A method and system for producing high-purity graphite

Abstract

The present invention relates to the technical field of high-purity graphite production, and proposes a high-purity graphite production method and system, including: preparation stage: obtaining solution 1, recording pure water, graphite material and solution 1 as reaction materials, and putting the reaction materials into a polyethylene reaction tank and mixing them evenly; first reaction stage: heating, repeatedly adding pure water, stirring and filtering until the reaction materials are neutral; second reaction stage: obtaining solution 2, adding solution 2 to the reaction materials and heating, repeatedly adding pure water, stirring and filtering until the reaction materials are neutral, and obtaining high-purity graphite; in the second reaction stage, collecting temperature data and pH value, obtaining and determining overall balance parameters, determining adaptive integral coefficients, and using PID control algorithm to obtain the amount of material for adjusting pH value. The present invention solves the problem that the unreasonable parameter setting of the PID control algorithm leads to unstable pH value in the preparation process of high-purity graphite, which affects the quality of high-purity graphite.

Description

Technical Field

[0001] The invention relates to the technical field of high-purity graphite production, and in particular to a high-purity graphite production method and system. Background Art

[0002] High-purity graphite is widely used in many fields such as metallurgy, military, nuclear energy, new energy batteries, electronic information, etc. In the process of obtaining high-purity graphite, the reaction temperature needs to be changed many times to ensure the key reaction. When the temperature changes, the chemical reaction rate of the reactants and the solubility of the substances will also change accordingly. When the temperature rises, the chemical reaction rate usually accelerates. At the same time, overheating or overcooling may inhibit key reactions or induce side reactions, resulting in pH fluctuations. In addition, the efficiency of the buffer system in the polyethylene reactor will also be affected by temperature changes, further affecting the stability of the pH value.

[0003] In order to ensure the stability of the pH value in the polyethylene reaction tank, it is necessary to add materials to adjust the pH value when the pH value of the reaction material changes. Generally, the PID control algorithm is used to obtain the amount of materials added to adjust the pH value, but the integral parameters of the PID control algorithm need to be set manually. It is easy to cause inaccurate calculation of the amount of materials added to the pH value due to inappropriate parameter settings, causing the reaction materials to react under inappropriate acid-base conditions, reducing the removal efficiency of impurities, affecting the graphite structure, and thus affecting the quality of high-purity graphite. Summary of the invention

[0004] The present invention provides a high-purity graphite production method and system to solve the problem that the unreasonable parameter setting of the PID control algorithm leads to the unstable pH value in the preparation process of high-purity graphite, which affects the quality of the high-purity graphite. The technical scheme adopted is as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for producing high-purity graphite, the method comprising the following steps:

[0006] Preparation stage: mixing a hydrofluoric acid aqueous solution and a hydrochloric acid aqueous solution to obtain a solution 1, recording pure water, a graphite material and the solution 1 as reaction materials, and putting the reaction materials into a polyethylene reaction tank and mixing them evenly;

[0007] The first reaction stage: Steam is introduced into the polyethylene reaction tank for heating, the waste acid water in the polyethylene reaction tank is extracted, and pure water is repeatedly added for stirring and filtering until the reaction materials are washed and filtered to neutrality;

[0008] Second reaction stage: Mix hydrochloric acid and nitric acid to obtain solution 2, add solution 2 to the polyethylene reaction tank and heat it, extract the waste acid water in the polyethylene reaction tank through a vacuum pump connected to a suction filtration device, repeatedly add pure water, stir and filter until the reaction materials are washed and filtered to neutrality, and obtain high-purity graphite;

[0009] During the second reaction stage, temperature data and pH values ​​are collected and preprocessed to obtain temperature and pH sequences; anomaly detection is performed on the pH sequence, outliers are obtained and assigned values, and characteristic values ​​of the variation pattern of the pH sequence are determined; reference comparison data of the temperature sequence is determined, the influencing deviation factor is determined, and the overall balance parameter is determined; the adaptive integral coefficient of the next adjacent data update interval is determined based on the overall balance parameter of the data update interval and the initial value of the integral coefficient, the adaptive integral coefficient of the next adjacent data update interval is used as the integral coefficient value of the next adjacent data update interval, and the PID control algorithm is used to obtain the amount of material for adjusting the pH value of the next adjacent data update interval, so as to ensure the stability of the pH value of the reaction material in the polyethylene reaction tank during the second reaction stage.

[0010] Furthermore, the solution 1 is prepared by mixing a hydrofluoric acid aqueous solution and a hydrochloric acid aqueous solution in a weight ratio of 1:2.

[0011] Further, the concentration of the hydrofluoric acid aqueous solution of Solution 1 was 47% by weight, and the concentration of the hydrochloric acid aqueous solution of Solution 1 was 30% by weight.

[0012] Furthermore, the conductivity of the pure water is 10 μS / cm and the pH is 7.

[0013] Further, the first reaction stage is heated to 80° C. and maintained at 80° C. for 15 hours, and the second reaction stage is heated to a temperature in the range of 100° C. to 150° C. and maintained for 6 hours.

[0014] Further, the nitric acid concentration of Solution 2 was 65% by weight.

[0015] Furthermore, the method of performing anomaly detection on the pH sequence, obtaining outliers and assigning values, and determining the abnormality pattern characteristic value of the pH sequence includes the following specific methods:

[0016] Perform anomaly detection on pH series, obtain outliers, and identify extreme values ​​in pH series;

[0017] When the outlier point is an extreme value, the outlier point is assigned a value of 1; when the outlier point is not an extreme value, the outlier point is assigned a value of 0;

[0018] A local anomaly window of a preset length is established with the outlier as the center, all data contained in the local anomaly window of the outlier are arranged in sequence, the local anomaly sequence of the outlier is obtained, and the first-order difference sequence of the local anomaly sequence of the outlier is recorded as the local anomaly difference sequence of the outlier;

[0019] The absolute value of the difference between the value in the local abnormal difference sequence of the outlier and the mean of all the data contained in the local abnormal difference sequence is recorded as the first absolute difference of the outlier;

[0020] The characteristic value of the variation pattern of pH sequence is positively correlated with the assignment of outliers, the first absolute difference of outliers, and the coefficient of variation of outliers.

[0021] Furthermore, the reference comparison data of the temperature sequence is determined, the influencing deviation factor is determined, and the overall balance parameter is determined, including the specific method of:

[0022] The data collection time corresponding to the outlier in the pH sequence is recorded as the reference time, and the data corresponding to the reference time in the temperature sequence is recorded as the reference comparison data. A local corresponding window of a preset length is established with the reference comparison data as the center, and all data contained in the local corresponding window of the reference comparison data are arranged in sequence to obtain a local corresponding sequence of the reference comparison data;

[0023] Clustering the temperature sequence to obtain clusters, recording the data in the temperature sequence that is different from the clusters corresponding to the adjacent data as boundary data, and recording the data that is both reference comparison data and boundary data as first feature data;

[0024] The difference between adjacent data in the local corresponding window of the reference comparison data is recorded as the first difference of adjacent data of the reference comparison data; the difference between adjacent data in the local abnormal window of the outlier is recorded as the second difference of adjacent data of the outlier; the ratio of the first difference and the second difference between adjacent data of the outlier and the reference comparison data at the same data collection time is recorded as the first ratio of adjacent data of the outlier and the reference comparison data at the data collection time;

[0025] The ratio of the local abnormal window of the outlier point at the same data collection time to the range in the local corresponding window of the reference comparison data is recorded as the second ratio of the outlier point at the data collection time to the reference comparison data;

[0026] The influencing deviation factor is positively correlated with the first ratio and the second ratio;

[0027] The overall balance parameter is positively correlated with the number of outliers in the pH sequence, and negatively correlated with the influencing deviation factor, the characteristic value of the mutation mode, and the number of the first characteristic data.

[0028] Further, the method of determining the adaptive integral coefficient of the next adjacent data update interval according to the overall balance parameter of the data update interval and the initial value of the integral coefficient, taking the adaptive integral coefficient of the next adjacent data update interval as the integral coefficient value of the next adjacent data update interval, and using the PID control algorithm to obtain the amount of material for adjusting the pH value of the next adjacent data update interval includes the following specific methods:

[0029] The adaptive integral coefficient is positively correlated with the overall balance parameter of the data update interval and the initial value of the integral coefficient;

[0030] The adaptive integral coefficient of the next adjacent data update interval is used as the integral coefficient value of the next adjacent data update interval, the amount of material for adjusting the pH value and the pH value of the data update interval are input into the PID control algorithm, and the amount of material for adjusting the pH value of the next adjacent data update interval is obtained according to the PID control algorithm.

[0031] In a second aspect, an embodiment of the present invention further provides a high-purity graphite production system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0032] The beneficial effects of the present invention are:

[0033] The present invention takes into account the problem that in the process of obtaining high-purity graphite, in the second reaction stage, the temperature in the polyethylene reaction tank will change, which will lead to insufficient stability of the pH value. The temperature sequence and pH sequence of each data update interval are obtained. First, based on the characteristics that the pH value change caused by multiple factors such as the chemical reaction rate of the reaction materials, the solubility of the substance, and the inertia of the system usually presents a delayed rather than instantaneous effect, combined with the characteristics that the random and irregular interference of noise will cause the data to deviate from its true and expected value and push the noise data point to the extreme value, the outliers and the abnormal variation mode characteristic values ​​of the pH sequence are obtained. The abnormal variation mode characteristic values ​​of the pH sequence are the evaluation of the degree of influence of the pH sequence by the noise, so as to improve the accuracy of the evaluation of the degree of influence of the pH sequence by the noise; then, based on According to the outliers, the fluctuations of the temperature series and the pH series are evaluated, the overall balance parameters are obtained, and the anti-interference ability between the temperature and the pH value is evaluated; finally, the adaptive integral coefficient of the next adjacent data update interval is determined according to the overall balance parameters of the data update interval and the initial value of the integral coefficient, and the adaptive integral coefficient of the next adjacent data update interval is used as the integral coefficient value of the next adjacent data update interval. The PID control algorithm is used to obtain the amount of material for adjusting the pH value of the next adjacent data update interval to ensure the stability of the pH value of the reaction material in the polyethylene reaction tank during the second reaction stage, and solve the problem that the unreasonable parameter setting of the PID control algorithm leads to unstable pH value in the preparation process of high-purity graphite, affecting the quality of high-purity graphite. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative labor.

[0035] Figure 1 A schematic flow chart of a method for producing high-purity graphite provided by one embodiment of the present invention;

[0036] Figure 2 This is the pH value control flow chart of the polyethylene reaction tank;

[0037] Figure 3 It is a diagram of extreme values. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0039] See also Figure 1 , which shows a flow chart of a method for producing high-purity graphite provided by an embodiment of the present invention, the method comprising the following steps:

[0040] Embodiment 1:

[0041] Step S1, preparation stage.

[0042] A hydrofluoric acid aqueous solution and a hydrochloric acid aqueous solution are prepared into a mixed acid in a weight ratio of 1:2 to obtain a solution 1, wherein the concentration of the hydrofluoric acid aqueous solution in the solution 1 is 47% by weight, and the concentration of the hydrochloric acid aqueous solution is 30% by weight;

[0043] Pure water, graphite material and solution 1 with a conductivity of 10 μS / cm and a pH of 7 are recorded as reaction materials;

[0044] Put the reaction materials into the polyethylene reaction tank, open the air pump, compressed air valve and tail gas valve, and pass compressed air into the polyethylene reaction tank to mix all the reaction materials in the polyethylene reaction tank evenly.

[0045] At this point, the preparation phase is completed.

[0046] Step S2: first reaction stage.

[0047] Open the steam valve and pass steam into the polyethylene reaction tank to heat all the reaction materials in the polyethylene reaction tank until the temperature rises to 80°C and maintain the temperature of 80°C for 15 hours;

[0048] A filtration device connected to a vacuum pump is placed in the polyethylene reaction tank, and the waste acid water in the polyethylene reaction tank is extracted by the vacuum pump. Pure water with a conductivity of 10 μS / cm and a pH of 7 is repeatedly added for stirring and filtration until all reaction materials in the polyethylene reaction tank are washed and filtered to neutrality, and the filtration device is taken out.

[0049] At this point, the first reaction stage is completed.

[0050] Step S3, second reaction stage.

[0051] mixing hydrochloric acid and nitric acid to obtain solution 2, wherein the concentration of nitric acid in solution 2 is 65% by weight;

[0052] Add solution 2 to the polyethylene reaction tank, heat all the reaction materials in the polyethylene reaction tank to 100° C. to 150° C. and maintain for 6 hours;

[0053] A filtration device connected to a vacuum pump is placed in a polyethylene reaction tank, and waste acid water in the polyethylene reaction tank is extracted by the vacuum pump. Pure water with a conductivity of 10 μS / cm and a pH of 7 is repeatedly added for stirring and filtration until all reaction materials in the polyethylene reaction tank are washed and filtered to neutrality to obtain high-purity graphite, and the filtration device is taken out.

[0054] At this point, the second reaction stage is completed and high-purity graphite is obtained.

[0055] During the second reaction phase, the temperature in the polyethylene reactor changes. When the temperature changes, the chemical reaction rate of the reactants and the solubility of the substances will also change. When the temperature rises, the chemical reaction rate usually accelerates. At the same time, overheating or overcooling may inhibit key reactions or induce side reactions, resulting in pH fluctuations. In addition, the effectiveness of the buffer system in the polyethylene reactor will also be affected by temperature changes, further affecting the stability of the pH value.

[0056] In order to ensure the stability of the pH value in the polyethylene reaction tank, it is necessary to add materials to adjust the pH value when the pH value of the reaction material changes. Generally, the PID control algorithm is used to obtain the amount of materials added to adjust the pH value. In order to more accurately control the pH value in the polyethylene reaction tank and ensure the stability of the pH value, it is necessary to set appropriate integral parameters.

[0057] During the second reaction stage, the pH value control flow chart of the polyethylene reactor is as follows: Figure 2 As shown in the figure, the setting of integral parameters in the PID control algorithm and the specific method for controlling the pH value in the polyethylene reaction tank are as follows:

[0058] Step S001, during the second reaction phase, temperature data and pH value are collected and preprocessed to obtain a temperature sequence and a pH sequence.

[0059] A thermal resistor sensor and a high temperature resistant pH meter are installed in the polyethylene reaction tank. During the second reaction stage, the thermal resistor sensor is used to collect temperature data in the polyethylene reaction tank, and the high temperature resistant pH meter is used to collect pH value in the polyethylene reaction tank. The data collection time of the temperature data and the pH value is the same, and the interval between the data collection times is t. In this embodiment, the value of t is 0.5 seconds.

[0060] The temperature data and pH value are normalized respectively to eliminate the influence of the data dimension on the subsequent analysis. In this embodiment, the Z-Score normalization method is selected for dimension removal. The specific means of dimension removal are well known to those skilled in the art and are not limited here.

[0061] In this embodiment, during the second reaction stage, the initial values ​​of the proportional gain, integral coefficient and differential coefficient of the PID control algorithm are set to 50, 2.5 and 0.5 respectively, and the data update interval of the proportional gain, integral coefficient and differential coefficient is set to T. The implementer can also set the initial values ​​of the proportional gain, integral coefficient and differential coefficient as needed, and the value of T in this embodiment is 10 seconds. According to the temperature data and pH value within the data update interval of the proportional gain, integral coefficient and differential coefficient, the integral parameter value of the next data update interval and the amount of material to be added to adjust the pH value are determined.

[0062] The temperature data collected within the same data update interval T are arranged in the order of data collection time to obtain a temperature sequence, and the pH values ​​collected within the same data update interval T are arranged in the order of data collection time to obtain a pH sequence.

[0063] At this point, the temperature sequence and pH sequence at each data update interval are obtained.

[0064] Step S002, perform anomaly detection on the pH sequence, obtain outliers, identify extreme values ​​in the pH sequence, assign values ​​to sequence outliers according to the extreme values, obtain local anomaly differential sequences of outliers according to the pH sequence, and determine the variation pattern characteristic value of the pH sequence according to the distribution of outliers in the pH sequence and the local anomaly differential sequence of each outlier.

[0065] For the convenience of analysis, the pH sequence is recorded as the sequence , the temperature sequence is recorded as the sequence .

[0066] Since the combined effects of multiple factors such as the chemical reaction rate of the reactants, the solubility of the substances, and the inertia of the system on the pH value require time to manifest, the changes in pH value caused by these external factors usually show a delayed rather than instantaneous effect.

[0067] Pair Sequence Use the LOF local anomaly factor detection algorithm to perform anomaly detection and obtain the sequence Outliers in the sequence The outliers in are abnormal pH mutation data caused by external factors such as temperature fluctuations, environmental interference, and noise interference. Each outlier in corresponds to the data collection moment. Calculate the sequence The coefficient of variation of all outliers in the system. The larger the coefficient of variation of all outliers, the more dispersed the outliers are in time, and the more likely the outliers are caused by random noise inside the system or other non-external factors.

[0068] Among them, the calculation of the coefficient of variation is a well-known definition in probability theory and statistics, and the specific calculation process will not be repeated; the use of the LOF local anomaly factor detection algorithm for anomaly detection is a well-known technology and will not be repeated; the implementer can use other methods of the existing technology to obtain outliers in the data and the degree of dispersion of outliers, and this application does not impose any special restrictions.

[0069] Noise is a random, irregular interference that will cause the data to deviate from its true, expected value. The true value is often concentrated in the center or average area of ​​the data set. Therefore, when random noise pushes the noisy data points to extreme values, it will lead to the generation of extreme values. Use the first-order difference maximum and minimum detection method to identify the sequence The extreme value diagram is as follows Figure 3 As shown, in Figure 3 In the figure, the horizontal axis is the sequence The data in the sequence The vertical axis is the sequence number in the order The values ​​of the data in the figure are pH values, and the points in the figure are the identified extreme values. It is a well-known technology to use the first-order difference maximum and minimum value detection method to identify the extreme value, which will not be described in detail.

[0070] Among them, the implementer can use other methods in the prior art to obtain the extreme values ​​in the sequence, and this application does not make any special restrictions.

[0071] According to the sequence The extreme value pair sequence in The outliers in the cluster are assigned values: when the outlier is an extreme value, the outlier is assigned a value of 1; when the outlier is not an extreme value, the outlier is assigned a value of 0.

[0072] The purpose of assigning values ​​to outliers is to treat extreme value outliers as outliers that are more likely to be caused by noise, and to assign greater weights to these outliers for evaluation, so that the deviations between the data contained in the local abnormal difference sequences of these outliers will have a greater impact on the evaluation of the degree to which the pH sequence is affected by noise.

[0073] Sequence A local anomaly window with a length of N is established with each outlier in as the center. In this embodiment, the value of N is 7. All data contained in the local anomaly window of the outlier are arranged in sequence to obtain the local anomaly sequence of the outlier, and then the first-order difference sequence of the local anomaly sequence of the outlier is obtained, and the first-order difference sequence is recorded as the local anomaly difference sequence of the outlier.

[0074] The sum of the absolute values ​​of the differences between the values ​​in the local abnormal difference sequence of the outlier and the mean of all the data contained in the local abnormal difference sequence is recorded as the first absolute difference of the outlier.

[0075] According to the distribution of outliers in the pH sequence and the local abnormal difference sequence of each outlier, the characteristic value of the abnormal pattern of the pH sequence is determined.

[0076] The characteristic value of the variation pattern of pH sequence is positively correlated with the assignment of outliers, the first absolute difference of outliers, and the coefficient of variation of outliers.

[0077] It can be understood that the positive correlation in this application refers to the relationship between the independent variable and the dependent variable. The positive correlation means that the independent variable increases (decreases) as the dependent variable increases (decreases), which can be an additive relationship, a multiplicative relationship, etc.

[0078] Preferably, as an embodiment of the present application, the product of the sum of the value assignment result of the outlier point and 0.1 and the first absolute difference of the outlier point is recorded as the first product of the outlier point, and the mean of the first products of all outliers is recorded as the first mean, and the variation pattern characteristic value of the pH sequence is the product of the first mean and the coefficient of variation of all outliers in the pH sequence.

[0079] When the coefficient of variation of all outliers is larger, the distribution of outliers in time is more dispersed, and the outliers are more likely to be caused by random noise inside the system or other non-external factors. At this time, the abnormal variation mode characteristic value of the pH sequence is larger. When the deviation between the data contained in the local abnormal difference sequence of the outliers is larger, the abnormal variation mode characteristic value of the pH sequence is larger, and at this time, the pH sequence is more affected by noise.

[0080] At this point, the mutation pattern characteristic value of the pH sequence is obtained.

[0081] Step S003, determine the reference comparison data of the temperature sequence according to the outliers of the pH sequence, establish a local corresponding window of the reference comparison data, screen the first characteristic data according to the temperature sequence and the reference comparison data, determine the influencing deviation factor according to the local corresponding window of the reference comparison data and the local abnormal window of the outliers, and obtain the overall balance parameter.

[0082] will sequence The data collection time corresponding to the outlier in is recorded as the reference time, and the sequence The data corresponding to the reference time are recorded as reference comparison data.

[0083] Sequence A local corresponding window of length N is established with each reference comparison data in the data as the center. In this embodiment, the value of N is 7. All data contained in the local corresponding window of the reference comparison data are arranged in sequence to obtain the local corresponding sequence of the reference comparison data. The range of all data contained in each local corresponding sequence and the local abnormal sequence is obtained.

[0084] Pair Sequence Use DBSCAN density clustering to perform clustering. In this example, the minimum number of points is set to 4 and the maximum radius is set to 2 to obtain clusters. The data that is different from the clusters corresponding to the adjacent data is recorded as boundary data. The data that is both reference comparison data and boundary data is recorded as the first feature data.

[0085] The difference between adjacent data in the local corresponding window of the reference comparison data is recorded as the first difference of adjacent data of the reference comparison data; the difference between adjacent data in the local abnormal window of the outlier is recorded as the second difference of adjacent data of the outlier; the ratio of the first difference to the second difference between adjacent data of the outlier and the reference comparison data at the same data collection time is recorded as the first ratio of adjacent data of the outlier and the reference comparison data at the data collection time.

[0086] The ratio of the local abnormal window of the outlier point at the same data collection time to the range in the local corresponding window of the reference comparison data is recorded as the second ratio of the outlier point to the reference comparison data at the data collection time.

[0087] According to the first ratio and the second ratio, an influencing deviation factor is determined, and the influencing deviation factor is positively correlated with the first ratio and the second ratio. It can be understood that the positive correlation in this application refers to the relationship between the independent variable and the dependent variable, and the positive correlation is that the independent variable increases (decreases) as the dependent variable increases (decreases), which can be an additive relationship, a multiplicative relationship, etc.

[0088] Preferably, as an embodiment of the present application, the product of the first ratio and the second ratio is recorded as the second product, and the influencing deviation factor is the sum of the second products of all outliers and all reference comparison data.

[0089] The second ratio is the relative value of the local variation range of the outlier and the reference comparison data at the same data collection time. When the second ratio is larger, the pH value is less affected by the overall temperature, and the deviation of the temperature on the pH value is larger. At this time, the influence deviation factor is larger. The first ratio is the relative variation of the data in the window of the outlier and the reference comparison data at the same data collection time. When the first ratio is larger, the pH value is less affected by the overall temperature, and the deviation of the temperature on the pH value is larger. At this time, the influence deviation factor is larger.

[0090] According to the number of outliers in the pH sequence, the influence deviation factor, the characteristic value of the abnormal mode, and the number of the first characteristic data, the overall balance parameter is determined. The overall balance parameter is positively correlated with the number of outliers in the pH sequence, and negatively correlated with the influence deviation factor, the characteristic value of the abnormal mode, and the number of the first characteristic data.

[0091] It can be understood that the positive correlation and negative correlation in this application refer to the relationship between the independent variable and the dependent variable. The positive correlation means that the independent variable increases (decreases) as the dependent variable increases (decreases), which can be an additive relationship, a multiplicative relationship, etc.; the negative correlation means that the independent variable decreases (increases) as the dependent variable increases (decreases), which can be an inverse relationship, a subtractive relationship, etc.

[0092] Preferably, as an embodiment of the present application, a power with a natural constant as the base and the opposite number of the influencing deviation factor as the exponent is recorded as a first power value, the product of the first power value and the number of outliers in the pH sequence is recorded as a third product, the product of the mutation pattern characteristic value and the number of first characteristic data is recorded as a fourth product, and the overall balance parameter is the ratio of the third product to the fourth product.

[0093] The larger the overall balance parameter is, the weaker the anti-interference ability between temperature and pH value is.

[0094] At this point, the overall balance parameters are obtained.

[0095] Step S004, based on the overall balance parameter of the data update interval and the initial value of the integral coefficient, determine the adaptive integral coefficient of the next adjacent data update interval, use the adaptive integral coefficient of the next adjacent data update interval as the integral coefficient value of the next adjacent data update interval, and use the PID control algorithm to obtain the amount of material for adjusting the pH value of the next adjacent data update interval to ensure the stability of the pH value of the reaction material in the polyethylene reaction tank during the second reaction stage.

[0096] According to the overall balance parameter of the data update interval and the initial value of the integral coefficient, the adaptive integral coefficient of the next adjacent data update interval is determined. The adaptive integral coefficient is positively correlated with the overall balance parameter of the data update interval and the initial value of the integral coefficient.

[0097] It can be understood that the positive correlation in this application refers to the relationship between the independent variable and the dependent variable. The positive correlation means that the independent variable increases (decreases) as the dependent variable increases (decreases), which can be an additive relationship, a multiplicative relationship, etc.

[0098] Preferably, as an embodiment of the present application, the adaptive integral coefficient is the product of the normalized value of the overall balance parameter of the data update interval, the initial value of the integral coefficient and the number 2.

[0099] The adaptive integral coefficient of the next adjacent data update interval is used as the integral coefficient value of the next adjacent data update interval, the amount of material for adjusting the pH value and the pH value of the data update interval are input into the PID control algorithm, and the amount of material for adjusting the pH value of the next adjacent data update interval is obtained according to the PID control algorithm.

[0100] The PID control algorithm can obtain the amount of material added to adjust the pH value in the next adjacent data update interval based on the amount of material added to adjust the pH value and the pH value in each data update interval, thereby ensuring the stability of the pH value of the reaction material in the polyethylene reactor, making the processing of the second reaction stage more stable and improving the production quality of high-purity graphite.

[0101] At this point, high-purity graphite production has been achieved.

[0102] Based on the same inventive concept as the above method, an embodiment of the present invention also provides a high-purity graphite production system, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned high-purity graphite production methods are implemented.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for producing high-purity graphite, characterized in that: The method comprises the following steps: Preparation stage: mixing a hydrofluoric acid aqueous solution and a hydrochloric acid aqueous solution to obtain a solution 1, recording pure water, a graphite material and the solution 1 as reaction materials, and putting the reaction materials into a polyethylene reaction tank and mixing them evenly; The first reaction stage: Steam is introduced into the polyethylene reaction tank for heating, the waste acid water in the polyethylene reaction tank is extracted, and pure water is repeatedly added for stirring and filtering until the reaction materials are washed and filtered to neutrality; Second reaction stage: Mix hydrochloric acid and nitric acid to obtain solution 2, add solution 2 to the polyethylene reaction tank and heat it, extract the waste acid water in the polyethylene reaction tank through a vacuum pump connected to a suction filtration device, repeatedly add pure water, stir and filter until the reaction materials are washed and filtered to neutrality, and obtain high-purity graphite; During the second reaction stage, temperature data and pH values ​​are collected and preprocessed to obtain temperature and pH sequences; anomaly detection is performed on the pH sequence, outliers are obtained and assigned values, and characteristic values ​​of the variation mode of the pH sequence are determined; reference comparison data of the temperature sequence is determined, the influencing deviation factor is determined, and the overall balance parameter is determined; the adaptive integral coefficient of the next adjacent data update interval is determined according to the overall balance parameter of the data update interval and the initial value of the integral coefficient, the adaptive integral coefficient of the next adjacent data update interval is used as the integral coefficient value of the next adjacent data update interval, and the PID control algorithm is used to obtain the amount of material for adjusting the pH value of the next adjacent data update interval, so as to ensure the stability of the pH value of the reaction material in the polyethylene reaction tank during the second reaction stage; The method for determining the characteristic value of the mutation pattern of the pH sequence is as follows: A local anomaly window of a preset length is established with the outlier as the center, all data contained in the local anomaly window of the outlier are arranged in sequence, the local anomaly sequence of the outlier is obtained, and the first-order difference sequence of the local anomaly sequence of the outlier is recorded as the local anomaly difference sequence of the outlier; The absolute value of the difference between the value in the local abnormal difference sequence of the outlier and the mean of all the data contained in the local abnormal difference sequence is recorded as the first absolute difference of the outlier; The characteristic value of the variation pattern of pH sequence is positively correlated with the assignment of outliers, the first absolute difference of outliers, and the coefficient of variation of outliers.

2. A method for producing high-purity graphite according to claim 1, characterized in that: The solution 1 is prepared by mixing a hydrofluoric acid aqueous solution and a hydrochloric acid aqueous solution in a weight ratio of 1:

2.

3. A method for producing high-purity graphite according to claim 2, characterized in that: The concentration of the hydrofluoric acid aqueous solution of solution 1 was 47% by weight, and the concentration of the hydrochloric acid aqueous solution of solution 1 was 30% by weight.

4. A method for producing high-purity graphite according to claim 1, characterized in that: The pure water has an electrical conductivity of 10 μS / cm and a pH of 7.

5. A method for producing high-purity graphite according to claim 1, characterized in that: The first reaction stage was heated to 80°C and maintained at 80°C for 15 hours, and the second reaction stage was heated to a temperature in the range of 100°C to 150°C and maintained for 6 hours.

6. A method for producing high-purity graphite according to claim 1, characterized in that: The nitric acid concentration of solution 2 was 65% by weight.

7. A method for producing high-purity graphite according to claim 1, characterized in that: The specific method of performing anomaly detection on the pH sequence, obtaining outliers and assigning values ​​includes: Perform anomaly detection on pH series, obtain outliers, and identify extreme values ​​in pH series; When the outlier point is an extreme value, the outlier point is assigned a value of 1; when the outlier point is not an extreme value, the outlier point is assigned a value of 0.

8. A method for producing high-purity graphite according to claim 1, characterized in that: The specific method of determining the reference comparison data of the temperature sequence, determining the influencing deviation factor, and obtaining and determining the overall balance parameter includes: The data collection time corresponding to the outlier in the pH sequence is recorded as the reference time, and the data corresponding to the reference time in the temperature sequence is recorded as the reference comparison data. A local corresponding window of a preset length is established with the reference comparison data as the center, and all data contained in the local corresponding window of the reference comparison data are arranged in sequence to obtain a local corresponding sequence of the reference comparison data; Clustering the temperature sequence to obtain clusters, recording the data in the temperature sequence that is different from the clusters corresponding to the adjacent data as boundary data, and recording the data that is both reference comparison data and boundary data as first feature data; The difference between adjacent data in the local corresponding window of the reference comparison data is recorded as the first difference of adjacent data of the reference comparison data; the difference between adjacent data in the local abnormal window of the outlier is recorded as the second difference of adjacent data of the outlier; the ratio of the first difference and the second difference between adjacent data of the outlier and the reference comparison data at the same data collection time is recorded as the first ratio of adjacent data of the outlier and the reference comparison data at the data collection time; The ratio of the local abnormal window of the outlier point at the same data collection time to the range in the local corresponding window of the reference comparison data is recorded as the second ratio of the outlier point at the data collection time to the reference comparison data; The influencing deviation factor is positively correlated with the first ratio and the second ratio; The overall balance parameter is positively correlated with the number of outliers in the pH sequence, and negatively correlated with the influencing deviation factor, the characteristic value of the mutation mode, and the number of the first characteristic data.

9. A method for producing high-purity graphite according to claim 1, characterized in that: The method of determining the adaptive integral coefficient of the next adjacent data update interval according to the overall balance parameter of the data update interval and the initial value of the integral coefficient, taking the adaptive integral coefficient of the next adjacent data update interval as the integral coefficient value of the next adjacent data update interval, and using the PID control algorithm to obtain the amount of material for adjusting the pH value of the next adjacent data update interval includes the following specific methods: The adaptive integral coefficient is positively correlated with the overall balance parameter of the data update interval and the initial value of the integral coefficient; The adaptive integral coefficient of the next adjacent data update interval is used as the integral coefficient value of the next adjacent data update interval, the amount of material for adjusting the pH value and the pH value of the data update interval are input into the PID control algorithm, and the amount of material for adjusting the pH value of the next adjacent data update interval is obtained according to the PID control algorithm.

10. A high-purity graphite production system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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