Arrangement parameter optimization method for full-tailing filling slurry conveying pipeline

Abstract

The invention discloses a method for optimizing arrangement parameters of a full-tailing filling slurry conveying pipeline. The method comprises the following steps: obtaining the particle size distribution range of coarse particles in the filling slurry; obtaining rheological parameters required by the following finite element simulation software; a three-dimensional simple conveying pipeline model is constructed, structured grid division is carried out, and the final grid number is obtained; establishing a control model in the conveying process of the full-tailing filling slurry to obtain a pressure and flow velocity distribution cloud picture and a pipeline wear cloud picture of the filling slurry in the conveying process of the three-dimensional simple conveying pipeline; setting a pipe diameter, a pipe bending angle and a bending diameter ratio as pipeline abrasion factors, and obtaining a plurality of groups of test schemes; according to the influence degree on the abrasion rate, the optimization value range of each pipeline abrasion factor is reset; and constructing a response surface prediction model, and selecting the bending pipe angle, different pipe diameters and bending diameter ratio values corresponding to the minimum predicted pipeline wear rate as optimal arrangement parameters of the conveying pipeline. The service life of the pipeline is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of mine filling, and particularly to an optimization method for the layout parameters of a pipeline for transporting full-tailings filling slurry. Background Art

[0002] With the country's increasing emphasis on mine environmental protection, filling mining methods are being adopted by more and more mines as the main mining methods. The filling mining method is to transport filling materials to the mined-out area, which can not only improve the ore recovery rate but also effectively reduce ground subsidence and environmental pollution. As one of the key technologies of the filling mining method, pipeline transportation plays a decisive role in the safety and stability of the entire filling system.

[0003] With the development of filling technology, the concentration of filling slurry has gradually increased. High-concentration filling slurry is a complex suspension system composed of coarse aggregates, gelling materials, water, etc. Since high-concentration filling slurry contains more solid coarse particles, the coarse particle matter will cause certain wear to the pipeline during the transportation process.

[0004] With the increasingly prominent problem of pipeline wear, many scholars have carried out research on the pipeline wear of filling slurry. However, in the existing technology, no reasonable and effective optimization method has been proposed for the layout parameters of the transportation pipeline. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention proposes an optimization method for the layout parameters of a pipeline for transporting full-tailings filling slurry. By optimizing the layout parameters of the pipeline for transporting filling slurry, the pipeline wear caused by non-full pipe flow is reduced, the service life of the pipeline is extended, the maintenance cost is reduced, and the efficiency of mine tailing treatment is improved.

[0006] To achieve the above object, an optimization method for the layout parameters of a pipeline for transporting full-tailings filling slurry designed by the present invention is characterized in that it includes the following steps:

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

[0008] S2) According to the particle size distribution range of the coarse particles, conduct a rheological test on the full-tailings filling slurry to obtain the rheological curve between different shear rates and shear stresses, and then fit the rheological curve. If the rheological model of the full-tailings filling slurry is determined to be the Bingham model according to the fitted rheological curve, the following rheological parameters required by the finite element simulation software are obtained;

[0009] 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 mesh division. When dividing the mesh, locally encrypt the mesh at the elbow and add a boundary layer. After mesh independence verification, obtain the final number of meshes;

[0010] S4) Establish a control model for the transportation process of the paste backfill with full tailings, and use the Euler-Lagrange model to obtain fluid information and solid particle information. Then, set boundary conditions according to the actual situation, and conduct fluent numerical simulation research to obtain the pressure, velocity distribution nephograms of the paste backfill in the transportation process of the three-dimensional simple transportation pipeline and the pipeline wear nephogram;

[0011] S5) From the pressure, velocity distribution nephograms and the pipeline wear nephogram, it is concluded that the pipeline wear is mainly distributed at the pipeline inlet and the outer side of the elbow. Therefore, set the pipe diameter, elbow angle, and bend diameter ratio as pipeline wear factors, and respectively obtain the preliminary value ranges of the elbow angle, pipe diameter, and bend diameter ratio. Take the combinations of different elbow angles, pipe diameters, and bend diameter ratios as a set of test schemes, and a total of multiple test schemes are obtained;

[0012] S6) According to the fluent numerical simulation, simulate the maximum erosion wear rate corresponding to different test schemes during the pipeline transportation process. Construct a response surface regression model between the simulated maximum erosion wear rate and the pipeline wear factors, conduct variance analysis, factor interaction verification, and significance analysis on the response surface regression model, obtain the influence degree of different pipeline wear factors on the pipeline wear rate, and reset the optimized value ranges of each pipeline wear factor according to the influence degree on the wear rate;

[0013] S7) According to the reset optimized value ranges of each pipeline wear factor, output the response surface prediction model constructed between the pipeline wear factors and the predicted pipeline wear rate, and select the elbow angle, different pipe diameters, and bend diameter ratio values corresponding to the minimum predicted pipeline wear rate as the optimized layout parameters of the transportation pipeline.

[0014] Furthermore, in S1), by conducting the full tailings particle size test, determine the particle size distribution range of the coarse particles.

[0015] Even further, in S3), use workbench to construct a three-dimensional simple transportation pipeline model, and use the finite element simulation software ANASYS FLUENT to import the three-dimensional simple transportation pipeline model.

[0016] Even further, in S4), the set boundary conditions include setting one end of the transportation pipeline as a velocity inlet, setting the other end of the transportation pipeline as a pressure outlet, setting the fine particles, cement, and water in the paste backfill as the continuous phase, setting the coarse particles in the paste backfill as the discrete phase, and setting the particle incidence conditions.

[0017] Even further, in S4), the control model for the transportation process of the paste backfill with full tailings includes the continuity equation, momentum conservation equation, discrete phase control equation, wear equation, and wall collision recovery equation;

[0018] The continuity equation is expressed as follows

[0019]

[0020] wherein,

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

[0022] v x , v y , v z represent the velocity components in the x, y, and z directions;

[0023] The momentum conservation equation is expressed as follows

[0024]

[0025] wherein,

[0026] ρ ω represents the continuous phase density,

[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 the pressure,

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

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

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

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

[0034] The discrete phase control equation is expressed as follows

[0035]

[0036] wherein,

[0037] v is expressed as the fluid phase velocity,

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

[0039] f represents other body forces,

[0040] g represents the acceleration due to gravity,

[0041] ρ g represents the particle density,

[0042] ρ represents the fluid density;

[0043] The wear equation is expressed by the following formula

[0044]

[0045] where,

[0046] R e represents the wear rate,

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

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

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

[0050] v p represents the particle impact velocity,

[0051] b ν represents the velocity exponent function,

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

[0053] P represents the colliding particles,

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

[0055] The wall collision recovery equation is expressed by the following formula

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

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

[0058] where,

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

[0060] α represents the particle incident angle.

[0061] Furthermore, in S5), multiple groups of experimental schemes are designed using Design-Expert software.

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

[0063] Even further, in S6), if the model significance does not meet the requirements, fluent numerical simulation is performed again until the constructed response surface regression model meets the significance requirements.

[0064] Even further, in S6), when performing model significance analysis, by obtaining the response surface contour maps and contour plots corresponding to different pipeline wear factors in the response surface regression model, the influence degree of different pipeline wear factors on the pipeline wear rate is obtained.

[0065] The advantages of the present invention are as follows:

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

[0067] The method for optimizing the layout parameters of the full-tailings filling slurry conveying pipeline of the present invention reduces pipeline wear caused by non-full pipe flow, extends the service life of the pipeline, reduces maintenance costs, and improves the efficiency of mine tailings treatment by optimizing the layout parameters of the filling slurry conveying pipeline. Brief Description of the Drawings

[0068] Figure 1 is the flow chart of the present invention;

[0069] Figure 2a is the response surface contour map of the pairwise interaction between the elbow angle and pipe diameter among different pipeline wear factors;

[0070] Figure 2b is the response surface contour map of the pairwise interaction between the pipe diameter and bend diameter ratio among different pipeline wear factors;

[0071] Figure 2c is the response surface contour map of the pairwise interaction between the elbow angle and bend diameter ratio among different pipeline wear factors;

[0072] Figure 2d is the contour plot of the pairwise interaction between the elbow angle and pipe diameter among different pipeline wear factors;

[0073] Figure 2e is the contour plot of the pairwise interaction between the pipe diameter and bend diameter ratio among different pipeline wear factors;

[0074] Figure 2f It is the contour map of the pairwise interaction between the elbow angle and the bend diameter ratio among different pipeline wear factors;

[0075] Figure 3 It is the conveying pipeline model after the layout parameters are optimized in this embodiment;

[0076] Figure 4 It is the velocity contour map of the conveying pipeline after the layout parameters are optimized in this embodiment when filling with filling slurry;

[0077] Figure 5 It is the pressure contour map of the conveying pipeline after the layout parameters are optimized in this embodiment when filling with filling slurry;

[0078] Figure 6 It is the pipeline wear contour map of the conveying pipeline after the layout parameters are optimized in this embodiment when filling with filling slurry. Detailed implementation manners

[0079] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0080] As Figure 1 shown, a method for optimizing the layout parameters of a conveying pipeline for full-tailings filling slurry of the present invention includes the following steps:

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

[0082] Specifically, by conducting full-tailings particle size tests, obtain the full-tailings particle size distribution range, determine that the particle gradation is good, and determine the particle size distribution range of the coarse particles.

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

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

[0085] Specifically, use workbench to construct a three-dimensional simple conveying pipeline model, and use the finite element simulation software ANASYS FLUENT to import the three-dimensional simple conveying pipeline model.

[0086] In this 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) Establish a control model for the transportation process of the full-tailings filling slurry, and use the Euler-Lagrange model to obtain fluid information and solid particle information. Then, set boundary conditions according to the actual situation, conduct fluent numerical simulation research, and obtain the pressure, flow velocity distribution cloud map of the filling slurry and the pipeline wear cloud map during the transportation process of the three-dimensional simple conveying pipeline.

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

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

[0090] Specifically, the control model for the transportation process of 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 、v z represent the velocity components in the x, y, and z directions.

[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 the velocity component of the continuous phase on the coordinate axis i,

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

[0102] p represents the pressure,

[0103] τij Denotes the viscous stress components generated on the unit microelement under the action of molecular viscosity.

[0104] f i Denotes the body forces in the x, y, and z directions.

[0105] x i Denotes the spatial coordinates (subscript i = 1, 2, 3 represent the x, y, z coordinate axes respectively, and i ≠ j).

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

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

[0108]

[0109] In the formula,

[0110] v represents the fluid phase velocity.

[0111] f d (ν - ν g ) represents the drag force per unit mass of the particle.

[0112] f represents other body 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 expressed 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 particle.

[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 ν Represents the velocity exponent function.

[0125] Α face Represents the unit surface area where the particles impact the wall.

[0126] P represents the colliding particles.

[0127] N P Represents the number of colliding particles;

[0128] The wall collision recovery equation is expressed by the following formula

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

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

[0131] In the formula,

[0132] е n ,е t respectively represent the wall restitution coefficients,

[0133] α represents the particle incidence angle.

[0134] S5) From the pressure, flow velocity distribution contour maps and the pipeline wear contour map, it is obtained that the pipeline wear is mainly distributed at the pipeline inlet and the outer side of the elbow. Therefore, the pipe diameter, elbow angle, and bend diameter ratio are set as the pipeline wear factors, and the preliminary value ranges of the elbow angle, pipe diameter, and bend diameter ratio are obtained respectively. The combinations of different elbow angles, pipe diameters, and bend diameter ratios are used as a set of test schemes, and a total of multiple sets of test schemes are obtained.

[0135] Specifically, Design-Expert software is used to design multiple sets of test schemes.

[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 radius of curvature 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 during the pipeline transportation 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 performed on the response surface regression model to obtain the influence degree of different pipeline wear factors on the pipeline wear rate, and the optimized value ranges of each pipeline wear factor are reset 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] In this embodiment, the pipeline wear rates corresponding to the 13 test schemes simulated are shown in Table 2.

[0144] Table 2 Results of Wear Rates of Test Schemes

[0145]

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

[0147] Table 3 Variance Analysis and Significance Analysis of Response Surface Regression Model

[0148] parameter response surface model <![CDATA[X 1 > <![CDATA[X 2 > <![CDATA[X 3 > <![CDATA[X 1 X 3 > <![CDATA[X 2 X 3 > <![CDATA[X 1 X 2 > 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 <![CDATA[5.929×10 -16 > <![CDATA[7.013×10 -16 > <![CDATA[2.208×10 -15 > <![CDATA[1.752×10 -15 > <![CDATA[1.444×10 -17 > <![CDATA[4.020×10 -16 > <![CDATA[2.031×10 -16 >

[0149] It can be seen from Table 3 that significance analysis is performed through the two parameters of P and F. According to F, the influence degrees of single factors and pairwise interaction factors on pipeline wear can be directly obtained. The larger F is, the greater the influence of the single factor on the response value. When P < 0.05, it indicates a significant difference. When P < 0.01, it indicates an extremely significant difference. The significant role of pipeline wear factors is judged according to the P value.

[0150] It can be seen from Table 3 that in this embodiment, the pipe diameter has the greatest influence on the pipeline wear rate, and the elbow angle has the smallest influence on the pipeline wear rate. Therefore, in this embodiment, the optimized value ranges of each pipeline wear factor are reset as follows: the optimized value range of the elbow angle X 1 is 60 - 90°, the optimized value range of the pipe diameter X 2 is 120 - 200 mm, and the optimized value range of the bend diameter ratio X 3 is 2 - 4.

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

[0152] Specifically, when the F value is greater than 0.05, the response surface model is not significant, and it is necessary to re-conduct the wear simulation and re-establish the response surface regression model; 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, where Y is the wear rate.

[0153] Y = 5.9×10 -8 -8.9×10 -9 X 1 -1.661×10 -8 X 2 -1.526×10 -8 X 3 +1.9×10 -9 X 1 X 2 -1.91×10 -9 X 1 X 3 +7.125×10 -9 X 2 X 3 -2.125×10 -10 X 1 2 +3.612×10 -9 X 2 2 -2.537×10 -9 X 3 2

[0154] Optimally, when conducting the model significance analysis, by obtaining the response surface contour maps and contour plots corresponding to different pipeline wear factors in the response surface regression model, the influence degree of different pipeline wear factors on the pipeline wear rate can be obtained.

[0155] As Figures 2a - 2f shown, they are the response surface contour maps and contour plots of the pairwise interactions of different pipeline wear factors. The response surface contour maps of different pipeline wear factors reflect the influence of pairwise interactions on the wear rate, and the contour plots are the influence of the pairwise interactions of two-dimensional pipeline wear factors on the pipeline wear rate. Combining Table 3, the influence of the pairwise interactions of different pipeline parameters on pipeline wear is obtained. According to the contour lines, under the pairwise interactions, the influence of a single wear factor on the pipeline wear parameters is relatively large, and finally the influence degree of the single factor on the pipeline wear rate is obtained.

[0156] S7) According to the optimized value ranges of each pipeline wear factor re-set, output the response surface prediction model constructed between the pipeline wear factors and the predicted pipeline wear rate, and select the elbow angle, different pipe diameters, and bend diameter ratio values corresponding to the minimum predicted pipeline wear rate as the optimized layout parameters of the conveying pipeline.

[0157] In this embodiment, the optimized layout parameters of the conveying pipeline are as follows: the elbow angle X 1 is 88.1716°, the pipe diameter X 2 is 193.012 mm, the bend diameter ratio X 3 is 2.4237, and the minimum predicted pipeline wear rate is 5.20182×10 -8 .

[0158] According to the above combination of optimized layout parameters of the conveying pipeline, repeat steps S3) and S4), establish a new geometric model, divide the mesh, set the boundary conditions, and conduct fluent wear simulation (as Figures 3 - 6 shown, which are respectively the conveying pipeline model after optimizing the layout parameters, the velocity, pressure, and pipeline wear nephogram when filling with filler slurry). The simulated wear rate is 5.48×10 -8 . The minimum predicted pipeline wear rate of 5.20182×10 -8 relative to the simulated wear rate of 5.48×10 -8 has an error value of 5%.

[0159] It can be seen from Figure 4 that when the slurry flows from the vertical pipeline into the elbow, the flow velocity in the middle of the pipeline increases continuously. As the turning angle changes, the area with the maximum flow velocity gradually moves towards the outer side of the elbow, and the velocity on the outer side of the elbow is greater than that on the inner side of the elbow.

[0160] It can be seen from Figure 5 that the pressure inside the pipeline gradually decreases from the inlet to the outlet, and the pressure outside the pipe is greater than the pressure inside the elbow. The reason why the pressure outside the elbow is greater than the pressure inside is that when the slurry moves to the elbow, the movement direction suddenly changes. Due to inertia, the slurry generates an extrusion force on the outer wall surface of the elbow.

[0161] It can be seen from Figure 6 that the two pipelines in the figure are the wear distribution nephograms of the same pipeline from different perspectives. The wear on the outer wall surface of the elbow is more serious than that on the inner wall surface because most of the particles move along the outer wall surface of the elbow during the movement process. Coupled with the existence of the pressure difference, the particles move from the outer side to the inner side gradually, so the wear mainly exists on the outer side of the elbow.

[0162] The above embodiments are the preferred embodiments of the present invention, but the embodiments of the present invention are not limited by the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A method for optimizing the layout parameters of a full tailings filling slurry delivery pipeline, characterized in that: The steps include: S1) obtaining the particle size distribution range of coarse particles in the full tailings filling slurry; S2) According to the particle size distribution range of the coarse particles, a rheological test of the full tailings filling slurry is carried out to obtain a rheological curve between different shear rates and shear stresses, and then the rheological curve is fitted. If the rheological model of the full tailings filling slurry is determined to be a Bingham model according to the fitted rheological curve, the rheological parameters required by the following finite element simulation software are obtained; S3) constructing a three-dimensional simple transportation pipeline model and importing it into finite element simulation software, assigning material properties to the three-dimensional simple transportation pipeline and performing structured mesh division, locally encrypting the mesh at the bend and adding a boundary layer when dividing the mesh, and obtaining the final number of meshes after mesh independence verification; S4) Establish a control model for the transportation of full tailings filling slurry, and use the Euler-Lagrangian model to obtain fluid information and solid particle information, then set boundary conditions according to actual conditions, carry out FLUENT numerical simulation research, and obtain the pressure and velocity distribution cloud map of the filling slurry in the three-dimensional simple transportation pipeline during the transportation process and the pipeline wear cloud map; S5) From the pressure and velocity 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. Therefore, the pipe diameter, elbow angle, and bend diameter ratio are set as pipeline wear factors, and the preliminary value ranges of the elbow angle, pipe diameter, and bend diameter ratio are obtained respectively. The combination of different elbow angles, pipe diameters, and bend diameter ratios is taken as a group of test schemes, and a total of multiple groups of test schemes are obtained; S6) Based on the FLUENT numerical simulation, the maximum erosion wear rate corresponding to different test schemes in the pipeline transportation process is simulated, and a response surface regression model is constructed between the simulated maximum erosion wear rate and the pipeline wear factor. The response surface regression model is subjected to variance analysis, factor interaction verification and significance analysis to obtain the influence of different pipeline wear factors on the pipeline wear rate, and the optimal value range of each pipeline wear factor is reset according to the influence on the wear rate; S7) According to the reset optimized 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 bending angle, different pipe diameters, and bending-diameter ratio corresponding to the minimum predicted pipeline wear rate are selected as the optimized layout parameters of the transmission pipeline.

2. The method for optimizing the layout parameters of the full tailings filling slurry delivery pipeline according to claim 1 is 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 full tailings filling slurry delivery pipeline according to claim 2 is characterized in that: In S3), a three-dimensional simple transportation pipeline model is constructed using workbench, and the three-dimensional simple transportation pipeline model is imported using finite element simulation software ANASYS FLUENT.

4. The method for optimizing the layout parameters of the full tailings filling slurry conveying pipeline according to claim 3 is characterized in that: S4), the boundary conditions set 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 a continuous phase, setting the coarse particles in the filling slurry as a discrete phase, and setting the particle incidence conditions.

5. The method for optimizing the layout parameters of the full tailings filling slurry delivery pipeline according to claim 4 is characterized in that: S4), the control model in the whole tailings filling slurry transportation process includes the continuity equation, the momentum conservation equation, the discrete phase control equation, the wear equation, and the wall collision recovery equation; The continuity equation is expressed by In the formula, x, y, and z represent three directions respectively. v x 、v y 、v z Represents the velocity components in the opposite directions of x, y, and z; The momentum conservation equation is expressed by the following formula: In the formula, ρ ω represents the density of the continuous phase, v i represents the velocity component of the continuous phase on coordinate axis i, v j represents the velocity component of the continuous phase on the coordinate axis j, p represents pressure, τ ij It represents the viscous stress component generated on the unit microelement under the action of molecular viscosity. f i represents the mass force in the x, y, and z directions, x i represents the spatial coordinates (subscript i = 1, 2, 3, representing the x, y, and z axes respectively, and i ≠ j), x j represents the spatial coordinates (subscript j = 1, 2, 3, representing the x, y, and z coordinate axes respectively, and i ≠ j); The discrete phase governing equation is expressed by In the formula, v is the fluid phase velocity, f d (ν-ν g ) represents the drag force per unit mass of the particle, f represents other mass forces, g is the acceleration due to gravity, ρ g represents the particle density, ρ represents the fluid density, f d (ν-ν g ) represents the drag force per unit mass of the particle, f represents other mass forces; The wear equation is expressed by the following formula: In the formula, R e represents the wear rate, m p represents the mass flow rate of particles, c(d p ) represents the particle diameter function, f(θ) represents the impact angle function, v p is the particle impact velocity, b ν represents the speed exponential function, Alpha face represents the unit surface area of ​​the particle impacting the wall, P represents the collision particle, N P Indicates the number of collision particles; The wall impact recovery equation is expressed by 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, е n , t are the wall restitution coefficients, α represents the particle incident angle.

6. The method for optimizing the layout parameters of a full tailings filling slurry delivery pipeline according to claim 1, characterized in that: In S5), Design-Expert software was used to design multiple test plans.

7. The method for optimizing the layout parameters of a full tailings filling slurry delivery pipeline according to claim 6, characterized in that: S6), the Design-Expert software was used to construct a response surface regression model between the simulated pipeline wear rate and pipeline wear factors.

8. The method for optimizing the layout parameters of a full tailings filling slurry delivery pipeline according to claim 7, characterized in that: In S6), if the model significance does not meet the requirement, the fluent numerical simulation is performed again until the constructed response surface regression model meets the significance requirement.

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

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