A sand mill process parameter optimization method and system based on stress energy model

Abstract

The invention discloses a sand mill process parameter optimization method and system based on a stress energy model, and relates to the field of process technology. The method analyzes the wet stirring grinding process of the sand mill based on the stress energy model, and obtains the target and constraint conditions of the sand mill process parameter optimization by revealing its energy transfer and dissipation mechanism; according to the adopted sand mill type and the physicochemical properties of the material, the range of the grinding process parameters to be optimized is determined in combination with empirical data, and used as the optimization space of the process variables; a hybrid frog leaping algorithm is used to optimize the target and constraint conditions of the process optimization, and the optimization values ​​of the required grinding process parameters are determined; a fluid-solid coupling simulation method is used to perform fluid-solid coupling simulation on the above parameters, and the specific stress energy thereof is determined, and the grinding process parameters corresponding to the maximum value of the specific stress energy are used as the optimal parameters, so as to solve the problem that the process parameters are difficult to optimize and the cost is high when the sand mill grinds the material, and the optimal process of the sand mill can be determined quickly, economically and conveniently.

Description

Technical Field

[0001] The invention relates to the field of process technology, and in particular to a method and system for optimizing process parameters of a sand mill based on a stress energy model. Background Art

[0002] Wet stirred media milling (WSMM) has become a mature grinding technology for producing fine and ultrafine materials such as paint pigments, pharmaceutical products, pesticides, ceramics and other materials. Its typical equipment is a sand mill. Compared with drum ball mills and vibrating ball mills, sand mills have higher energy density and lower specific energy consumption due to their higher bead collision stress intensity and stress number in the barrel. Therefore, WSMM is a robust, reproducible, scalable, and environmentally friendly mechanized physical particle reduction process. WSMM is a complex process and is considered a "black box". It depends on several process variables, which can be divided into two categories: 1) mill parameters such as agitator peripheral speed, mill volume, grinding media diameter and density / packing ratio; 2) suspension properties such as solid volume concentration, viscosity, shear rate, flow rate and particle size / Young's modulus.

[0003] Different material product characteristics and sand mill types make it difficult to determine the grinding process variables, especially when grinding new materials. At present, WSMM process parameter optimization relies heavily on empirical methods, experimental design and first rule models. In the actual production process, due to the expensive materials, high energy consumption, and numerous parameters and uncontrollable factors of the machine, it has become increasingly difficult to obtain the optimal parameters of the grinding process based on empirical and experimental methods. How to effectively reduce or eliminate the cost of obtaining optimized process parameters and use simple and convenient methods to obtain optimized grinding process parameters still faces great challenges. Summary of the invention

[0004] In order to overcome the above-mentioned prior art, how to effectively reduce or eliminate the disadvantage of high cost of obtaining optimized process parameters, the main purpose of the present invention is to provide a sand mill process parameter optimization method and system based on a stress energy model.

[0005] To achieve the above object, the present invention adopts the following technical scheme, a method for optimizing process parameters of a sand mill based on a stress energy model, comprising the following steps:

[0006] The wet stirred media grinding process of the sand mill is analyzed based on the stress model of material crushing. Through the energy transfer and dissipation mechanism, the goal and constraint conditions of process optimization are obtained. The goal is the set material particle size, and the constraint condition is to maximize the single particle stress number and single particle stress intensity of the material particles.

[0007] According to the type of sand mill used and the physical and chemical properties of the material, combined with empirical data, the range of grinding process parameters that need to be optimized is determined as the optimization space for process parameter variables;

[0008] The hybrid frog leaping algorithm is used to optimize the process optimization objectives and constraints to determine the optimal values ​​of the required grinding process parameters;

[0009] The CFD-DEM fluid-solid coupling simulation method is used to perform fluid-solid coupling simulation on the optimized values ​​of the required grinding process parameters to determine the specific stress energy, and the grinding process parameters corresponding to the maximum value of the specific stress energy are taken as the optimal parameters.

[0010] The objectives and constraints of the process optimization include:

[0011]

[0012] Among them, SN p,s is the single particle stress number, is the single particle stress intensity. n sr is the stirring disc speed, is the bead filling rate, C v is the material concentration, d gm is the bead diameter, φ is the porosity, x p is the particle size of the material, Material particle size target, t g is the grinding time, MaxSN p,s and It represents the maximization of process optimization objectives, and subjectto represents the constraints.

[0013] The hybrid frog leaping algorithm is used to optimize the process optimization objectives and constraints to determine the optimal values ​​of the required grinding process parameters, including the following steps:

[0014] Initialize the process optimization objectives and constraints, and randomly generate a set of initial solutions based on the set optimization space, representing potential optimization parameter combinations;

[0015] Evaluate the objective function of the process optimization goals and constraints, evaluate each initial solution, and confirm whether the corresponding objective function value and constraints are met;

[0016] In each iteration, local and global search strategies are used to improve the current solution and ensure that the constraints are satisfied;

[0017] Based on the evaluation results, the solutions in the initial solution are updated, and solutions with good performance are selectively retained or new solutions are introduced;

[0018] Iterations are repeated until the convergence condition is met, the maximum number of iterations is reached, or the objective function change is less than the set threshold.

[0019] The fluid-solid coupling simulation method is used to perform fluid-solid coupling simulation on the above parameters, including the following steps:

[0020] Based on the determined parameters, fluid mechanics model and solid dynamics model are established;

[0021] The finite element method is used to solve the coupled fluid-solid problem;

[0022] According to the simulation results, the specific stress energy, i.e. the maximum stress energy density in the system, is determined;

[0023] The grinding process parameters that maximize the specific stress energy are determined as the basis for selecting the optimal parameters.

[0024] The method of establishing a fluid mechanics model and a solid dynamics model based on the determined parameters comprises the following steps:

[0025] According to the actual equipment and process conditions, a geometric model of the fluid is established, including the grinding material and the sand mill rotor structure, and boundary conditions are determined, wherein the boundary conditions include: inlet velocity and outlet pressure;

[0026] According to the incompressibility, viscosity and interaction between fluid and solid particles, appropriate fluid mechanics equations are selected to establish a fluid mechanics model;

[0027] A motion model of solid particles is established, and the discrete element method (DEM) is used to describe the dynamic behavior of particles based on the contact force and friction between the stirring medium and the solid particles.

[0028] Determine the boundary conditions between the particles and the container wall, including friction coefficient and boundary elasticity.

[0029] The method of using the finite element method to solve the coupled fluid-solid problem includes:

[0030] Couple the fluid dynamics model and the solid dynamics model to form an overall coupled problem;

[0031] Generate appropriate finite element meshes for the fluid region and particle region respectively. The mesh density and accuracy should be determined according to actual needs and simulation accuracy requirements to confirm the fluid and solid.

[0032] The maximum stress energy density in the system is determined by the stress fields of the fluid and the solid.

[0033] A system for optimizing a sand mill process parameter based on a stress energy model, comprising:

[0034] The process analysis module analyzes the wet stirred medium grinding process of the sand mill based on the stress model of material crushing, and obtains the goals and constraints of process optimization through energy transfer and dissipation mechanisms. The goal is the set material particle size, and the constraint is to maximize the single particle stress number and single particle stress intensity of the material particles.

[0035] The optimization module is used to determine the range of grinding process parameters that need to be optimized based on the type of sand mill used and the physicochemical properties of the material, combined with empirical data, as the optimization space for process parameter variables;

[0036] The optimization processing module is used to optimize the objectives and constraints of process optimization by using a hybrid frog leaping algorithm to determine the optimal values ​​of the required grinding process parameters;

[0037] The optimal parameter acquisition module is used to use the CFD-DEM fluid-solid coupling simulation method to perform fluid-solid coupling simulation on the optimized values ​​of the required grinding process parameters, determine the specific stress energy, and take the grinding process parameters corresponding to the maximum value of the specific stress energy as the optimal parameters.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows: based on the stress energy theory of material crushing and combined with practical experience, the present invention proposes an intelligent optimization process optimization method based on stress energy. The present invention solves the problem of difficulty in determining material grinding process parameters by effectively analyzing the crushing mechanism of micron and nanometer particles during mechanical crushing of materials, as well as the energy transfer and dissipation mechanism in the sand mill. Based on computers and intelligent optimization technology, the sand mill optimization process can be determined quickly, economically and conveniently, effectively reducing the cost of factories in wet preparation of micron and nanometer pigments, coatings and pesticides, reducing environmental pollution, and having good social and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0040] Figure 1 It is a schematic diagram of the process structure of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0042] The current WSMM process parameter optimization mainly relies on empirical methods, experimental design and first rule models. For a new grinding material, engineers first determine the specific sand mill rotor structure based on the physicochemical properties of the material, such as viscosity, concentration, specific surface area, refractive index, etc., and then determine the approximate process parameter range based on the specific production output and the product particle size to be met. Secondly, a small batch of material grinding experiments are carried out on a small experimental sand mill. According to the experimental grinding effect, the grinding process parameters are continuously adjusted so that it can meet the required particle size requirements and preliminarily determine the appropriate grinding process parameters. Finally, the data obtained from the experiment is transplanted to the industrial machine for pilot testing. According to the required output, the cylinder volume of the sand mill is determined, and the pilot grinding experiment is carried out using the grinding process parameters determined by the experiment. The grinding process parameters are continuously adjusted according to the experimental results. After a long period of repeated pilot experiments, the grinding process parameters of the sand mill are finally determined. If the pilot test fails, it is necessary to return to the small sand mill in the laboratory for a small test, such as re-changing / optimizing the rotor structure, re-determining the grinding process parameters, and then conducting a pilot test. Only after the pilot test is successful can subsequent industrial production be arranged. From this point of view, the current method of determining the grinding process parameters is long and cumbersome, with a high risk of failure and a large waste of manpower and material resources. With the continuous influx of new materials into the market, the existing methods face serious challenges; in addition, some grinding materials are very expensive, which leads to a great restriction on the number of grinding experiments, and in some cases even does not allow repeated experiments; finally, since the sand mill is an energy-intensive equipment, repeated experiments will cause a lot of energy waste, and the intermediate materials in the experimental process will also cause a lot of environmental pollution control costs. In summary, it can be seen that the current determination of the grinding process parameters of the sand mill faces great cost pressure and waste of resources for sand mill manufacturers. Whether it is possible to seek a method for optimizing the process based on the material crushing theory to quickly, economically and conveniently determine the optimization process of the sand mill has important theoretical and industrial application practical value.

[0043] Based on the stress energy theory of material crushing and combined with practical experience, this invention proposes an intelligent optimization process optimization method based on stress energy. The specific scheme is as follows:

[0044] Firstly, based on the stress model, the energy transfer and dissipation mechanism of WSMM was analyzed and determined, and the objectives and constraints of process optimization were proposed:

[0045]

[0046] Among them, SN p,s is the single particle stress number, is the single particle stress intensity. n sr is the stirring disc speed, is the bead filling rate, Cv is the material concentration, d gm is the bead diameter, φ is the porosity, x p is the particle size of the material, Material particle size target, t g It's grinding time.

[0047] Secondly, according to the type of sand mill used and the physical and chemical properties of the material, the engineer determines the range of grinding process parameters that need to be optimized and uses this as the optimization space for process variables.

[0048] Then, the shuffled frog leaping algorithm (SFLA) is used to optimize equation (1) to determine the optimal values ​​of the required grinding process parameters.

[0049] Finally, the CFD-DEM (Computational fluid dynamics-discrete element method) fluid-solid coupling simulation method was used to perform fluid-solid coupling simulation on the above parameters to determine their specific stress energy, and the grinding process parameters corresponding to the maximum value of the specific stress energy were taken as the optimal parameters.

[0050] The specific flow chart is as follows Figure 1 shown.

[0051] The present invention is based on the stress energy theory of material crushing and combines practical experience to propose an intelligent optimization process optimization method based on stress energy. The present invention solves the problem of difficulty in determining material grinding process parameters by effectively analyzing the crushing mechanism of micron and nanometer particles during mechanical crushing of materials, as well as the energy transfer and dissipation mechanism in the sand mill. Based on computers and intelligent optimization technology, the sand mill optimization process can be determined quickly, economically and conveniently, effectively reducing the cost of factories in wet preparation of micron and nanometer pigments, coatings and pesticides, reducing environmental pollution, and having good social and economic benefits.

[0052] Example 1

[0053] Antimony-containing refractory gold / silver ore samples were used as grinding materials, with a feed particle size of d80 = 60 μm. Ultrafine grinding was performed using a vertical pin sand mill. During the grinding process, the slurry concentration was maintained at 35% w / w. Three stirring shaft speeds were used: 250 rpm, 500 rpm, and 750 rpm. The grinding media had an average density of 7.68 g / cm 3 The stainless steel beads of three diameters (1mm, 3mm, 5mm) were used; the bead filling rates were (60%, 70%, 80%). Under each working condition, the sand mill was ground for 10 minutes. Table 1 shows the measured particle size data under each grinding process condition.

[0054] Table 1. Grinding process conditions and measured particle size data

[0055]

[0056]

[0057] use Figure 1 The process is optimized, where the SFLA algorithm is implemented in a mathematical software package such as MATLAB 2022b, and the SFLA parameters are set as follows: the number of memes m = 30, the number of frogs in each meme n = 50, the number of evolutionary individuals selected from each meme q = 50, the number of local iterations in each meme Lmax = 30, and the maximum step size Smax = 1 taken by the frog after being infected. After optimization and fluid-solid coupling analysis, the optimal process parameters are finally obtained: grinding bead diameter = 1mm, bead filling rate = 80%, stirring shaft speed = 750rpm. The corresponding material particle size is d 80 =2.88 μm. Compared with other levels listed in Table 3, the optimized process parameters can achieve the minimum product particle size within the same grinding time and improve the grinding efficiency.

[0058] Example 2

[0059] In this embodiment, low-grade chromite containing platinum group elements was used as the grinding material, and a wet grinding experiment was carried out in a vertical stirred mill (Netzsch, GmbH) with a disc stirrer. A Malvern particle size analyzer (CILAS, 1040) was used to measure and analyze the particle size of a representative sample of 10-15g. The grinding media used was a mixed medium of 4.89mm and 3.6mm ceramic grinding beads. The mill was filled to the height of the second disc with 10 kg of media balls with a volume density of 3.9 g / cm3. In order to better compare the grinding efficiency, according to the actual needs of the company, the grinding time was set to a constant value, t=8min. The grinding process parameters used and the measured material particle size data are shown in Table 2.

[0060] Table 2. Grinding process conditions and measured particle size data

[0061]

[0062]

[0063] use Figure 1The SFLA algorithm was implemented in a mathematical software package (MATLAB 2022b) and the SFLA parameters were set as follows: the number of memes m = 30, the number of frogs in each meme n = 50, the number of evolved individuals selected from each meme q = 50, the number of local iterations within each meme Lmax = 30, and the maximum step size Smax = 1 taken by the frog after being infected.

[0064] After optimization and fluid-solid coupling analysis, the optimal process parameters were finally obtained: agitator speed = 700 rpm, feed particle size = 23 μm, solid concentration = 45%, grinding time = 8 min, and the corresponding material particle size is P 80 =13.87 μm. Compared with the actual levels listed in Table 2 above, the optimized process parameters can achieve the minimum product particle size within the same grinding time (t=8 min). The parameter optimization method proposed in the present invention has achieved good results in factory application and improved production quality and economic benefits.

[0065] Relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0066] The above embodiments are merely examples of the present invention and do not limit the protection scope of the present invention. All designs that are the same or similar to the present invention fall within the protection scope of the present invention.

Claims

1. A method for optimizing process parameters of a sand mill based on a stress energy model, characterized in that: The following steps are involved: The wet stirred media grinding process of the sand mill is analyzed based on the stress model of material crushing, and the goals and constraints of process optimization are obtained through the energy transfer and dissipation mechanism; The target is the set material particle size, and the constraint is to maximize the single particle stress number and single particle stress intensity of the material particles; According to the type of sand mill used and the physical and chemical properties of the material, combined with empirical data, the range of grinding process parameters that need to be optimized is determined as the optimization space for process parameter variables; The hybrid frog leaping algorithm is used to optimize the process optimization objectives and constraints to determine the optimal values ​​of the required grinding process parameters; The CFD-DEM fluid-solid coupling simulation method is used to perform fluid-solid coupling simulation on the optimized values ​​of the required grinding process parameters, determine the specific stress energy, and take the grinding process parameters corresponding to the maximum value of the specific stress energy as the optimal parameters; The hybrid frog leaping algorithm is used to optimize the process optimization objectives and constraints to determine the optimal values ​​of the required grinding process parameters, including the following steps: Initialize the process optimization objectives and constraints, and randomly generate a set of initial solutions based on the set optimization space, representing potential optimization parameter combinations; Evaluate the objective function of the process optimization goals and constraints, evaluate each initial solution, and confirm whether the corresponding objective function value and constraints are met; In each iteration, local and global search strategies are used to improve the current solution and ensure that the constraints are satisfied; Based on the evaluation results, the solutions in the initial solution are updated, and solutions with good performance are selectively retained or new solutions are introduced; Repeat the iteration until the convergence condition is met, the maximum number of iterations is reached, or the objective function changes less than the set threshold; And the CFD-DEM fluid-solid coupling simulation method is used to perform fluid-solid coupling simulation on the optimized values ​​of the required grinding process parameters, including the following steps: Based on the determined parameters, fluid mechanics model and solid dynamics model are established; The finite element method is used to solve the coupled fluid-solid problem; According to the simulation results, the specific stress energy, i.e. the maximum stress energy density in the system, is determined; Determine the grinding process parameters that maximize the specific stress energy as the basis for selecting the optimal parameters; The method of establishing a fluid mechanics model and a solid dynamics model based on the determined parameters comprises the following steps: According to the actual equipment and process conditions, a geometric model of the fluid is established, including the grinding material and the sand mill rotor structure, and boundary conditions are determined, wherein the boundary conditions include: inlet velocity and outlet pressure; According to the incompressibility, viscosity and interaction between fluid and solid particles, appropriate fluid mechanics equations are selected to establish a fluid mechanics model; A motion model of solid particles is established, and the discrete element method (DEM) is used to describe the dynamic behavior of particles based on the contact force and friction between the stirring medium and the solid particles. Determine the boundary conditions between the particles and the container wall, including friction coefficient and boundary elasticity; The method of solving the coupled fluid-solid problem by using the finite element method includes: Couple the fluid dynamics model and the solid dynamics model to form an overall coupled problem; Generate appropriate finite element meshes for the fluid region and particle region respectively. The mesh density and accuracy should be determined according to actual needs and simulation accuracy requirements to confirm the fluid and solid; The maximum stress energy density in the system is determined by the stress fields of the fluid and the solid.

2. The method for optimizing process parameters of a sand mill based on a stress energy model according to claim 1, characterized in that: The objectives and constraints of the process optimization include: Among them, SN p，s is the single particle stress number, is the single particle stress intensity, Max: SN p，s and Represents the maximization of process optimization objectives, n sr is the stirring disc speed, is the bead filling rate, C v is the material concentration, d gm is the bead diameter, is the porosity, x p is the particle size of the material, Material particle size target, t g is the grinding time, and subject to represents the constraint condition.

3. A system for optimizing the process parameters of a sand mill based on a stress energy model, characterized in that: include: The process analysis module analyzes the wet stirred medium grinding process of the sand mill based on the stress model of material crushing, and obtains the goals and constraints of process optimization through energy transfer and dissipation mechanisms; The target is the set material particle size, and the constraint is to maximize the single particle stress number and single particle stress intensity of the material particles; The optimization module is used to determine the range of grinding process parameters that need to be optimized based on the type of sand mill used and the physicochemical properties of the material, combined with empirical data, as the optimization space for process parameter variables; The optimization processing module is used to optimize the objectives and constraints of process optimization by using a hybrid frog leaping algorithm to determine the optimal values ​​of the required grinding process parameters; The hybrid frog leaping algorithm is used to optimize the objectives and constraints of process optimization and determine the optimal values ​​of the required grinding process parameters, including the following steps: initializing the objectives and constraints of process optimization, randomly generating a set of initial solutions according to the set optimization space, representing potential optimization parameter combinations; evaluating the objective function of the objectives and constraints of process optimization, evaluating each initial solution, and confirming whether the corresponding objective function value and constraint conditions are satisfied; in each iteration, using local and global search strategies to improve the current solution and ensure that the constraints are satisfied; according to the evaluation results, updating the solutions in the initial solution, selectively retaining solutions with good performance or introducing new solutions; repeating the iteration until the convergence condition is met, the maximum number of iterations is reached, or the objective function change is less than the set threshold; The optimal parameter acquisition module is used to adopt the CFD-DEM fluid-solid coupling simulation method to perform fluid-solid coupling simulation on the optimized values ​​of the required grinding process parameters, determine the specific stress energy, and use the grinding process parameters corresponding to the maximum specific stress energy as the optimal parameters; wherein, the CFD-DEM fluid-solid coupling simulation method is used to perform fluid-solid coupling simulation on the optimized values ​​of the required grinding process parameters, including the following steps: based on the determined parameters, a fluid mechanics model and a solid dynamics model are established; the finite element method is used to solve the coupled fluid-solid problem; according to the simulation results, the specific stress energy, that is, the maximum stress energy density in the system, is confirmed; the grinding process parameters that maximize the specific stress energy are determined as the basis for selecting the optimal parameters; the fluid mechanics model and the solid dynamics model are established based on the determined parameters, including the following steps: according to the actual equipment and process conditions, Establish a geometric model of the fluid, including the grinding material and the sand mill rotor structure, determine the boundary conditions, which include: inlet velocity, outlet pressure; select appropriate fluid mechanics equations according to the incompressibility, viscosity and interaction between the fluid and solid particles, and establish a fluid mechanics model; establish a motion model of solid particles, and use the discrete element method DEM to describe the dynamic behavior of particles according to the contact force and friction between the stirring medium and the solid particles; determine the boundary conditions between the particles and the container wall, including the friction coefficient and boundary elasticity; the use of the finite element method to solve the coupled fluid-solid problem includes: coupling the fluid mechanics model and the solid dynamics model to form an overall coupling problem; generate appropriate finite element grids for the fluid region and the particle region respectively, where the grid density and accuracy should be determined according to actual needs and simulation accuracy requirements, and confirm the fluid and solid; The maximum stress energy density in the system is determined by the stress fields of the fluid and the solid.

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