Method for optimizing arrangement parameters of full tailings filling slurry conveying pipeline

Abstract

The application discloses a kind of layout parameter optimization method of full tailings filling slurry conveying pipeline.It includes the following steps: obtaining the particle size distribution range of coarse particles in filling slurry;Obtain the rheological parameters required by the following finite element simulation software;Construct a three-dimensional simple conveying pipeline model, perform structured meshing, and obtain the final number of grids;Establish a control model during the conveying process of full tailings filling slurry, and obtain the pressure, flow velocity distribution cloud map of filling slurry and pipeline wear cloud map during the conveying process of three-dimensional simple conveying pipeline;Set the pipe diameter, bend angle, bend diameter ratio as the pipeline wear factors, and obtain multiple test schemes;According to the influence degree of wear rate, reset the optimization value range of each pipeline wear factor;Construct a response surface prediction model, select the bend angle corresponding to the minimum predicted pipeline wear rate, different pipe diameters and bend diameter ratio values as the optimized layout parameters of the conveying pipeline.The application prolongs the service life of the pipeline.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mine filling, in particular to a method for optimizing arrangement parameters of a full tailings filling slurry conveying pipeline. BACKGROUND

[0002] With the attention of the state to the environmental protection of mines, the filling mining method is used as the main mining method by more and more mines. The filling mining method can not only improve the ore recovery rate, but also effectively reduce the ground subsidence and environmental pollution by conveying the filling material to the goaf. As one of the key technologies of the filling mining method, the pipeline conveying plays a decisive role in the safety and stability of the entire filling system.

[0003] With the development of filling technology, the filling slurry concentration is gradually increased, and the high-concentration filling slurry is a complex suspension system composed of coarse aggregate, cementing material and water. Since the high-concentration filling slurry contains a large amount of solid coarse particles, the coarse particles will cause a certain wear to the pipeline in the conveying process.

[0004] With the increasingly prominent problem of pipeline wear, many scholars have carried out research on the pipeline wear of the filling slurry. However, in the prior art, no reasonable and effective optimization method for the arrangement parameters of the conveying pipeline is proposed. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a method for optimizing arrangement parameters of a full tailings filling slurry conveying pipeline, which optimizes the arrangement parameters of the filling slurry conveying pipeline, reduces the pipeline wear caused by the unfulfilled pipe flow, prolongs the service life of the pipeline, reduces the maintenance cost, and improves the efficiency of mine tailings treatment.

[0006] To achieve the above purpose, the method for optimizing arrangement parameters of a full tailings filling slurry conveying pipeline designed by the present application has the following special features:

[0007] S1) obtaining a particle size distribution range of coarse particles in the full tailings filling slurry;

[0008] S2) conducting a rheological test of the full tailings filling slurry according to the particle size distribution range of the coarse particles, obtaining a rheological curve between different shear rates and shear stresses, fitting the rheological curve, and if it is determined that the rheological model of the full tailings filling slurry is a Bingham model according to the fitted rheological curve, then the rheological parameters required by the following finite element simulation software are obtained;

[0009] S3) constructing a three-dimensional simple conveying pipeline model and importing the finite element simulation software, assigning material properties to the three-dimensional simple conveying pipeline and performing structured grid division, locally encrypting the grid at the elbow pipe and adding a boundary layer when dividing the grid, and obtaining the final grid number after grid independence verification;

[0010] S4) Establish a control model in the whole tailings filling slurry conveying process, and use the Euler-Lagrange model to obtain fluid information and solid particle information, and then set boundary conditions according to the actual situation, carry out fluent numerical simulation research, and obtain the pressure, flow velocity distribution cloud picture and pipeline wear cloud picture of the filling slurry in the conveying process of the three-dimensional simple conveying pipeline;

[0011] S5) From the pressure, flow velocity distribution cloud picture and pipeline wear cloud picture, it is concluded that the pipeline wear is mainly distributed at the inlet of the pipeline and the outer side of the elbow pipe, so the pipe diameter, elbow angle and bend diameter ratio are set as the pipeline wear factors, and the preliminary value range of the elbow angle, pipe diameter and bend diameter ratio is obtained, and different combinations of the elbow angle, pipe diameter and bend diameter ratio are taken as a group of test schemes, a total of multiple groups of test schemes are obtained;

[0012] S6) According to the fluent numerical simulation, the maximum erosion wear rate corresponding to different test schemes in the pipeline conveying process is simulated, a response surface regression model is constructed between the simulated maximum erosion wear rate and the pipeline wear factors, variance analysis, factor interaction verification and significance analysis are carried out on the response surface regression model, the influence degree of different pipeline wear factors on the pipeline wear rate is obtained, and the optimization value range of each pipeline wear factor is re-set according to the influence degree on the wear rate;

[0013] S7) According to the re-set optimization value range of each pipeline wear factor, the response surface prediction model constructed between the pipeline wear factors and the predicted pipeline wear rate is output, and the elbow angle, different pipe diameter and bend diameter ratio values corresponding to the minimum predicted pipeline wear rate are selected as the optimization arrangement parameters of the conveying pipeline.

[0014] Further, in S1), the particle size distribution range of coarse particles is determined by carrying out full tailings particle size test.

[0015] Further, in S3), the three-dimensional simple conveying pipeline model is constructed using workbench, and the three-dimensional simple conveying pipeline model is imported using the finite element simulation software ANASYS FLUENT.

[0016] Further, in S4), the boundary conditions include setting one end of the conveying pipeline as a velocity inlet, setting the other end of the conveying pipeline as a pressure outlet, setting the fine particles, cement and water in the filling slurry as continuous phases, setting the coarse particles in the filling slurry as discrete phases, and setting the particle incidence condition.

[0017] Further, in S4), the control model in the whole tailings filling slurry conveying process includes continuity equation, momentum conservation equation, discrete phase control equation, wear equation and wall collision recovery equation;

[0018] The continuity equation is represented by the following equation

[0019]

[0020] wherein,

[0021] x, y, z respectively represent three directions,

[0022] v x , v y , v z represent velocity components of x, y, z in the opposite direction;

[0023] The momentum conservation equation is represented by the following equation

[0024]

[0025] wherein,

[0026] p ω represents the density of the continuous phase,

[0027] v i represents the velocity component of the continuous phase on the coordinate axis i,

[0028] v j represents the velocity component of the continuous phase on the coordinate axis j,

[0029] p represents pressure,

[0030] t ij represents the viscous stress component generated on the unit microelement under the action of molecular viscosity,

[0031] f i represents the mass force in the x, y, z directions,

[0032] x i represents the spatial coordinate (subscript i = 1, 2, 3, respectively representing the x, y, z coordinate axes, and i ≠ j),

[0033] x j represents the spatial coordinate (subscript j = 1, 2, 3, respectively representing the x, y, z coordinate axes, and i ≠ j);

[0034] The discrete phase control equation is represented by the following equation

[0035]

[0036] wherein,

[0037] v represents the fluid phase velocity,

[0038] f d (ν-ν g ) represents the unit mass drag force of the particle,

[0039] f represents other mass forces,

[0040] g represents the acceleration of gravity,

[0041] p g represents the particle density,

[0042] p represents the fluid density;

[0043] The wear equation is represented by the following equation

[0044]

[0045] wherein,

[0046] R e represents the wear rate,

[0047] m p represents the mass flow rate of the particles,

[0048] c(d p ) represents a particle diameter function,

[0049] f(θ) represents an impact angle function,

[0050] v p represents the particle impact velocity,

[0051] b r represents a velocity exponent function,

[0052] Α face represents the unit surface area of the particle impacting the wall surface,

[0053] P represents the colliding particles,

[0054] N P represents the number of colliding particles;

[0055] The wall surface collision restitution equation is represented by the following equation

[0056] e n = 0.993 - 1.76a + 1.56a 2 - 0.49a 3

[0057] e t = 0.988 - 1.66a + 2.11a 2 - 0.67a 3

[0058] wherein,

[0059] е n , е t represent the wall restitution coefficients, respectively,

[0060] Alpha represents the particle incident angle.

[0061] Further, in S5), a plurality of test schemes are designed by using Design-Expert software.

[0062] Further, in S6), a response surface regression model is constructed between the simulated pipeline wear rate and the pipeline wear factors by using Design-Expert software.

[0063] Further, in S6), if the model significance does not meet the requirement, the fluent numerical simulation is re-performed until the constructed response surface regression model meets the significance requirement.

[0064] Further, in S6), when the model significance is analyzed, the influence degree of different pipeline wear factors on the pipeline wear rate is obtained by acquiring the response surface cloud diagram and the contour diagram corresponding to different pipeline wear factors in the response surface regression model.

[0065] The present application has the advantages of:

[0066] 1. The present application realizes effective optimization of the layout parameters of the filling slurry conveying pipeline by systematic experimental design, rheological test, numerical simulation and response surface analysis.

[0067] The layout parameter optimization method of the full tailings filling slurry conveying pipeline of the present application reduces the pipeline wear caused by unfulfilled pipe flow, prolongs the service life of the pipeline, reduces the maintenance cost, and improves the efficiency of mine tailings treatment. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 The flowchart of the present application;

[0069] Figure 2a The response surface cloud diagram of the two-way interaction of the bend angle and the pipe diameter in different pipeline wear factors;

[0070] Figure 2b The response surface cloud diagram of the two-way interaction of the pipe diameter and the bend diameter ratio in different pipeline wear factors;

[0071] Figure 2c The response surface cloud diagram of the two-way interaction of the bend angle and the bend diameter ratio in different pipeline wear factors;

[0072] Figure 2d The contour diagram of the two-way interaction of the bend angle and the pipe diameter in different pipeline wear factors;

[0073] Figure 2e The contour diagram of the two-way interaction of the pipe diameter and the bend diameter ratio in different pipeline wear factors;

[0074] Figure 2f is a contour plot of the elbow angle and bend diameter ratio two-by-two interaction of different pipeline wear factors;

[0075] Figure 3 is a pipeline model after layout parameter optimization in this embodiment;

[0076] Figure 4 is a velocity cloud chart of the pipeline after layout parameter optimization in this embodiment when filling the slurry;

[0077] Figure 5 is a pressure cloud chart of the pipeline after layout parameter optimization in this embodiment when filling the slurry;

[0078] Figure 6 is a pipeline wear cloud chart of the pipeline after layout parameter optimization in this embodiment when filling the slurry. DETAILED DESCRIPTION

[0079] The present application will be further described in detail below in combination with the drawings and specific embodiments.

[0080] As shown in Figure 1 , the layout parameter optimization method of the full tailings filling slurry pipeline of the present application comprises the following steps:

[0081] S1) Obtain the particle size distribution range of coarse particles in the full tailings filling slurry.

[0082] Specifically, by conducting full tailings particle size testing, the particle size distribution range of the full tailings is obtained, the particle gradation is determined to be good, and the particle size distribution range of the coarse particles is determined.

[0083] S2) According to the particle size distribution range of the coarse particles, rheological test of the full tailings filling slurry is carried out to obtain the rheological curve between different shear rates and shear stresses, and then the rheological curve is fitted. If it is determined according to the fitted rheological curve that the rheological model of the full tailings filling slurry is a Bingham model, the rheological parameters required by the following finite element simulation software are obtained.

[0084] S3) Construct a three-dimensional simple pipeline model and import it into the finite element simulation software. Assign material properties to the three-dimensional simple pipeline and perform structured meshing. When meshing, locally encrypt the mesh at the elbow and add a boundary layer. After mesh independence verification, the final number of meshes is obtained.

[0085] Specifically, the three-dimensional simple pipeline model is constructed using workbench, and the three-dimensional simple pipeline model is imported into the finite element simulation software ANASYS FLUENT.

[0086] In the embodiment, a three-dimensional simple conveying pipeline model with a horizontal length of 4 m and a vertical length of 2 m is constructed.

[0087] S4) A control model in the process of transporting the full tailings filling slurry is established, the Euler-Lagrange model is used to obtain fluid information and solid particle information, boundary conditions are set according to actual conditions, a fluent numerical simulation research is carried out, and a pressure, flow velocity distribution cloud diagram and a pipeline wear cloud diagram of the filling slurry in the conveying process of the three-dimensional simple conveying pipeline are obtained.

[0088] Optimally, the set boundary conditions include setting one end of the conveying pipeline as a velocity inlet, setting the other end of the conveying pipeline as a pressure outlet, setting fine particles, cement and water in the filling slurry as continuous phases, setting coarse particles in the filling slurry as a discrete phase, and setting a particle incidence condition.

[0089] In the embodiment, the vertical pipeline inlet is a velocity inlet, and the horizontal pipeline outlet is a pressure outlet.

[0090] Specifically, the control model in the process of transporting the full tailings filling slurry includes a continuity equation, a momentum conservation equation, a discrete phase control equation, a wear equation and a wall collision recovery equation.

[0091] The continuity equation is expressed by the following formula

[0092]

[0093] In the formula,

[0094] x, y and z respectively represent three directions,

[0095] v x , v y and v z represent velocity components in the opposite directions of x, y and z.

[0096] The momentum conservation equation is expressed by the following formula

[0097]

[0098] In the formula,

[0099] ρ ω represents the density of the continuous phase,

[0100] v i represents a velocity component of the continuous phase on the coordinate axis i,

[0101] v j represents a velocity component of the continuous phase on the coordinate axis j,

[0102] p represents pressure,

[0103] τij represents the viscous stress component generated on the unit microelement under the action of molecular viscosity,

[0104] f i represents the mass force in x, y, z directions,

[0105] x i represents the spatial coordinate (subscript i = 1, 2, 3, respectively representing x, y, z coordinate axes, and i≠j),

[0106] x j represents the spatial coordinate (subscript j = 1, 2, 3, respectively representing x, y, z coordinate axes, and i≠j).

[0107] The discrete phase control equation is represented by the following formula

[0108]

[0109] In the formula,

[0110] v represents the fluid phase velocity,

[0111] f d represents the particle unit mass drag force, g

[0112] f represents other mass forces,

[0113] g represents the gravitational acceleration,

[0114] ρ g represents the particle density,

[0115] ρ represents the fluid density.

[0116] The wear equation is represented by the following formula

[0117]

[0118] In the formula,

[0119] R e represents the wear rate,

[0120] m p represents the mass flow rate of the particles,

[0121] c(d p ) represents the particle diameter function,

[0122] f(θ) represents the impact angle function,

[0123] v p represents the particle impact velocity,

[0124] b r represents the velocity exponent function,​

[0125] A face unit surface area of a particle impinging a wall surface,

[0126] P represents a colliding particle,

[0127] N P number of colliding particles;

[0128] The wall surface collision restitution equation is represented by the following formula

[0129] e n = 0.993 - 1.76a + 1.56a 2 - 0.49a 3

[0130] e t = 0.988 - 1.66a + 2.11a 2 - 0.67a 3

[0131] In the formula,

[0132] e n , e t represent wall restitution coefficients, respectively,

[0133] a represents a particle incidence angle.

[0134] S5) From the pressure, flow rate distribution cloud map and the pipeline wear cloud map, it is concluded that the pipeline wear is mainly distributed at the pipeline inlet and the outer side of the elbow pipe. Therefore, the pipe diameter, the elbow angle and the bend diameter ratio are set as the pipeline wear factors, and the preliminary value ranges of the elbow angle, the pipe diameter and the bend diameter ratio are obtained respectively. Different combinations of the elbow angle, the pipe diameter and the bend diameter ratio are taken as a group of test schemes, and a plurality of groups of test schemes are obtained in total.

[0135] Specifically, a plurality of groups of test schemes are designed by using Design-Expert software.

[0136] In this embodiment, the preliminary value range of the elbow angle is 60-120°, the preliminary value range of the pipe diameter is 120-200 mm, and the preliminary value range of the bend diameter ratio is 2-4. The bend diameter ratio is the ratio between the curvature radius and the pipe diameter.

[0137] The 13 groups of test schemes obtained in this embodiment are shown in Table 1.

[0138] Table 1 Test schemes

[0139]

[0140]

[0141] S6) According to the fluent numerical simulation, the maximum erosion wear rate corresponding to different test schemes in the pipeline transportation process is simulated, a response surface regression model between the simulated maximum erosion wear rate and the pipeline wear factors is constructed, variance analysis, factor interaction verification and significance analysis are performed on the response surface regression model, the influence degree of different pipeline wear factors on the pipeline wear rate is obtained, and the optimization value range of each pipeline wear factor is re-set according to the influence degree on the wear rate.

[0142] Specifically, the Design-Expert software is used to construct a response surface regression model between the simulated pipeline wear rate and the pipeline wear factors.

[0143] The pipeline wear rates corresponding to the 13 test schemes simulated in the embodiment are shown in Table 2.

[0144] Table 2 Test scheme wear rate results

[0145]

[0146] In the embodiment, the results of the variance analysis and the significance analysis of the response surface regression model are shown in Table 3.

[0147] Table 3 Variance analysis and significance analysis of the response surface regression model

[0148] Parameters Response surface model X1 X2 X3 X1X3 X2X3 X1X2 P <0.0001 0.0093 <0.0001 0.0323 0.6248 0.0643 0.1344 F 112.04 50.42 451.70 143.3 0.368 8.21 4.15 Mean square error 5.929 x 10 -16 ]] 7.013 x 10 -16 ]] 2.208 x 10 -15 ]] 1.752 x 10 -15 ]]> 1.444 x 10 -17 ]]> 4.020 x 10 -16 ]]> 2.031 x 10 -16 ]]>

[0149] As can be seen from Table 3, the significance analysis is performed through the two parameters P and F. According to F, the influence degree of single factors and two-way interaction factors on the pipeline wear is directly obtained, and the greater F is, the greater the influence of the single factor on the response value is. When P<0.05, it indicates that the difference is significant, and when P<0.01, it indicates that the difference is extremely significant. According to the P value, the significant role of the pipeline wear factors is judged.

[0150] As can be seen from Table 3, the influence degree of the pipe diameter on the pipeline wear rate is the largest in the embodiment, and the influence degree of the elbow angle on the pipeline wear rate is the smallest. Therefore, the optimization value range of each pipeline wear factor is re-set in the embodiment as follows: the optimization value range of the elbow angle X1 is 60-90°, the optimization value range of the pipe diameter X2 is 120-200 mm, and the optimization value range of the bend diameter ratio X3 is 2-4.

[0151] In addition, if the model significance does not meet the requirement, the fluent numerical simulation is performed again until the response surface regression model constructed meets the significance requirement.

[0152] Specifically, when the F value is greater than 0.05, the response surface model is not significant, and the wear simulation needs to be carried out again, and the response surface regression model is re-established; when the P value is less than 0.05, the response surface regression model is significant, and the response surface regression equation is fitted, as shown in the following formula, wherein Y is the wear rate.

[0153] Y = 5.9 x 10 -8 -8.9 x 10 -9 X1-1.661 x 10 -8 X2-1.526 x 10 -8 X3+1.9 x 10 -9 X1X2-1.91 x 10 -9 X1X3+7.125 x 10 -9 X2X3-2.125 x 10 -10 X1 2 +3.612 x 10 -9 X2 2 -2.537 x 10 -9 X3 2

[0154] Optimally, when the model significance analysis is performed, the influence degree of different pipeline wear factors on the pipeline wear rate is obtained by obtaining the response surface cloud diagram and the contour diagram corresponding to different pipeline wear factors in the response surface regression model.

[0155] As shown in Table 3, the response surface cloud diagram of different pipeline wear factors and the contour diagram of different pipeline wear factors are shown. Figures 2a-2f The response surface cloud diagram of different pipeline wear factors reflects the influence of two-way interaction on the wear rate, and the contour diagram is the influence of two-way interaction of two-dimensional pipeline wear factors on the pipeline wear rate. According to Table 3, the influence of two-way interaction of different pipeline parameters on the pipeline wear is obtained, and according to the contour, the influence of the single wear factor on the pipeline wear parameter under two-way interaction is obtained, and finally the influence degree of the single factor on the pipeline wear rate is obtained.

[0156] S7) According to the re-established optimization value range of each pipeline wear factor, the response surface prediction model constructed between the pipeline wear factor and the predicted pipeline wear rate is output, and the elbow angle, different pipe diameter and bend diameter ratio value corresponding to the minimum predicted pipeline wear rate are selected as the optimization arrangement parameters of the conveying pipeline.

[0157] In this embodiment, the optimization arrangement parameters of the conveying pipeline are as follows: the elbow angle X1 is 88.1716°, the pipe diameter X2 is 193.012 mm, the bend diameter ratio X3 is 2.4237, and the minimum predicted pipeline wear rate is 5.20182 x 10 -8 .

[0158] According to the above-mentioned optimized arrangement parameter combination of the conveying pipeline, steps S3) and S4) are repeated, a new geometric model is established, a grid is divided, boundary conditions are set, and the fluent wear simulation is carried out (as shown in Figures 3-6 The simulated wear rate is 5.48*10 -8 The minimum predicted pipeline wear rate is 5.20182*10 -8 The relative error of the simulated wear rate is 5.48*10 -8 The error value is 5%.

[0159] As can be seen from Figure 4 , when the slurry flows from the vertical pipeline into the elbow pipeline, the flow rate in the middle of the pipeline becomes larger and larger, with the change of the turning angle, the area with the largest flow rate gradually moves to the outside of the elbow pipeline, and the flow rate on the outside of the elbow pipeline is larger than that on the inside of the elbow pipeline.

[0160] As can be seen from Figure 5 , the pressure in the pipeline gradually decreases from the inlet to the outlet, and the pressure on the outside of the pipeline is larger than that on the inside of the pipeline. The reason why the pressure on the outside of the elbow pipeline is larger than that on the inside of the elbow pipeline is that when the slurry moves to the elbow pipeline, the moving direction suddenly changes, and due to the inertia effect, the slurry generates extrusion force on the outer wall surface of the elbow pipeline.

[0161] As can be seen from Figure 6 , the two pipelines in the figure are wear distribution cloud diagrams of the same pipeline at different viewing angles. The reason why the wear on the outer wall surface of the elbow pipeline is more serious than that on the inner wall surface of the elbow pipeline is that most of the particles move along the outer wall surface of the elbow pipeline during the movement of the particles, and in addition to the existence of the pressure difference, the particles gradually move from the outside to the inside, so that the wear mainly exists on the outside of the elbow pipeline.

[0162] The above-mentioned embodiments are the preferred embodiments of the present application, but the embodiments of the present application are not limited to the above-mentioned embodiments, and any changes, modifications, substitutions, combinations, simplifications made without departing from the spirit and principles of the present application should be equivalent replacement methods, and all are included in the protection scope of the present application.

Claims

1. A method for optimizing the layout parameters of a pipeline for conveying tailings backfill slurry, characterized in that, Includes the following steps: S1) Obtain the particle size distribution range of coarse particles in the tailings backfill slurry; S2) Based on the particle size distribution range of coarse particles, conduct rheological tests on the full tailings filling slurry to obtain the rheological curves between different shear rates and shear stresses. Then fit the rheological curves. If the rheological model of the full tailings filling slurry is determined to be the Bingham model based on the fitted rheological curves, then the rheological parameters required by the following finite element simulation software are obtained. S3) Construct a simple three-dimensional conveying pipeline model and import it into finite element simulation software. Assign material properties to the simple three-dimensional conveying pipeline and perform structured mesh generation. When generating the mesh, locally refine the mesh at the bend and add a boundary layer. After verifying the mesh independence, obtain the final number of meshes. S4) Establish a control model for the transportation process of the tailings backfill slurry, and use the Euler-Lagrange model to obtain fluid information and solid particle information. Then, set boundary conditions according to the actual situation and carry out fluent numerical simulation research to obtain the pressure and velocity distribution cloud map and pipeline wear cloud map of the backfill slurry in the transportation process of the three-dimensional simplified transportation pipeline. S5) From the pressure, flow velocity distribution cloud map and the pipe wear cloud map, it was found that the pipe wear is mainly distributed at the pipe inlet and the outside of the bend. Therefore, the pipe diameter, bend angle and bend ratio were set as pipe wear factors, and the preliminary value range of bend angle, pipe diameter and bend ratio were obtained respectively. The combination of different bend angle, pipe diameter and bend ratio was used as a test scheme, and a total of multiple test schemes were obtained. S6) Based on Fluent numerical simulation, the maximum erosion wear rate corresponding to different test schemes during pipeline transportation is simulated. Based on the simulated maximum erosion wear rate and pipeline wear factors, a response surface regression model is constructed. Variance analysis, factor interaction verification and significance analysis are performed on the response surface regression model to obtain the degree of influence of different pipeline wear factors on pipeline wear rate. Based on the degree of influence on wear rate, the optimal value range of each pipeline wear factor is reset. S7) Based on the optimized value range of each pipeline wear factor that has been reset, output the response surface prediction model constructed between the pipeline wear factor and the predicted pipeline wear rate, and select the bend angle, different pipe diameters, and bend ratio values ​​corresponding to the minimum predicted pipeline wear rate as the optimized layout parameters of the conveying pipeline.

2. The method for optimizing the layout parameters of the tailings filling slurry conveying pipeline according to claim 1, characterized in that: In S1), the particle size distribution range of coarse particles is determined by conducting a full tailings particle size test.

3. The method for optimizing the layout parameters of the tailings filling slurry conveying pipeline according to claim 2, characterized in that: In S3), a simplified three-dimensional transport pipeline model is built using Workbench, and then imported into the finite element simulation software ANASYS FLUENT.

4. The method for optimizing the layout parameters of the tailings filling slurry conveying pipeline according to claim 3, characterized in that: In S4), the boundary conditions set include setting one end of the conveying pipe as a velocity inlet, setting the other end of the conveying pipe as a pressure outlet, setting the fine particles, cement, and water in the filling slurry as continuous phases, setting the coarse particles in the filling slurry as discrete phases, and setting particle incident conditions.

5. The method for optimizing the layout parameters of the tailings filling slurry conveying pipeline according to claim 4, characterized in that: In S4), the control model for the transportation process of the tailings filling slurry includes the continuity equation, momentum conservation equation, discrete phase control equation, wear equation, and wall collision recovery equation. The continuity equation is expressed by the following formula: In the formula, x, y, and z represent the three directions, respectively. v x v y v z Represents the velocity components in the x, y, and z directions; The momentum conservation equation is expressed by the following formula: In the formula, ρ ω Represents the density of the continuous phase. v i This represents the velocity components of the continuous phase on coordinate axis i. v j This represents the velocity components of the continuous phase along coordinate axis j. p represents pressure. τ ij This represents the viscous stress component generated on a single micro-element under the influence of molecular viscosity. f i Represents the mass forces in the x, y, and z directions. x i x j Represents spatial coordinates, where the subscripts i and j = 1, 2, 3, representing the x, y, and z coordinate axes respectively, and i ≠ j; The discrete phase control equation is expressed by the following equation. In the formula, v represents the fluid phase velocity. g represents the acceleration due to gravity. ρ g Indicates particle density, ρ represents the fluid density. f d (ν-ν g () represents the drag force per unit mass of particles. f represents other mass forces; The wear equation is expressed by the following formula. In the formula, R e Indicates the wear rate. m p Indicates the mass flow rate of the particles. c(d p ) represents the particle diameter function. f(θ) represents the impact angle function. v p Indicates particle impact velocity. b r Represents the velocity exponential function, A face This represents the unit surface area of ​​the wall impacted by the particle. P represents the colliding particle. N P Indicates the number of colliding particles; The wall collision recovery equation is expressed by the following formula: e n ＝0.993-1.76α+1.56α 2 -0.49α 3 e t ＝0.988-1.66α+2.11α 2 -0.67α 3 In the formula, e n , e t To represent the wall recovery coefficient, α represents the incident angle of the particle.

6. The method for optimizing the layout parameters of the tailings filling slurry conveying pipeline according to claim 1, characterized in that: In S5), multiple experimental schemes were designed using Design-Expert software.

7. The method for optimizing the layout parameters of the tailings filling slurry conveying pipeline according to claim 6, characterized in that: In S6), the Design-Expert software was used to construct the response surface regression model.

8. The method for optimizing the layout parameters of the tailings filling slurry conveying pipeline according to claim 7, characterized in that: In S6), if the model significance does not meet the requirements, the Fluent numerical simulation is repeated until the constructed response surface regression model meets the significance requirements.

9. The method for optimizing the layout parameters of a full tailings filling slurry conveying pipeline according to claim 8, characterized in that: In S6), when performing model significance analysis, the influence of different pipeline wear factors on the pipeline wear rate is obtained by acquiring the response surface cloud map and contour map corresponding to different pipeline wear factors in the response surface regression model.

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