Full-tailing filling slurry pipeline erosion model parameter optimization method and full-tailing filling slurry pipeline erosion model parameter optimization device

Abstract

The invention provides a full-tailing filling slurry pipeline erosion model parameter optimization method and device, and the method comprises the steps: mixing full tailings and a cementing material based on the coarse particle size distribution and mineral content of the full tailings to obtain filling slurry, determining a filling slurry rheological model to obtain filling slurry rheological parameters, dividing structured grids for a to-be-filled pipeline, and determining the filling slurry rheological parameters of the to-be-filled pipeline. The method comprises the following steps: establishing a fluid numerical simulation model, setting erosive wear factors as inlet flow velocity, mass concentration and mass flow rate, establishing a pipeline erosive wear scheme, and obtaining the influence degree of different erosive wear single factors on the maximum wear rate and the influence of erosive wear interaction factors on the pipeline erosive wear; and obtaining an RM-BBD test result according to the influence of a single factor or an interactive factor on the erosive wear of the pipeline, and optimizing the inlet flow rate, the mass concentration and the mass flow rate. The method can quickly and accurately identify the erosive wear influence degree of the filling pipeline, optimize the erosive wear parameters, stabilize the filling conveying system and prolong the conveying life of the pipeline.

Description

Technical Field

[0001] The present invention relates to the technical field of filling mining, and particularly relates to a method and device for optimizing parameters of a pipeline erosion model of a paste backfill containing full tailings. Background Art

[0002] At present, the filling mining method has gradually been favored by many mines due to its environmental protection and high efficiency characteristics. As one of the cores of the filling mining method, the stability and safety of the pipeline transportation system have a decisive impact on the normal operation of the entire mining system.

[0003] At the present stage, the research on the wear problem during the pipeline transportation of the paste backfill has attracted extensive attention, and efforts have been made to find effective methods to reduce wear and increase the service life of the pipeline. Among them, pipeline wear mainly involves two types: erosion wear and corrosion wear. Since the paste backfill is mostly neutral or alkaline in most cases, the influence of corrosion wear is relatively limited. Therefore, the pipeline wear is mainly erosion wear.

[0004] In the prior art, when analyzing the erosion wear of the system pipeline, most studies are carried out from single factors such as filling technology, slurry factors, and pipeline characteristics, often ignoring the influence degree of the interaction of multiple factors on pipeline wear, resulting in incorrect analysis results. At the same time, during the transportation of the paste backfill in the filling pipeline, the pipeline will be eroded and worn due to the change of the flow state, causing failure phenomena such as wear, leakage, and pipe explosion in the filling system, thus affecting the normal operation of the filling transportation system. As an important means for the treatment of mine tailings, the full tailings filling technology has been widely used. However, in practical applications, due to the serious erosion wear phenomenon of the paste backfill during pipeline transportation, the service life of the pipeline is short and the maintenance cost is high. The traditional empirical design method is difficult to accurately predict and control the erosion wear of the pipeline. Therefore, a scientific method is urgently needed to optimize the parameters of the pipeline erosion model of the paste backfill, so as to reduce the pipeline erosion wear. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a method and device for optimizing parameters of a pipeline erosion model of a paste backfill containing full tailings, which can quickly and accurately identify the influence degree of erosion wear of the filling pipeline, optimize the erosion wear parameters, stabilize the filling transportation system, and extend the service life of pipeline transportation.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0007] A method for optimizing parameters of a pipeline erosion model of a paste backfill containing full tailings, comprising:

[0008] Mixing full tailings with a cementitious material to obtain a paste backfill based on the coarse particle size distribution and mineral content of the full tailings;

[0009] Determine the rheological model of the filling slurry and obtain the rheological parameters of the filling slurry;

[0010] Divide the structured grid for the pipeline to be filled;

[0011] Based on the rheological parameters of the filling slurry and the structured grid, establish a control model. The control model includes the continuity equation, the momentum conservation equation, the solid particle force equation, the erosion wear equation, and the wall collision recovery equation. Set the boundary conditions and parameters to carry out fluent numerical simulation research;

[0012] Set the erosion wear factors as the inlet velocity, mass concentration, and mass flow rate, and establish a pipeline erosion wear scheme for different erosion wear factors;

[0013] Based on the pipeline erosion wear scheme and the control model, obtain the influence degree of different single erosion wear factors on the maximum wear rate and the influence of the erosion wear interaction factors on the pipeline erosion wear;

[0014] According to the influence of single factors or interaction factors on the pipeline erosion wear, obtain the RM-BBD test results and obtain the optimized inlet velocity, mass concentration, and mass flow rate.

[0015] Furthermore, the method for determining the rheological model of the filling slurry is as follows:

[0016] Set one end of the pipeline as the velocity inlet and the other end as the pressure outlet. The calculation formula of the filling slurry rheological model is:

[0017]

[0018] In the formula: G k is the generation of turbulent kinetic energy led by the average velocity; G b is the generation of turbulent kinetic energy led by the buoyancy force; S k , S ε are user-defined parameters; C iε is the empirical coefficient, i = 1, 2, 3, and the empirical coefficient can be obtained from basic turbulence experiments; ρ is the density of the fluid; g is the acceleration of gravity; k is the kinetic energy in turbulence; u j is the velocity component of the discrete phase in the coordinate axis; x i and x j are the coordinates of the fluid in the i direction and the j direction respectively, i, j = 1, 2, 3, representing the x-axis, y-axis, and z-axis respectively, i ≠ j; μ is the dynamic viscosity coefficient; μ t is the turbulent viscosity coefficient; ε is the local density, usually used in non-uniform fluids; μ i is the equivalent dynamic viscosity of the influence of turbulence on viscosity; is the turbulent Prandtl number of the turbulent dissipation rate, indicating the turbulent diffusion characteristics; σ k , σε is an empirical constant; t is time.

[0019] Furthermore, the calculation formula of the solid particle force equation is:

[0020]

[0021] In the formula: t represents time; ν represents 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 represents the acceleration due to gravity; ρ g represents the particle density; ν g represents the velocity of the background fluid; ρ represents the fluid density;

[0022] The calculation formula of the erosion wear equation is:

[0023]

[0024] In the formula: R e represents the wear rate; u p represents the particle impact velocity; b r represents the velocity exponential function; m p represents the mass flow rate of the particle; c(d p ) represents the particle diameter function; f(θ) represents the impact angle function; A face represents the unit surface area of the particle impacting the wall; N P represents the total number of colliding particles; P represents the Pth colliding particle;

[0025] The calculation formula of the wall collision recovery equation is:

[0026] e n = 0.993 - 1.76α + 1.56α 2 - 0.49α 3

[0027] e t = 0.988 - 1.66α + 2.11α 2 - 0.67α 3

[0028] Among them, e n , e t are the normal wall recovery coefficient and the tangential wall recovery coefficient respectively; α is the particle incident angle.

[0029] Furthermore, the Deign-Expert software is used to design the pipeline erosion wear schemes with different inlet flow rates, mass concentrations, and mass flow rates.

[0030] Further, based on the pipeline erosion wear solution and control model, the method for obtaining the influence degree of different single erosion wear factors on the maximum wear rate and the influence of erosion wear interaction factors on pipeline erosion wear is as follows:

[0031] Adopt RM response surface analysis of the erosion wear rate response surface model, fit the regression model of the maximum wear rate and erosion wear parameters, optimize the regression model through variance analysis, factor interaction verification and significance analysis, and output the response surface analysis prediction model;

[0032] According to fluid numerical simulation, obtain the erosion wear rate of the pipeline under the influence of different erosion wear factors, including single-factor erosion wear rate and pairwise interaction erosion wear rate;

[0033] Perform multiple regression analysis on the obtained erosion pipeline wear rate for the test results, and combine the least squares method to obtain a second-order polynomial response surface model.

[0034] Further, it is also necessary to construct a data set, and the construction method of the data set is as follows:

[0035] Set the initial values of the three influencing factors of erosion wear factors: inlet flow velocity, mass concentration, and mass flow rate;

[0036] Take range values for different erosion wear factors and conduct three-factor and three-level tests;

[0037] Use deign software to obtain the test schemes of different erosion wear factors.

[0038] Further, the quadratic polynomial regression model is:

[0039]

[0040] Among them, a 1 、a 2 、a 3 、a 4 、a 5 、a 6 、a 7 、a 8 、a 9 、a 10 are all fitting parameters, X 11 is the inlet flow velocity, X 2 is the mass concentration, X 3 is the mass flow rate.

[0041] An optimized device for the parameters of the full-tailings filling slurry pipeline erosion model includes:

[0042] A filling slurry acquisition module, which is used to mix the full tailings with the cementitious material to obtain the filling slurry based on the coarse particle size distribution and mineral content of the full tailings;

[0043] The rheological parameter acquisition module is used to determine the rheological model of the filling slurry and obtain the rheological parameters of the filling slurry;

[0044] The mesh generation module is used to generate structured meshes for the pipelines to be filled;

[0045] The model establishment module is used to establish a control model based on the rheological parameters of the filling slurry and the structured meshes. The control model includes the continuity equation, the momentum conservation equation, the solid particle force equation, the erosion wear equation, and the wall collision recovery equation. Set boundary conditions and parameters to conduct fluent numerical simulation research;

[0046] The scheme establishment module is used to set the erosion wear factors as the inlet velocity, mass concentration, and mass flow rate, and establish the pipeline erosion wear schemes for different erosion wear factors;

[0047] The wear influence acquisition module is used to obtain the influence degree of different single erosion wear factors on the maximum wear rate and the influence of the erosion wear interaction factors on the pipeline erosion wear based on the pipeline erosion wear schemes and the control model;

[0048] The optimization module is used to obtain the RM-BBD test results according to the influence of single factors or interaction factors on the pipeline erosion wear, and obtain the optimized inlet velocity, mass concentration, and mass flow rate.

[0049] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned method for optimizing the parameters of the pipeline erosion model of the whole-tailings filling slurry.

[0050] A non-transitory computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned method for optimizing the parameters of the pipeline erosion model of the whole-tailings filling slurry.

[0051] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0052] Based on the existing coarse particle size distribution and mineral content of the whole tailings, the present invention mixes the whole tailings with the cementitious material to obtain the filling slurry, and establishes a fluid numerical simulation model. The fluid numerical simulation model includes the continuity equation, the momentum conservation equation, the solid particle force equation, the erosion wear equation, and the wall collision recovery equation. The discrete phase model (DPM) is used as the fluid numerical simulation model, which can consider various forces and physical phenomena, is applicable to various complex fluid-solid two-phase flow problems, and can generate detailed visualization results, facilitating the analysis and understanding of the physical phenomena of fluid-solid two-phase flow.

[0053] The present invention selects the erosion wear model existing in DPM to predict the wear rate of discrete phase particles on the material wall surface. The material filling the pipeline is generally a rigid material, so the Generic wear model is selected to solve the wear rate of the particles relative to the filling elbow. After the particles in the pipeline collide with the wall surface, the rebound velocity is always lower than the incident velocity due to the energy transfer and loss effect, and its value will change with the number of collisions. This characteristic is usually expressed by the wall collision recovery function. At the same time, the wall recovery coefficient is related to the wall material. Since the filling elbow belongs to a rigid material, this model is selected. In summary, the motion state and force condition of the coarse particles in the pipeline can be obtained, and the pipeline wear condition can be analyzed.

[0054] The present invention considers the influences of the inlet flow velocity, mass concentration, and mass flow rate, and conducts a study on the erosion wear characteristics of the full-tailings cemented filling slurry pipeline under different erosion wear factors. Through simulation, the data of the pipeline wear rate under different erosion wear factors are obtained, and the optimized erosion wear parameters are output for the filling and transportation of the full-tailings filling slurry, which can extend the pipeline life and reduce the maintenance cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The drawings described herein are used to provide a further understanding of the present invention and form a part of this application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0056] Figure 1 is a flowchart of the method for optimizing the erosion model parameters of the full-tailings filling slurry pipeline of the present invention;

[0057] Figure 2 is a three-dimensional structure diagram of the simple model for pipeline transportation related to the present invention;

[0058] Figure 3 is a grid diagram of the simple model for pipeline transportation related to the present invention;

[0059] Figure 4(a) is a response surface diagram of the erosion wear factors of the inlet flow velocity and mass concentration in the present invention;

[0060] Figure 4(b) is a response surface diagram of the erosion wear factors of the inlet flow velocity and mass concentration in the present invention;

[0061] Figure 4(c) is a response surface diagram of the erosion wear factors of the mass concentration and mass flow rate in the present invention;

[0062] Figure 4(d) is a contour diagram of the erosion wear factors of the inlet flow velocity and mass concentration in the present invention;

[0063] Figure 4(e) is a contour diagram of the erosion wear factors of the inlet flow velocity and mass concentration in the present invention;

[0064] Figure 4(f) is the contour map of the mass concentration and the mass concentration erosion wear factor in the present invention;

[0065] Figure 5 is the flow chart of the device for optimizing the parameters of the full-tailings filling slurry pipeline erosion model of the present invention. Specific embodiments

[0066] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0067] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "plurality" is two or more.

[0068] Embodiment 1

[0069] Embodiment 1 provides a method for optimizing the parameters of the full-tailings filling slurry pipeline erosion model, as Figure 1 shown, including:

[0070] Step 1: Based on the coarse particle size distribution and mineral content of the full-tailings, mix the full-tailings with the cementitious material to obtain the filling slurry;

[0071] Step 2: Determine the rheological model of the filling slurry and obtain the rheological parameters of the filling slurry;

[0072] Step 3: Divide the structured grid for the pipeline to be filled;

[0073] Step 4: Based on the rheological parameters of the filling slurry and the structured grid, establish a control model. The control model includes the continuity equation, the momentum conservation equation, the solid particle force equation, the erosion wear equation, and the wall collision recovery equation. Set the boundary conditions and parameters to carry out the fluent numerical simulation study;

[0074] Step 5: Set the erosion wear factors as the inlet velocity, the mass concentration, and the mass flow rate, and establish the pipeline erosion wear schemes for different erosion wear factors;

[0075] Step 6: Based on the pipeline erosion wear scheme and control model, obtain the influence degree of different single erosion wear factors on the maximum wear rate and the influence of erosion wear interaction factors on pipeline erosion wear;

[0076] Step 7: According to the influence of single factors or interaction factors on pipeline erosion wear, obtain the results of the RM-BBD test, and obtain the optimized inlet flow rate, mass concentration, and mass flow rate.

[0077] A method for optimizing the parameters of a full-tailings filling slurry pipeline erosion model provided in this embodiment mixes full-tailings with a cementitious material to obtain a filling slurry based on the existing coarse particle size distribution and mineral content of the full-tailings.

[0078] And establish a fluid numerical simulation model. The fluid numerical simulation model includes a continuity equation, a momentum conservation equation, a solid particle force equation, an erosion wear equation, and a wall collision recovery equation. The discrete phase model (DPM) is used as the fluid numerical simulation model. The discrete phase model is a computational fluid dynamics (CFD) method that can simulate and analyze the motion and interaction of solid particles in a fluid. The solid particles are regarded as discrete mass points, and the trajectory of each particle is tracked separately, and the interaction force between the particles and the fluid is calculated. It can consider various acting forces and physical phenomena, is applicable to various complex fluid-solid two-phase flow problems, and can generate detailed visualization results, which is convenient for analyzing and understanding the physical phenomena of fluid-solid two-phase flow.

[0079] In this embodiment, an erosion wear model existing in DPM is selected. The Generic wear model is a generally applicable erosion wear model in FLUENT to predict the wear rate of discrete phase particles on the material wall. The material of the filling pipeline is generally a rigid material, so the Generic wear model is selected to solve the wear rate of the particles relative to the filling elbow. After the particles in the pipeline collide with the wall, the rebound velocity is always lower than the incident velocity due to the energy transfer and loss effect, and its value will change with the number of collisions. This characteristic is usually expressed by the wall collision recovery function. At the same time, the wall recovery coefficient is related to the wall material. The filling elbow belongs to a rigid material, and this model is selected. In summary, the motion state and force condition of the coarse particles in the pipeline can be obtained, and the pipeline wear condition can be analyzed.

[0080] This embodiment considers the influence of the inlet flow rate (X 1 ), mass concentration (X 2 ), and mass flow rate (X 3 ), conducts a study on the erosion wear characteristics of the full-tailings cementitious filling slurry pipeline under different erosion wear factors, obtains the pipeline wear rate data under different erosion wear factors through simulation, outputs the optimized erosion wear parameters, and conducts the filling and transportation of the full-tailings filling slurry, which can extend the pipeline life and reduce the maintenance cost.

[0081] In step 1 of this embodiment, according to the particle size distribution of the full tailings, it is determined that the particle gradation is good, and the coarse particle size distribution is obtained. Based on the physical and chemical parameters of the tailings, the main mineral content is obtained. Combined with the cementitious material, the filling slurry has good fluidity.

[0082] In step 2 of this embodiment, a rheological test of the filling slurry is carried out to obtain the distribution curves of different shear rates and shear stresses, fit the curves, and determine the rheological model of the filling slurry according to the rheological curves, so as to obtain the rheological parameters of the filling slurry.

[0083] In step 2 of this embodiment, pipeline wear analysis is carried out. One end of the pipeline is set as the inlet with a velocity inlet, and the other end is set as the outlet with a pressure outlet. The rheological model of the filling slurry is a viscous model, and the kε model is selected. The calculation formula is:

[0084]

[0085] In the formula: G k is the generation of turbulent kinetic energy induced by the average velocity; G b is the generation of turbulent kinetic energy induced by the buoyancy effect; S k 、S ε are user-defined parameters; C iε is an empirical coefficient, i = 1, 2, 3. The empirical coefficient can be obtained from basic turbulence tests. ρ is the density of the fluid, with the unit of kg / m 3 ; g is the acceleration due to gravity, with the unit of m / s 2 ; k is the kinetic energy in turbulence, with the unit of J; u j is the velocity component of the discrete phase in the coordinate axis, with the unit of m / s; x i and x j are the coordinates of the fluid in the i direction and j direction respectively. i, j = 1, 2, 3 represent the x-axis, y-axis, and z-axis respectively, i ≠ j, with the unit of m; μ is the dynamic viscosity coefficient, with the unit of Pa·s; μ t is the turbulent viscosity coefficient, with the unit of Pa·s; ε is the local density, with the unit of kg / m 3 , usually used in non-uniform fluids; μ i is the equivalent dynamic viscosity of the influence of turbulence on viscosity, with the unit of Pa·s; is the turbulent Prandtl number of the turbulent dissipation rate, indicating the turbulent diffusion characteristics; σ k 、σ ε are empirical constants; t is the time, with the unit of s.

[0086] In this embodiment, in step 3, structured grids are divided according to the existing filling pipeline layout, with local refinement at the elbows, boundary layers are added, and after grid independence verification, the final number of grids is obtained. Generally, grids are all verified for grid independence to reduce the impact of the number of grids on pipeline wear. When the variance value does not reach significance, considering the consistency of the erosion wear settings, re-dividing the grids can be considered. Boundary layers can be added and the elbows can be locally refined. (Locally refining the grids will capture more coarse particles). For example, when the grid size is 10 mm, considering dividing the grids into 9 mm to increase the erosion wear rate magnitude of the simulation results.

[0087] In step 4 of this embodiment, based on the rheological parameters of the filling slurry and the structured grids, a control model is established. The Euler-Lagrange model is adopted to obtain fluid information and solid particle information, and boundary conditions and parameters are set according to the actual situation to facilitate subsequent fluent numerical simulation research. The control model includes the continuity equation, the momentum conservation equation, the solid particle force equation, the erosion wear equation, and the wall collision recovery equation, specifically including:

[0088] The filling slurry is usually regarded as an incompressible fluid and follows the continuity equation and momentum equation during the flow process, etc.

[0089] Continuity equation:

[0090]

[0091] In the formula: μ x 、μ y 、μ z are the velocity components of the fluid in the x, y, and z directions respectively, with the unit of m / s; x, y, and z are the x-axis, y-axis, and z-axis respectively;

[0092] Momentum conservation equation:

[0093]

[0094] In the formula: p is the pressure, with the unit of pa; τ ij is the component of the viscous stress generated on the unit microelement under the action of molecular viscosity, with the unit of pa; f i is the body force in the x, y, and z directions, with the unit of m / s; ρ w is the density of the continuous phase, with the unit of kg / m 3 ; u i is the velocity component of the continuous phase along the coordinate axis, with the unit of m / s; u j is the velocity component of the discrete phase along the coordinate axis, with the unit of m / s;

[0095] Discrete phase control equation:

[0096] The force of solid particles is in the Cartesian coordinate system. Therefore, the calculation formula for the solid particle force equation is:

[0097]

[0098] In the formula: t represents time, with the unit of s; ν represents the fluid phase velocity, with the unit of m / s; F D (ν - ν g ) represents the drag force per unit mass of the particle, with the unit of N; F represents other body forces, with the unit of N; g represents the acceleration due to gravity, with the unit of m / s 2 ; ρ g represents the particle density, with the unit of kg / m 3 ; ν g represents the velocity of the background fluid, with the unit of m / s; ρ represents the fluid density, with the unit of kg / m 3 ;

[0099] The calculation formula for the erosion wear equation is:

[0100]

[0101] In the formula: R e represents the wear rate, with the unit of kg / (m 2 ·s); u p represents the particle impact velocity, with the unit of m / s; b r represents the velocity exponential function; m p represents the mass flow rate of the particles, with the unit of kg / s; c(d p ) represents the particle diameter function; f(θ) represents the impact angle function; A face represents the unit surface area of the particle impacting the wall, with the unit of m 2 ; N P represents the total number of colliding particles; P represents the Pth colliding particle, which is determined according to the wall boundary conditions in the model. By default, c(d p ) = 1, f(θ) = 1, b r = 0;

[0102] The calculation formula for the wall collision recovery equation is:

[0103] e n = 0.993 - 1.76α + 1.56α 2 - 0.49α 3

[0104] e t = 0.988 - 1.66α + 2.11α 2 - 0.67α 3

[0105] In the formula: en and e t are the normal wall recovery coefficient and the tangential wall recovery coefficient respectively; α is the particle incident angle, with the unit of °.

[0106] In step 4 of this embodiment, the method for setting boundary conditions and parameters to carry out fluent numerical simulation research is specifically as follows:

[0107] Establish a basic calculation model and select the erosion wear model as the erosion wear equation.

[0108] In step 5 of this embodiment, use Deign-Expert software to design erosion wear schemes for different inlet flow rates, mass concentrations, and mass flow rates of the pipeline.

[0109] In step 6 of this embodiment, based on the pipeline erosion wear scheme and the fluid numerical simulation model, the method for obtaining the influence degree of different erosion wear single factors on the maximum wear rate and the influence of erosion wear interaction factors on pipeline erosion wear is specifically as follows:

[0110] Step 601: Use RM response surface to analyze the erosion wear rate response surface model, use Deign-Expert software to fit the regression model of the maximum wear rate and erosion wear parameters, optimize the regression model through variance analysis, factor interaction verification and significance analysis, and output the response surface analysis prediction model;

[0111] Step 602: According to fluid numerical simulation, obtain the erosion wear rates of the pipeline under the influence of different erosion wear factors, including single-factor erosion wear rates and pairwise interaction erosion wear rates;

[0112] Step 603: Substitute the obtained erosion wear rate of the pipeline into Deign-expert software, conduct multiple regression analysis on the test results, and combine the least squares method to obtain a second-order polynomial response surface model. The model results combined with the model variance analysis show good significance.

[0113] In this embodiment, RM-BBD is a design scheme in the response surface method, which is often used in optimization processes or experiments to find the best conditions or parameter settings. The advantage of the BBD design method is that it can effectively explore and model nonlinear relationships in experiments. Through step 2, rheological parameters are obtained, combined with the boundary conditions of the actual fluid, and research on the influence of different erosion wear factors on the pipeline wear rate is carried out. Combining with step 603, the response surface models of different erosion wear factors are obtained, and variance analysis is performed on the obtained erosion wear rate response surface model. In step 7, parameter optimization of different erosion wear factors is obtained in Deign-expert software. Specifically, set the inlet flow rate (X 1 ), mass concentration (X 2 ) and mass flow rate (X3 ) range, obtain the optimized inlet flow velocity (X according to the value of the maximum erosion wear rate 1 ), mass concentration (X 2 ), and mass flow rate (X 3 ). The value of the maximum erosion wear rate can be the maximum value, the minimum value, or other values.

[0114] In step 601 of this embodiment, it is also necessary to construct a data set. The construction method of the data set is as follows:

[0115] Step 6011: Set the initial values of the three influencing factors of the erosion wear factors inlet flow velocity (X 1 ), mass concentration (X 2 ), and mass flow rate (X 3 ).

[0116] Step 6012: Take range values for different erosion wear factors and conduct a three-factor three-level experiment;

[0117] Step 6013: Use deign software to obtain the experimental schemes of different erosion wear factors, with a total of multiple groups of experiments.

[0118] In step 603 of this embodiment, the maximum erosion wear rate is set as the dependent variable, with the unit of kg / (m 2 ). By inputting the data obtained from the simulation and performing least squares variance analysis, through optimization, the pvalue of the model < 0.0001, and a quadratic polynomial regression model is obtained. The quadratic polynomial regression model:

[0119]

[0120] Among them, a 1 , a 2 , a 3 , a 4 , a 5 , a 6 , a 7 , a 8 , a 9 , a 10 are all fitting parameters.

[0121] In step 603 of this embodiment, the least squares method is used to analyze different erosion wear factors. By selecting different factor terms, different analysis of variance results are obtained. In the analysis of variance, two parameters, p-value and F-value, are obtained, and then the significance is analyzed. When p-value < 0.05, it indicates a significant difference; when p-value < 0.01, it indicates an extremely significant difference. The significant effect of the factor is judged according to the p-value of the factor; F-value is the influence of each factor on the response value. The larger the F-value, the greater the influence of the single factor on the response value.

[0122] At present, the more commonly used experimental design methods are uniform design and orthogonal experimental design. Both of these design methods use linear mathematical models to fit data. Although the number of experiments in these two designs is small, the predictability of the experimental results is poor, and the influencing factors of the experiment cannot be comprehensively analyzed.

[0123] Therefore, the response surface method (Response surface methodology), also known as regression design, is a multivariate analysis method that analyzes the levels of influencing factors and their interactions by establishing a surface model of influencing factors and optimizes the experimental method.

[0124] Generally, the selection of the response surface function should meet two requirements: 1) The selected function should use as few undetermined coefficients as possible to minimize the workload of structural calculation; 2) The mathematical expression of the selected response surface function should not only reflect the characteristics of the true function but also be as concise and clear as possible. Therefore, a quadratic polynomial can meet the requirements of the experiment.

[0125] In the parameter optimization affected by multiple factors, multiple factors act on the objective function (quadratic polynomial) together. In the process of parameter optimization, sensitivity analysis can be used to identify which factors have the greatest influence on the objective function (which parameter has a large erosion wear rate). The interaction between multiple factors, that is, how the change of one factor affects the effect of another factor, and the ultimate goal is to obtain a regression equation with high accuracy.

[0126] In steps 6 and 7, the finally optimized erosion wear parameters are obtained from the RM test results. The Deign-Expert software is used to fit p with the quadratic polynomial regression model, and the fitting formula is optimized and iterated through analysis of variance, factor interaction verification and significance analysis, and finally the erosion wear parameters are obtained.

[0127] In summary, for the method for optimizing the parameters of the full-tailings filling slurry pipeline erosion model provided in this embodiment, the rheological parameters required for finite element simulation are obtained through rheological tests, and the pressure and flow velocity distribution nephograms of the slurry during pipeline transportation are obtained using the fluent simulation software. Through the response surface method (RSM), combined with the Box-Behnken design (BBD) method, considering the influence of the inlet flow velocity (X 1 ), mass concentration (X 2 ), and mass flow rate (X 3 ), the research on the erosion and wear characteristics of the full-tailings cemented filling slurry pipeline under different erosion and wear factors is carried out. The pipeline wear rate data under different erosion and wear factors are obtained through simulation. After optimizing the fitting formula through variance analysis, factor interaction verification, and significance analysis, the optimized erosion and wear parameters are output, which can extend the pipeline life and reduce the maintenance cost.

[0128] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0129] Taking the model with a simple Figure 1 model length of 4m, model width of 2m, pipe diameter of 120mm, curvature radius of 240mm, and the model being a 90° right-angle elbow as an example, as Figure 1 shown, the specific implementation steps are as follows;

[0130] Use workbench to construct a 90° right-angle elbow model with a model length of 4m, model width of 2m, pipe diameter of 120mm, and curvature radius of 240mm. On this basis, use the finite element simulation software ANASYS to import the model, assign material properties, and perform mesh division. When dividing the mesh, locally encrypt the mesh at the elbow to ensure the accuracy of the simulation results, as Figure 2 shown.

[0131] Select different erosion and wear factors, namely the inlet flow velocity (X 1 ), mass concentration (X 2 ), and mass flow rate (X 3 ), and use Design Expert software to design the pipeline erosion and wear schemes for different erosion and wear factors, as shown in Table 1.

[0132] Table 1 Pipeline erosion and wear schemes for different erosion and wear factors

[0133]

[0134] The response surface analysis erosion wear rate response surface model is obtained, and the least squares method analysis is carried out on different erosion wear factors. According to the selection of different factor items, different variance analysis results are obtained. In the variance analysis, two parameters, p-value and F-value, are obtained, and then the significance is analyzed. When p-value < 0.05, it indicates a significant difference, and when p-value < 0.01, it indicates an extremely significant difference. The significant role of the factor is judged according to the p-value of the factor; F-value is the influence of each factor on the response value, and the larger the F-value, the greater the influence of the single factor on the response value.

[0135] The maximum erosion wear rate is set as the dependent variable, with the unit of kg / (m 2 ·s). Through the input of the data obtained from the simulation, the least squares method variance analysis is carried out. Through optimization, the pvalue of the model < 0.0001, and a quadratic polynomial regression model is obtained. The quadratic polynomial regression model:

[0136]

[0137] When the accuracy does not meet the standard, different erosion wear factors are re-divided and analyzed again until the accuracy meets the standard, and then the final RSM response surface analysis prediction model is output. The response surface model and contour cloud map of the pairwise interaction on the maximum erosion wear rate are obtained as Figure 4(a) - Figure 4(f) shown. Furthermore, the parameter values of different erosion wear factors are optimized through DesignExpert.

[0138] Example 2

[0139] Example 2 provides an optimization device for the parameters of the full-tailings filling slurry pipeline erosion model, as Figure 5 shown, including:

[0140] The filling slurry acquisition module is used to mix the full tailings with the cementitious material to obtain the filling slurry based on the coarse particle size distribution and mineral content of the full tailings;

[0141] The rheological parameter acquisition module is used to determine the rheological model of the filling slurry and obtain the rheological parameters of the filling slurry;

[0142] The mesh generation module is used to generate structured meshes for the pipeline to be filled;

[0143] The model establishment module is used to establish a control model based on the rheological parameters of the filling slurry and the structured mesh. The control model includes the continuity equation, the momentum conservation equation, the solid particle force equation, the erosion wear equation, and the wall collision recovery equation, and set boundary conditions and parameters to carry out fluent numerical simulation research;

[0144] A scheme establishment module, configured to set the erosion wear factors as the inlet flow velocity, mass concentration, and mass flow rate, and establish a pipeline erosion wear scheme for different erosion wear factors;

[0145] A wear influence acquisition module, configured to obtain the influence degree of different single erosion wear factors on the maximum wear rate and the influence of erosion wear interaction factors on pipeline erosion wear based on the pipeline erosion wear scheme and the control model;

[0146] An optimization module, configured to obtain the RM-BBD test results according to the influence of single factors or interaction factors on pipeline erosion wear, and obtain the optimized inlet flow velocity, mass concentration, and mass flow rate.

[0147] Embodiment 3

[0148] Embodiment 3 provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above-mentioned method for optimizing the parameters of the full-tailings filling slurry pipeline erosion model is implemented.

[0149] Embodiment 4

[0150] Embodiment 4 provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method for optimizing the parameters of the full-tailings filling slurry pipeline erosion model is implemented.

[0151] The memory in the embodiments of the present invention is used to store various types of data to support the operation of the electronic device. Examples of such data include: any computer program for operating on the electronic device.

[0152] The parameter optimization of the full-tailings filling slurry pipeline erosion model disclosed in the embodiments of the present invention can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the parameter optimization of the full-tailings filling slurry pipeline erosion model can be completed by the integrated logic circuit of the hardware in the processor or the instructions in the form of software. The aforementioned processor may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor, etc. Combining the steps of the method disclosed in the embodiments of the present invention can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor. The software module may be located in a storage medium, and this storage medium is located in the memory. The processor reads the information in the memory and combines its hardware to complete the steps of the parameter optimization of the full-tailings filling slurry pipeline erosion model provided in the embodiments of the present invention.

[0153] In an exemplary embodiment, the electronic device may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), FPGAs, general-purpose processors, controllers, microcontroller units (MCUs), microprocessors, or other electronic components for performing the foregoing method.

[0154] It can be understood that the memory can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM, Read Only Memory), a programmable read-only memory (PROM, Programmable Read-Only Memory), an erasable programmable read-only memory (EPROM, Erasable Programmable Read-Only Memory), an electrically erasable programmable read-only memory (EEPROM, Electrically Erasable Programmable Read-Only Memory), a ferromagnetic random access memory (FRAM, ferromagnetic random access memory), a flash memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM, Compact Disc Read-Only Memory); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM, RandomAccessMemory), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as a static random access memory (SRAM, Static Random Access Memory), a synchronous static random access memory (SSRAM, Synchronous Static Random Access Memory), a dynamic random access memory (DRAM, Dynamic Random Access Memory), a synchronous dynamic random access memory (SDRAM, SynchronousDynamic Random Access Memory), a double data rate synchronous dynamic random access memory (DDRSDRAM, Double Data Rate Synchronous Dynamic Random Access Memory), an enhanced synchronous dynamic random access memory (ESDRAM, Enhanced Synchronous Dynamic Random Access Memory), a synchronous link dynamic random access memory (SLDRAM, Sync Link Dynamic Random Access Memory), a direct rambus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present invention are intended to include but are not limited to these and any other suitable types of memories.

[0155] The above embodiments are merely illustrative examples of the technical solutions of the present invention. The methods involved in the present invention are not limited solely to the content described in the above embodiments, but are subject to the scope defined by the claims. Any modifications, supplements, or equivalent replacements made by those skilled in the art to which the present invention pertains based on this embodiment are within the scope protected by the claims of the present invention.

Claims

1. A method for optimizing the parameters of a full tailings-filled slurry pipeline erosion model, characterized in that: include: Based on the coarse particle size distribution and mineral content of the whole tailings, the whole tailings are mixed with a cementing material to obtain a filling slurry; Determine the rheological model of the filling slurry and obtain the rheological parameters of the filling slurry; Divide the pipeline to be filled into a structured grid; Based on the rheological parameters of the filling slurry and the structured grid, a control model is established. The control model includes the continuity equation, momentum conservation equation, solid particle force equation, erosion wear equation and wall collision recovery equation. The boundary conditions and parameters are set to carry out the FLUENT numerical simulation study. The erosion wear factors are set as inlet flow velocity, mass concentration, and mass flow rate, and the erosion wear scheme for pipelines with different erosion wear factors is established; Based on the pipeline erosion wear scheme and control model, the influence of different erosion wear single factors on the maximum wear rate and the influence of erosion wear interaction factors on pipeline erosion wear are obtained; According to the influence of single factors or interactive factors on pipeline erosion and wear, the RM-BBD test results are obtained to obtain the optimized inlet flow velocity, mass concentration, and mass flow rate.

2. The method for optimizing the erosion model parameters of the full tailings filling slurry pipeline according to claim 1 is characterized in that: The method to determine the rheological model of filling slurry is: One end of the pipeline is set as the velocity inlet and the other end is set as the pressure outlet. The calculation formula of the filling slurry rheological model is: Where: G k is the generation of turbulent kinetic energy caused by the average velocity; G b It is the generation of turbulent kinetic energy caused by buoyancy; S k , S ε is a custom parameter; C iε is the empirical coefficient, i = 1, 2, 3, which can be obtained from basic turbulence experiments; ρ is the density of the fluid; g is the acceleration of gravity; k is the kinetic energy in turbulence; u j is the velocity component of the discrete phase on the coordinate axis; x i and x j are the coordinates of the fluid in the i and j directions, i, j = 1, 2, 3, representing the x-axis, y-axis, and z-axis, respectively, i≠j; μ is the dynamic viscosity coefficient; μ t is the turbulent viscosity coefficient; ε is the local density, usually used in inhomogeneous fluids; μ i is the equivalent dynamic viscosity of the effect of turbulence on viscosity; is the turbulent Prandtl number of the turbulent dissipation rate, indicating the turbulent diffusion characteristics; σ k , σ ε is an empirical constant; t is time.

3. The method for optimizing the erosion model parameters of the full tailings filling slurry pipeline according to claim 1 is characterized in that: The calculation formula of the solid particle force equation is: Where: t represents time; ν represents fluid phase velocity; F D (ν-ν g ) represents the drag force per unit mass of the particle; F represents other mass forces; g represents the gravitational acceleration; ρ g represents the particle density; ν g represents the velocity of the background fluid; ρ represents the fluid density; The calculation formula of the erosion wear equation is: Where: R e Indicates the wear rate; u p represents the particle impact velocity; b r represents the speed exponential function; m p represents the mass flow rate of particles; c(d p ) represents the particle diameter function; f(θ) represents the impact angle function; A face represents the unit surface area of ​​the particle impacting the wall; N P represents the total number of collision particles; P represents the Pth collision particle; The calculation formula of the wall impact recovery equation is: e n ＝0.993-1.76α+1.56α 2 -0.49α 3 e t ＝0.988-1.66α+2.11α 2 -0.67α 3 Among them, e n 、e t are the normal wall restitution coefficient and the tangential wall restitution coefficient respectively; α is the particle incident angle.

4. The method for optimizing the erosion model parameters of the full tailings filling slurry pipeline according to claim 1 is characterized in that: Deign-Expert software was used to design pipeline erosion and wear solutions with different inlet flow rates, mass concentrations, and mass flow rates.

5. The method for optimizing the erosion model parameters of the full tailings filling slurry pipeline according to claim 1 is characterized in that: Based on the pipeline erosion wear scheme and control model, the method for obtaining the influence of different erosion wear single factors on the maximum wear rate and the influence of erosion wear interaction factors on pipeline erosion wear is as follows: The RM response surface analysis was used to analyze the erosion wear rate response surface model, and the regression model of the maximum wear rate and erosion wear parameters was fitted. The regression model was optimized through variance analysis, factor interaction verification and significance analysis, and the response surface analysis prediction model was output. According to the fluid numerical simulation, the erosion wear rate of the pipeline under the influence of different erosion wear factors is obtained, including the single factor erosion wear rate and the two-to-two interactive erosion wear rate; The obtained erosion pipe wear rate was used to perform multivariate regression analysis on the test results, and a second-order polynomial response surface model was obtained by combining the least squares method.

6. The method for optimizing the erosion model parameters of the full tailings filling slurry pipeline according to claim 5 is characterized in that: You also need to build a data set. The method for building a data set is: Set the initial values ​​of the three influencing factors of erosion wear, namely, inlet flow velocity, mass concentration and mass flow rate; The range of values ​​for different erosion wear factors was selected, and a three-factor three-level test was carried out; The test schemes for different erosion and wear factors were obtained using deign software.

7. The method for optimizing the erosion model parameters of the full tailings filling slurry pipeline according to claim 6 is characterized in that: The quadratic multinomial regression model is: Among them, a1, a2, a3, a4, a5, a6, a7, a8, a9, a 10 All are fitting parameters, X1 is the inlet flow velocity, X2 is the mass concentration, and X3 is the mass flow rate.

8. A device for optimizing the erosion model parameters of a full tailings-filled slurry pipeline, characterized in that: include: A filling slurry acquisition module, used for mixing the whole tailings with a cementing material to obtain a filling slurry based on the coarse particle size distribution and mineral content of the whole tailings; A rheological parameter acquisition module is used to determine the rheological model of the filling slurry and obtain the rheological parameters of the filling slurry; A grid division module is used to divide the pipeline to be filled into structured grids; Model building module, which is used to build control models based on rheological parameters of filling slurry and structured grids. The control models include continuity equation, momentum conservation equation, solid particle force equation, erosion wear equation and wall collision recovery equation, and set boundary conditions and parameters to carry out FLUENT numerical simulation research; The scheme establishment module is used to set the erosion wear factors as inlet flow rate, mass concentration, and mass flow rate, and establish the erosion wear scheme for pipelines with different erosion wear factors; Wear influence acquisition module, used to obtain the influence of different erosion wear single factors on the maximum wear rate and the influence of erosion wear interaction factors on pipeline erosion wear based on pipeline erosion wear scheme and control model; The optimization module is used to obtain the RM-BBD test results and the optimized inlet flow velocity, mass concentration, and mass flow rate according to the influence of single factors or interactive factors on pipeline erosion and wear.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the full tailings filling slurry pipeline erosion model parameter optimization method as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing the parameters of the full tailings filling slurry pipeline erosion model as described in any one of claims 1 to 7 is implemented.

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