High-temperature impurity removal and purification method of graphite material

Abstract

The invention relates to the technical field of impurity removal and purification, in particular to a high-temperature impurity removal and purification method for a graphite material. Obtaining the furnace temperature and the output power at each moment in each monitoring period in each high-temperature purification process and the temperature and the voltage of each induction coil at each moment; calculating a first evaluation value, a power fluctuation degree, a second evaluation value and an evaluation coefficient of each monitoring period; determining the bridging confidence coefficient of each monitoring period in each high-temperature purification process; and the bridging phenomenon occurring in each monitoring period in the high-temperature purification process is evaluated, and the bridging phenomenon existing in the high-temperature purification furnace is eliminated, so that the graphite material is subjected to high-temperature purification. According to the method, the bridging phenomenon in the high-temperature purification furnace can be accurately evaluated, and stable operation of a high-temperature purification process and high-quality purification of a graphite material are ensured.

Description

Technical Field

[0001] This application relates to the field of purification technology, specifically to a high-temperature purification method for graphite materials. Background Technology

[0002] High-purity graphite is an indispensable key material in cutting-edge fields such as semiconductors, photovoltaics, aerospace, and nuclear industry. Its purity directly determines the performance and reliability of the final product. High-temperature purification is the core process for preparing high-purity graphite. In the high-temperature purification process of graphite materials, the high-temperature purification furnace is one of the key pieces of equipment. By placing the graphite material in a high-temperature environment and precisely controlling the temperature inside the furnace, impurities in the graphite material are removed, thereby improving the purity of the graphite material.

[0003] Existing technologies using high-temperature purification furnaces do not fully consider the uneven temperature within the furnace and the bridging phenomenon caused by localized sintering of graphite powder at high temperatures. Bridging not only causes discharge blockage and interrupts continuous production, but also hinders the uniform transfer of heat and reactant gases, resulting in incomplete purification of graphite powder below the bridging area and affecting the stability of the high-temperature purification process. Furthermore, severe bridging necessitates furnace shutdown for maintenance, reducing overall purification and production efficiency. Summary of the Invention

[0004] To address the aforementioned technical problems, a high-temperature purification method for graphite materials is provided to resolve existing issues.

[0005] The solution to the technical problem of this application is to provide a high-temperature purification method for graphite materials, including the following steps: When using a high-temperature purification furnace to purify graphite materials at high temperatures, the furnace temperature, output power, and temperature and voltage of each induction coil at each moment are obtained during each monitoring cycle of the high-temperature purification process. Analyze the discrete changes in temperature and voltage of different induction coils at the same time within each monitoring cycle, and calculate the first evaluation value for each monitoring cycle; The power fluctuation of each monitoring cycle is calculated by observing the fluctuation of output power in different local areas within each monitoring cycle; the second evaluation value of each monitoring cycle is calculated by combining the synchronization of the furnace temperature and output power change trends within each monitoring cycle. Based on the first and second evaluation values, the evaluation coefficients for each monitoring period are obtained. Based on the changing trends and differences of the evaluation coefficients for each monitoring period in different high-temperature purification processes, the bridging confidence level for each monitoring period in each high-temperature purification process is determined. Anomaly detection is performed on the bridging confidence level of this and previous high-temperature purification processes under each monitoring period to evaluate the bridging phenomenon that occurs in each monitoring period during this high-temperature purification process, eliminate the bridging phenomenon in the high-temperature purification furnace, and thus purify the graphite material at high temperature.

[0006] Preferably, the calculation of the first evaluation value for each monitoring period includes: Calculate the degree of dispersion of temperature and voltage of all induction coils at each time point, and denote them as the first degree of dispersion and the second degree of dispersion, respectively; The sum of the first dispersion and the second dispersion at all times within each monitoring cycle is used as the temperature difference and voltage difference for each monitoring cycle, respectively. The first evaluation value is the result of fusing the temperature difference and voltage difference.

[0007] Preferably, the specific process of the fusion is as follows: the product of the temperature difference and the voltage difference is used as the first evaluation value for each monitoring cycle.

[0008] Preferably, the calculation of power fluctuation for each monitoring period includes: The output power at all times within each monitoring cycle is divided into multiple local time periods; Analyze the overall trend of output power deviation at different times within a local time period, and calculate the deviation; The power fluctuation is the average of the offsets for all local time periods within each monitoring cycle.

[0009] Preferably, the calculation of the offset includes: linearly fitting the output power at all times within a local time period, and calculating the fitting error as the offset.

[0010] Preferably, the calculation of the second evaluation value for each monitoring period includes: Calculate the correlation between furnace temperature and output power at all times within each monitoring cycle, and perform a positive mapping on the absolute value of the correlation. The second evaluation value is the ratio between the power fluctuation and the result of the positive mapping.

[0011] Preferably, the evaluation coefficient is the product of the first evaluation value and the second evaluation value.

[0012] Preferably, determining the bridging confidence level for each monitoring cycle during each high-temperature purification process includes: The evaluation coefficients of each monitoring cycle in each high-temperature purification process and the evaluation coefficients of the same monitoring cycle in the previous high-temperature purification processes are linearly fitted, and the slope of the fitted line is calculated. The average difference between the evaluation coefficients of each monitoring cycle in each high-temperature purification process and the same monitoring cycle in previous high-temperature purification processes is calculated as the cumulative deviation of each monitoring cycle in each high-temperature purification process. The bridging confidence level is the product of the slope and the cumulative deviation.

[0013] Preferably, the assessment of the bridging phenomenon occurring in each monitoring cycle during the current high-temperature purification process includes: performing anomaly detection on the bridging confidence of each monitoring cycle during the current high-temperature purification process and its previous high-temperature purification processes in the same monitoring cycle; if the bridging confidence of each monitoring cycle during the current high-temperature purification process is an abnormal value, it is determined that a bridging phenomenon has occurred; otherwise, no bridging phenomenon has occurred.

[0014] Preferably, eliminating bridging in the high-temperature purification furnace includes: using a vibration device to vibrate the furnace body of the high-temperature purification furnace for a short time and at a low amplitude, causing the graphite powder with bridging in the furnace to loosen and fall.

[0015] This application has at least the following beneficial effects: This application analyzes the temperature and voltage fluctuations of different induction coils at the same time to calculate the first evaluation value for each monitoring cycle. Its beneficial effect is that it reflects the heating non-uniformity of the induction coils within the monitoring cycle and preliminarily assesses the possibility of bridging phenomena within the high-temperature purification furnace. It also calculates the power fluctuation for each monitoring cycle, which benefits from considering irregular and abnormal fluctuations in the power supply's output power, reflecting the stability of the power supply's output power, and assessing the localized decrease in heating efficiency or load changes caused by bridging phenomena. Furthermore, it calculates the second evaluation value for each monitoring cycle, which benefits from considering the synchronicity between furnace temperature and output power, reflecting the fluctuation of the power supply's output power and its synchronous change trend with the furnace temperature within the monitoring cycle, further assessing the possibility of bridging phenomena within the high-temperature purification furnace. Finally, it obtains the evaluation coefficient for each monitoring cycle, which benefits from comprehensively assessing the possibility of bridging phenomena occurring within the monitoring cycle from multiple dimensions, thereby reflecting the purification process. The stability of the process; determining the bridging confidence level of each monitoring cycle in each high-temperature purification process. The beneficial effect is that by comparing the evaluation coefficients of the same monitoring cycle in different high-temperature purification processes, abnormal changes compared with historical normal operating conditions can be identified, thereby more accurately determining the possibility of bridging phenomena; anomaly detection is performed on the bridging confidence level of the current and previous high-temperature purification processes under each monitoring cycle, assessing the bridging phenomena that occur in each monitoring cycle during the current high-temperature purification process, eliminating the existence of bridging phenomena in the high-temperature purification furnace, and thus purifying graphite materials at high temperatures. The beneficial effect is that by performing anomaly detection on the same monitoring cycle in the current high-temperature purification process and different historical high-temperature purification processes, the occurrence of bridging phenomena in the high-temperature purification furnace under each monitoring cycle during the current high-temperature purification process can be accurately assessed, and intervention measures can be initiated in a timely manner to avoid production interruptions, product quality degradation, and equipment damage caused by bridging. This ensures the stable operation of the high-temperature purification process and the high-quality purification of graphite materials, improving purification efficiency and graphite purification quality. Attached Figure Description

[0016] The following description, in conjunction with the accompanying drawings, provides a more detailed explanation of a high-temperature purification method for graphite materials according to this application.

[0017] Figure 1 A flowchart illustrating the steps of a high-temperature purification method for graphite materials provided in this application embodiment; Figure 2 A flowchart illustrating the steps of the method for obtaining bridging confidence provided in this application embodiment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of a high-temperature purification method for graphite materials, in conjunction with the accompanying drawings and embodiments, is provided. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a high-temperature purification method for graphite materials according to an embodiment of this application. The method includes the following steps: Step 1: When purifying graphite materials at high temperature using a high-temperature purification furnace, obtain the furnace temperature, output power, and temperature and voltage of each induction coil at each moment during each monitoring cycle of the high-temperature purification process.

[0021] Graphite, as an important carbon-based material, has broad application prospects in energy storage, sensors, catalysts, and other fields. The purity of graphite directly affects its performance and application effects. The purification process utilizes the high melting and boiling points of graphite, reducing the impurity content in the graphite powder raw material through high-temperature calcination decomposition and volatilization, and the introduction of oxidizing gases to react with impurities such as metals.

[0022] The specific processing steps for graphite materials are as follows: Mixing and isostatic pressing: Weigh asphalt coke and asphalt according to a certain weight ratio, and put them into a mixing device. Mix for 6-8 hours to obtain a mixture. Put the mixture into a rubber sleeve and vacuum the sleeve. When the vacuum degree inside the sleeve reaches 0.06MPa, seal the sleeve and put it into an isostatic press. Press the mixture for 60-70 minutes under a pressure of 150MPa-180MPa. Densification and Shaping: After isostatic pressing, the product is removed and subjected to high-temperature firing for 600-750 hours, with a maximum firing temperature of 900℃-1000℃. The fired product is then impregnated under pressure for 1-2 hours at a pressure of 4MPa. The impregnated product is then fired again, and this process is repeated until the product's bulk density reaches 1.72 g / cm³. 3The above steps are followed by coarse crushing of the densified product and grinding in an air jet mill, where the average particle size of the powder is 35μm~45μm. The powder is then placed in a shaping device to remove burrs and sharp edges. After shaping for 8-10 minutes, the powder is classified by cyclone separation to remove particles with a particle size ≥100μm and particles with a particle size <5μm, thereby obtaining uniformly sized powder. The content of particles with a particle size <32μm in the uniformly sized powder is 60%~70%. High-temperature purification: The uniformly sized powder is placed in a crucible, sealed, and then placed in a high-temperature purification furnace for purification. The heating rate is controlled at 6℃ / min. When the furnace temperature reaches 2000℃, chlorine gas is introduced into the furnace, with an average of 35kg~40kg of chlorine gas per ton of product. When the furnace temperature reaches 2450℃, Freon is introduced into the furnace, with an average of 20kg~25kg of Freon per ton of product. When the furnace temperature reaches 3000℃, the high-temperature treatment lasts for 36~48 hours. Then, the high-temperature treated product is cooled to obtain the purified graphite powder product.

[0023] In this embodiment, a medium-frequency vertical graphite purification furnace is used to purify graphite materials at high temperatures. During the purification process, bridging may occur in the furnace. Bridging refers to the formation of arch-like structures by graphite powder within the furnace, hindering the normal flow of the charge. When bridging occurs, it not only leads to discharge blockage and affects the efficiency of subsequent impurity removal processes, but also impacts the removal of impurities from the graphite powder at the bridging points. Therefore, to ensure the efficiency and effectiveness of graphite material purification, the bridging problem needs to be addressed promptly.

[0024] Secondly, the heating process of the medium-frequency vertical graphite purification furnace mainly relies on the induction coil to achieve energy conversion. Its working principle is as follows: the induction coil is powered by a medium-frequency power supply, and generates a strong alternating magnetic field under the action of alternating voltage and current. When graphite, as a conductor, is placed in the magnetic field, it will generate eddy currents due to electromagnetic induction. During the flow of the eddy currents inside the graphite, the electrical energy is directly converted into heat energy due to the resistance effect, thereby achieving the heating of the graphite.

[0025] Based on the above analysis, the furnace temperature and the output power of the heating power supply are collected at each moment during each monitoring cycle in the purification furnace through the heating control system of the purification furnace; secondly, the temperature and voltage of each induction coil at each moment are measured through tungsten-rhenium thermocouples. In this embodiment, the data acquisition time interval is 1 second and the monitoring period is 20 minutes. As for other implementation methods, the implementer can set them according to the actual situation.

[0026] The collected data is normalized for each monitoring cycle. In this embodiment, the maximum-minimum normalization method is used for normalization. The maximum-minimum normalization method is a well-known technique and will not be described in detail here. As other implementation methods, implementers may use other methods of existing technology, such as Z-score normalization. This embodiment does not impose any special restrictions on this.

[0027] Thus, the furnace temperature, output power, and temperature and voltage of each induction coil at each moment are obtained during each monitoring cycle in each high-temperature purification process.

[0028] Step 2: Analyze the discrete changes in temperature and voltage of different induction coils at the same time within each monitoring cycle, and calculate the first evaluation value for each monitoring cycle.

[0029] Medium-frequency vertical graphite purification furnaces use induction coils to heat the furnace interior. If the induction coils experience uneven magnetic field distribution or malfunction, it will lead to uneven heating of the graphite powder. In some areas, the powder may sinter more densely, making bridging more likely. Therefore, uneven magnetic field distribution or malfunctions in the induction coils will result in temperature differences between different induction coils.

[0030] Secondly, since the induction coils rely on a power supply to operate, the heating load of the coils is directly reflected in their voltage characteristics. If bridging does not occur, the heating load of each coil is consistent, and the voltage of each coil will be relatively consistent. If bridging occurs, the graphite powder in a local area becomes denser and accumulates due to sintering, which affects the conductivity and permeability of the graphite powder in that local area. Since the principle of induction heating is to generate a magnetic field through the coil and induce eddy currents for heating, when the electromagnetic properties of the graphite powder in the local area facing the coil change, the heating load of the corresponding induction coil in that local area will also change, resulting in a change in voltage.

[0031] Therefore, by analyzing the temperature and voltage changes of different induction coils at the same time, and calculating the first evaluation value, we can assess whether there is a potential bridging risk inside the purification furnace. Specifically: Calculate the degree of temperature dispersion of all induction coils at each time point, and denote it as the first degree of dispersion; In this embodiment, the degree of dispersion is measured by calculating the coefficient of variation of the temperature of all induction coils at each time point. As other implementation methods, implementers may use other methods of the prior art, such as standard deviation, etc. This embodiment does not impose any special restrictions on this.

[0032] The sum of the first dispersion of all times within each monitoring cycle is taken as the temperature difference of each monitoring cycle. Calculate the degree of dispersion of the voltage of all induction coils at each time point, and denote it as the second degree of dispersion; In this embodiment, the degree of dispersion is measured by calculating the coefficient of variation of the voltage of all induction coils at each time point. As in other implementations, implementers may use other methods of the prior art, such as standard deviation, etc. This embodiment does not impose any special restrictions on this.

[0033] The sum of the second dispersion of all times within each monitoring cycle is taken as the voltage difference of each monitoring cycle. The product of temperature difference and voltage difference is used as the first evaluation value for each monitoring cycle. It should be noted that the larger the first dispersion, the greater the temperature difference between different induction coils at the same time. The greater the temperature difference, the greater the temperature difference between the induction coils, reflecting a more significant heating non-uniformity of the induction coils within the monitoring period. The larger the second dispersion, the greater the voltage difference between different induction coils at the same time. The greater the voltage difference, the greater the voltage difference between the induction coils, reflecting a more significant load non-uniformity of the induction coils within the monitoring period. The larger the obtained first evaluation value, the greater the difference between the heating temperature and load voltage of the induction coils within the monitoring period, the more significant the heating non-uniformity, and the greater the possibility of bridging.

[0034] Thus, the first evaluation value for each monitoring cycle is obtained.

[0035] Step 3: Calculate the power fluctuation of each monitoring cycle by observing the power fluctuations in different local areas within each monitoring cycle; and calculate the second evaluation value for each monitoring cycle by combining the synchronization of the furnace temperature and power change trends within each monitoring cycle.

[0036] Furthermore, when bridging occurs, it not only affects the induction coil but also the output power of the heating power supply to the purification furnace. During normal high-temperature purification, the power supply's output power increases steadily with the furnace temperature, and it tends to stabilize during the holding phase. However, if bridging occurs, the heating load and efficiency of the induction coil will be affected. In order to ensure the furnace temperature rises at the set rate, the power supply's output power may experience irregular and abnormal fluctuations, thus disrupting the stability of the output power change and its synchronization with the furnace temperature trend.

[0037] Secondly, since different temperature stages exist during the high-temperature purification process of graphite materials, in order to accurately analyze the changes in power output, the power output at all times within the monitoring period is divided to analyze the changes in power output. Therefore, by analyzing the fluctuations in power output within a local range, the power fluctuation is calculated, specifically: The output power at all times within each monitoring cycle is divided into multiple local time periods; In this embodiment, the BG (Bernaola-Galvan) segmentation algorithm is used to divide the output power at all times within each monitoring period. The BG segmentation algorithm is a well-known technology and will not be described in detail here. As other implementation methods, implementers may use other methods of existing technology, such as the MK segmentation algorithm, etc. This embodiment does not impose any special restrictions on this.

[0038] The output power at all times within a local time period is linearly fitted, and the fitting error is calculated as the offset. In this embodiment, the least squares method is used for linear fitting. The least squares method is a well-known technique and will not be elaborated upon here. Secondly, the fitting error is measured by calculating the mean square error. The calculation of the mean square error is a well-known technique, and the formula for calculating the mean square error is as follows: ,in, Mean square error, Let be the output power at time i. To fit the predicted value at time i on the straight line, The number of all time points.

[0039] Calculate the average offset of all local time periods within each monitoring period as the power fluctuation of each monitoring period; It should be noted that the larger the offset, the more drastic the fluctuation of the output power in a local period, the greater the possibility of deviating from the linear trend, and the greater the possibility of abnormal power output during the purification process; the greater the power fluctuation, the larger the fluctuation of the power output within the monitoring period, and the poorer the stability of the power output.

[0040] Furthermore, by analyzing the synchronization of the changes in furnace temperature and output power during the monitoring period, and combining this with the power fluctuation, a second evaluation value is calculated, specifically: Calculate the correlation between furnace temperature and output power at all times within each monitoring cycle, and perform a positive mapping on the absolute value of the correlation. In this embodiment, the correlation is measured by calculating the Pearson correlation coefficient between the furnace temperature and output power at all times within each monitoring period. The Pearson correlation coefficient is a well-known technique and will not be elaborated upon here. As other implementation methods, the implementer may use other methods from the prior art, such as the Spearman correlation coefficient, etc. This embodiment does not impose any special limitations on this. Secondly, the specific process of the positive mapping is as follows: a positive mapping is performed using an exponential function. Assuming the absolute value of the correlation is denoted as... Then The result is taken as the result of the positive mapping, where, Let be an exponential function with the natural constant as its base. Through the process of positive mapping, the result of the positive mapping is made to be greater than 0.

[0041] The ratio between the power fluctuation and the positive mapping result is used as the second evaluation value for each monitoring period; It should be noted that the smaller the positive mapping result, the lower the synchronicity between furnace temperature and output power, and the greater the possibility of a disconnect between output power and temperature requirements; the larger the second evaluation value, the more drastic the fluctuation of power output power and the lower the synchronicity trend with furnace temperature during the monitoring period, and the greater the possibility of bridging.

[0042] Thus, the second evaluation value for each monitoring cycle is obtained.

[0043] Step 4: Based on the first and second evaluation values, obtain the evaluation coefficients for each monitoring period; based on the changing trends and differences of the evaluation coefficients for each monitoring period in different high-temperature purification processes, determine the bridging confidence level for each monitoring period in each high-temperature purification process; perform anomaly detection on the bridging confidence level of this and previous high-temperature purification processes under each monitoring period, evaluate the bridging phenomenon that occurs in each monitoring period during this high-temperature purification process, eliminate the bridging phenomenon in the high-temperature purification furnace, and thus purify the graphite material at high temperature.

[0044] Furthermore, based on the first and second evaluation values, the evaluation coefficients are determined as follows: The product of the first and second evaluation values ​​is used as the evaluation coefficient for each monitoring period. It should be noted that the larger the evaluation coefficient, the greater the possibility of bridging phenomenon during the monitoring period. In this case, the bridging phenomenon needs to be dealt with in a timely manner to improve the purification efficiency and purification effect.

[0045] Secondly, during the high-temperature purification of graphite materials, the furnace conditions evolve continuously and in stages, and the furnace conditions are not consistent at different temperature stages. Therefore, it is difficult to accurately distinguish whether the changes in the furnace are due to normal process transition or abnormal disturbances based on the data changes in a single monitoring cycle. In order to avoid misjudgment, the furnace conditions are compared with those in previous high-temperature purification processes to determine whether there are significant differences in the furnace characteristics during the monitoring cycle in this high-temperature purification process.

[0046] The evaluation coefficients of each monitoring cycle in each high-temperature purification process and the same monitoring cycle in previous high-temperature purification processes were linearly fitted, and the slope of the fitted line was calculated. In this embodiment, the least squares method is used for linear fitting. The least squares method and the calculation of the slope are well-known techniques and will not be described in detail here.

[0047] It should be noted that there are multiple monitoring cycles in each high-temperature purification process. Therefore, the evaluation coefficients of the qth monitoring cycle in each high-temperature purification process and the qth monitoring cycle in the previous 50 high-temperature purification processes are linearly fitted as another implementation method. The implementer can set it according to the actual situation.

[0048] The average difference between the evaluation coefficients of each monitoring cycle in each high-temperature purification process and the same monitoring cycle in previous high-temperature purification processes is calculated as the cumulative deviation of each monitoring cycle in each high-temperature purification process. In this embodiment, the average difference between the evaluation coefficients of the qth monitoring cycle in each high-temperature purification process and the qth monitoring cycle in the previous 50 high-temperature purification processes is calculated as the cumulative deviation of the qth monitoring cycle in each high-temperature purification process.

[0049] The product of the slope and the cumulative deviation is used as the bridging confidence level for each monitoring cycle during each high-temperature purification process. It should be noted that the larger the slope, the greater the degradation of furnace temperature and power compared to historical operating conditions, and the greater the possibility of bridging risk. The larger the accumulated deviation, the higher the probability of abnormal furnace operating conditions during the monitoring period compared to historical operating conditions, and the greater the possibility of bridging. Therefore, the higher the bridging confidence score, the more closely the furnace characteristics match the abnormal load characteristics caused by bridging during the monitoring period, reflecting a higher probability of bridging occurring during that monitoring period. The flowchart of the bridging confidence score acquisition method provided in this application embodiment is shown below. Figure 2 As shown.

[0050] Anomaly detection was performed on the bridging confidence of each monitoring cycle during this high-temperature purification process and its previous high-temperature purification processes in the same monitoring cycle. If the bridging confidence of each monitoring cycle during this high-temperature purification process was an outlier, it was determined that a bridging phenomenon had occurred; otherwise, no bridging phenomenon had occurred. In this embodiment, 3 is used The algorithm performs anomaly detection, where 3 The algorithm is a well-known technology and will not be described in detail here. As other implementation methods, implementers may use other methods of existing technology, such as Bayesian mutation detection algorithm, LOF anomaly detection algorithm, etc. This embodiment does not impose any special restrictions on this.

[0051] If bridging occurs during a monitoring cycle in this high-temperature purification process, the control system sends a vibration command to the electromagnetic vibrator to activate the vibration device and perform short-term, low-amplitude vibration on the purification furnace body. This causes the graphite powder in the bridging area to loosen and fall, thereby ensuring the uniform transfer of heat and reaction gases during subsequent high-temperature purification. At the same time, it avoids furnace shutdowns due to severe bridging and improves the overall purification efficiency.

[0052] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0053] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0054] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application, without departing from the content of the technical solution of this application, shall fall within the protection scope of the technical solution of this application.

Claims

1. A high-temperature purification method for graphite materials, characterized in that, The method includes the following steps: When using a high-temperature purification furnace to purify graphite materials at high temperatures, the furnace temperature, output power, and temperature and voltage of each induction coil at each moment are obtained during each monitoring cycle of the high-temperature purification process. Analyze the discrete changes in temperature and voltage of different induction coils at the same time within each monitoring cycle, and calculate the first evaluation value for each monitoring cycle; The power fluctuation of each monitoring cycle is calculated by observing the fluctuation of output power in different local areas within each monitoring cycle; the second evaluation value of each monitoring cycle is calculated by combining the synchronization of the furnace temperature and output power change trends within each monitoring cycle. Based on the first and second evaluation values, the evaluation coefficients for each monitoring period are obtained. Based on the changing trends and differences of the evaluation coefficients for each monitoring period in different high-temperature purification processes, the bridging confidence level for each monitoring period in each high-temperature purification process is determined. Anomaly detection is performed on the bridging confidence level of this and previous high-temperature purification processes under each monitoring period to evaluate the bridging phenomenon that occurs in each monitoring period during this high-temperature purification process, eliminate the bridging phenomenon in the high-temperature purification furnace, and thus purify the graphite material at high temperature.

2. The high-temperature purification method for graphite materials as described in claim 1, characterized in that, The calculation of the first evaluation value for each monitoring period includes: Calculate the degree of dispersion of temperature and voltage of all induction coils at each time point, and denote them as the first degree of dispersion and the second degree of dispersion, respectively; The sum of the first dispersion and the second dispersion at all times within each monitoring cycle is used as the temperature difference and voltage difference for each monitoring cycle, respectively. The first evaluation value is the result of fusing the temperature difference and voltage difference.

3. The high-temperature purification method for graphite materials as described in claim 2, characterized in that, The specific process of the fusion is as follows: the product of the temperature difference and the voltage difference is used as the first evaluation value for each monitoring cycle.

4. The high-temperature purification method for graphite materials as described in claim 1, characterized in that, The calculation of power fluctuation for each monitoring period includes: The output power at all times within each monitoring cycle is divided into multiple local time periods; Analyze the overall trend of output power deviation at different times within a local time period, and calculate the deviation; The power fluctuation is the average of the offsets for all local time periods within each monitoring cycle.

5. The high-temperature purification method for graphite materials as described in claim 4, characterized in that, The calculation of the offset includes: linearly fitting the output power at all times within a local time period, and calculating the fitting error as the offset.

6. The high-temperature purification method for graphite materials as described in claim 1, characterized in that, The calculation of the second evaluation value for each monitoring period includes: Calculate the correlation between furnace temperature and output power at all times within each monitoring cycle, and perform a positive mapping on the absolute value of the correlation. The second evaluation value is the ratio between the power fluctuation and the result of the positive mapping.

7. The high-temperature purification method for graphite materials as described in claim 1, characterized in that, The evaluation coefficient is the product of the first evaluation value and the second evaluation value.

8. The high-temperature purification method for graphite materials as described in claim 1, characterized in that, The determination of the bridging confidence level for each monitoring cycle during each high-temperature purification process includes: The evaluation coefficients of each monitoring cycle in each high-temperature purification process and the evaluation coefficients of the same monitoring cycle in the previous high-temperature purification processes are linearly fitted, and the slope of the fitted line is calculated. The average difference between the evaluation coefficients of each monitoring cycle in each high-temperature purification process and the same monitoring cycle in previous high-temperature purification processes is calculated as the cumulative deviation of each monitoring cycle in each high-temperature purification process. The bridging confidence level is the product of the slope and the cumulative deviation.

9. The high-temperature purification method for graphite materials as described in claim 1, characterized in that, The assessment of bridging phenomena occurring in each monitoring cycle during this high-temperature purification process includes: performing anomaly detection on the bridging confidence of each monitoring cycle during this high-temperature purification process and its previous high-temperature purification processes in the same monitoring cycle. If the bridging confidence of each monitoring cycle during this high-temperature purification process is an anomaly, it is determined that bridging has occurred; otherwise, no bridging has occurred.

10. The high-temperature purification method for graphite materials as described in claim 1, characterized in that, Eliminating bridging in the high-temperature purification furnace includes: using a vibration device to vibrate the furnace body for a short time and at a low amplitude, causing the graphite powder with bridging to loosen and fall off.

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