Flotation equipment optimization method and system based on pressure pulsation wavelet analysis

Abstract

The invention relates to a flotation equipment optimization method and system based on pressure pulsation wavelet analysis, and belongs to the technical field of flotation equipment design and optimizing.The method comprises the steps that flotation equipment structure parameters and pressure pulsation signals are obtained; performing continuous wavelet transform processing on the pressure pulsation signal, calculating to obtain a wavelet coefficient, converting the wavelet coefficient into a wavelet transform coefficient, then calculating a frequency energy spectral density corresponding to the frequency of the pressure pulsation signal, and obtaining a turbulence characteristic parameter in the average energy spectral density; and constructing a mapping relation model between the flotation equipment structure parameters and the turbulence characteristic parameters, and performing reverse optimization solution on the mapping relation model to obtain the optimal flotation equipment structure parameters. According to the method, quantitative optimization of the turbulence structure in the flotation equipment is achieved by building the mapping relation model between the flotation equipment structure parameters and the turbulence characteristic parameters and conducting reverse optimization solution, and the method is used for guiding design and development of large flotation equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of flotation equipment design and optimization, and particularly to an optimization method and system for flotation equipment based on pressure pulsation wavelet analysis. Background Art

[0002] The statements in this section merely provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] Flotation is one of the very fine separation methods in the mineral processing process, and particularly occupies a core position in the recovery of coal, non-ferrous metals and rare and dispersed element resources. With the dual demands of reducing resource grade and increasing processing capacity, the existing flotation equipment is developing towards the direction of large-scale, high-efficiency and intelligent.

[0004] During the flotation process, the modification of pulp conditioning, as well as the effective collision, adhesion and entrainment of bubbles and ore particles, are the key microscopic mechanisms determining the flotation efficiency. And microscale turbulence, as the physical basis for strengthening this mechanism, can not only promote the frequent perturbation and collision between particles and bubbles, but also play a regulatory role in suppressing bubble coalescence and particle desorption. Therefore, the turbulence regulation ability has become one of the most core performance parameters in the design of flotation equipment.

[0005] There are many drawbacks in the development of existing flotation equipment in terms of turbulence optimization. On the one hand, the design of existing flotation equipment generally relies on empirical rules or structural analogy, and the improvement path is mainly to conduct multi-round experimental optimization through trial-and-error strategies such as changing the form of impeller stator and the way of aeration. Moreover, although the existing technology involves the structural optimization of flotation equipment or the improvement of the tank body, the existing technology does not start from the perspective of physical measurement, making the existing improvements have certain engineering feasibility, but with a long development cycle, low efficiency, lack of quantitative basis and predictability.

[0006] On the other hand, in recent years, the CFD (Computational Fluid Dynamics) numerical simulation method has been widely used for the flow field prediction of flotation equipment to optimize the impeller structure and tank layout. However, it is found in the research that the problems of this method are as follows: 1. The model scale does not match the actual equipment scale, and the amplification effect is significant; 2. The simulation ability for the unsteady state and multi-scale characteristics of turbulence is limited; 3. Lack of experimental data support, and the problem of model closure is prominent.

[0007] At the same time, although the existing experimental measurement methods such as PIV, LDV, CTA, etc. have high precision, due to their dependence on transparent media and optical paths, it is difficult to be applied to the complex multiphase system inside the flotation tank, especially for on-line detection in industrial environments with high solid concentration, dense bubbles and particle occlusion. In addition, their high cost and layout difficulty also limit their wide deployment in engineering practice. Summary of the Invention

[0008] In order to solve the above problems, the present invention proposes an optimization method and system for flotation equipment based on wavelet analysis of pressure pulsation. By constructing a mapping relationship model between the structural parameters and turbulent characteristic parameters of the flotation equipment and performing reverse optimization to solve, quantitative optimization of the turbulent structure in the flotation equipment is realized, which is used to guide the design and development of large flotation equipment.

[0009] To achieve the above object, the present invention adopts the following technical solutions: In the first aspect, the present invention provides an optimization method for flotation equipment based on wavelet analysis of pressure pulsation, including: Obtain the structural parameters of the flotation equipment, and arrange pressure sensors in the turbulent region of the flotation equipment tank body, and obtain the pressure pulsation signal based on the structural parameters of the flotation equipment; Perform continuous wavelet transform processing on the obtained pressure pulsation signal, calculate the wavelet coefficients and convert them into wavelet transform coefficients; calculate the frequency energy spectral density corresponding to the frequency of the pressure pulsation signal based on the wavelet transform coefficients, and obtain the turbulent characteristic parameters in the frequency energy spectral density; Construct a mapping relationship model between the structural parameters and turbulent characteristic parameters of the flotation equipment, determine the target turbulent characteristic parameters, and obtain the optimal structural parameters of the flotation equipment by performing reverse optimization to solve the mapping relationship model.

[0010] In a further technical solution, the structural parameters of the flotation equipment include the number of impeller blades, impeller diameter, stator spacing, rotational speed, and air inflow; the turbulent characteristic parameters include the main frequency, main frequency energy, high-frequency energy ratio, energy spectral width, total turbulent energy, and turbulent intermittency coefficient.

[0011] In a further technical solution, the wavelet transform processing selects the Morlet mother wavelet, and the formula for calculating the wavelet coefficients is: ; where represents the wavelet coefficient, represents the pressure pulsation signal, represents the wavelet scale parameter, represents the translation parameter, represents the sampling time, [[ID=DoubleRightTee]] represents the conjugate of the Morlet mother wavelet; Convert the wavelet scale parameter into the physical frequency of the pressure pulsation signal through the scale-frequency conversion formula, and replace the translation parameter with the sampling time to obtain the wavelet transform coefficient , and the scale-frequency conversion formula is: ; where represents the physical frequency of the pressure pulsation signal, Represents the dimensionless center frequency of the Morlet mother wavelet.

[0012] A further technical solution is that the formula for calculating the frequency energy spectral density corresponding to the pressure pulsation signal is: ; Wherein, Represents the frequency energy spectral density, Represents the sampling duration, Represents the wavelet transform coefficient.

[0013] A further technical solution is that the process of constructing the mapping relationship model between the structural parameters of the flotation equipment and the turbulent characteristic parameters includes: adjusting the structural parameters of the flotation equipment, obtaining different turbulent characteristic parameters under different combinations of the structural parameters of the flotation equipment, and forming a data set. According to the data set, define the form of the mapping relationship model. First, use a multiple linear regression model to fit the turbulent characteristic parameters. If the goodness of fit of the multiple linear regression model is lower than the design threshold, then use a non-linear regression model, and use the model with the optimal goodness of fit as the mapping relationship model.

[0014] A further technical solution is that the multiple linear regression model is expressed as: ; Wherein, Represents the turbulent characteristic parameter, Represents the fitting coefficient, Represents the number of impeller blades, Represents the impeller diameter, Represents the stator spacing, Represents the rotational speed, Represents the aeration rate, Represents the fitting residual term.

[0015] A further technical solution is that the non-linear regression model uses a support vector regression model, wherein the kernel function is selected as the radial basis function, and is specifically expressed as: ; Wherein, Represents the radial basis function, Represents the structural parameters of the flotation equipment, Represents the Structural parameters of the flotation equipment under the jth group of working conditions, Is the support vector weight coefficient corresponding to the jth turbulent characteristic parameter, Represents the bias term of the jth turbulent characteristic parameter.

[0016] In the second aspect, the present invention provides a flotation equipment optimization system based on pressure pulsation wavelet analysis, including: The data acquisition module is configured to: acquire structural parameters of the flotation device, and arrange pressure sensors in the turbulent area of the flotation device tank to acquire pressure pulsation signals based on the structural parameters of the flotation device; The wavelet analysis module is configured to: perform continuous wavelet transform processing on the acquired pressure pulsation signal, calculate the wavelet coefficients and convert them into wavelet transform coefficients; calculate the frequency energy spectral density corresponding to the frequency of the pressure pulsation signal based on the wavelet transform coefficients, and obtain turbulence characteristic parameters from the frequency energy spectral density; The parameter optimization module is configured to: construct a mapping relationship model between the flotation equipment structural parameters and the turbulence characteristic parameters, determine the target turbulence characteristic parameters, and obtain the optimal flotation equipment structural parameters by performing reverse optimization on the mapping relationship model.

[0017] Compared with the prior art, the present invention has the following advantages and beneficial effects: The present invention performs continuous wavelet transform processing on the pressure pulsation signal, uses wavelet analysis to effectively decompose the non-steady-state signal, and extracts the turbulence energy changes at different time scales, with high time-frequency resolution. In combination with the constructed mapping relationship model, the optimal flotation equipment structural parameters are reversely solved according to the target turbulence characteristic parameters, which significantly shortens the development cycle, improves efficiency, reduces structural trial and error, and realizes the quantitative optimization of the turbulence structure in the flotation equipment, which is used to guide the design and development of large-scale flotation equipment.

[0018] The present invention arranges a pressure sensor in the turbulent area of the flotation equipment tank, is applicable to industrial flotation equipment with opaque and high solid content systems, and solves the problem that optical measurement means cannot be deployed. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their description are used to explain the present invention but do not constitute a limitation of the present invention.

[0020] Figure 1 1 is a flow chart of a flotation equipment optimization method based on pressure pulsation wavelet analysis in Example 1 of the present invention; Figure 2 Schematic diagram of the structure of a flotation equipment optimization system based on pressure pulsation wavelet analysis in Example 2 of the present invention; Among them, 1. Flotation tank; 2. Impeller assembly; 3. Slurry; 4. Pressure sensor; 5. Data acquisition module; 6. Wavelet analysis and processing module. DETAILED DESCRIPTION

[0021] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0022] It should be noted that the following detailed description is exemplary and is intended to provide further illustration of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0023] Without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0024] Embodiment 1 This embodiment proposes an optimization method for flotation equipment based on wavelet analysis of pressure pulsation, as Figure 1 shown. Specifically, it includes the following technical solutions: First, obtain and design the structural parameters of the flotation equipment, including the number of impeller blades, impeller diameter, stator spacing, rotational speed, and air inflow rate. Among them, as an implementation method, the number of impeller blades is designed as 4, 5, or 6, the impeller diameter is designed as 300 mm, 350 mm, or 400 mm, the stator spacing is designed as 20 mm or 30 mm, the rotational speed is designed as 500 rpm, 600 rpm, or 700 rpm, and the air inflow rate is designed as 0.8 m3 / h, 1.0 m3 / h, or 1.2 m3 / h.

[0025] Then, as Figure 2 shown, arrange pressure sensors 4 in the turbulent region inside the flotation equipment tank. Specifically, arrange 6 high-frequency dynamic pressure sensors at typical positions in the impeller assembly 2 (impeller outlet area, stator outer edge area) and the tank wall surface inside the flotation tank 1 of the flotation equipment. The frequency response range of the high-frequency dynamic pressure sensors is 10 - 15 kHz, the sampling frequency is not less than 20 kHz, which is set to 20 kHz in this embodiment, and the sampling duration is not less than 60 s, which is set to 60 s in this embodiment. Collect the pressure pulsation signals at each measurement point through a 16-channel synchronous data acquisition module. .

[0026] Then, perform continuous wavelet transform processing on the obtained pressure pulsation signals. Among them, the Morlet mother wavelet is selected for wavelet transform processing, and the formula for calculating the wavelet coefficient is: ; where represents the wavelet coefficient, represents the pressure pulsation signal, represents the wavelet scale parameter, represents the translation parameter, represents the sampling time, represents the conjugate of the Morlet mother wavelet.

[0027] Then, the wavelet scale parameter Convert it into the physical frequency of the pressure pulsation signal through the scale-frequency conversion formula, and replace the translation parameter with the sampling time , and obtain the wavelet transform coefficient , where the scale-frequency conversion formula is: ; The above represents the physical frequency of the pressure pulsation signal, represents the dimensionless center frequency of the Morlet mother wavelet.

[0028] Then, calculate the frequency energy spectral density corresponding to the frequency of the pressure pulsation signal based on the wavelet transform coefficient , and obtain the turbulence characteristic parameters in the frequency energy spectral density. Among them, the formula for calculating the frequency energy spectral density corresponding to the frequency of the pressure pulsation signal is: ; Among them, represents the frequency energy spectral density, represents the sampling duration.

[0029] In this embodiment, the obtained turbulence characteristic parameters include the main frequency f p , the main frequency energy E p , the high-frequency energy ratio R hf , the energy spectrum width Δf, the total turbulence energy E total , the turbulence intermittency coefficient I t .

[0030] For the designed structural parameter X of the flotation device, repeat the above process, correspondingly obtain the turbulence characteristic parameter Y, and form a data set for constructing the mapping relationship model between the structural parameter of the flotation device and the turbulence characteristic parameter.

[0031] Then, construct the mapping relationship model between the structural parameter of the flotation device and the turbulence characteristic parameter. The specific steps are as follows: 1. Adjust the structural parameter of the flotation device. Under different combinations of the structural parameters of the flotation device, obtain different turbulence characteristic parameters, and form a data sample containing n groups (Xi, Yi) as the model training set; 2. Define the form of the mapping relationship model according to the data set. First, use the multiple linear regression model for each turbulence characteristic parameter (including the main frequency f p , the main frequency energy E p , the high-frequency energy ratioR hf , energy spectrum width Δf, total turbulent energy E total , turbulence intermittency coefficient I t ) are fitted. Among them, the multiple linear regression model is expressed as: ; Among them, represents the turbulent characteristic parameter, represents the fitting coefficient, represents the number of impeller blades, represents the impeller diameter, represents the stator spacing, represents the rotational speed, represents the aeration rate, represents the fitting residual term.

[0032] 3. If the goodness of fit of the multiple linear regression model is lower than the design threshold (0.9), then a non-linear regression model is further adopted. In this embodiment, the non-linear regression model adopts a support vector regression model. Among them, the kernel function is selected as the radial basis function, which is specifically expressed as: ; Among them, represents the radial basis function, represents the structural parameters of the flotation equipment, represents the structural parameters of the flotation equipment under the jth group of working conditions, is the support vector weight coefficient corresponding to the jth turbulent characteristic parameter,

[0033] 4. After training is completed, the model with the best goodness of fit is retained as the final mapping relationship model for subsequent reverse optimization design.

[0034] Then, according to the optimization design goal of the flotation equipment, the target turbulent characteristic parameters are determined. According to the target turbulent characteristic parameters, the mapping relationship model is reversely optimized and solved by genetic algorithm and particle swarm optimization algorithm to obtain the optimal combination of structural parameters of the flotation equipment that meets the target turbulence characteristics. Verify the optimization design effect and complete the closed-loop process of equipment optimization design.

[0035] Embodiment 2 This embodiment provides an optimization system for flotation equipment based on pressure pulsation wavelet analysis, which specifically includes the following modules: A data acquisition module, configured to: acquire the structural parameters of the flotation equipment, arrange pressure sensors in the turbulent region within the flotation equipment tank body, and acquire pressure pulsation signals based on the structural parameters of the flotation equipment; A wavelet analysis module, configured to: perform continuous wavelet transform processing on the acquired pressure pulsation signals, calculate wavelet coefficients and convert them into wavelet transform coefficients; calculate the frequency energy spectral density corresponding to the frequency of the pressure pulsation signals based on the wavelet transform coefficients, and acquire turbulent characteristic parameters from the frequency energy spectral density; A parameter optimization module, configured to: construct a mapping relationship model between the structural parameters of the flotation equipment and the turbulent characteristic parameters, determine the target turbulent characteristic parameters, and obtain the optimal structural parameters of the flotation equipment by performing reverse optimization and solution on the mapping relationship model.

[0036] For the implementation of the specific modules in this embodiment, refer to the steps of the optimization method for the flotation equipment based on pressure pulsation wavelet analysis described in Embodiment 1, and no specific description will be given here.

[0037] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the appended claims.

Claims

1. An optimization method for flotation equipment based on wavelet analysis of pressure pulsation, characterized in that Including: Obtain the structural parameters of the flotation equipment, deploy pressure sensors in the turbulent region within the flotation equipment tank body, and obtain pressure pulsation signals based on the structural parameters of the flotation equipment; Perform continuous wavelet transform processing on the obtained pressure pulsation signals, calculate the wavelet coefficients and convert them into wavelet transform coefficients; calculate the frequency energy spectral density corresponding to the frequency of the pressure pulsation signals based on the wavelet transform coefficients, and obtain the turbulent characteristic parameters in the frequency energy spectral density; Construct a mapping relationship model between the structural parameters of the flotation equipment and the turbulent characteristic parameters, determine the target turbulent characteristic parameters, and obtain the optimal structural parameters of the flotation equipment by performing reverse optimization and solution on the mapping relationship model.

2. The optimization method of the flotation equipment based on the wavelet analysis of pressure pulsation according to claim 1, characterized in that The structural parameters of the flotation equipment include the number of impeller blades, impeller diameter, stator spacing, rotational speed, and gas injection volume; the turbulent characteristic parameters include the main frequency, main frequency energy, high-frequency energy ratio, energy spectral width, total turbulent energy, and turbulent intermittency coefficient.

3. The optimization method of the flotation equipment based on pressure pulsation wavelet analysis according to claim 1, characterized in that The wavelet transform processing selects the Morlet mother wavelet, and the formula for calculating the wavelet coefficients is: ; wherein, represents the wavelet coefficient, represents the pressure pulsation signal, represents the wavelet scale parameter, represents the translation parameter, represents the sampling time, represents the conjugate of the Morlet mother wavelet; Convert the wavelet scale parameter to the physical frequency of the pressure pulsation signal through the scale-frequency conversion formula, and replace the translation parameter with the sampling time to obtain the wavelet transform coefficient . The scale-frequency conversion formula is as follows: ; where represents the physical frequency of the pressure pulsation signal, and represents the dimensionless center frequency of the Morlet mother wavelet.

4. The optimization method of the flotation equipment based on pressure pulsation wavelet analysis according to claim 1, wherein, The formula for calculating the frequency energy spectral density corresponding to the frequency of the pressure pulsation signals is: ； Among them, represents the frequency energy spectral density, represents the duration of sampling, represents the wavelet transform coefficient.

5. The optimization method of the flotation equipment based on pressure pulsation wavelet analysis according to claim 1, wherein, The process of constructing a mapping relationship model between the structural parameters of the flotation equipment and the turbulent characteristic parameters includes: adjusting the structural parameters of the flotation equipment, obtaining different turbulent characteristic parameters under different combinations of the structural parameters of the flotation equipment, and forming a data set. Define the form of the mapping relationship model according to the data set. First, use a multiple linear regression model to fit the turbulent characteristic parameters. If the goodness of fit of the multiple linear regression model is lower than the design threshold, then use a non-linear regression model, and use the model with the best goodness of fit as the mapping relationship model.

6. The optimization method of the flotation equipment based on pressure pulsation wavelet analysis according to claim 5, characterized in that, The multiple linear regression model is expressed as: ; among them, represents the turbulent flow characteristic parameter, represents the fitting coefficient, represents the number of impeller blades, represents the impeller diameter, represents the stator spacing, represents the rotational speed, represents the aeration rate, represents the fitting residual term.

7. The optimization method of the flotation equipment based on the wavelet analysis of pressure pulsation according to claim 5, characterized in that The nonlinear regression model adopts the support vector regression model, wherein the kernel function adopts the radial basis function, which is specifically expressed as: ;in, represents the radial basis function, Indicates the structural parameters of the flotation equipment, Indicates the Structural parameters of flotation equipment under group working conditions, is the support vector weight coefficient corresponding to the j-th turbulence characteristic parameter, Represents the bias term of the j-th turbulence characteristic parameter.

8. A flotation equipment optimization system based on wavelet analysis of pressure pulsation, characterized in that, Including: A data acquisition module, configured to: obtain the structural parameters of the flotation equipment, deploy pressure sensors in the turbulent region within the flotation equipment tank body, and obtain pressure pulsation signals based on the structural parameters of the flotation equipment; A wavelet analysis module, configured to: perform continuous wavelet transform processing on the obtained pressure pulsation signals, calculate the wavelet coefficients and convert them into wavelet transform coefficients; calculate the energy spectral density corresponding to the frequency of the pressure pulsation signals based on the wavelet transform coefficients, and obtain the turbulent characteristic parameters in the frequency energy spectral density; A parameter optimization module, configured to: construct a mapping relationship model between the structural parameters of the flotation equipment and the turbulent characteristic parameters, determine the target turbulent characteristic parameters, and obtain the optimal structural parameters of the flotation equipment by performing reverse optimization and solution on the mapping relationship model.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps in the optimization method for a flotation equipment based on pressure pulsation wavelet analysis according to any one of claims 1-7.

10. An electronic device, comprising a memory, a processor, and a program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the optimization method for a flotation equipment based on pressure pulsation wavelet analysis according to any one of claims 1-7.

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