A predictive evaluation method for Raymond Mill operation classification

Abstract

A predictive evaluation method for the operation classification of a Raymond Mill blower is disclosed. Currently, there is no relevant processing method that can simultaneously evaluate the classification efficiency and the overall pressure loss of a Raymond Mill blower. The predictive evaluation method of the present invention is based on the inlet air velocity D, the main shaft speed E, and the classifier speed F. By combining the Box-Behnken method, the Distributed Partial Planarization (DPM) model, and the Multi-Coordinate Reference System (MRF) multi-coordinate reference system, a response surface simulation analysis is performed using the discrete phase of the R-R distribution to calculate the maximum value of the classification efficiency M and the minimum value of the overall pressure loss N. A comparative analysis process is then performed with the actual acquired data based on the maximum value of the classification efficiency M and the minimum value of the overall pressure loss N.

Description

Technical Field

[0001] The invention belongs to the technical field of mechanical engineering, and in particular relates to a prediction and evaluation method for operation classification of a Raymond mill. Background Art

[0002] Raymond mills, a type of mining equipment, primarily grind ore into fine powder through mechanical processing. During operation, the internal flow field is primarily gas-solid two-phase flow, often exhibiting complex fluid-particle multiphase flow patterns. This includes vortex flow between the rollers and grinding rings, contraction flow in the gravity classification zone, and strong vortex flow in the centrifugal separation zone. Their complex internal structure requires multi-point monitoring and a long product lifecycle. During the operation of these coupled particle-fluid systems, the internal fluid exerts drag, lift, or pressure on the particles, causing them to move. This process involves not only particle-to-particle and particle-to-solid collisions, but also particle motion that in turn influences fluid flow. This complex interaction complicates and challenges the comprehensive and accurate understanding of the physical processes involved in the movement, distribution, and collision characteristics of particle populations within fluid systems. Currently, there is a lack of methods to accurately understand the fluid-solid two-phase flow patterns in powder systems to improve equipment conveying efficiency and predict surface wear. This often creates challenges when selecting classifiers as the grading equipment commonly used with Raymond mills, particularly for coarse-fine grading of limestone powder. These issues primarily stem from mismatches between performance parameters such as the air box inlet velocity, spindle speed, and classifier speed. This phenomenon results in inefficient material classification by the Raymond Mill, leading to severe coarseness and requiring more classification cycles to meet operational requirements. Currently, there is no systematic and quantitative evaluation method for this issue. Summary of the Invention

[0003] In view of the fact that there is no existing evaluation method for the classification efficiency and the pressure loss of the Raymond Mill blower taking into account both, a predictive evaluation method for the operation classification of the Raymond Mill blower is proposed.

[0004] A predictive evaluation method for the operation classification of a Raymond Mill blower is proposed. The predictive evaluation method is based on the inlet air velocity D, the main shaft speed E, and the classifier speed F. By combining the Box-Behnken method, the DPM model, and the MRF multi-coordinate reference system, a response surface simulation analysis is performed using the discrete phase of the RR distribution to calculate the maximum value of the classification efficiency M and the minimum value of the whole machine pressure loss N. A comparative analysis process is completed with the actual data based on the maximum value of the classification efficiency M and the minimum value of the whole machine pressure loss N.

[0005] As a preferred solution: with an inlet wind speed D of 25-33m / s, a main shaft speed E of 72-92r / min, and a classifier speed F of 308-338r / min as the flow field boundary conditions, experiments were conducted through Ansys Fluent simulation software according to the Box-Behnken design method. The collection efficiency calculation method in the Box-Behnken design method is: after the Fluent steady-state simulation calculation is completed, the statistics of the particle information captured at the outlet of the whole machine are completed in the Sample of Discrete Phase under Reports, and then the mass flow rate of particles of each mesh size is obtained, and the collection efficiency is calculated accordingly. The collection efficiency is the initial preparation data of the classification efficiency M.

[0006] As a preferred solution, the Box-Behnken method, DPM model and MRF multi-coordinate reference system are combined to perform response surface simulation analysis with the discrete phase of RR distribution and calculate the pressure loss N of the whole machine as follows:

[0007] Raymond mill flow field model construction and boundary condition setting:

[0008] The 3D model of the Raymond Mill machine was constructed using AutoCAD and UG software. The Spaceclaim software under ANSYS was then used to simplify the whole machine model and extract the flow field model inside the Raymond Mill machine to ensure that the flow field model was obtained correctly. Secondly, the meshing software under ANSYS was used to mesh the flow field of the Raymond Mill machine. Finally, the drawn mesh file was imported into Fluent. The appropriate calculation model was used to set the boundary conditions and solution method for numerical simulation. The numerical simulation results were compared and analyzed with the actual data obtained from the Raymond Mill machine to verify the calculation model, boundary conditions and solution conditions. Accuracy, using steady-state, pressure solver for solution, turbulence model using RNG model in k-ε double equation, using DPM model and MRF multi-coordinate reference system, discrete phase using RR distribution, particles with zero initial velocity perpendicular to the roller surface launch, the hydraulic diameter at the air inlet is 0.485m, using coupling solution method for solution, using the default under-relaxation factor, Box-Behnken test design and results statistics, the fitting model is subjected to variance analysis, forming the main engine pressure loss variance analysis standard table 1, based on the data of the main engine pressure loss variance analysis standard table 1, the main engine pressure loss N fitting regression equation is established, specifically:

[0009] N=949.06-145.46D+38.81E-0.42F-2.70DE-0.02DF+0.01EF+

[0010] 4.91D 2 +0.48E 2 +1.63×10 -5 F2 (1)

[0011] The Pr value of the model used in the regression analysis results was compared with the significance level standard value of 0.05. When the Pr value is less than the significance level standard value of 0.05, it indicates that the model is reliable; when the Pr value is greater than the significance level standard value of 0.05, it indicates that the model is unreliable. The lack of fit term is used as an indicator to evaluate the degree of fit between the model and the experimental data. Its value is 0.052. The lack of fit term is greater than 0.05, indicating that there is no lack of fit phenomenon, that is, there is good consistency between the model and the experimental data.

[0012] The correlation coefficient r of the model 2 It indicates the degree to which the model can explain the response value. According to the fitting regression equation of the main engine pressure loss Y, the factors affecting the comprehensive score are ranked in order of importance as follows: inlet air speed D, main shaft speed E, and whole machine outlet pressure. An increase in inlet air speed D will lead to an increase in pressure loss, while an increase in main shaft speed E will help reduce pressure loss.

[0013] As a preferred solution: statistics are conducted on the Box-Behnken test design and results, and variance analysis is performed on the fitting model to form the standard table 2 for variance analysis of the fractional efficiency M. Based on the data in the standard table 2 for variance analysis of the main engine pressure loss, a fitting regression equation for the fractional efficiency M is established. The response surface fitting equation for the fractional efficiency M is:

[0014] M=-31.42314-43.83017D+6.52373E+3.12442F+0.12819DE+0.038208DF-0.037308EF+0.35797D 2 +0.013450E 2 -2.09167e -3 F 2 ; (2)

[0015] The response surface fitting equation of the classification efficiency M was input into Origin 2022 to obtain the response surface of the classification efficiency M. When the spindle speed E is fixed, the classification efficiency M increases with the increase of the inlet wind speed D and the decrease of the classifier speed F. When the inlet wind speed D is constant, the collection efficiency first decreases and then increases with the increase of the spindle speed E and the decrease of the classifier speed F. The optimal values ​​of the spindle speed E and the classifier speed F are determined when the collection efficiency is close to 100%.

[0016] As the preferred solution, we start the optimization calculation with the goal of maximizing the graded efficiency M and minimizing the overall pressure loss N, and establish the optimization equation:

[0017]

[0018] According to the response surface equation, the maximum value of the classification efficiency M and the minimum value of the whole machine pressure loss N are obtained by the inlet air velocity D, the main shaft speed E, and the classifier speed F. By comparing and analyzing the data obtained in practice, the evaluation process of the data obtained in practice is obtained. When the classification efficiency M in the data obtained in practice is lower than 60% of the maximum value of the classification efficiency M, and the whole machine pressure loss N in the data obtained in practice is higher than 60% of the minimum value of the whole machine pressure loss N, the data obtained in practice are of low quality, indicating that the corresponding Raymond Mill machine is in poor operation and classification status and should be maintained and adjusted accordingly. When the classification efficiency M in the data obtained in practice is 60% to 80% of the maximum value of the classification efficiency M, and the whole machine pressure loss N in the data obtained in practice is 10% to 40% of the minimum value of the whole machine pressure loss N, the data obtained in practice are of high quality, indicating that the corresponding Raymond Mill machine is in good operation and classification status.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The present invention realizes the in-depth regular optimization processing process of the classifier of the Raymond mill, optimizes the velocity and pressure flow field inside the classifier, thereby achieving the goals of improving classification efficiency, reducing energy consumption and reducing the particle size distribution range. In the optimization evaluation process, the present invention determines the coordination of key performance parameters combining the air inlet wind speed, the spindle speed and the classifier speed. By adjusting these parameters, we can achieve better material classification effects and reduce the occurrence of coarse running. Through in-depth research and optimization, it is possible to conduct corresponding evaluations on the operating classification and the pressure loss of the entire machine of the existing Raymond mill, and provide favorable and reliable basis support for subsequent rectification, so that the Raymond mill can more efficiently cope with the coarse and fine classification of limestone powder, thereby helping to improve the overall production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a schematic diagram of the force analysis of particles in the gravity classification area;

[0022] Figure 2a Schematic diagram of response surface simulation of DE factor in the response surface of fractional efficiency M;

[0023] Figure 2b Schematic diagram of response surface simulation of DF factor in the response surface of fractional efficiency M;

[0024] Figure 2c Schematic diagram of response surface simulation of EF factor in the response surface of fractional efficiency M;

[0025] Figure 3aSchematic diagram of response surface simulation of the interaction between inlet wind speed D and main shaft speed E;

[0026] Figure 3b Schematic diagram of response surface simulation of the interaction between inlet wind speed D and the outlet pressure of the whole machine;

[0027] Figure 3c This is a schematic diagram of the response surface simulation of the interaction between the spindle speed E and the outlet pressure of the whole machine;

[0028] Figure 4 This is a schematic diagram of the wind field trajectory simulation during the test at 9 o'clock inside the Raymond machine;

[0029] Figure 5 This is a schematic diagram of the wind field trajectory simulation during the test at 14 o'clock inside the Raymond machine;

[0030] Figure 6 This is the velocity cloud diagram of the internal flow field during test 2 inside the Raymond machine;

[0031] Figure 7 This is the velocity cloud diagram of the internal flow field inside the Raymond machine during the test at 7 o'clock;

[0032] Figure 8 This is the internal pressure cloud diagram of the Raymond machine during test 2;

[0033] Figure 9 This is the internal pressure cloud diagram of the Raymond machine during test 7;

[0034] Figure 10 This is a comparative diagram of the effects of classifier speed on the passing rate and classification rate of the gravity classification zone;

[0035] Figure 11 This is a schematic diagram of the retention time of finished particles;

[0036] Figure 12 Velocity cloud diagram of coarse particle trajectory. DETAILED DESCRIPTION

[0037] To make the objectives, technical solutions, and advantages of the present invention more clearly apparent, the present invention is described below using specific embodiments shown in the accompanying drawings. However, it should be understood that these descriptions are merely illustrative and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present invention.

[0038] Specific implementation method 1: Combination Figures 1 to 12This embodiment describes the prediction and evaluation method for the operation classification of the Raymond mill in this embodiment. It is based on the inlet wind speed D, the main shaft speed E, and the classifier speed F. By combining the Box-Behnken method, the DPM model and the MRF multi-coordinate reference system, the response surface simulation analysis is performed with the discrete phase of the RR distribution, and the maximum value of the classification efficiency M and the minimum value of the whole machine pressure loss N are calculated. The comparison and analysis process with the actual obtained data is completed based on the maximum value of the classification efficiency M and the minimum value of the whole machine pressure loss N, wherein the inlet wind speed D is the inlet wind speed D of the air inlet box.

[0039] Combine Figure 1 As shown, the force analysis process of the particles in the classifier in this embodiment is as follows:

[0040] During the working process of the classifier, the classification of material particles is mainly carried out under the combined action of three forces, including the spiral rising gas drag force F d , centrifugal inertia force F c and gravity G. The interaction of these forces determines the trajectory of the particles in the classifier, which in turn affects the classification effect. The particle velocity can be decomposed into the axial velocity (u p ) L , radial velocity (u p ) r and tangential velocity (u p ) t Three mutually perpendicular velocity components are used for analysis:

[0041] 1) Axial speed (u p ) L Because the particles inside the Raymond Mill are subject to the interaction of rising airflow and gravity, and because the two forces are in opposite directions, the particles may move upward or downward in the axial direction. For some coarse particles, when the rising velocity of the airflow is less than the settling velocity due to gravity, the particles move downward. When the rising velocity of the airflow is greater than the settling velocity due to gravity, the particles move upward. Therefore, the smaller the particle diameter, the closer the rising velocity of the particle is to the rising velocity of the airflow.

[0042] 2) Radial velocity (u p ) r It is caused by the inertial centrifugal force generated by the material particles around the center of the classifier shaft.

[0043] 3) Tangential velocity (u p ) t It is caused by the circumferential speed of the material particles and airflow rotating around the center of the classifier shaft.

[0044] Based on the theory of particle centrifugal classification, when particles move in a circular motion with the airflow, the combined effect of the axial and radial velocities causes the particles to adopt a spiral trajectory. This spiral motion can be upward or downward. Fine particles tend to spiral upward compared to coarse particles.

[0045] The particle force calculation method process is as follows:

[0046] In a centrifugal field, the resistance experienced by particles in the medium is opposite to the centrifugal force, which can be expressed using the Tosks resistance formula:

[0047] F d =3πηdV r (5-1)

[0048] Where η is the medium viscosity, Pa·s; δ and ρ are the density of the particle material and the medium, kg / m 3 ; V r Medium flow rate, m / s;

[0049] The centrifugal force on a particle of density δ is:

[0050]

[0051] Where r is the particle's radius of gyration, m; ω is the particle's gyration speed, r / min; V t is the tangential velocity of the particle, m / s;

[0052] If F d >F c , that is, the resistance to the particles is greater than the centrifugal force, the particles will move toward the wall of the classifier and be discharged and collected as coarse particles. If F d <F c When the centrifugal force on the particles is greater than the resistance, the particles will be discharged with the airflow and finally collected at the outlet. d =F c When the particle size is 0.01, the particle group will theoretically continue to move in a circular motion. At this time, the final centrifugal sedimentation velocity and the classification particle size can be obtained as follows:

[0053]

[0054] Where: d r -Classification particle size, m; Vt-impeller average speed, m / s; Vr-medium flow rate, m / s; η-air viscosity; δ-material density, kg / m 3 ; r-impeller average radius, m; ρ-air flow density, kg / m 3 .

[0055] Specific embodiment 2: This embodiment is a further limitation of specific embodiment 1. In this embodiment, an inlet wind speed D of 25 to 33 m / s, a spindle speed E of 72 to 92 r / min, and a classifier speed F of 308 to 338 r / min are used as flow field boundary conditions. The experiments are conducted through Ansys Fluent simulation software according to the Box-Behnken design method. The collection efficiency calculation method in the Box-Behnken design method is as follows: after the Fluent steady-state simulation calculation is completed, the statistics of the particle information captured at the outlet of the whole machine are completed in the Sample of Discrete Phase under Reports, and then the mass flow rate of particles of each mesh size is obtained, and the collection efficiency is calculated accordingly. The collection efficiency is the initial preparation data of the classification efficiency M.

[0056] In this implementation, inlet air velocity D, spindle speed E, and classifier speed F are selected as the three factors, and classification efficiency M and overall pressure drop N are selected as the target optimization parameters. To gain a deeper understanding of the combined impact of these three factors on overall machine performance, inlet air velocity D is selected at 25, 29, and 33 m / s, spindle speed E at 72, 82, and 92 r / min, and classifier speed at 308, 338, and 368 r / min. The boundary conditions are shown in Table 1 below.

[0057] Table 1 Boundary conditions of the flow field of the Raymond mill

[0058]

[0059] The steady-state and pressure solvers are used for solution, and the turbulence model adopts the RNG model in the k-ε double equation. Because it has better effect in handling flows with high strain rates and large streamline curvature, it can better simulate the internal flow field of the Raymond mill. The DPM model and MRF multi-coordinate reference system are used, and the discrete phase adopts RR distribution. The particles are launched perpendicular to the grinding roller surface with an initial velocity of zero. The hydraulic diameter at the air inlet is 0.711m, and the hydraulic diameter at the air inlet is 0.721m. The coupled solution method is used for solution, and the default under-relaxation factor is used.

[0060] Specific implementation method three: This implementation method is a further limitation of specific implementation method one or two. In this implementation method, Ansys Fluent simulation software is used to perform experimental design based on the Box-Behnken design method in Design-Expert10.0 software. Table 2 below shows the Box-Behnken experimental design and result statistics. The collection efficiency calculation method is as follows: after the Fluent steady-state simulation calculation is completed, the statistics of the particle information captured at the outlet of the whole machine are completed in the Sample of Discrete Phase under Reports, and the mass flow rate of particles of each mesh size is obtained. For example, when the inlet wind speed D is 25m / s, the spindle speed E is 72r / min, and the classifier speed F is 338r / min, the collected 200-mesh mass flow rate is 3.3114 respectively, then the collection efficiency m is

[0061]

[0062] The calculation method for the Raymond Mill collection efficiency of the other simulation conditions is the same as above. The obtained fractional efficiency M and the whole machine pressure loss N are input into the result statistical table to obtain the variance analysis table of the fractional efficiency M and the whole machine pressure loss N in Table 3:

[0063] Table 2 Box-Behnken test design and result statistics

[0064]

[0065] Table 3 Variance analysis of classification efficiency M and overall pressure loss N

[0066]

[0067] In the above table, when Pr is less than 0.05, ※ indicates that the factor has a significant impact on the test index.

[0068] Specific implementation method 4: This implementation method is a further limitation of specific implementation methods 1, 2 or 3. Figure 3a 、 Figure 3b and Figure 3c As shown, in this embodiment, the Box-Behnken method, the DPM model and the MRF multi-coordinate reference system are combined to perform response surface simulation analysis with the discrete phase of the RR distribution and calculate the process of the whole machine pressure loss N as follows:

[0069] Raymond mill flow field model construction and boundary condition setting:

[0070] A 3D model of the Raymond Mill machine was constructed using AutoCAD and UG software. The entire machine model was then simplified using ANSYS's Spaceclaim software to extract the flow field model within the Raymond Mill machine and ensure the accuracy of the flow field model. The meshing software within ANSYS was then used to mesh the flow field of the Raymond Mill machine. Finally, the meshed file was imported into Fluent, and numerical simulation was performed using an appropriate calculation model, boundary conditions, and solution method. The numerical simulation results were compared and analyzed with the actual data obtained from the Raymond Mill machine to verify the accuracy of the calculation model, boundary conditions, and solution conditions. , the steady-state and pressure solvers are used for solution, the turbulence model adopts the RNG model in the k-ε double equation, the DPM model and the MRF multi-coordinate reference system are used, the discrete phase adopts the RR distribution, the particles are launched perpendicular to the grinding roller surface with an initial velocity of zero, the hydraulic diameter at the air inlet is 0.485m, and the coupled solution method is used for solution. The default under-relaxation factor is used to statistically analyze the Box-Behnken test design and results to form Table 4, and the variance analysis of the fitting model is performed to form the standard table 5 for the variance analysis of the main engine pressure loss. According to the data in the standard tables 4 and 5 for the variance analysis of the main engine pressure loss, the fitting regression equation of the main engine pressure loss N is established, specifically:

[0071] Table 4 Statistics of Box-Behnken test results

[0072]

[0073]

[0074] Table 5 Standard table for variance analysis of main engine pressure loss

[0075]

[0076]

[0077] The fitting regression equation of the main engine pressure loss N established based on the data in Table 4 and Table 5 of the main engine pressure loss variance analysis standard is:

[0078] N=949.06-145.46D+38.81E-0.42F-2.70DE-0.02DF+0.01EF+

[0079] 4.91D 2 +0.48E 2 +1.63×10 -5 F 2 (1)

[0080] The Pr value of the model used in the regression analysis results is 0.0309, which is less than 0.05, indicating that the model is statistically reliable. The lack of fit term, an indicator of the degree of fit between the model and the experimental data, is 0.052 and greater than 0.05, indicating that there is no lack of fit, that is, there is good consistency between the model and the experimental data. The correlation coefficient r of the model 2 The value is 0.8511, indicating that the model can explain 85.11% of the response variation. According to this model, the factors influencing the comprehensive score are ranked in order of importance as follows: inlet air velocity D, spindle speed E, and overall outlet pressure F. Inlet air velocity D and spindle speed E have a significant impact on the model: an increase in inlet air velocity D leads to an increase in pressure loss, while an increase in spindle speed E helps reduce pressure loss.

[0081] The final conclusion is: the Pr value of the model used is compared with the significance level standard value of 0.05. When the Pr value is less than the significance level standard value of 0.05, it means that the model is reliable; when the Pr value is greater than the significance level standard value of 0.05, it means that the model is unreliable. The lack of fit term is an indicator of the degree of fit between the model and the experimental data. Its value is 0.052. The lack of fit term is greater than 0.05, indicating that there is no lack of fit phenomenon, that is, there is good consistency between the model and the experimental data.

[0082] The correlation coefficient r of the model 2 It indicates the degree to which the model can explain the response value. According to the fitting regression equation of the main engine pressure loss Y, the factors affecting the comprehensive score are ranked in order of importance as follows: inlet air speed D, main shaft speed E, and whole machine outlet pressure F. An increase in inlet air speed D will lead to an increase in pressure loss, while an increase in main shaft speed E will help reduce pressure loss.

[0083] The main engine pressure loss analysis process in this embodiment is as follows:

[0084] The energy equation for a steady flow with energy input is described as:

[0085]

[0086] In the above formula, W shaft，net in is the unit mass shaft work input to the fluid; is the flow energy per unit mass; is the kinetic energy per unit mass; gz is the potential energy per unit mass of the fluid; e is the internal energy; q net in is the unit mass heat transferred to the fluid; intermolecular collisions are not considered in this paper, so the internal energy and the heat transferred to the fluid (u2-u1-q net in ) is negligible; P is absolute pressure, Pa; ρ is density, kg / m3; V is velocity, m / s; g is acceleration due to gravity, m / s2; z represents spatial position, m;

[0087] Based on the above analysis, formula 6 is sorted out, and the change law of the flow energy of gas at the inlet and outlet of the mill main engine is as follows formula 7:

[0088]

[0089] In the above formula, the difference between P2 and P1 is the pressure loss of the Raymond mill.

[0090] When the fluid flows through the wall structure of the mill, the pressure inevitably decreases due to the generation of structural resistance and the dissipation of flow energy by vortices. Combined with Formula 7, it can be seen that the flow energy of the pressure component is a component of the energy balance equation. The greater the pressure loss, the greater the flow energy loss of the mill. For the collection of finished products, the kinetic energy change caused by the speed change should remain stable, and the potential energy change should be consistent with the equipment height; therefore, the increase in pressure loss will result in the need for greater shaft power input to maintain energy conservation, that is, to ensure a larger main engine air intake volume, spindle speed E and classifier speed F. The motor energy input. Therefore, pressure loss is an important component that can reflect the energy consumption of the mill.

[0091] Box-Behnken test results combined with response surface Figure 3a 、 Figure 3b and Figure 3c As shown in the figure, when the outlet pressure of the whole machine remains unchanged, the main engine pressure loss increases with the increase of inlet wind speed D and the continuous decrease of main shaft speed E. When the inlet wind speed D is 46m / s and the main shaft speed E is 100r / min, the main engine pressure loss reaches a maximum of 1660Pa. When the main shaft speed E remains unchanged, as the inlet wind speed D continues to increase and the main engine outlet pressure continues to decrease, the main engine pressure loss also continues to increase, with a maximum value of approximately 1320Pa. When the inlet wind speed D remains unchanged, as the main shaft speed E and the main engine outlet pressure continue to decrease, the main engine pressure loss increases more slowly, with a maximum value of approximately 1260Pa.

[0092] Specific implementation method 5: This implementation method is a further limitation of specific implementation methods 1, 2, 3 or 4, combined with Table 4, Table 5, Figure 4 and Figure 5 As shown, the parameters corresponding to test 9 are 38m / s, 120r / min, 3500Pa, and the parameters corresponding to test 14 are 46m / s, 100r / min, 3500Pa. The two are used as comparative tests. The minimum and maximum values ​​of the corresponding host pressure loss results are 880Pa and 1660Pa respectively. Figure 4 and Figure 5The wind field traces in the grinding roller area are shown. Fluid experiments show that above the critical Reynolds number, a series of complex changes will occur, resulting in a sharp change in flow characteristics, and the flow is in a disordered and chaotic state, which is called turbulence. Turbulence with a vortex flow structure is a turbulent vortex. The wind field characteristics inside the Raymond mill show complex turbulent flow characteristics, and there are a large number of vortex areas. There are moving parts in the main machine and the classifier area that rotate around the center of the structure and have a high speed, which will cause drastic changes in the flow field. Combined with Figure 4 and Figure 5 It can be seen that the wind field traces in the grinding roller area show an overall trend of circling upward, and local vortices caused by wall effects are generated in the lower side and top areas of the plum blossom rack. The main engine pressure loss in test 9 is the smallest, and the wind field traces emitted from the grinding roller have a spiral upward trend and enter the classifier area more concentratedly. However, the main engine pressure loss in test 14 is the largest. Specifically, the distribution of the wind field traces emitted from the grinding roller is relatively chaotic, and there are large-scale vortex areas. Large-scale vortices will continuously obtain energy from the mainstream, resulting in huge flow field energy losses, which is reflected in the significant increase in pressure loss in test 14. In order to further determine the influence of wind speed and rotation speed on pressure loss, a single-factor comparative study is conducted below.

[0093] Specific implementation method 6: This implementation method is a further limitation of specific implementation methods 1, 2, 3, 4 or 5. Figure 2a 、 2b As shown in Figure 2c, in this embodiment, the Box-Behnken test design and results are statistically analyzed, and the variance analysis is performed on the fitting model to form the standard table 2 for variance analysis of the graded efficiency M. Based on the data in the standard table 2 for variance analysis of the main engine pressure loss, a fitting regression equation for the graded efficiency M is established. The response surface fitting equation for the graded efficiency M is:

[0094] M=-31.42314-43.83017D+6.52373E+3.12442F+0.12819DE+

[0095] 0.038208DF-0.037308EF+0.35797D 2 +0.013450E 2 -2.09167e -3 F 2 ; (2)

[0096] The response surface fitting equation of the classification efficiency M was input into Origin 2022 to obtain the response surface of the classification efficiency M. When the spindle speed E is fixed, the classification efficiency M increases with the increase of the inlet wind speed D and the decrease of the classifier speed F. When the inlet wind speed D is constant, the collection efficiency first decreases and then increases with the increase of the spindle speed E and the decrease of the classifier speed F. The optimal values ​​of the spindle speed E and the classifier speed F are determined when the collection efficiency is close to 100%.

[0097] Specific embodiment seven: This embodiment is a further limitation of specific embodiments one, two, three, four, five or six, and starts the optimization calculation with the goal of maximizing the graded efficiency M and minimizing the overall pressure loss N, and establishes an optimization equation, specifically:

[0098]

[0099] According to the response surface equation above, the optimal target result is achieved when the inlet air velocity D is 23.21 m / s, the spindle speed E is 70 r / min, and the classifier speed F is 320.70 r / min. At this point, the classification efficiency M is 99.95% and the pressure drop is 910.43 Pa. This parameter combination was set in the simulation, and the simulation results are shown in Table 6. After optimization, the classification efficiency M increased by 4.33%. Since pressure drop cannot be measured during current testing at the company, the simulation results show that the overall pressure drop N decreased by 140.99 Pa after optimization compared to actual data.

[0100] Table 6 Response surface optimization results and simulation results

[0101]

[0102] In summary, the inlet air velocity D, main shaft speed E and classifier speed F have all decreased compared with the initial operating parameters. While improving the classification efficiency M of the Raymond mill, the energy consumption of the whole machine has also been reduced.

[0103] According to the response surface equation, the maximum value of the classification efficiency M and the minimum value of the whole machine pressure loss N are obtained by the inlet air velocity D, the main shaft speed E, and the classifier speed F. By comparing and analyzing the data obtained in practice, the evaluation process of the data obtained in practice is obtained. When the classification efficiency M in the data obtained in practice is lower than 60% of the maximum value of the classification efficiency M, and the whole machine pressure loss N in the data obtained in practice is higher than 60% of the minimum value of the whole machine pressure loss N, the data obtained in practice are of low quality, indicating that the corresponding Raymond Mill machine is in poor operation and classification status and should be maintained and adjusted accordingly. When the classification efficiency M in the data obtained in practice is 60% to 80% of the maximum value of the classification efficiency M, and the whole machine pressure loss N in the data obtained in practice is 10% to 40% of the minimum value of the whole machine pressure loss N, the data obtained in practice are of high quality, indicating that the corresponding Raymond Mill machine is in good operation and classification status.

[0104] Specific embodiment eight: This embodiment is a further limitation of specific embodiments one, two, three, four, five, six or seven. In this embodiment, the influence of the classifier rotation speed F on the flow field velocity of the Raymond mill and the analysis process are as follows:

[0105] Combine Figure 6 and Figure 7 As shown in the figure, based on the analysis of the response surface, in order to further analyze the influence of the classifier speed F on the collection efficiency, Experiment 2 and Experiment 7 were selected as comparative experiments to analyze the changes in the internal flow field of the Raymond mill, as well as the pass rate and classification efficiency M in the gravity classification area, to provide a reference for optimizing the classifier speed F. Among them, the operating parameters of Experiment 2 are 25m / s, 82r / min, 308r / min, and the operating parameters of Experiment 7 are 25m / s, 82r / min, 368r / min.

[0106] When other operating parameters remain unchanged, the classifier speed F reaches the maximum value of 368r / min. Figure 6 and Figure 7As shown, it can be observed that the local wind field formed on the periphery of the classifier has a relatively high speed. Especially at the edge of the frame position, this wind field speed is more significant, even as high as 157m / s. This local wind field allows the particles to obtain greater kinetic energy, accelerates the movement of the particles, and causes more coarse particles to be brought into the classifier, or causes the "coarse running" phenomenon. When the classifier speed F drops to 308r / min, the velocity flow field distribution inside the Raymond mill is more uniform. This uniform velocity distribution contributes to the uniform distribution and collection of particles, thereby improving the efficiency and accuracy of classification. A relatively low speed means a smoother wind field, and the particles will no longer obtain greater kinetic energy locally, making it easier for the particles to be attracted to the collection area. Therefore, choosing an appropriate speed can effectively improve the efficiency and accuracy of the entire classification process.

[0107] Combine Figure 8 and Figure 9 As shown in the figure, the influence of the classifier speed F on the flow field pressure of the Raymond mill in this embodiment and the analysis process are as follows: Test 2 and Test 7 are also used to compare and analyze the flow field pressure of the whole machine. When other operating parameters remain unchanged, if the speed of the classifier is adjusted to the maximum value of 368r / min, according to Figure 8 and Figure 9 As shown, it can be observed that a local high-pressure area is formed on the periphery of the classifier, with a pressure as high as -25.6Pa. This area is almost consistent with the high-speed area of ​​the velocity field, which means that the particles with greater kinetic energy have intensified collisions with the classifier shell and the rotating cage. This collision causes increased energy consumption, resulting in a large pressure loss in this area, and thus the particle collection efficiency is reduced. When the classifier speed F is 308r / min, the pressure distribution inside the Raymond mill is more uniform, the pressure loss is smaller, and the particles can enter the classifier more smoothly and be collected. Therefore, by adjusting the speed of the classifier to 308r / min, the pressure distribution inside the Raymond mill can be uniformed, the pressure loss can be reduced, thereby promoting the smooth flow of particles and improving the overall performance of the classifier and the particle collection efficiency.

[0108] The influence of the classifier speed F on the Raymond mill classification efficiency M and the analysis process in this embodiment are as follows:

[0109] according to Figure 3a 、 3bFrom the response surface analysis results shown in Figure 3c, it can be seen that the classifier speed F is a significant factor. When the inlet wind speed D is 25m / s, it meets the relevant requirements for the actual cutting particle size of 50 mesh. The operating parameters of this experiment are inlet wind speed D of 25m / s and main spindle speed E of 82r / min. On this basis, experiment 2 with operating parameters of 25m / s, 82r / min, 308r / min and experiment 7 with operating parameters of 25m / s, 82r / min, 368r / min are selected as comparative experiments to study the relationship between gravity classification pass rate and collection efficiency at different classifier speeds F.

[0110] Depend on Figure 10 As can be seen, the finished product pass rate in the gravity classification zone is 80.41%. When the classifier rotates at a speed of 308 r / min, the Raymond mill shaft work increases, providing more kinetic energy to the particles, and more finished products are collected. Therefore, the overall collection efficiency increases to 97.9%, which is a high collection efficiency. However, when the classifier speed F increases to 368 r / min, the overall collection efficiency decreases to 64.41%. This is because the local wind field velocity is high, and the wind field force is greater than the centrifugal force, so a lot of coarse products are collected.

[0111] Specific implementation method 9: This implementation method is a further limitation of specific implementation methods 1, 2, 3, 4, 5, 6, 7 or 8. Figure 11 and Figure 12 It can be seen that in this embodiment, the finished particle ID is searched in the collected particle information file, named as ID1 and ID2 respectively, and the particle code is entered in Fluent to obtain the combined Figure 11 Particle trajectories are shown.

[0112] By analyzing the particle trajectory, we can see that the particles, under the action of the classifier, drill into the rotating cage along the classifier shell and complete the final classification process. In this process, the retention time of the finished particles with ID 1 and 2 is 0.534s and 1.46s respectively. In particular, from the particle trajectory of ID2, we can see that when the diameter of the classifier increases, the particle's running trajectory is almost the same as Figure 1 The running trajectories of the particles in the simulation remain consistent, all spiraling up along the wall, indicating that the simulation can accurately reflect the physical processes in the real system, providing a reliable basis for further engineering analysis and optimization.

[0113] Refer to the selection method of finished product particle ID, combined with Figure 12As shown, by selecting two groups of coarse particles (ID 3 and 4) and observing their movement under the action of the classifier, it can be found that the particle speed increases under the action of the classifier. After spiraling upward, the speed decreases to 0 m / s at the classifier wall, and finally adheres to the wall. After a period of operation of the Raymond Mill, this phenomenon may cause dust to accumulate on the wall. The accumulated dust affects the movement path of the particles, resulting in a decrease in the screening performance of the classifier and even equipment failure or shutdown.

[0114] The present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for predicting and evaluating the operation classification of a Raymond Mill machine, characterized by: The prediction and evaluation method is based on the inlet air velocity D, the main shaft speed E, and the classifier speed F. By combining the Box-Behnken method, the DPM model, and the MRF multi-coordinate reference system, a response surface simulation analysis is performed using the discrete phase of the RR distribution to calculate the maximum value of the classification efficiency M and the minimum value of the whole machine pressure loss N. The analysis process is then compared with the actual data based on the maximum value of the classification efficiency M and the minimum value of the whole machine pressure loss N. By combining the Box-Behnken method, the DPM model, and the MRF multi-coordinate reference system, the response surface simulation analysis is performed with the discrete phase of the RR distribution to calculate the pressure loss N of the whole machine as follows: Raymond mill flow field model construction and boundary condition setting: A 3D model of the Raymond Mill machine was constructed using AutoCAD and UG software. The entire machine model was then simplified using ANSYS's Spaceclaim software to extract the flow field model within the Raymond Mill machine and ensure the accuracy of the flow field model. Next, the meshing software within ANSYS was used to mesh the flow field of the Raymond Mill machine. Finally, the meshed file was imported into Fluent, and numerical simulation was performed using an appropriate calculation model, boundary conditions, and solution method. The numerical simulation results were compared and analyzed with the actual data obtained from the Raymond mill to verify the accuracy of the calculation model, boundary conditions and solution conditions. The steady-state and pressure solvers were used for solution. The turbulence model adopted the RNG model in the k-ε equation, the DPM model and the MRF multi-coordinate reference system were used, the discrete phase adopted the RR distribution, the particles were launched perpendicular to the grinding roller surface with an initial velocity of zero, the hydraulic diameter at the air inlet was 0.485m, and the coupled solution method was used for solution. The default under-relaxation factor was used to perform statistics on the Box-Behnken test design and results, and the variance analysis was performed on the fitting model to form the standard table 1 for the variance analysis of the main engine pressure loss. Based on the data in the standard table 1 for the variance analysis of the main engine pressure loss, the fitting regression equation of the main engine pressure loss N was established, specifically:

2. The method for predicting and evaluating the operation classification of a Raymond mill according to claim 1, characterized in that: The experiment was conducted using Ansys Fluent simulation software according to the Box-Behnken design method with an inlet wind speed D of 25-33 m / s, a spindle speed E of 72-92 r / min, and a classifier speed F of 308-338 r / min as flow field boundary conditions. The collection efficiency calculation method in the Box-Behnken design method is as follows: after the Fluent steady-state simulation calculation is completed, the statistics of the particle information captured at the outlet of the whole machine are completed in the Sample of the Discrete Phase under Reports, and then the mass flow rate of particles of each mesh size is obtained, and the collection efficiency is calculated accordingly. The collection efficiency is the initial preparation data of the classification efficiency M.

3. The method for predicting and evaluating the operation classification of a Raymond mill according to claim 1 or 2, characterized in that: The Box-Behnken test design and results were statistically analyzed, and the variance analysis of the fitting model was performed to form the standard table 2 for variance analysis of the fractional efficiency M. Based on the data in the standard table 2 for variance analysis of the main engine pressure loss, the fitting regression equation of the fractional efficiency M was established. The response surface fitting equation of the fractional efficiency M is: M=-31.42314-43.83017D+6.52373E+3.12442F+0.12819DE+0.038208DF-0.037308EF+0.35797D 2 +0.013450E 2 -2.09167e -3 F 2 (2) The response surface fitting equation of the classification efficiency M was input into Origin 2022 to obtain the response surface of the classification efficiency M. When the spindle speed E is fixed, the classification efficiency M increases with the increase of the inlet air speed D and the decrease of the classifier speed F. When the inlet wind speed D is constant, the collection efficiency shows a trend of first decreasing and then increasing with the increase of the main shaft speed E and the decrease of the classifier speed F. The optimal values ​​of the main shaft speed E and the classifier speed F are determined when the collection efficiency is close to 100%.

4. The method for predicting and evaluating the operation classification of a Raymond Mill according to claim 3, characterized in that: The optimization calculation was started with the goal of maximizing the graded efficiency M and minimizing the overall pressure loss N, and the optimization equation was established, specifically: According to the response surface equation, the maximum value of the classification efficiency M and the minimum value of the whole machine pressure loss N are obtained by the inlet air velocity D, the main shaft speed E, and the classifier speed F. By comparing and analyzing the data obtained in practice, the evaluation process of the data obtained in practice is obtained. When the classification efficiency M in the data obtained in practice is lower than 60% of the maximum value of the classification efficiency M, and the whole machine pressure loss N in the data obtained in practice is higher than 60% of the minimum value of the whole machine pressure loss N, the data obtained in practice are of low quality, indicating that the corresponding Raymond Mill machine is in poor operation and classification status and should be maintained and adjusted accordingly. When the classification efficiency M in the data obtained in practice is 60% to 80% of the maximum value of the classification efficiency M, and the whole machine pressure loss N in the data obtained in practice is 10% to 40% of the minimum value of the whole machine pressure loss N, the data obtained in practice are of high quality, indicating that the corresponding Raymond Mill machine is in good operation and classification status.

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