Washing and impurity removing system for lithium carbonate preparation

Abstract

The invention discloses a washing and impurity removing system for lithium carbonate preparation, and the system specifically comprises a historical data extraction and arrangement module which is used for extracting and arranging historical data, and the historical data comprises lithium carbonate particle size diameter data and corresponding washing liquid nozzle angle data. According to the method, the correlation between the particle size diameter of lithium carbonate and the angle of the washing liquid nozzle can be accurately judged by extracting and analyzing historical data, and meanwhile, comprehensive calculation is performed in a multi-dimensional data combination mode when the correlation is judged, so that the accuracy and the reliability of correlation judgment are further improved; data support is provided for washing and impurity removal in the lithium carbonate preparation process through correlation judgment, the accuracy and efficiency of decision making are improved, refined control over the washing process can be achieved by determining the correlation type between the lithium carbonate particle size and the washing liquid spray head angle, the washing liquid spray head angle is optimized, and the washing effect is improved. Therefore, the quality and the production efficiency of lithium carbonate products are improved.

Description

Technical Field

[0001] The invention relates to the technical field of lithium carbonate preparation, and in particular to a washing and impurity removal system for lithium carbonate preparation. Background Art

[0002] Lithium carbonate is an inorganic compound with the chemical formula Li2CO3 and a molecular weight of 73.89. It is a colorless monoclinic crystal that is slightly soluble in water and dilute acid, but insoluble in ethanol and acetone. It needs to be washed and impurities removed during the preparation of carbonate.

[0003] The relationship between lithium carbonate particle size and washing liquid nozzle angle is mutually influential. On the one hand, the particle size of lithium carbonate determines the contact area between the washing liquid and the particle surface and the difficulty of entraining attached liquid, thereby affecting the demand for washing liquid and washing time. On the other hand, the washing liquid nozzle angle determines the distribution and impact force of the washing liquid, further affecting the washing effect and product quality.

[0004] In actual operation, the angle of the washing liquid nozzle needs to be adjusted according to the particle size and washing requirements of the lithium carbonate. For example, for lithium carbonate particles with smaller particle size, a smaller nozzle angle may be required to avoid particle breakage and uneven washing, while for particles with larger particle size, a larger nozzle angle can be used to improve washing efficiency and effect;

[0005] However, the adjustment of the washing liquid nozzle angle mainly relies on manual experience and intuitive judgment, and there is a lack of in-depth understanding of the complex relationship between the lithium carbonate particle size and the washing liquid nozzle angle. As a result, in actual operation, the adjustment of the washing liquid nozzle angle is often not precise enough, making it difficult to meet the refined control requirements in the production process. Summary of the invention

[0006] The object of the present invention is to provide a washing and impurity removal system for preparing lithium carbonate to solve at least one of the above-mentioned problems of the prior art.

[0007] A washing and impurity removal system for preparing lithium carbonate, specifically comprising:

[0008] Historical data extraction and sorting module: extracting and sorting historical data, wherein the historical data includes lithium carbonate particle diameter data and corresponding washing liquid nozzle angle data;

[0009] Historical data analysis module: obtains the sorted historical data, performs data analysis, and outputs the correlation judgment value between the lithium carbonate particle size diameter and the corresponding washing liquid nozzle angle;

[0010] The process of obtaining the correlation judgment value is as follows: by analyzing the particle size diameter of lithium carbonate and the nozzle angle of the washing liquid, the slope characterization value and the correlation coefficient are calculated, and the slope characterization value and the correlation coefficient are substituted into the formula to calculate the correlation judgment value;

[0011] Correlation type judgment module: judging the correlation type between the lithium carbonate particle diameter and the corresponding washing liquid nozzle angle according to the output correlation judgment value;

[0012] Among them, the correlation types include linear relationship and nonlinear relationship;

[0013] Model building module: based on the correlation type judgment results, build a particle size diameter-nozzle angle optimization model;

[0014] Angle optimization and adjustment module: measure and obtain the real-time lithium carbonate particle size diameter, substitute it into the particle size diameter-nozzle angle optimization model, output the nozzle angle prediction value, and optimize the real-time nozzle angle value.

[0015] As a further technical solution of the present invention, the process of obtaining the correlation judgment value is as follows:

[0016] Obtain the slope characterization value and the correlation coefficient, substitute the slope characterization value XL and the correlation coefficient R into the formula, and calculate the correlation judgment value XG;

[0017] The calculation formula of relevant judgment XG is: Among them, s1 and s2 are weight coefficients.

[0018] As a further technical solution of the present invention, the process of obtaining the slope characterization value is as follows:

[0019] Obtain the approximate slope proportion value, the slope mean difference ratio, and the slope ratio extreme value difference, substitute the approximate slope proportion value, the slope mean difference ratio, and the slope ratio extreme value difference into the formula, and calculate the slope characterization value;

[0020] The calculation formula of the slope characterization value XL is: The slope characterization value XL is calculated, where JZ represents the approximate slope ratio, JB represents the slope mean difference ratio, JX represents the slope extreme value difference, and a1, a2, and a3 are weight coefficients.

[0021] As a further technical solution of the present invention, the process of obtaining the approximate slope proportion, the slope mean difference ratio, and the slope ratio extreme value difference is as follows:

[0022] Obtain the slope value of the sub-curve and the slope of the endpoint connecting line, analyze the slope value of the sub-curve and the slope of the endpoint connecting line, and obtain the approximate slope and slope ratio;

[0023] Count the number of approximate slopes, and perform a ratio process on the number of approximate slopes and the total number of sub-curves to obtain the approximate slope ratio.

[0024] The slope values ​​of all sub-curves are summed and averaged to obtain the slope mean, the slope mean is calculated with the slope of the endpoint connection line, the difference is taken as the absolute value, and the difference is calculated with the slope of the endpoint connection line to obtain the slope mean difference ratio;

[0025] The maximum and minimum values ​​in the slope ratio are extracted, and the difference between the maximum and minimum values ​​is calculated to obtain the slope ratio extreme value difference.

[0026] As a further technical solution of the present invention, the process of obtaining the slope value of the sub-curve and the slope of the endpoint connecting line is as follows:

[0027] Obtain the sorted array, establish a two-dimensional model with the lithium carbonate particle diameter as the X-axis and the nozzle angle of the washing liquid as the Y-axis, and draw a particle diameter-nozzle angle curve;

[0028] Connect the two end points of the particle diameter-nozzle angle curve to obtain an end point connecting line, and calculate the slope of the end point connecting line;

[0029] The curves corresponding to the adjacent lithium carbonate particle size diameter values ​​are marked as sub-curves, and the slope values ​​of all sub-curves are calculated.

[0030] The slope of the sub-curve is calculated to be different from the slope of the endpoint connection line, the absolute value of the difference is taken to obtain the slope difference, and the slope difference is processed to be a ratio of the slope of the endpoint connection line to obtain the slope ratio;

[0031] The slope ratio is compared with the slope ratio threshold. If the slope ratio is less than the slope ratio threshold, it is marked as an approximate slope. If the slope ratio is greater than the slope ratio threshold, it is marked as a difference slope.

[0032] As a further technical solution of the present invention, the process of obtaining the correlation coefficient is:

[0033] Substitute the array of lithium carbonate particle diameter and washing liquid nozzle angle in the historical data into the Pearson correlation coefficient calculation formula: The correlation coefficient R is calculated, where X represents the particle diameter of lithium carbonate, Y represents the nozzle angle of the washing liquid, n represents the number of arrays, and i represents the array number.

[0034] As a further technical solution of the present invention, the process of determining the type of correlation between the particle size diameter of lithium carbonate and the corresponding washing liquid nozzle angle is as follows:

[0035] Obtaining a relevant judgment value, and comparing the relevant judgment value with a relevant judgment threshold range;

[0036] If the relevant judgment value is within the relevant judgment threshold range, there is a linear relationship between the lithium carbonate particle diameter and the corresponding washing liquid nozzle angle, and the particle diameter-nozzle angle curve is marked as a linear change curve;

[0037] If the relevant judgment value is not within the relevant judgment threshold range, there is a nonlinear relationship between the lithium carbonate particle diameter and the corresponding washing liquid nozzle angle, and the particle diameter-nozzle angle curve is marked as a nonlinear change curve.

[0038] As a further technical solution of the present invention, the process of constructing the particle size diameter-nozzle angle optimization model includes:

[0039] If the particle size diameter-nozzle angle curve is marked as a linear change curve, a linear regression model is used for fitting. The lithium carbonate particle size diameter and the corresponding washing liquid nozzle angle in the historical data are used to fit the linear regression model through the least squares method, and the linear regression model is obtained, which is the particle size diameter-nozzle angle optimization model.

[0040] As a further technical solution of the present invention, the process of constructing the particle size diameter-nozzle angle optimization model also includes:

[0041] If the particle size diameter-nozzle angle curve is marked as a nonlinear change curve, a neural network model is selected for training, and the neural network model obtained by training is the particle size diameter-nozzle angle optimization model.

[0042] As a further technical solution of the present invention, the process of optimizing the real-time nozzle angle value is as follows:

[0043] Measure and obtain the real-time lithium carbonate particle size diameter, substitute the real-time lithium carbonate particle size diameter into the optimization model, and output the predicted value of the nozzle angle;

[0044] The real-time value of the nozzle angle is measured, the difference between the real-time value of the nozzle angle and the predicted value of the nozzle angle is calculated, and the absolute value of the difference is taken to obtain the nozzle angle deviation value;

[0045] Compare the nozzle angle deviation value with the nozzle angle deviation threshold, wherein the nozzle angle deviation threshold is set by a person skilled in the art based on a summary of multiple historical experimental data;

[0046] If the nozzle angle deviation value is less than or equal to the nozzle angle deviation threshold, the difference between the nozzle angle real-time value and the nozzle angle prediction value is within a reasonable range, and an angle non-adjustment signal is generated;

[0047] If the nozzle angle deviation value is greater than the nozzle angle deviation threshold, the difference between the nozzle angle real-time value and the nozzle angle prediction value is not within a reasonable range, and an angle adjustment signal is generated;

[0048] Based on the angle adjustment signal, the nozzle angle is adjusted to the nozzle angle prediction value.

[0049] Beneficial effects of the present invention:

[0050] 1. The present invention can accurately judge the correlation between the particle size diameter of lithium carbonate and the angle of the washing liquid nozzle by extracting and analyzing historical data. At the same time, a comprehensive calculation is performed by combining multi-dimensional data when judging the correlation, which further improves the accuracy and reliability of the correlation judgment. The correlation judgment provides data support for washing and impurity removal in the lithium carbonate preparation process, improves the accuracy and efficiency of decision-making, and can achieve refined control of the washing process and optimize the washing liquid nozzle angle by determining the correlation type between the lithium carbonate particle size and the washing liquid nozzle angle, thereby improving the quality and production efficiency of lithium carbonate products;

[0051] 2. The present invention can predict the nozzle angle required under different particle size diameters, thereby optimizing the washing and impurity removal process and improving the quality and production efficiency of lithium carbonate products. The system can flexibly select linear regression or neural network model for fitting according to the correlation type between particle size diameter and nozzle angle to adapt to the processing requirements of different data relationships. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 This is a flow chart of a washing and impurity removal system for preparing lithium carbonate according to the present invention;

[0054] Figure 2 The present invention is a schematic diagram of the calculation process of the relevant judgment value in the washing and impurity removal system for preparing lithium carbonate. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0056] Embodiment 1

[0057] like Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a washing and impurity removal system for preparing lithium carbonate, which specifically includes:

[0058] Historical data extraction and sorting module: extracting and sorting historical data, wherein the historical data includes lithium carbonate particle diameter data and corresponding washing liquid nozzle angle data;

[0059] In some embodiments, the particle size of lithium carbonate is obtained by a laser particle size analyzer. The specific acquisition process is: ensure that the laser particle size analyzer is in a stable environment, away from vibration and interference sources, turn on the host power, and preheat for 15 minutes to ensure the stability of the instrument;

[0060] Turn on the disperser power supply and prepare the corresponding sample processing equipment;

[0061] Obtain an appropriate amount of sample from the lithium carbonate preparation process and open the laser particle size analyzer application software on the computer;

[0062] Setting appropriate measurement parameters according to sample characteristics, wherein the measurement parameters include but are not limited to laser power, scattering angle, and detection range;

[0063] Set the shading limit in the software operation interface to ensure the accuracy of the measurement results;

[0064] Inject the processed sample into the sample chamber or introduce it into the sample pool, adjust the sample chamber or light path alignment to ensure that the particle stream is properly illuminated in the laser beam;

[0065] Start the measurement program, and the instrument will analyze and determine the particle size distribution through the scattered light signal;

[0066] After the measurement is completed, the data is recorded, and the recorded data is the particle size diameter of lithium carbonate;

[0067] The angle of the washing liquid nozzle is obtained by an angle measuring device, wherein the angle measuring device includes but is not limited to an angle meter. The specific acquisition process is: the angle measuring device is correctly installed on the washing liquid nozzle and calibrated to ensure the accuracy of the measurement result;

[0068] Start the measuring device, record the angle data of the washing liquid nozzle in the working state for many times, sum and average the angle data of the multiple distances, and the average is the required angle data of the washing liquid nozzle;

[0069] The lithium carbonate particle size diameter and washing liquid nozzle angle values ​​in the historical data are sorted out, wherein the sorting process is:

[0070] The corresponding lithium carbonate particle diameter and washing liquid nozzle angle values ​​are marked as the same array, and then the abnormal values ​​are cleaned, and the lithium carbonate particle diameter limit range and the washing liquid nozzle angle limit range are set, and the arrays that are not within the limit range are deleted;

[0071] Historical data analysis module: obtains the sorted historical data, performs data analysis, and outputs the correlation judgment value between the lithium carbonate particle size diameter and the corresponding washing liquid nozzle angle;

[0072] In some embodiments, the sorted array is obtained, and a two-dimensional model is established with the particle diameter of lithium carbonate as the X-axis and the nozzle angle of the washing liquid as the Y-axis to draw a particle diameter-nozzle angle curve;

[0073] It should be noted that, as mentioned in the background technology, for lithium carbonate particles with smaller particle sizes, a smaller nozzle angle may be required, while for particles with larger particle sizes, a larger nozzle angle may be used, so the particle diameter-nozzle angle curve shows an increasing trend;

[0074] Connect the two end points of the particle diameter-nozzle angle curve to obtain an end point connecting line, and calculate the slope of the end point connecting line;

[0075] The curves corresponding to the adjacent lithium carbonate particle size diameter values ​​are marked as sub-curves, and the slope values ​​of all sub-curves are calculated.

[0076] The slope of the sub-curve is calculated to be different from the slope of the endpoint connection line, the absolute value of the difference is taken to obtain the slope difference, and the slope difference is processed to be a ratio of the slope of the endpoint connection line to obtain the slope ratio;

[0077] Compare the slope ratio with a slope ratio threshold, if the slope ratio is less than the slope ratio threshold, mark it as an approximate slope, if the slope ratio is greater than the slope ratio threshold, mark it as a difference slope;

[0078] The slope ratio threshold is set by those skilled in the art based on historical experimental data.

[0079] Count the number of approximate slopes, and perform a ratio process on the number of approximate slopes and the total number of sub-curves to obtain the approximate slope ratio.

[0080] The slope values ​​of all sub-curves are summed and averaged to obtain the slope mean, the slope mean is calculated with the slope of the endpoint connection line, the difference is taken as the absolute value, and the difference is calculated with the slope of the endpoint connection line to obtain the slope mean difference ratio;

[0081] Extract the maximum and minimum values ​​in the slope ratio, calculate the difference between the maximum and minimum values, and obtain the slope ratio extreme value difference;

[0082] Substitute the approximate slope proportion, slope mean difference ratio, and slope extreme value difference into the formula to calculate the slope representation value;

[0083] In this embodiment, since the slope mean difference ratio and the slope extreme value difference are proportional to the slope characterization value, and the approximate slope ratio is inversely proportional to the slope characterization value, the calculation formula of the slope characterization value XL is: The slope representation value XL is calculated, where JZ represents the approximate slope ratio, JB represents the slope mean difference ratio, JX represents the slope extreme value difference, a1, a2, and a3 are weight coefficients, a1 is 0.37, a2 is 0.32, and a3 is 0.31;

[0084] Substitute the array of lithium carbonate particle diameter and washing liquid nozzle angle in the historical data into the Pearson correlation coefficient calculation formula: The correlation coefficient R is calculated, where X represents the particle diameter of lithium carbonate, Y represents the nozzle angle of the washing liquid, n represents the number of arrays, and i represents the array number;

[0085] It needs to be explained that, since the particle size diameter-nozzle angle curve shows an increasing trend, the correlation coefficient is between 0 and 1;

[0086] Substitute the slope representation value XL and the correlation coefficient R into the formula to calculate the correlation judgment value XG;

[0087] In this embodiment, the calculation formula of the relevant judgment XG is: Among them, s1 and s2 are weight coefficients, the value of s1 is 0.48, and the value of s2 is 0.52;

[0088] Correlation type judgment module: judging the correlation type between the lithium carbonate particle diameter and the corresponding washing liquid nozzle angle according to the output correlation judgment value;

[0089] Among them, the correlation types include linear relationship and nonlinear relationship;

[0090] In some embodiments, a relevant judgment value is obtained, and the relevant judgment value is compared with a relevant judgment threshold range, wherein the relevant judgment threshold range is summarized and set by a person skilled in the art based on historical multiple experimental data;

[0091] If the relevant judgment value is within the relevant judgment threshold range, it means that there is a linear relationship between the lithium carbonate particle diameter and the corresponding washing liquid nozzle angle, and the particle diameter-nozzle angle curve is marked as a linear change curve;

[0092] If the relevant judgment value is not within the relevant judgment threshold range, it means that there is a nonlinear relationship between the lithium carbonate particle diameter and the corresponding washing liquid nozzle angle, and the particle diameter-nozzle angle curve is marked as a nonlinear change curve;

[0093] The technical solution of this embodiment is: historical data extraction and sorting module is used to obtain and sort historical data of lithium carbonate particle size diameter and washing liquid nozzle angle, historical data analysis module analyzes these data, calculates slope characterization value and correlation coefficient, and then obtains correlation judgment value, finally, correlation type judgment module judges the correlation type between lithium carbonate particle size diameter and washing liquid nozzle angle according to correlation judgment value, so that by extracting and analyzing historical data, the correlation between lithium carbonate particle size diameter and washing liquid nozzle angle can be accurately judged, and at the same time, when judging the correlation, a comprehensive calculation is performed by combining multi-dimensional data, which further improves the accuracy and reliability of correlation judgment, and correlation judgment provides data support for washing and impurity removal in the lithium carbonate preparation process, improves the accuracy and efficiency of decision-making, and by determining the correlation type between lithium carbonate particle size and washing liquid nozzle angle, it is possible to achieve refined control of the washing process and optimize the washing liquid nozzle angle, thereby improving the quality and production efficiency of lithium carbonate products.

[0094] Embodiment 2

[0095] like Figure 1 As shown, an embodiment of the present invention provides a washing and impurity removal system for preparing lithium carbonate, which specifically includes:

[0096] Model building module: based on the correlation type judgment results, build a particle size diameter-nozzle angle optimization model;

[0097] In some embodiments, if the particle size diameter-nozzle angle curve is marked as a linear change curve, a linear regression model is used for fitting, and the lithium carbonate particle size diameter and the corresponding washing liquid nozzle angle in the historical data are used for fitting by the least squares method to obtain a linear regression model, which is a particle size diameter-nozzle angle optimization model;

[0098] If the particle size diameter-nozzle angle curve is marked as a nonlinear change curve, the neural network model is selected for training, and the neural network model obtained by training is the particle size diameter-nozzle angle optimization model;

[0099] It needs to be explained that neural networks have powerful nonlinear modeling capabilities and can handle complex data relationships. At the same time, neural networks are suitable for large-scale data processing and can process large amounts of historical data to improve the prediction accuracy of the model.

[0100] In this embodiment, the specific process of training the neural network model is as follows: designing a suitable artificial neural network structure, including an input layer, a hidden layer and an output layer, wherein the input layer receives the lithium carbonate particle diameter data, the hidden layer may include one or more layers, each layer including a certain number of neurons for extracting the nonlinear characteristics of the data, and the output layer outputs the predicted detergent nozzle angle;

[0101] Select appropriate activation functions (such as ReLU, si gmo id, etc.) and loss functions (such as mean square error MSE, etc.) to optimize network performance;

[0102] Use historical data to train the neural network, and adjust the network parameters through the back propagation algorithm to minimize the error between the predicted results and the actual results. During the training process, optimization algorithms such as batch gradient descent and stochastic gradient descent can be used to update the network weights;

[0103] Set appropriate hyperparameters such as the number of training rounds and learning rate to ensure that the model can fully learn and generalize;

[0104] Exemplarily, the input layer: 1 neuron, receiving the lithium carbonate particle size diameter X, the hidden layer: 1 layer, including 10 neurons, using ReLU as the activation function, the output layer: 1 neuron, outputting the predicted detergent nozzle angle Y, and selecting the mean square error (MSE) as the loss function to measure the error between the predicted value and the actual value;

[0105] Initialization parameters: Randomly initialize the network weights and biases, set hyperparameters: set the learning rate to 0.01, the number of training rounds to 1000, the batch size to 32, and divide the historical data into training set and test set with a ratio of 8:2;

[0106] Use the training set data to train the neural network. In each round of training, randomly select a batch size of data, calculate the prediction value through forward propagation, then calculate the loss function value, use the backpropagation algorithm to calculate the gradient, and update the network weights and biases;

[0107] Repeat the above process until the set number of training rounds is reached or the loss function value no longer decreases significantly;

[0108] Angle optimization and adjustment module: measure and obtain the real-time lithium carbonate particle diameter, substitute it into the particle diameter-nozzle angle optimization model, output the nozzle angle prediction value, and optimize the real-time nozzle angle value;

[0109] In some embodiments, before washing, the real-time lithium carbonate particle size diameter is measured and obtained, the real-time lithium carbonate particle size diameter is substituted into the optimization model, and the nozzle angle prediction value is output;

[0110] It should be noted that after the above measurement obtains the real-time lithium carbonate particle size diameter, it is necessary to determine whether the particle size diameter is within the lithium carbonate particle size diameter limit range;

[0111] The real-time value of the nozzle angle is measured, the difference between the real-time value of the nozzle angle and the predicted value of the nozzle angle is calculated, and the absolute value of the difference is taken to obtain the nozzle angle deviation value;

[0112] Compare the nozzle angle deviation value with the nozzle angle deviation threshold, wherein the nozzle angle deviation threshold is set by a person skilled in the art based on a summary of multiple historical experimental data;

[0113] If the nozzle angle deviation value is less than or equal to the nozzle angle deviation threshold, the difference between the nozzle angle real-time value and the nozzle angle prediction value is within a reasonable range, and an angle non-adjustment signal is generated;

[0114] If the nozzle angle deviation value is greater than the nozzle angle deviation threshold, the difference between the nozzle angle real-time value and the nozzle angle prediction value is not within a reasonable range, and an angle adjustment signal is generated;

[0115] Based on the angle adjustment signal, the nozzle angle is adjusted to the nozzle angle prediction value;

[0116] The technical solution of this embodiment is: according to the type of correlation between the particle size diameter and the nozzle angle, a linear regression or neural network model is constructed as a particle size diameter-nozzle angle optimization model through a model construction module. The angle optimization adjustment module uses the real-time measured lithium carbonate particle size diameter, substitutes it into the optimization model to predict the nozzle angle, and compares it with the real-time nozzle angle. It decides whether to adjust the nozzle angle according to the deviation value, so as to predict the nozzle angle required under different particle size diameters, thereby optimizing the washing and impurity removal process, and improving the quality and production efficiency of lithium carbonate products. The system can flexibly select linear regression or neural network models for fitting according to the type of correlation between the particle size diameter and the nozzle angle to adapt to the processing requirements of different data relationships.

[0117] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0118] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0119] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0120] 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, that is, they 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.

[0121] 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.

[0122] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A washing and impurity removal system for lithium carbonate preparation, characterized in that: Specifically include: Historical data extraction and sorting module: extracting and sorting historical data, wherein the historical data includes lithium carbonate particle diameter data and corresponding washing liquid nozzle angle data; Historical data analysis module: obtains the sorted historical data, performs data analysis, and outputs the correlation judgment value between the lithium carbonate particle size diameter and the corresponding washing liquid nozzle angle; The process of obtaining the correlation judgment value is as follows: by analyzing the particle size diameter of lithium carbonate and the nozzle angle of the washing liquid, the slope characterization value and the correlation coefficient are calculated, and the slope characterization value and the correlation coefficient are substituted into the formula to calculate the correlation judgment value; Correlation type judgment module: judging the correlation type between the lithium carbonate particle diameter and the corresponding washing liquid nozzle angle according to the output correlation judgment value; Among them, the correlation types include linear relationship and nonlinear relationship; Model building module: based on the correlation type judgment results, build a particle size diameter-nozzle angle optimization model; Angle optimization and adjustment module: measure and obtain the real-time lithium carbonate particle size diameter, substitute it into the particle size diameter-nozzle angle optimization model, output the nozzle angle prediction value, and optimize the real-time nozzle angle value.

2. A washing and impurity removal system for preparing lithium carbonate according to claim 1, characterized in that: The process of obtaining the correlation judgment value is as follows: Obtain the slope characterization value and the correlation coefficient, substitute the slope characterization value XL and the correlation coefficient R into the formula, and calculate the correlation judgment value XG; The calculation formula of relevant judgment XG is: Among them, s1 and s2 are weight coefficients.

3. A washing and impurity removal system for preparing lithium carbonate according to claim 1, characterized in that: The process of obtaining the slope characterization value is as follows: Obtain the approximate slope proportion value, the slope mean difference ratio, and the slope ratio extreme value difference, substitute the approximate slope proportion value, the slope mean difference ratio, and the slope ratio extreme value difference into the formula, and calculate the slope characterization value; The calculation formula of the slope characterization value XL is: The slope characterization value XL is calculated, where JZ represents the approximate slope ratio, JB represents the slope mean difference ratio, JX represents the slope extreme value difference, and a1, a2, and a3 are weight coefficients.

4. A washing and impurity removal system for lithium carbonate preparation according to claim 3, characterized in that: The process of obtaining the approximate slope ratio, slope mean difference ratio, and slope ratio extreme value difference is as follows: Obtain the slope value of the sub-curve and the slope of the endpoint connecting line, analyze the slope value of the sub-curve and the slope of the endpoint connecting line, and obtain the approximate slope and slope ratio; Count the number of approximate slopes, and perform a ratio process on the number of approximate slopes and the total number of sub-curves to obtain the approximate slope ratio. The slope values ​​of all sub-curves are summed and averaged to obtain the slope mean, the slope mean is calculated with the slope of the endpoint connection line, the difference is taken as the absolute value, and the difference is calculated with the slope of the endpoint connection line to obtain the slope mean difference ratio; The maximum and minimum values ​​in the slope ratio are extracted, and the difference between the maximum and minimum values ​​is calculated to obtain the slope ratio extreme value difference.

5. A washing and impurity removal system for preparing lithium carbonate according to claim 4, characterized in that: The process of obtaining the slope value of the sub-curve and the slope of the endpoint connecting line is as follows: Obtain the sorted array, establish a two-dimensional model with the lithium carbonate particle diameter as the X-axis and the nozzle angle of the washing liquid as the Y-axis, and draw a particle diameter-nozzle angle curve; Connect the two end points of the particle diameter-nozzle angle curve to obtain an end point connecting line, and calculate the slope of the end point connecting line; The curves corresponding to the adjacent lithium carbonate particle size diameter values ​​are marked as sub-curves, and the slope values ​​of all sub-curves are calculated. The slope of the sub-curve is calculated to be different from the slope of the endpoint connection line, the absolute value of the difference is taken to obtain the slope difference, and the slope difference is processed to be a ratio of the slope of the endpoint connection line to obtain the slope ratio; The slope ratio is compared with the slope ratio threshold, and if the slope ratio is less than the slope ratio threshold, it is marked as an approximate slope.

6. A washing and impurity removal system for preparing lithium carbonate according to claim 1, characterized in that: The process of obtaining the correlation coefficient is: Substitute the array of lithium carbonate particle diameter and washing liquid nozzle angle in the historical data into the Pearson correlation coefficient calculation formula: The correlation coefficient R is calculated, where X represents the particle diameter of lithium carbonate, Y represents the nozzle angle of the washing liquid, n represents the number of arrays, and i represents the array number.

7. A washing and impurity removal system for preparing lithium carbonate according to claim 1, characterized in that: The process of determining the correlation type between the lithium carbonate particle size diameter and the corresponding washing liquid nozzle angle is as follows: Obtaining a relevant judgment value, and comparing the relevant judgment value with a relevant judgment threshold range; If the relevant judgment value is within the relevant judgment threshold range, there is a linear relationship between the lithium carbonate particle diameter and the corresponding washing liquid nozzle angle, and the particle diameter-nozzle angle curve is marked as a linear change curve; If the relevant judgment value is not within the relevant judgment threshold range, there is a nonlinear relationship between the lithium carbonate particle diameter and the corresponding washing liquid nozzle angle, and the particle diameter-nozzle angle curve is marked as a nonlinear change curve.

8. A washing and impurity removal system for lithium carbonate preparation according to claim 7, characterized in that: The process of constructing the particle size diameter-nozzle angle optimization model includes: If the particle size diameter-nozzle angle curve is marked as a linear change curve, a linear regression model is used for fitting. The lithium carbonate particle size diameter and the corresponding washing liquid nozzle angle in the historical data are used to fit the linear regression model through the least squares method, and the linear regression model is obtained, which is the particle size diameter-nozzle angle optimization model.

9. A washing and impurity removal system for preparing lithium carbonate according to claim 7, characterized in that: The process of constructing the particle size diameter-nozzle angle optimization model also includes: If the particle size diameter-nozzle angle curve is marked as a nonlinear change curve, a neural network model is selected for training, and the neural network model obtained by training is the particle size diameter-nozzle angle optimization model.

10. A washing and impurity removal system for preparing lithium carbonate according to claim 1, characterized in that: The process of optimizing the real-time nozzle angle value is as follows: Measure and obtain the real-time lithium carbonate particle size diameter, substitute the real-time lithium carbonate particle size diameter into the optimization model, and output the predicted value of the nozzle angle; The real-time value of the nozzle angle is measured, the difference between the real-time value of the nozzle angle and the predicted value of the nozzle angle is calculated, and the absolute value of the difference is taken to obtain the nozzle angle deviation value; Compare the nozzle angle deviation value with the nozzle angle deviation threshold, wherein the nozzle angle deviation threshold is set by a person skilled in the art based on a summary of multiple historical experimental data; If the nozzle angle deviation value is less than or equal to the nozzle angle deviation threshold, the difference between the nozzle angle real-time value and the nozzle angle prediction value is within a reasonable range, and an angle non-adjustment signal is generated; If the nozzle angle deviation value is greater than the nozzle angle deviation threshold, the difference between the nozzle angle real-time value and the nozzle angle prediction value is not within a reasonable range, and an angle adjustment signal is generated; Based on the angle adjustment signal, the nozzle angle is adjusted to the nozzle angle prediction value.

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