Step-by-step optimization method for variable section parameters of spiral chute

Abstract

The invention provides a step-by-step optimization method for variable section parameters of a spiral chute, and particularly relates to the technical field of spiral chute optimization design. The method comprises the following steps: constructing a spiral chute Euler Mti-fluid VOF model, combining a Kriging interpolation method with an optimization algorithm, selecting a total lower oblique angle as an optimization parameter and sorting efficiency as an optimization target, and optimizing a first optimal total lower oblique angle; the local lower oblique angle and the progressive turn number are optimization parameters, the sorting efficiency is an optimization target, a second optimal local lower oblique angle and an optimal optimization progressive turn number are optimized, and an initial optimization section shape is determined; setting a flow field monitoring section, and determining an initial optimization section position; and gradually optimizing the variable section parameters of the spiral chute according to the initial optimization section position, the first optimal overall lower oblique angle, the second optimal local lower oblique angle and the optimal optimization gradual turn number optimization local lower oblique angle. The separation efficiency of the spiral chute can be improved, and efficient separation of minerals is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of spiral chute optimization design, and in particular to a method for gradually optimizing variable-section parameters of a spiral chute. Background Art

[0002] The spiral chute is a device that uses the physical properties of the minerals, gravity, centrifugal force, etc. to separate minerals. It has many advantages, such as large processing capacity, low energy consumption, no pollution, high separation efficiency, and wide separation range. It is widely used in the field of mineral processing. However, the spiral chute currently has problems such as low concentrate yield, large loss of fine minerals, and low separation accuracy, which largely limit its large-scale application.

[0003] The structural characteristics of the spiral chute have an important influence on the mineral sorting effect. Among them, the cross-sectional shape of the spiral chute is the key to determining the sorting results of minerals with different particle sizes. Nowadays, the cross-sectional shape curve generally adopts the form of a cubic parabola, and the lower oblique angle is an important parameter that determines the change trend of the cubic parabola. A larger lower oblique angle is conducive to the inward aggregation of minerals with larger specific gravity, and reducing the lower oblique angle can increase the tendency of minerals with smaller specific gravity to move to the outer edge. The lower oblique angle of the traditional spiral chute is usually kept consistent as a whole, so it is difficult to take into account the sorting efficiency of both heavier and lighter minerals during sorting. Nowadays, in the optimization design of spiral chutes, there are few optimization methods for cross-sectional shapes, such as changing the curve function expression, using curve segmentation optimization strategies, etc., but these methods have low universality, and the sorting effect is not ideal, and there is currently little research on variable cross-sectional optimization methods. Therefore, developing a method for gradually optimizing the variable cross-section parameters of a spiral chute, gradually optimizing the lower inclination angle and the overall lower inclination angle of the spiral chute, and reasonably determining the position of the variable cross-section have important theoretical research value and practical engineering application significance for improving the sorting efficiency of the spiral chute and realizing efficient mineral sorting.

[0004] At present, optimization algorithms based on deep learning have begun to be applied to the optimization design of spiral chutes, and optimization calculations can be performed under the condition of small samples. However, some optimization algorithms have problems of slow convergence speed and high computational complexity when facing nonlinear systems or systems with complex interactions, and applications in this regard are not yet mature enough. At the same time, when verifying the results of the optimization algorithm, numerical simulation calculation methods are usually used. Although the results obtained in this way have high accuracy, the cost is high and the calculation requires a lot of resources, which does not have good universality. The above information disclosed in the background technology section is only used to enhance the understanding of the background of the present disclosure, so it may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0005] The object of the present invention is to provide a method for gradually optimizing the variable cross-section parameters of a spiral chute to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for gradually optimizing the variable cross-section parameters of a spiral chute, the specific steps comprising: Step 1: Use the Eulerian Muti-fluid VOF model to perform numerical calculations of multiphase flow in spiral chute, and optimize the Eulerian Muti-fluid VOF model through model tests; Step 2: Take the overall down-angle as the first optimization parameter, select the separation efficiency of the heavier mineral as the first optimization target, design the optimization target threshold according to the design efficiency, and select Different overall lower oblique angles are used as initial samples to construct the initial sample plan; Step 3: Substitute the overall down-angle in the initial sample scheme into the Eulerian Muti-fluid VOF model, obtain the mass flow data under the initial different sample schemes, calculate the first optimization target value according to the mass flow data, and generate an initial data set based on the overall down-angle and the separation efficiency of the heavier mineral among the two minerals; Step 4: Set constraints, analyze the initial data set based on the Kriging interpolation method, establish an approximate functional relationship between the first optimization parameter and the first optimization target, and establish the overall down-slope angle variation range based on the approximate functional relationship between the first optimization parameter and the first optimization target and in combination with the optimization target threshold; Step 5: Constrain the feasible solution space with the overall down-slope angle variation range, and use the particle swarm optimization algorithm to search in the feasible solution space to obtain the first optimal overall down-slope angle; Step 6: Setup A spiral chute flow field monitoring section is used, and the Eulerian Muti-fluid VOF model is used to simulate the sorting data. The optimization interface judgment coefficient is generated according to the sorting data, and the starting optimization section position is determined by the optimization interface judgment coefficient. Step 7: Select the local downward bevel angle and the number of progressive turns as the second optimization parameters, select the sorting efficiency of the lighter mineral among the two minerals as the second optimization target, repeat steps 2 to 5 to obtain the second optimal local downward bevel angle and the optimal optimized number of progressive turns, and optimize the local downward bevel angle based on the starting optimized section position, the first optimal overall downward bevel angle, the second optimal local downward bevel angle and the optimal optimized number of progressive turns.

[0007] Furthermore, in step 1, the numerical calculation uses the simulation results of the spiral chute gas-liquid two-phase flow as the initial flow field, and the gas-liquid-solid three-phase flow simulation is performed after the calculation is stable. The multiphase flow model selected in the numerical calculation is the Eulerian Muti-fluid VOF model; the turbulence model is the RNG k-ε turbulence model; the inlet boundary condition adopts the velocity inlet, and the outlet boundary adopts the pressure outlet; the lower wall and the side wall are no-slip walls, and the upper surface is the free liquid surface. At the same time, the inlet velocity of the mineral phase is the same as that of water. The stability of the calculation is determined by the residual of each calculation of the model. If , then the calculation is considered stable, where is the residual error at each calculation of the model.

[0008] Furthermore, the optimization target threshold is ,in, Optimize target threshold, For design efficiency; Further, the mass flow data includes the mass flow of the two minerals in the concentrate zone at the spiral chute outlet, the total mass flow of the two minerals at the spiral chute outlet, and the mass flow of the two minerals in the tailings zone at the spiral chute outlet; The specific formula for calculating the sorting efficiency is: ; ; in, is the separation efficiency of the heavier mineral among the two minerals, is the separation efficiency of the lighter mineral of the two minerals; At the outlet of the spiral chute, the total mass flow rate of the heavier mineral of the two minerals, At the outlet of the spiral chute, the total mass flow rate of the lighter mineral of the two minerals, is the mass flow rate of the heavier mineral among the two minerals in the concentrate zone at the outlet of the spiral chute. It is the mass flow rate of the lighter mineral in the concentrate zone at the outlet of the spiral chute. is the mass flow rate of the heavier mineral among the two minerals in the tailings zone at the outlet of the spiral chute, It is the mass flow rate of the lighter mineral among the two minerals in the tailings zone at the outlet of the spiral chute; The initial data set is represented as: ,in, For the The overall downward slope of the group sample, The overall downward slope is The sorting efficiency of the sample.

[0009] Furthermore, the constraints include unbiasedness constraints and optimality constraints, wherein the unbiasedness constraints are that the expected predicted value is equal to the expected true value, and the optimality constraints are that the predicted variance is minimized; Based on the Kriging interpolation method, the specific logic for establishing the approximate functional relationship between the first optimization parameter and the first optimization target is as follows: randomly select The samples constitute a random data set, construct a mathematical expression for predicting the sorting efficiency, construct a covariance function, calculate the predicted variance through the covariance function, minimize the predicted variance under the unbiased constraint, construct a weight calculation function, and construct a complete Kriging equation group based on the weight calculation function. The Kriging equation group is solved to obtain the sorting efficiency weight, and the predicted sorting efficiency is calculated based on the sorting efficiency weight and the mathematical expression of the predicted sorting efficiency. The corresponding predicted sorting efficiency is calculated for each value within the range of the overall lower oblique angle, and the overall lower oblique angle-predicted sorting efficiency image is drawn to obtain the image. and The corresponding overall lower bevel angle, the overall lower bevel angle Surrounded by: ,in, is the overall downward bevel angle, for and The smaller overall downslope angle among the corresponding overall downslope angles, for and the larger overall downslope angle among the corresponding overall downslope angles; For heavy minerals, the overall downward angle increases, and they are more likely to concentrate on the inside of the chute, which will lead to an enhanced enrichment effect of heavy minerals in the concentrate zone and improve the sorting efficiency; for light minerals, the overall downward angle decreases, and light minerals are more likely to move with the water flow, and the movement toward the outer edge increases, which may enhance the separation effect and improve the overall sorting efficiency; The predicted sorting efficiency is expressed as: ; The overall downward slope is The predicted sorting efficiency, is the overall downslope angle for which the sorting efficiency needs to be predicted, For the The overall downward slope of the samples is is the sorting efficiency weight, and , is the number of samples in the random data set, is the index of a sample from a random dataset, and ; The covariance function is expressed as: ; is the covariance function, For the random data set The overall downward slope of the samples is For the random data set The overall downward slope of the samples is and is the index of the sample of the random data set, , and are model parameters, which can be obtained by fitting the variation experimental function; Get , and The specific logic is as follows: first construct a mutation experiment function, input the samples in the initial data set into the mutation experiment function, obtain a large amount of fitting data, form a fitting data set, select an exponential covariance fitting function, fit the fitting data set to the exponential covariance fitting function, and obtain model parameters; The mutation experiment function is expressed as: ; in, is the value of the variation experiment function, is the reference spacing distance, is the interval distance in the initial sample set The number of point pairs, The first The overall downward slope of the samples is The first The overall downward slope of the sample and is the index of the sample in the initial sample set; The exponential covariance fitting function is expressed as: ; The specific formula used to calculate the forecast variance is: ; in, is the prediction variance; Minimize the prediction variance under the unbiased constraint, and the constructed weight calculation function is expressed as: ; in, is the weight calculation function, is the Lagrange multiplier; The complete Kriging equations constructed based on the weight calculation function are expressed as: .

[0010] Furthermore, setting The setting method is as follows: use the RNG k-ε turbulence model in the Eulerian Multi-fluid VOF model in step 1 to monitor the flow field of the spiral chute at intervals along the flow direction. Set a longitudinal section, the section range is the range of the concentrate zone, ; The sorting data includes the mass flow rate of each spiral chute flow field monitoring section and the mass flow rate of the concentrate belt at the spiral chute outlet, the average inlet velocity of each spiral chute flow field monitoring section, the average outlet velocity of each spiral chute flow field monitoring section, the inlet turbulent kinetic energy of each spiral chute flow field monitoring section and the outlet turbulent kinetic energy of each spiral chute flow field monitoring section; Using the Eulerian Muti-fluid VOF model, the sorting data of each spiral chute flow field monitoring section is simulated starting from the spiral chute inlet direction; the sorting data is analyzed to obtain the optimization interface judgment coefficient, and the optimization interface judgment threshold is preset. When the optimization interface judgment coefficient is less than or equal to the optimization interface judgment threshold, the position at this time is recorded as the optimization section position. If there are multiple positions that meet the above requirements, the first optimization section position is set as the starting optimization section position of the spiral chute; The specific formula for calculating the optimization interface judgment coefficient is: ; in, To optimize the interface judgment coefficient, For the The mass flow rate of the spiral chute flow field monitoring section, is the mass flow rate of the concentrate belt at the spiral chute outlet, For the The average inlet velocity of the spiral chute flow field monitoring section, For the The average outlet velocity of the spiral chute flow field monitoring section, For the The inlet turbulent kinetic energy of the spiral chute flow field monitoring section, For the The outlet turbulent kinetic energy of the monitoring section of the spiral chute flow field, The index of the monitoring section of the spiral chute flow field.

[0011] Furthermore, the specific formula for optimizing the local lower bevel angle is: ; ; in, To optimize the local down-slope angle, is the first optimal overall downslope angle, is the second optimal local downward slope angle; the progressive circles inside the spiral chute are numbered in sequence along the flow direction. is the index of the progressive cycle, and , is the total number of progressive circles inside the spiral chute, is the index of the progressive circle at the starting optimization section position, To optimize the index of the progressive circle at the end section position, Optimize the number of progressive turns for best performance.

[0012] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for gradually optimizing the variable cross-section parameters of a spiral chute. By constructing a numerical simulation calculation model for multiphase flow of a spiral chute, the downward angle of different areas of the spiral chute is gradually optimized, while taking into account the sorting efficiency of two minerals with heavier and lighter specific gravity, providing a new idea and method for the optimization design of the spiral chute structure. In addition, the present invention combines the advantages of the Kriging interpolation model and the optimization algorithm, constructs an approximate response surface of the objective function through a small number of initial samples, replaces the time-consuming numerical simulation calculation, and then uses the algorithm for iterative optimization, saving more than 90% of computing resources. At the same time, the Kriging model also provides a prediction variance, and the optimization algorithm can design a fitness function based on it, automatically balancing "developing known optimal areas" and "exploring potential new areas". It saves optimization time and numerical calculation verification costs, while improving the sorting efficiency of the spiral chute, and realizing efficient sorting of minerals. It has important theoretical research value and engineering practical application significance for improving the performance of the spiral chute, and lays a foundation for the development of intelligent and automated spiral chutes. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a schematic diagram of the flow monitoring section of the spiral chute of the present invention; Figure 3 It is a schematic diagram of the overall downward inclination angle, local downward inclination angle and asymptotic number of turns of the spiral chute of the present invention; Description of reference numerals: 1. lower wall surface, 2. side wall surface, 3. flow field monitoring section, , the number of progressive circles from the first spiral chute flow field monitoring section to the starting optimized section position, , the number of progressive turns from the first spiral chute flow field monitoring section to the optimization end position. DETAILED DESCRIPTION

[0014] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0015] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0016] Example: See also Figure 1 , the present invention provides a technical solution: A method for gradually optimizing the variable cross-section parameters of a spiral chute, the specific steps comprising: Step 1: Use the Eulerian Muti-fluid VOF model to perform numerical calculations of multiphase flow in spiral chute, and optimize the Eulerian Muti-fluid VOF model through model tests; In step 1, the numerical calculation uses the simulation results of the spiral chute gas-liquid two-phase flow as the initial flow field, and the gas-liquid-solid three-phase flow simulation is performed after the calculation is stable. The multiphase flow model selected in the numerical calculation is the Eulerian Muti-fluid VOF model; the turbulence model is the RNG k-ε turbulence model; the inlet boundary condition uses the velocity inlet, and the outlet boundary uses the pressure outlet; the lower wall and side wall are no-slip walls, and the upper surface is the free liquid surface. At the same time, the inlet velocity of the mineral phase is the same as that of water. The stability of the calculation is determined by the residual of each calculation of the model. If , then the calculation is considered stable, where is the residual error at each calculation of the model.

[0017] The Eulerian Multi-fluid VOF model is optimized through model tests to reflect the multiphase flow field in the spiral chute with higher accuracy. Step 2: Take the overall down-angle as the first optimization parameter, select the separation efficiency of the heavier mineral as the first optimization target, design the optimization target threshold according to the design efficiency, and select Different overall lower oblique angles are used as initial samples to construct the initial sample plan; The optimization target threshold is ,in, Optimize target threshold, It is the design efficiency; the design efficiency is set with reference to industry standards.

[0018] Step 3: Substitute the overall down-angle in the initial sample scheme into the Eulerian Muti-fluid VOF model, obtain the mass flow data under the initial different sample schemes, calculate the first optimization target value according to the mass flow data, and generate an initial data set based on the overall down-angle and the separation efficiency of the heavier mineral among the two minerals; The mass flow data include the mass flow of the two minerals in the concentrate zone at the spiral chute outlet, the total mass flow of the two minerals at the spiral chute outlet, and the mass flow of the two minerals in the tailings zone at the spiral chute outlet; The specific formula for calculating the sorting efficiency is: ; ; in, is the separation efficiency of the heavier mineral among the two minerals, is the separation efficiency of the lighter mineral of the two minerals; At the outlet of the spiral chute, the total mass flow rate of the heavier mineral of the two minerals, At the outlet of the spiral chute, the total mass flow rate of the lighter mineral of the two minerals, is the mass flow rate of the heavier mineral among the two minerals in the concentrate zone at the outlet of the spiral chute. It is the mass flow rate of the lighter mineral in the concentrate zone at the outlet of the spiral chute. is the mass flow rate of the heavier mineral among the two minerals in the tailings zone at the outlet of the spiral chute, It is the mass flow rate of the lighter mineral among the two minerals in the tailings zone at the outlet of the spiral chute; The initial data set is represented as: ,in, For the The overall downward slope of the group sample, The overall downward slope is The sorting efficiency of the sample.

[0019] Step 4: Set constraints, analyze the initial data set based on the Kriging interpolation method, establish an approximate functional relationship between the first optimization parameter and the first optimization target, and establish the overall down-slope angle variation range based on the approximate functional relationship between the first optimization parameter and the first optimization target and in combination with the optimization target threshold; The constraints include unbiasedness constraints and optimality constraints, wherein the unbiasedness constraints are that the expected predicted value is equal to the expected true value, and the optimality constraints are that the predicted variance is minimized; Based on the Kriging interpolation method, the specific logic for establishing the approximate functional relationship between the first optimization parameter and the first optimization target is as follows: randomly select The samples constitute a random data set, construct a mathematical expression for predicting the sorting efficiency, construct a covariance function, calculate the predicted variance through the covariance function, minimize the predicted variance under the unbiased constraint, construct a weight calculation function, and construct a complete Kriging equation group based on the weight calculation function. The Kriging equation group is solved to obtain the sorting efficiency weight, and the predicted sorting efficiency is calculated based on the sorting efficiency weight and the mathematical expression of the predicted sorting efficiency. The corresponding predicted sorting efficiency is calculated for each value within the range of the overall lower oblique angle, and the overall lower oblique angle-predicted sorting efficiency image is drawn to obtain the image. and The corresponding overall lower bevel angle, the overall lower bevel angle Surrounded by: ,in, is the overall downward bevel angle, for and The smaller overall downslope angle among the corresponding overall downslope angles, for and the larger overall downslope angle among the corresponding overall downslope angles; For heavy minerals, the overall downward angle increases, and they are more likely to concentrate on the inside of the chute, which will lead to an enhanced enrichment effect of heavy minerals in the concentrate zone and improve the sorting efficiency; for light minerals, the overall downward angle decreases, and light minerals are more likely to move with the water flow, and the movement toward the outer edge increases, which may enhance the separation effect and improve the overall sorting efficiency.

[0020] The predicted sorting efficiency is expressed as: ; The overall downward slope is The predicted sorting efficiency, is the overall downslope angle for which the sorting efficiency needs to be predicted, For the The overall downward slope of the samples is is the sorting efficiency weight, and , is the number of samples in the random data set, is the index of a sample from a random dataset, and ; The covariance function is expressed as: ; is the covariance function, For the random data set The overall downward slope of the samples is For the random data set The overall downward slope of the samples is and is the index of the sample of the random data set, , and are model parameters, which can be obtained by fitting the variation experimental function; Get , and The specific logic is as follows: first construct a mutation experiment function, input the samples in the initial data set into the mutation experiment function, obtain a large amount of fitting data, form a fitting data set, select an exponential covariance fitting function, fit the fitting data set to the exponential covariance fitting function, and obtain model parameters; The mutation experiment function is expressed as: ; in, is the value of the variation experiment function, is the reference spacing distance, is the interval distance in the initial sample set The number of point pairs, The first The overall downward slope of the samples is The first The overall downward slope of the sample and is the index of the sample in the initial sample set; The exponential covariance fitting function is expressed as: ; The specific formula used to calculate the forecast variance is: ; in, is the prediction variance; Minimize the prediction variance under the unbiased constraint, and the constructed weight calculation function is expressed as: ; in, is the weight calculation function, is the Lagrange multiplier; The complete Kriging equations constructed based on the weight calculation function are expressed as: .

[0021] Step 5: Constrain the feasible solution space with the overall down-slope angle variation range, and use the particle swarm optimization algorithm to search in the feasible solution space to obtain the first optimal overall down-slope angle; In the initial data set, each group of optimization parameters that meets the overall down-slope variation range is taken as an individual to form a particle swarm; the particle swarm is initialized, and each particle has an initial position and speed; the fitness value of each particle on the objective function is calculated, that is, the performance under multiple objectives, and the individual optimal position and individual optimal fitness of the particle are updated; the particles are sorted according to the dominance relationship to screen out non-dominated solutions. The individual optimal solution and the global optimal solution of each particle are determined, and the speed of each particle is updated, and the position of each particle is updated according to the updated speed; the fitness evaluation, non-dominated solution selection and speed and position update are repeated until the maximum number of iterations is reached or the fitness is no longer significantly improved, and the solution set with the largest sorting efficiency is selected as the final solution in the output non-dominated sorting, and the overall down-slope of this group of final solutions is the first optimal overall down-slope, where the fitness is the overall down-slope and the sorting efficiency.

[0022] Step 6: Setup A spiral chute flow field monitoring section is used, and the Eulerian Muti-fluid VOF model is used to simulate the sorting data. The optimization interface judgment coefficient is generated according to the sorting data, and the starting optimization section position is determined by the optimization interface judgment coefficient. set up The setting method is as follows: use the RNG k-ε turbulence model in the Eulerian Muti-fluid VOF model in step 1 to monitor the flow field of the spiral chute at intervals along the flow direction. Set a longitudinal section, the section range is the range of the concentrate zone, ; The sorting data includes the mass flow rate of each spiral chute flow field monitoring section and the mass flow rate of the concentrate belt at the spiral chute outlet, the average inlet velocity of each spiral chute flow field monitoring section, the average outlet velocity of each spiral chute flow field monitoring section, the inlet turbulent kinetic energy of each spiral chute flow field monitoring section and the outlet turbulent kinetic energy of each spiral chute flow field monitoring section; Using the Eulerian Muti-fluid VOF model, the sorting data of each spiral chute flow field monitoring section is simulated starting from the spiral chute inlet direction; the sorting data is analyzed to obtain the optimization interface judgment coefficient, and the optimization interface judgment threshold is preset. When the optimization interface judgment coefficient is less than or equal to the optimization interface judgment threshold, the position at this time is recorded as the optimization section position. If there are multiple positions that meet the above requirements, the first optimization section position is set as the starting optimization section position of the spiral chute; The specific formula for calculating the optimization interface judgment coefficient is: ; in, To optimize the interface judgment coefficient, For the The mass flow rate of the spiral chute flow field monitoring section, is the mass flow rate of the concentrate belt at the spiral chute outlet, For the The average inlet velocity of the spiral chute flow field monitoring section, For the The average outlet velocity of the spiral chute flow field monitoring section, For the The inlet turbulent kinetic energy of the spiral chute flow field monitoring section, For the The outlet turbulent kinetic energy of the monitoring section of the spiral chute flow field, The index of the monitoring section of the spiral chute flow field.

[0023] It is the overall mass flow deviation, reflecting the relative deviation between the mineral flow at the inlet section of each spiral chute flow field monitoring section and the mineral flow at the final outlet concentrate zone. The larger the value, the more unstable the mineral flow is, and there may be particle loss or deposition in the middle, resulting in lower sorting efficiency. It is the flow velocity deviation, which reflects the average deviation of the flow velocity changes at the inlet and outlet of each spiral chute flow field monitoring section. The larger the value, the more uneven the flow velocity, which will cause the movement trajectory of the mineral in the chute to be unstable and reduce the sorting accuracy. It is the turbulent kinetic energy deviation, which reflects the difference in turbulence intensity between the inlet and outlet of each spiral chute flow field monitoring section along the flow direction. The larger its value, the stronger the turbulence fluctuation, and the turbulent disturbance of mineral particles may occur, which will reduce the sorting effect. The optimization interface judgment coefficient reflects the sorting stability of the spiral chute flow field monitoring section. The smaller its value, the higher the sorting stability of the spiral chute flow field monitoring section; the generation of this coefficient can provide an important basis for determining the optimization position.

[0024] Step 7: Select the local downward bevel angle and the number of progressive turns as the second optimization parameters, select the sorting efficiency of the lighter mineral among the two minerals as the second optimization target, repeat steps 2 to 5 to obtain the second optimal local downward bevel angle and the optimal optimized number of progressive turns, and optimize the local downward bevel angle based on the starting optimized section position, the first optimal overall downward bevel angle, the second optimal local downward bevel angle and the optimal optimized number of progressive turns.

[0025] Repeat steps 2 to 5, including selecting the local down-angle and the number of progressive turns as the second optimization parameters, selecting the separation efficiency of the lighter mineral of the two minerals as the second optimization target, and designing the efficiency as the optimization target threshold. different local down-bevel angles and progressive turns as initial samples, and construct an initial sample scheme; substitute the local down-bevel angle and progressive turns in the initial sample scheme into the Eulerian Muti-fluid VOF model, obtain the mass flow data under the initial different sample schemes, calculate the second optimization target value according to the mass flow data, generate an initial data set based on the local down-bevel angle, progressive turns and the sorting efficiency of the lighter mineral in the two minerals, set constraints, and analyze the initial data set based on the Kriging interpolation method to establish an approximate functional relationship between the second optimization parameter and the second optimization target, and establish the variation range of the local down-bevel angle and the progressive turns according to the approximate functional relationship between the second optimization parameter and the second optimization target and in combination with the optimization target threshold; constrain the feasible solution space with the local down-bevel angle and the progressive turns, and use the particle swarm optimization algorithm to search for the optimal local down-bevel angle and the optimal optimized progressive turns in the feasible solution space.

[0026] The specific formula for optimizing the local lower bevel angle is: ; ; in, To optimize the local down-slope angle, is the first optimal overall downslope angle, is the second optimal local downward slope angle; the progressive circles inside the spiral chute are numbered in sequence along the flow direction. is the index of the progressive cycle, and , is the total number of progressive circles inside the spiral chute, is the index of the progressive circle at the starting optimization section position, To optimize the index of the progressive circle at the end section position, Optimize the number of progressive turns for best performance.

[0027] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0028] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0029] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0030] The above is only a specific implementation method of the present application, but the protection scope of the present application is not limited thereto. Any technical personnel familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A method for gradually optimizing the variable cross-section parameters of a spiral chute, characterized in that: The specific steps include: Step 1: Use the Eulerian Muti-fluid VOF model to perform numerical calculations of multiphase flow in spiral chute, and optimize the Eulerian Muti-fluid VOF model through model tests; Step 2: Take the overall down-angle as the first optimization parameter, select the separation efficiency of the heavier mineral as the first optimization target, design the optimization target threshold according to the design efficiency, and select Different overall lower oblique angles are used as initial samples to construct the initial sample plan; Step 3: Substitute the overall down-angle in the initial sample scheme into the Eulerian Muti-fluid VOF model, obtain the mass flow data under the initial different sample schemes, calculate the first optimization target value according to the mass flow data, and generate an initial data set based on the overall down-angle and the separation efficiency of the heavier mineral among the two minerals; Step 4: Set constraints, analyze the initial data set based on the Kriging interpolation method, establish an approximate functional relationship between the first optimization parameter and the first optimization target, and establish the overall down-slope angle variation range based on the approximate functional relationship between the first optimization parameter and the first optimization target and in combination with the optimization target threshold; Step 5: Constrain the feasible solution space with the overall down-slope angle variation range, and use the particle swarm optimization algorithm to search in the feasible solution space to obtain the first optimal overall down-slope angle; Step 6: Setup A spiral chute flow field monitoring section was built, and the Eulerian Muti-fluid VOF model was used to simulate the sorting data. The optimized interface judgment coefficient was generated according to the sorting data, and the starting optimized section position was determined by optimizing the interface judgment coefficient. Step 7: Select the local downward bevel angle and the number of progressive turns as the second optimization parameters, select the sorting efficiency of the lighter mineral among the two minerals as the second optimization target, repeat steps 2 to 5 to obtain the second optimal local downward bevel angle and the optimal optimized number of progressive turns, and optimize the local downward bevel angle based on the starting optimized section position, the first optimal overall downward bevel angle, the second optimal local downward bevel angle and the optimal optimized number of progressive turns.

2. A method for gradually optimizing the variable cross-section parameters of a spiral chute according to claim 1, characterized in that: In step 1, the numerical calculation uses the simulation results of the spiral chute gas-liquid two-phase flow as the initial flow field, and the gas-liquid-solid three-phase flow simulation is performed after the calculation is stable. The multiphase flow model selected in the numerical calculation is the Eulerian Muti-fluid VOF model; the turbulence model is the RNG k-ε turbulence model; the inlet boundary condition uses the velocity inlet, and the outlet boundary uses the pressure outlet; the lower wall and side wall are no-slip walls, and the upper surface is the free liquid surface. At the same time, the inlet velocity of the mineral phase is the same as that of water. The stability of the calculation is determined by the residual of each calculation of the model. If , then the calculation is considered stable, where is the residual error at each calculation of the model.

3. A method for gradually optimizing the variable cross-section parameters of a spiral chute according to claim 1, characterized in that: The optimization target threshold is ,in, Optimize target threshold, For design efficiency.

4. A method for gradually optimizing the variable cross-section parameters of a spiral chute according to claim 1, characterized in that: The mass flow data include the mass flow of the two minerals in the concentrate zone at the spiral chute outlet, the total mass flow of the two minerals at the spiral chute outlet, and the mass flow of the two minerals in the tailings zone at the spiral chute outlet; The specific formula for calculating the sorting efficiency is: ； ； in, is the separation efficiency of the heavier mineral among the two minerals, is the separation efficiency of the lighter mineral of the two minerals; At the outlet of the spiral chute, the total mass flow rate of the heavier mineral of the two minerals, At the outlet of the spiral chute, the total mass flow rate of the lighter mineral of the two minerals, is the mass flow rate of the heavier mineral among the two minerals in the concentrate zone at the outlet of the spiral chute. It is the mass flow rate of the lighter mineral in the concentrate zone at the outlet of the spiral chute. is the mass flow rate of the heavier mineral among the two minerals in the tailings zone at the outlet of the spiral chute, It is the mass flow rate of the lighter mineral among the two minerals in the tailings zone at the outlet of the spiral chute; The initial data set is represented as: ,in, For the The overall downward slope of the group sample, The overall downward slope is The sorting efficiency of the sample.

5. A method for gradually optimizing the variable cross-section parameters of a spiral chute according to claim 4, characterized in that: The constraints include unbiasedness constraints and optimality constraints, wherein the unbiasedness constraints are that the expected predicted value is equal to the expected true value, and the optimality constraints are that the predicted variance is minimized; Based on the Kriging interpolation method, the specific logic for establishing the approximate functional relationship between the first optimization parameter and the first optimization target is as follows: randomly select The samples constitute a random data set, construct a mathematical expression for predicting the sorting efficiency, construct a covariance function, calculate the predicted variance through the covariance function, minimize the predicted variance under the unbiased constraint, construct a weight calculation function, and construct a complete Kriging equation group based on the weight calculation function. The Kriging equation group is solved to obtain the sorting efficiency weight, and the predicted sorting efficiency is calculated based on the sorting efficiency weight and the mathematical expression of the predicted sorting efficiency. The corresponding predicted sorting efficiency is calculated for each value within the range of the overall lower oblique angle, and the overall lower oblique angle-predicted sorting efficiency image is drawn to obtain the image. and The corresponding overall lower bevel angle, the overall lower bevel angle Surrounded by: ,in, is the overall downward bevel angle, for and The smaller overall downslope angle among the corresponding overall downslope angles, for and the larger overall downslope angle among the corresponding overall downslope angles; The predicted sorting efficiency is expressed as: ； The overall downward slope is The predicted sorting efficiency, is the overall downslope angle for which the sorting efficiency needs to be predicted, For the The overall downward slope of the samples is is the sorting efficiency weight, and , is the number of samples in the random data set, is the index of a sample of a random dataset, and ; The covariance function is expressed as: ； is the covariance function, For the random data set The overall downward slope of the samples is For the random data set The overall downward slope of the samples is and is the index of the sample of the random data set, , and are model parameters, which can be obtained by fitting the variation experimental function; Get , and The specific logic is as follows: first construct a mutation experiment function, input the samples in the initial data set into the mutation experiment function, obtain a large amount of fitting data, form a fitting data set, select an exponential covariance fitting function, fit the fitting data set to the exponential covariance fitting function, and obtain model parameters; The mutation experiment function is expressed as: ； in, is the value of the variation experiment function, is the reference spacing distance, is the interval distance in the initial sample set The number of point pairs, The first The overall downward slope of the samples is The first The overall downward slope of the samples is For the allowable error, and is the index of the sample in the initial sample set; The exponential covariance fitting function is expressed as: ； The specific formula used to calculate the forecast variance is: ； in, is the prediction variance; Minimize the prediction variance under the unbiased constraint, and the constructed weight calculation function is expressed as: ； in, is the weight calculation function, is the Lagrange multiplier; The complete Kriging equations constructed based on the weight calculation function are expressed as: 。 6. A method for gradually optimizing the variable cross-section parameters of a spiral chute according to claim 1, characterized in that: set up The setting method is as follows: use the RNG k-ε turbulence model in the Eulerian Muti-fluid VOF model in step 1 to monitor the flow field of the spiral chute at intervals along the flow direction. Set a longitudinal section, the section range is the range of the concentrate zone, ; The sorting data includes the mass flow rate of each spiral chute flow field monitoring section and the mass flow rate of the concentrate belt at the spiral chute outlet, the average inlet velocity of each spiral chute flow field monitoring section, the average outlet velocity of each spiral chute flow field monitoring section, the inlet turbulent kinetic energy of each spiral chute flow field monitoring section and the outlet turbulent kinetic energy of each spiral chute flow field monitoring section; Using the Eulerian Muti-fluid VOF model, the sorting data of each spiral chute flow field monitoring section is simulated starting from the spiral chute inlet direction; the sorting data is analyzed to obtain the optimization interface judgment coefficient, and the optimization interface judgment threshold is preset. When the optimization interface judgment coefficient is less than or equal to the optimization interface judgment threshold, the position at this time is recorded as the optimization section position. If there are multiple positions that meet the above requirements, the first optimization section position is set as the starting optimization section position of the spiral chute; The specific formula for calculating the optimization interface judgment coefficient is: ； in, To optimize the interface judgment coefficient, For the The mass flow rate of the spiral chute flow field monitoring section, is the mass flow rate of the concentrate belt at the spiral chute outlet, For the The average inlet velocity of the spiral chute flow field monitoring section, For the The average outlet velocity of the spiral chute flow field monitoring section, For the The inlet turbulent kinetic energy of the spiral chute flow field monitoring section, For the The outlet turbulent kinetic energy of the monitoring section of the spiral chute flow field, The index of the monitoring section of the spiral chute flow field.

7. A method for gradually optimizing the variable cross-section parameters of a spiral chute according to claim 6, characterized in that: The specific formula for optimizing the local lower bevel angle is: ； ； in, To optimize the local down-slope angle, is the first optimal overall downslope angle, is the second optimal local downward slope angle; the progressive circles inside the spiral chute are numbered in sequence along the flow direction. is the index of the progressive cycle, and , is the total number of progressive circles inside the spiral chute, is the index of the progressive circle at the starting optimization section position, To optimize the index of the progressive circle at the end section position, Optimize the number of progressive turns for best performance.

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