index when all parameters are at zero. Let be the coefficient of the first-order term, representing the first term. The linear influence of individual changes in key parameters on the segregation index; The coefficient of the quadratic term represents the th term. The effect of the squared terms of the key parameters on the segregation index. Subscript This indicates a self-interaction of the same parameter; The coefficients of the interaction term, and , indicating the first The and the first The influence of the coupling interaction between key parameters on the segregation index, subscript This represents the cross-effect of different parameters; , The first The and the first The encoded values ​​of key parameters.

4. The method according to claim 2, characterized in that, The key parameters include rotational speed, filling rate, and particle size ratio.

5. The method according to claim 2, characterized in that, The operating parameters include rotational speed, filling rate, and wall temperature; the material parameters include particle size ratio, density ratio, and internal friction angle.

6. The method according to claim 1, characterized in that, Before performing parameter combination optimization, the local segregation index is mapped to the spatial coordinates of the pseudo-particles to generate a segregation index cloud map, identify high-incidence segregation areas, and adjust the parameter value range.

7. The method according to claim 6, characterized in that, The verification and further optimization of the optimal parameter combination includes: If the degree of segregation in the high-incidence segregation area identified by the local segregation index exceeds the set range, the optimal parameter combination shall be further adjusted or the rotary kiln structure shall be improved.

8. The method according to claim 1, characterized in that, The calculation of the local segregation index includes: ， in, S i pseudo-particles i Local segregation index, For the first i Each pseudo-particle represents the volume fraction or mass fraction of the target component within the region; The average concentration of the entire particle bed represented by all SPH pseudoparticles.

9. The method according to claim 8, characterized in that, The calculation of the global segregation index includes: ， in, S global The global segregation index for the entire particle bed. N This represents the total number of pseudo-particles.

10. An apparatus for performing the method according to any one of claims 1-9.

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