Numerical simulation method for glass-lined agitator

Abstract

The application relates to the technical field of numerical simulation, in particular to a numerical simulation method for a glass lining stirrer, which comprises the following steps: analyzing the differences of various stirring parameter offset coefficients and the differences of offset indexes between any two stirring processes, so as to screen a target stirring process from all stirring processes; analyzing the differences of all inflection point numbers of various stirring parameters and the differences of fitting curves of various stirring parameters between any two target stirring processes, and combining the differences of fitting values of the fitting curves of various stirring parameters at the same inflection point number between any two target stirring processes, so as to determine a same frequency coefficient and screen stirring parameters for numerical simulation. The application solves the influence of vibration interference on the change trend of various stirring parameters in the stirring process, and improves the accuracy of numerical simulation of the glass lining stirrer.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of numerical simulation, in particular to a numerical simulation method for a glass-lined agitator. BACKGROUND

[0002] The glass-lined agitator is a core component for realizing the stirring action in the glass-lined reactor. By moving the impeller with the rotating shaft, mechanical energy is applied to the liquid material. The main function is to accelerate the reaction speed of the liquid material in the reactor, strengthen the mixing, and enhance the mass and heat transfer effect. At the same time, based on the physical characteristics of different liquid materials, the type of agitator can be changed. For example, for liquid materials with high flowability and low viscosity, a paddle-type agitator can be selected to improve the stirring efficiency and process effect.

[0003] Due to the existence of a monitoring blind area inside the reactor, in order to accurately evaluate the working state of the agitator and the reaction effect of the material, numerical simulation technology is often used in engineering practice. When collecting data during the stirring process of the glass-lined agitator, abnormal vibration pseudo-features caused by the asymmetry of the stirring shaft system during the stirring process interfere with the collected data, resulting in deviation of the numerical simulation results from the real scene, and reducing the accuracy of the numerical simulation of the glass-lined agitator. SUMMARY

[0004] In order to solve the above technical problems, the present application provides a numerical simulation method for a glass-lined agitator to solve the existing problems.

[0005] The numerical simulation method for the glass-lined agitator of the present application adopts the following technical scheme:

[0006] One embodiment of the present application provides a numerical simulation method for a glass-lined agitator, which comprises the following steps:

[0007] Using the glass-lined agitator to stir the material, real-time acquisition of various stirring parameters in each stirring process;

[0008] In each stirring process, by analyzing the difference between the numerical value of each stirring parameter at the initial time and the preset standard value, the offset coefficient of each stirring parameter in each stirring process is determined to determine the offset index of each stirring process; analyzing the difference between the offset coefficients of each stirring parameter and the difference between the offset indices between any two stirring processes, the similarity index between any two stirring processes is determined to select the target stirring process from all stirring processes;

[0009] fitting curves of the stirring parameters in each target stirring process, screening the inflection points on the fitting curves of the stirring parameters and numbering all the inflection points in sequence, analyzing the difference of all the inflection point numbers of the stirring parameters and the difference of the fitting curves of the stirring parameters between any two target stirring processes, and determining the difference index of the stirring parameters between any two target stirring processes;

[0010] analyzing the difference of the fitting values of the fitting curves of the stirring parameters at the same inflection point number between any two target stirring processes, determining the parameter similarity eigenvalue of the stirring parameters at the same inflection point number between any two target stirring processes, and determining the characteristic index of the stirring parameters between any two target stirring processes;

[0011] based on the characteristic index and the difference index, determining the same frequency coefficient of the stirring parameters between any two target stirring processes, and screening the stirring parameters for numerical simulation.

[0012] Preferably, the offset coefficient of the stirring parameters in each stirring process is the difference between the value of the stirring parameters at the initial moment of each stirring process and the preset standard value divided by the preset error value.

[0013] Preferably, the determination method of the offset index of each stirring process is:

[0014] calculating the difference between the offset coefficient of each stirring parameter in each stirring process and the minimum value of all the stirring parameter offset coefficients, calculating the average value of the difference of all the stirring parameters in each stirring process, and taking the ratio of the average value and the range of the offset coefficients of all the stirring parameters in the corresponding stirring process as the offset index of each stirring process.

[0015] Preferably, the determination method of the similarity index between any two stirring processes is:

[0016] the difference between the offset coefficients of the stirring parameters between any two stirring processes is recorded as the offset difference value of the stirring parameters between any two stirring processes, and the cumulative sum of the offset difference values of all the stirring parameter offset coefficients between any two stirring processes is recorded as the comprehensive offset difference between any two stirring processes.

[0017] the reciprocal of the product of the difference of the offset index between any two stirring processes and the comprehensive offset difference value is taken as the normalization value, as the similarity index between any two stirring processes.

[0018] Preferably, the target stirring process is screened from all the stirring processes, comprising:

[0019] Calculate the normalized value of the mean similarity index between each stirring process and all other target stirring processes, and record it as the similarity feature value. Stirring processes with similarity feature values ​​greater than or equal to a preset threshold are taken as target stirring processes.

[0020] Preferably, the method for determining the difference index of various stirring parameters between any two target stirring processes is as follows:

[0021] In each stirring process, the corresponding numbers of all inflection points on the fitted curves of various stirring parameters are arranged into an inflection point sequence according to time sequence. The difference between the inflection point sequences of various stirring parameters between any two target stirring processes is calculated and recorded as the inflection point difference of various stirring parameters between any two target stirring processes.

[0022] The normalized value of the product of the difference in the fitted curves of various mixing parameters between any two target mixing processes and the difference in the inflection point is used as the difference index of various mixing parameters between any two target mixing processes.

[0023] Preferably, the expression for the parameter similarity characteristic values ​​of various stirring parameters under the same inflection point number between any two target stirring processes is: In the formula, This represents the parameter similarity feature value of the k-th type of stirring parameters between the i-th target stirring process and the j-th target stirring process at the inflection point number m; This represents the difference in the fitted values ​​of the k-th type of stirring parameter on the fitted curve at the inflection point number m between the i-th and j-th target stirring processes. represents the mean difference between the fitted values ​​of the k-th type of stirring parameter at the inflection point m among all sub-target stirring processes; exp() represents an exponential function with the natural constant as the base.

[0024] Preferably, the expression for the characteristic indices of various stirring parameters between any two target stirring processes is: In the formula, The characteristic index represents the k-th type of stirring parameter between the i-th target stirring process and the j-th target stirring process; This represents the difference in similarity characteristic values ​​of the k-th type of stirring parameter between the i-th target stirring process and the j-th target stirring process at the inflection point number m and its adjacent previous number. This represents the number of inflection points with the same number between the k-th type of stirring parameters in the i-th target stirring process and the j-th target stirring process.

[0025] Preferably, the frequency coefficients of various stirring parameters between any two target stirring processes are normalized values ​​of the product of the difference index and the characteristic index of various stirring parameters between any two target stirring processes.

[0026] Preferably, the screening of stirring parameters for numerical simulation includes:

[0027] In all sub-target stirring processes, the mean value of the frequency coefficients of various stirring parameters between each target stirring process and all other sub-target stirring processes is normalized and denoted as the frequency index. If the frequency index of the k-th type of stirring parameter in the n-th target stirring process is greater than the preset frequency threshold, then the k-th type of stirring parameter in the n-th target stirring process is included in the numerical simulation process of the glass-lined agitator; otherwise, the k-th type of stirring parameter in the n-th target stirring process is not included in the numerical simulation process of the glass-lined agitator. All sub-target stirring processes are traversed to obtain the stirring parameters used for numerical simulation.

[0028] This application has at least the following beneficial effects:

[0029] This application quantifies the deviation of initial parameters from standard values ​​by calculating the offset coefficients and offset indices of various stirring parameters during each stirring process. Furthermore, by analyzing the differences in these coefficients and indices between any two stirring operations, a similarity index is calculated, effectively identifying stirring processes with similar process backgrounds and parameter characteristics. This method can initially screen out "target stirring processes" with less interference and smaller parameter fluctuations, providing a high-quality data foundation for subsequent more accurate numerical simulations, thereby improving the reliability and prediction accuracy of simulation results. Furthermore, this application, through in-depth analysis of the target stirring process data, first uses fitting curves and inflection point analysis to calculate a difference index, quantifying the overall difference in parameter changes between any two stirring operations, and further compares the difference in fitting values ​​at the same inflection point. The method calculates parameter similarity feature values ​​that reflect the similarity of parameters. Finally, by summarizing the changes in these feature values, a feature index is obtained, which is used to determine whether the fluctuation trends of two mixing processes on a specific parameter are consistent. This method can more accurately identify mixing batches with high process similarity, providing a higher quality and more consistent data foundation for subsequent numerical simulations, thereby improving the accuracy of simulation results. Furthermore, the difference index and feature index obtained in this application are used to calculate the same frequency coefficient and set the same frequency threshold to screen out parameters that are highly "same frequency" as most processes for subsequent numerical simulations. This method can effectively eliminate data that is disturbed or has abnormal operating conditions, ensuring that the simulation is based on stable and consistent real operating condition data, thereby significantly improving the accuracy of numerical simulations. Attached Figure Description

[0030] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A flowchart illustrating the steps of a numerical simulation method for a glass-lined stirrer provided in one embodiment of this application;

[0032] Figure 2 This is a schematic diagram of the same frequency coefficient extraction process provided in one embodiment of this application. Detailed Implementation

[0033] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the numerical simulation method for glass-lined stirrers proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0035] The following description, in conjunction with the accompanying drawings, details the specific scheme of the numerical simulation method for glass-lined stirrers provided in this application.

[0036] This application provides a numerical simulation method for glass-lined stirrers in one embodiment. Specifically, the following numerical simulation method for glass-lined stirrers is provided. Please refer to [link / reference]. Figure 1 The method includes the following steps:

[0037] Step S1: Use a glass-lined agitator to stir the material and obtain various stirring parameters in real time during each stirring process.

[0038] When using multi-source data obtained during the stirring process of materials with a glass-lined stirrer for numerical simulation, false features such as vibration interference may be introduced, causing the numerical simulation results to deviate from the real scene and rendering the simulation results worthless. Therefore, this embodiment collects different types of stirring parameters during the stirring process, analyzes the variation characteristics of different types of stirring parameters, and eliminates the influence of vibration interference and other features on the numerical simulation. The specific data collection process is as follows:

[0039] The material is stirred using a glass-lined agitator. Various stirring parameters are acquired in real time during each stirring process. The data acquisition frequency is set to y, and a total of R stirring processes are acquired. The stirring parameters include: temperature data, stirring speed of the glass-lined agitator, sealing pressure, and pH value during the stirring process.

[0040] It should be noted that the values ​​of the data acquisition frequency y and the number of stirring processes R are both set manually. In this embodiment, the value of y is 2Hz and the value of R is 100. In actual applications, as other implementation methods, implementers can also set them according to specific circumstances. This embodiment does not impose any special restrictions.

[0041] It should be noted that all of the above-mentioned stirring parameters are collected simultaneously.

[0042] Step S2: In each stirring process, by analyzing the difference between the initial values ​​of various stirring parameters and the preset standard values, the offset coefficient of various stirring parameters in each stirring process is determined, so as to determine the offset index of each stirring process; by analyzing the difference between the offset coefficients of various stirring parameters and the difference between the offset index between any two stirring processes, the similarity index between any two stirring processes is determined, so as to screen out the target stirring process from all stirring processes.

[0043] In the application of glass-lined agitators, even if adjacent batches are stirred with the same initial parameters to stir materials such as porcelain enamel, factors such as vibration interference may still cause abnormal differences in the stirring effect. If the initial parameters themselves are different, this deviation in the stirring result may be more obvious.

[0044] Therefore, to effectively handle this complexity, during each stirring process, the deviation coefficients of various stirring parameters are determined by analyzing the differences between their initial values ​​and preset standard values, thus determining the deviation index for each stirring process. Furthermore, the differences in the deviation coefficients and deviation indices of various stirring parameters between any two stirring processes are analyzed to determine the similarity index between any two stirring processes. This allows for the selection of target stirring processes from all stirring processes, thereby initially identifying stirring processes with similar process backgrounds or parameter characteristics. Specifically:

[0045] In this embodiment, firstly, during each stirring process, the deviation coefficients of various stirring parameters are determined by analyzing the differences between the initial values ​​of each stirring parameter and the preset standard values, thereby determining the deviation index for each stirring process. Specifically:

[0046] In this embodiment, the difference between the initial value of each stirring parameter and the preset standard value during each stirring process is divided by the preset error value, and the result is used as the offset coefficient of each stirring parameter during each stirring process. This is used to characterize the relative degree to which the initial value of each stirring parameter deviates from the standard value during each stirring process.

[0047] Furthermore, in this embodiment, the difference between the deviation coefficient of each type of stirring parameter and the minimum value among all types of stirring parameter deviation coefficients is calculated in each stirring process. The mean value of the difference among all types of stirring parameters in each stirring process is calculated, and the ratio of the mean value to the range of the deviation coefficients of all types of stirring parameters in the corresponding stirring process is used as the deviation index of each stirring process, which is used to characterize the overall situation of all types of stirring parameters deviating from their respective standard values ​​in each stirring process.

[0048] It should be noted that the initial mixing parameters may vary between different batches when mixing materials. For example, temperature data may differ due to variations in ambient temperature, making it difficult to guarantee a consistent initial temperature. Therefore, there will be an acceptable range of temperature error. The initial temperature data during the mixing process is generally within a certain range. ,in, Standard values ​​for temperature data For different types of stirring parameters, the preset standard values ​​and preset error values ​​are different. In this embodiment, the preset standard value and preset error value of temperature data are set to 20℃ and 10℃ respectively for all types of stirring parameters; the preset standard value and preset error value of stirring speed of glass-lined stirrer are 70RPM and 20RPM respectively; the preset standard value and preset error value of sealing pressure during stirring are 0.5MPa and 0.3MPa respectively; and the preset standard value and preset error value of pH value during stirring are 7 and 1 respectively. In actual application, implementers can also set them according to specific circumstances. This embodiment does not impose any special restrictions.

[0049] Furthermore, this embodiment determines the similarity index between any two mixing processes by analyzing the differences in the offset coefficients and offset indices of various mixing parameters between any two mixing processes, in order to screen out the target mixing process from all mixing processes, specifically as follows:

[0050] In this embodiment, the difference between the offset coefficients of various types of stirring parameters between any two stirring processes is recorded as the offset difference value of various types of stirring parameters between any two stirring processes, and the sum of the offset difference values ​​of all types of stirring parameter offset coefficients between any two stirring processes is recorded as the comprehensive offset difference between any two stirring processes.

[0051] Furthermore, the reciprocal of the product of the difference in offset index between any two stirring processes and the comprehensive offset difference is normalized and used as the similarity index between any two stirring processes.

[0052] It should be noted that there are many methods to measure the difference between data. In this embodiment, the absolute value of the difference is used to measure the difference between data. In practical applications, as other implementation methods, implementers may also use other methods such as the square or ratio of the difference to measure the difference between data, depending on the specific circumstances. This embodiment does not impose any special restrictions on the selection of methods to measure the difference between data.

[0053] It should be noted that, unless otherwise specified, all methods for measuring differences between data in this embodiment use the method of taking the absolute value of the difference.

[0054] Based on the similarity index between any two mixing processes, it can be understood that the similarity index reflects the overall fluctuation similarity of the mixing parameters between different mixing processes. If there is a larger offset index between two mixing processes, it indicates that the difference between the mixing parameters between these two mixing processes is very high. Therefore, the similarity between the two mixing processes is smaller, and the corresponding similarity index is also smaller. At the same time, if the difference in the offset coefficient of the current class of mixing parameters between these two mixing processes is larger, it indicates that the fluctuation difference of the current class of mixing parameters between these two mixing processes is larger. Therefore, considering the difference in the offset coefficients of all classes of mixing parameters between these two mixing processes, if the resulting comprehensive offset index is larger, it indicates that the difference between these two mixing processes is greater, the similarity is smaller, and the corresponding similarity index is smaller.

[0055] Conversely, the smaller the offset index between two mixing processes, the lower the difference in mixing parameters between them. Therefore, the greater the similarity between the two mixing processes, and the larger the corresponding similarity index. At the same time, the smaller the difference in the offset coefficient of the current type of mixing parameter between the two mixing processes, the smaller the fluctuation difference of the current type of mixing parameter between them. Therefore, considering the differences in the offset coefficients of all types of mixing parameters between the two mixing processes, the smaller the overall offset index, the smaller the difference between the two mixing processes, the greater the similarity, and the larger the corresponding similarity index.

[0056] Furthermore, based on the aforementioned similarity index, this embodiment selects the target stirring process from all stirring processes, specifically:

[0057] In this embodiment, the normalized value of the mean similarity index between each stirring process and all other target stirring processes is calculated and recorded as the similarity feature value. The stirring process with a similarity feature value greater than or equal to a preset threshold is taken as the target stirring process.

[0058] It should be noted that the preset threshold value is set manually. In this embodiment, the preset threshold value is 0.8. In actual applications, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0059] Thus, this embodiment quantifies the degree of deviation of the initial parameters from the standard values ​​by calculating the offset coefficients and offset indices of various stirring parameters during each stirring process. Furthermore, by analyzing the differences of these coefficients and indices between any two stirring processes, a similarity index is calculated, effectively identifying stirring processes with similar process backgrounds and parameter characteristics. This method can initially screen out "target stirring processes" with less interference and smaller parameter fluctuations, providing a high-quality data foundation for subsequent more accurate numerical simulations, thereby improving the reliability and prediction accuracy of the simulation results.

[0060] Step S3: Analyze the differences in the inflection point numbers of all types of stirring parameters and the differences in the fitted curves of various types of stirring parameters between any two target stirring processes. Combine the differences in the fitted values ​​of the fitted curves of various types of stirring parameters at the same inflection point number between any two target stirring processes to determine the characteristic index of various types of stirring parameters between any two target stirring processes.

[0061] During the mixing process, different offsets have different effects on the process data. When the glass-lined agitator is undisturbed, the monitored data show similar change characteristics over time. However, when the glass-lined agitator is subjected to vibration, the material movement during operation increases. Friction between particles during this movement generates heat, leading to abnormal temperature changes during mixing. Therefore, this embodiment analyzes the differences in the inflection point numbers of all types of mixing parameters and the differences in the fitted curves of various mixing parameters between any two target mixing processes. Combined with the differences in the fitted values ​​of the fitted curves of various mixing parameters at the same inflection point number between any two target mixing processes, the characteristic indices of various mixing parameters between any two target mixing processes are determined. The specific process is as follows:

[0062] S301: Fit various stirring parameters for each target stirring process, screen the inflection points on the fitting curves of various stirring parameters and number all inflection points in chronological order, analyze the differences in the inflection point numbers of various stirring parameters and the differences in the fitting curves of various stirring parameters between any two target stirring processes, and determine the difference index of various stirring parameters between any two target stirring processes, specifically:

[0063] In this embodiment, firstly, various stirring parameters in each target stirring process are fitted to obtain fitting curves for various stirring parameters. Then, inflection points on the fitting curves of various stirring parameters are screened and all inflection points are numbered in chronological order.

[0064] It should be noted that there are many commonly used fitting methods. In this embodiment, the values ​​of various stirring parameters at all times during each stirring process are used as the input of the least squares fitting method, with time as the independent variable and the values ​​of various stirring parameters as the dependent variable, and the fitting curves corresponding to various stirring parameters are output. In practical applications, as other implementation methods, implementers may also use other fitting methods such as polynomial fitting algorithms according to specific circumstances. This embodiment does not impose any special restrictions on the selection of fitting methods.

[0065] The least squares fitting method is a well-known technique, and its specific principles will not be elaborated here.

[0066] Furthermore, this embodiment determines the difference index of various stirring parameters between any two target stirring processes by analyzing the differences in the inflection point numbers of all stirring parameters and the differences in the fitted curves of various stirring parameters. Specifically:

[0067] In each stirring process, the corresponding numbers of all inflection points on the fitted curves of various stirring parameters are arranged into an inflection point sequence according to time sequence. The difference between the inflection point sequences of various stirring parameters between any two target stirring processes is calculated and recorded as the inflection point difference of various stirring parameters between any two target stirring processes.

[0068] Furthermore, the normalized value of the product of the difference in the fitted curves of various mixing parameters between any two target mixing processes and the difference in the inflection point is used as the difference index of various mixing parameters between any two target mixing processes.

[0069] It should be noted that there are many methods to measure the difference between fitted curves. In this embodiment, the DTW distance between the fitted curves of various stirring parameters between any two target stirring processes is taken as the difference between the fitted curves of various stirring parameters between any two target stirring processes. Specifically, the fitted values ​​of the fitted curves of various stirring parameters at the same time between any two target stirring processes are taken as the input of the DTW calculation formula. By traversing all the same time, the DTW distance between the two fitted curves is obtained. In practical applications, implementers may also use other methods to measure the difference between fitted curves, such as Euclidean distance or the reciprocal of cosine similarity, depending on the specific circumstances. This embodiment does not impose any special restrictions on the selection of methods to measure the difference between fitted curves.

[0070] Similarly, in this embodiment, the difference between inflection point sequences is also calculated using the DTW distance method.

[0071] The calculation method and formula for DTW are well-known technologies, and the specific calculation process will not be elaborated here.

[0072] Based on the difference index of various stirring parameters between any two target stirring processes, it can be understood that the difference index reflects the overall degree of difference between the two target stirring processes. If the difference in the inflection point of the current type of stirring parameter between the two target stirring processes is larger, it indicates that the similarity of the inflection point change of the current type of stirring parameter between the two target stirring processes is low, indicating that the difference in the change of the current type of stirring parameter between the two target stirring processes is larger, and therefore the corresponding difference index is larger. At the same time, if the difference in the fitting curve of the current type of stirring parameter between the two target stirring processes is larger, it indicates that the difference in the change trend of the current type of stirring parameter between the two target stirring processes is larger, and therefore the corresponding difference index is also larger, indicating that the difference in the change of the current type of stirring parameter between the two target stirring processes is larger.

[0073] Conversely, if the difference in the inflection point of the current type of stirring parameter between two target stirring processes is smaller, it indicates a higher similarity in the inflection point changes of the current type of stirring parameter between the two target stirring processes, indicating a smaller difference in the change of the current type of stirring parameter between the two target stirring processes, and therefore a smaller difference index. At the same time, if the difference in the fitting curve of the current type of stirring parameter between two target stirring processes is smaller, it indicates a smaller difference in the change trend of the current type of stirring parameter between the two target stirring processes, and therefore a smaller difference index, indicating a smaller difference in the change of the current type of stirring parameter between the two target stirring processes.

[0074] S302: Analyze the differences in the fitted values ​​of various stirring parameters at the same inflection point number between any two target stirring processes, determine the parameter similarity characteristic values ​​of various stirring parameters at the same inflection point number between any two target stirring processes, and determine the characteristic index of various stirring parameters between any two target stirring processes, specifically:

[0075] As a specific implementation, in this embodiment, the parameter similarity characteristic value of the k-th type of stirring parameter between the i-th target stirring process and the j-th target stirring process at the inflection point number m. The expression is: In the formula, This represents the difference in the fitted values ​​of the k-th type of stirring parameter on the fitted curve at the inflection point number m between the i-th and j-th target stirring processes. represents the mean difference between the fitted values ​​of the k-th type of stirring parameter at the inflection point m among all sub-target stirring processes; exp() represents an exponential function with the natural constant as the base.

[0076] Based on the parameter similarity characteristic values ​​of various mixing parameters under the same inflection point number between any two target mixing processes, it can be understood that if the parameter similarity characteristic value of the k-th type of mixing parameter under the inflection point number m is larger between the i-th and j-th target mixing processes, it indicates that the similarity of the changing trend of the k-th type of mixing parameter is greater between the i-th and m-th target mixing processes; conversely, if the parameter similarity characteristic value of the k-th type of mixing parameter under the inflection point number m is smaller between the i-th and m-th target mixing processes, it indicates that the similarity of the changing trend of the k-th type of mixing parameter is smaller.

[0077] Furthermore, based on parameter similarity eigenvalues, characteristic indices of various stirring parameters between any two target stirring processes are determined, specifically:

[0078] In this embodiment, the characteristic index of the k-th type of stirring parameter between the i-th target stirring process and the j-th target stirring process is... The expression is: In the formula, This represents the difference in similarity characteristic values ​​of the k-th type of stirring parameter between the i-th target stirring process and the j-th target stirring process at the inflection point number m and its adjacent previous number. This represents the number of inflection points with the same number between the k-th type of stirring parameters in the i-th target stirring process and the j-th target stirring process.

[0079] Based on the characteristic indices of various mixing parameters between any two target mixing processes, it can be understood that if the characteristic index of the k-th type of mixing parameter between the i-th and j-th target mixing processes is smaller, it indicates that the fluctuation trend of the k-th type of mixing parameter between the i-th and j-th target mixing processes is less different from the average fluctuation trend among all target mixing processes, meaning their trends are more consistent. Conversely, if the characteristic index of the k-th type of mixing parameter between the i-th and j-th target mixing processes is larger, it indicates that the fluctuation trend of the k-th type of mixing parameter between the i-th and j-th target mixing processes is more different from the average fluctuation trend among all target mixing processes, meaning their trends are more different.

[0080] Thus, this application, through in-depth analysis of the target mixing process data, firstly calculates the difference index using fitted curves and inflection point analysis to quantify the overall difference in parameter changes between any two mixing processes. Further, it compares the differences in fitted values ​​at the same inflection point to calculate parameter similarity characteristic values ​​reflecting parameter similarity. Finally, by summarizing the changes in these characteristic values, a characteristic index is obtained, which is used to determine whether the fluctuation trends of two mixing processes on specific parameters are consistent. This method can more precisely identify mixing batches with high process similarity, providing a higher quality and more consistent data foundation for subsequent numerical simulations, thereby improving the accuracy of simulation results.

[0081] Step S4: Based on the characteristic index and the difference index, determine the frequency coefficients of various stirring parameters between any two target stirring processes, so as to screen out the stirring parameters for numerical simulation.

[0082] Based on the difference index and characteristic index obtained in step S3, the frequency coefficients of various stirring parameters between any two target stirring processes are further determined to screen out the stirring parameters used for numerical simulation, specifically:

[0083] In this embodiment, the normalized value of the product of the difference index and the characteristic index of various stirring parameters between any two target stirring processes is used as the frequency coefficient of various stirring parameters between any two target stirring processes.

[0084] Preferably, the schematic diagram of the same frequency coefficient extraction process provided in this embodiment is as follows: Figure 2 As shown.

[0085] Based on the frequency coefficients of various stirring parameters between any two target stirring processes, it can be understood that if the difference index of the current type of stirring parameter between two target stirring processes is larger, it indicates that the overall change pattern of the stirring parameter between the two target stirring processes is more different. In other words, the two target stirring processes are less similar in macroscopic form, and may be subject to different disturbances or have significant differences in actual process conditions. Therefore, the corresponding frequency coefficient is also smaller. At the same time, if the characteristic index of the current type of stirring parameter between two target stirring processes is larger, it indicates that the local change trend of the parameter between the two target stirring processes is more different. That is, the fluctuation trend of the current type of stirring parameter between the two target stirring processes is more different, which means that the two target stirring processes may be affected by vibration disturbances on that parameter. Therefore, the corresponding frequency coefficient is also smaller.

[0086] Conversely, if the difference index of the current type of stirring parameter between two target stirring processes is smaller, it indicates that the overall change pattern of the stirring parameter between the two target stirring processes is smaller. In other words, the two target stirring processes are more similar in macroscopic form and have more consistent process states. Therefore, the corresponding frequency coefficient is also larger. At the same time, if the characteristic index of the current type of stirring parameter between two target stirring processes is smaller, it indicates that the difference in the local change trend of the parameter between the two target stirring processes is smaller. That is, the difference in the fluctuation trend of the current type of stirring parameter between the two target stirring processes is lower. This means that the two target stirring processes are less disturbed by this parameter and the change trend is highly consistent. Therefore, the corresponding frequency coefficient is also larger.

[0087] Furthermore, in all sub-target stirring processes, the mean value of the frequency coefficients of various stirring parameters between each target stirring process and all other sub-target stirring processes is normalized and denoted as the frequency index. If the frequency index of the k-th type of stirring parameter in the n-th target stirring process is greater than the preset frequency threshold, then the k-th type of stirring parameter in the n-th target stirring process is included in the numerical simulation process of the glass-lined agitator; otherwise, the k-th type of stirring parameter in the n-th target stirring process is not included in the numerical simulation process of the glass-lined agitator. By traversing all sub-target stirring processes, stirring parameters for numerical simulation are obtained.

[0088] It should be noted that the preset frequency threshold is set manually. In this embodiment, the preset frequency threshold is 0.6. In actual applications, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0089] At this point, step S4 calculates the frequency coefficient based on the difference index and characteristic index obtained in S3. The smaller the frequency coefficient, the greater the difference between the two mixing processes in terms of macroscopic morphology and microscopic trend, which may be due to interference or different operating conditions. The larger the frequency coefficient, the more similar the two mixing processes are and the more stable the operating conditions. By setting a frequency threshold, parameters that are highly "in sync" with most processes are selected for subsequent numerical simulation. This method can effectively eliminate data that is interfered with or has abnormal operating conditions, ensuring that the simulation is based on stable and consistent real operating condition data, thereby significantly improving the accuracy of numerical simulation.

[0090] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0091] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0092] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A numerical simulation method for glass-lined stirrers, characterized in that, The method includes the following steps: The material is stirred using a glass-lined agitator, and various stirring parameters are acquired in real time for each stirring process. In each stirring process, by analyzing the difference between the initial values ​​of various stirring parameters and the preset standard values, the deviation coefficient of various stirring parameters in each stirring process is determined, so as to determine the deviation index of each stirring process; by analyzing the difference between the deviation coefficients of various stirring parameters and the difference between the deviation index between any two stirring processes, the similarity index between any two stirring processes is determined, so as to screen out the target stirring process from all stirring processes. Fit various stirring parameters in each target stirring process, screen the inflection points on the fitting curves of various stirring parameters and number all inflection points in time sequence, analyze the differences in the inflection point numbers of various stirring parameters and the differences in the fitting curves of various stirring parameters between any two target stirring processes, and determine the difference index of various stirring parameters between any two target stirring processes. Analyze the differences in fitting values ​​of various stirring parameters at the same inflection point between any two target stirring processes, determine the parameter similarity characteristic values ​​of various stirring parameters at the same inflection point between any two target stirring processes, and thus determine the characteristic index of various stirring parameters between any two target stirring processes. Based on the characteristic index and the difference index, the frequency coefficients of various stirring parameters between any two target stirring processes are determined to screen out the stirring parameters for numerical simulation.

2. The numerical simulation method for glass-lined stirrers as described in claim 1, characterized in that, The offset coefficient of each stirring parameter in each stirring process is the result of the difference between the initial value of each stirring parameter and the preset standard value in each stirring process, multiplied by the preset error value.

3. The numerical simulation method for glass-lined stirrers as described in claim 1, characterized in that, The method for determining the offset index for each stirring process is as follows: Calculate the difference between the offset coefficient of each type of stirring parameter and the minimum value among all types of stirring parameter offset coefficients during each stirring process. Calculate the mean of the difference among all types of stirring parameters during each stirring process. Use the ratio of the mean to the range of the offset coefficients of all types of stirring parameters in the corresponding stirring process as the offset index for each stirring process.

4. The numerical simulation method for glass-lined stirrers as described in claim 1, characterized in that, The method for determining the similarity index between any two stirring processes is as follows: The difference between the offset coefficients of various mixing parameters between any two mixing processes is recorded as the offset difference value of various mixing parameters between any two mixing processes. The sum of the offset difference values ​​of all types of mixing parameter offset coefficients between any two mixing processes is recorded as the comprehensive offset difference between any two mixing processes. The reciprocal of the product of the difference in offset index between any two stirring processes and the comprehensive offset difference is normalized and used as the similarity index between any two stirring processes.

5. The numerical simulation method for glass-lined stirrers as described in claim 1, characterized in that, The process of selecting the target mixing process from all mixing processes includes: Calculate the normalized value of the mean similarity index between each stirring process and all other target stirring processes, and record it as the similarity feature value. Stirring processes with similarity feature values ​​greater than or equal to a preset threshold are taken as target stirring processes.

6. The numerical simulation method for glass-lined stirrers as described in claim 1, characterized in that, The method for determining the difference index of various stirring parameters between any two target stirring processes is as follows: In each stirring process, the corresponding numbers of all inflection points on the fitted curves of various stirring parameters are arranged into an inflection point sequence according to time sequence. The difference between the inflection point sequences of various stirring parameters between any two target stirring processes is calculated and recorded as the inflection point difference of various stirring parameters between any two target stirring processes. The normalized value of the product of the difference in the fitted curves of various mixing parameters between any two target mixing processes and the difference in the inflection point is used as the difference index of various mixing parameters between any two target mixing processes.

7. The numerical simulation method for glass-lined stirrers as described in claim 1, characterized in that, The expression for the parameter similarity characteristic values ​​of various stirring parameters under the same inflection point number between any two target stirring processes is: In the formula, This represents the parameter similarity feature value of the k-th type of stirring parameters between the i-th target stirring process and the j-th target stirring process at the inflection point number m; This represents the difference in the fitted values ​​of the k-th type of stirring parameter on the fitted curve at the inflection point number m between the i-th and j-th target stirring processes. represents the mean difference between the fitted values ​​of the k-th type of stirring parameter at the inflection point m among all sub-target stirring processes; exp() represents an exponential function with the natural constant as the base.

8. The numerical simulation method for glass-lined stirrers as described in claim 1, characterized in that, The expression for the characteristic indices of various stirring parameters between any two target stirring processes is: In the formula, The characteristic index represents the k-th type of stirring parameter between the i-th target stirring process and the j-th target stirring process; This represents the difference in similarity characteristic values ​​of the k-th type of stirring parameter between the i-th target stirring process and the j-th target stirring process at the inflection point number m and its adjacent previous number. This represents the number of inflection points with the same number between the k-th type of stirring parameters in the i-th target stirring process and the j-th target stirring process.

9. The numerical simulation method for glass-lined stirrers as described in claim 1, characterized in that, The frequency coefficients of various stirring parameters between any two target stirring processes are the normalized values ​​of the product of the difference index and the characteristic index of various stirring parameters between any two target stirring processes.

10. The numerical simulation method for glass-lined stirrers as described in claim 1, characterized in that, The selected stirring parameters for numerical simulation include: In all sub-target stirring processes, the mean value of the frequency coefficients of various stirring parameters between each target stirring process and all other sub-target stirring processes is normalized and denoted as the frequency index. If the frequency index of the k-th type of stirring parameter in the n-th target stirring process is greater than the preset frequency threshold, then the k-th type of stirring parameter in the n-th target stirring process is included in the numerical simulation process of the glass-lined agitator; otherwise, the k-th type of stirring parameter in the n-th target stirring process is not included in the numerical simulation process of the glass-lined agitator. All sub-target stirring processes are traversed to obtain the stirring parameters used for numerical simulation.

Images