Aluminum electrolysis single anode real-time height calculation simulation model construction, anode fault diagnosis and aluminum electrolysis process optimization control method

Abstract

The invention discloses an aluminum electrolysis single anode real-time height calculation simulation model construction method, an anode fault diagnosis method and an aluminum electrolysis process optimization control method. The method comprises the following steps: S1, collecting real-time current data of each anode; s2, collecting the height, the horizontal section area, the volume density, the current efficiency data and the accumulated current flux data of the anode scrap during normal anode replacement of each anode; s3, analyzing an anode correlation influence coefficient; s4, analyzing a post-intervention coefficient; s5, analyzing the comprehensive index coefficient of the electrolytic cell; and S6, obtaining a final calculation model of the real-time height of the single anode. According to the method, the related factors of single anode consumption in the electrolytic cell are decomposed, the main influence factors of the single anode in the electrolytic cell are dynamically split, and the horizontal size error is eliminated in combination with the anode post-intervention treatment, so that the real-time consumption height of the single anode is accurately estimated, and the result digitization is realized; and high-efficiency control of the single anode of the electrolytic cell is realized and anode carbon consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of nonferrous metal metallurgy, and in particular to a method for constructing a simulation model for real-time height calculation of a single anode of aluminum electrolysis. Background Art

[0002] In 2022, global electrolytic aluminum production will be approximately 68.34 million tons, of which China will produce approximately 40.21 million tons, a year-on-year increase of 4.45%, accounting for 58.7% of the global total and ranking first in the world. In 2022, China's anode production capacity will be approximately 29.87 million tons, and its output will be approximately 21.42 million tons, a year-on-year increase of 2.1%. Of this total, commercial anode production will be 11.09 million tons, with a capacity utilization rate of 69.9%, and supporting anode production will be 10.33 million tons, with a capacity utilization rate of 79.6%. From January to June 2023, China's anode production will be 10.748 million tons, with a capacity utilization rate of 72.2%. The aluminum industry is a major energy consumer in the metallurgical sector, with comprehensive electricity consumption of approximately 13,500 kW / h per ton of aluminum and anode carbon consumption of approximately 480 kg per ton of aluminum. The costs of electricity and anode carbon are significant components of primary aluminum production costs, and energy conservation and consumption reduction in aluminum electrolysis are key to controlling production costs.

[0003] The Hall-Heroult electrolysis process is used industrially to produce metallic aluminum. The main reactor for aluminum electrolysis is the aluminum reduction cell. During the aluminum electrolysis process, it is critical to keep various parameters within the optimal process range. Therefore, how to obtain process parameters related to the core reactions of aluminum electrolysis and how to achieve automation and digitalization of the aluminum electrolysis process are widely and key concerns in the industry.

[0004] Existing technologies, such as online anode current monitoring, are not linked to online acquisition of anode carbon block parameters. Anode simulation technology, which uses simulation software to calculate anode carbon block consumption and determine anode dimensional changes, is not coupled with online anode current monitoring and presents several challenges.

[0005] (1) The state of each electrolytic cell in different aluminum electrolysis plants is different, and it is impossible to analyze the distribution of the current density of the anode corresponding to each cell only by the electric field law;

[0006] (2) The relationship between the three major fields of electric field, thermal field and magnetic field to which the anode is subjected in the electrolytic cell is only the basic environmental condition. In fact, there are many factors related to anode consumption, and the model obtained by simple simulation does not have high accuracy;

[0007] (3) The structural dimensions of the electrolytic cell cannot be accurately designed. There are large errors between the actual product dimensions and the designed dimensions. Simple normalization cannot adapt to long-term changes.

[0008] (4) The differences in physical and chemical properties of anode production lead to differences in the oxidation of anodes in the electrolytic cell. The specific oxidation conditions cannot be determined simply, and the peeling and slagging of the anode exterior will affect the overall size prediction.

[0009] (5) After a thorough analysis of the material conditions and environment, there is still a large difference between the actual situation and the actual situation. The results obtained by analysis without combining historical data will still deviate from the actual situation.

[0010] It should also be noted that 70% of aluminum electrolytic cell failures are related to prebaked anodes. Common anode failures (symptoms) in electrolytic cells include: localized anode overheating, uneven current distribution, anode block dropout, anode "bulking," anode cracks and fractures, anode block shedding, large amounts of carbon slag entering the electrolyte, carbonization around the anode, and anode tilt. These failures are primarily caused by prebaked anode quality issues and improper electrolytic operation. However, the real-time status and detection of anode problems in a single anode can currently only be determined by inferring the problematic anode through the cell control curve and then determining the problem through anode current distribution measurements. This method is not very accurate, requires high-level production management skills and experience, and is also difficult to automate and digitize. When an anode abnormality occurs in the electrolytic cell, it is generally necessary to react to it through post-processing data before appropriate measures can be taken, which prevents timely implementation of optimal measures. Summary of the Invention

[0011] The technical problem to be solved by the present invention is to overcome the shortcomings of the existing technology and provide a real-time state prediction calculation simulation method for a single anode of an aluminum electrolytic cell. By combining the overall environmental impact with the properties of each anode itself, the operating state of each anode in the electrolytic cell can be accurately determined with high precision, strong real-time performance, accurate prediction results, and accurate anode defect determination.

[0012] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0013] A method for constructing a simulation model for calculating the real-time height of a single anode in aluminum electrolysis comprises the following steps:

[0014] S1. Number the anodes at various locations in the electrolytic cell and collect real-time current data for each anode;

[0015] S2. Collect the height, horizontal cross-sectional area, volume density, current efficiency and cumulative current flux data of the residual anode at each numbered position during normal pole replacement, obtain a database of the corresponding relationship between the cumulative current flux of a single anode and the anode height, and establish a basic calculation model;

[0016] S3. Collect the anode physical and chemical index data of each numbered anode and deduce the anode quality and index coefficient;

[0017] S4. Collect the corresponding coefficients of the post-intervention measures and the quantitative indicators of the post-intervention treatment for each numbered anode, and derive the post-intervention coefficient;

[0018] S5. Collect comprehensive index coefficients including electrolytic cell capacity, busbar configuration and magnetic field distribution, cathode and lining configuration, magnetic fluid stability, thermal balance and thermal field distribution, and raw material conditions, and combine empirical data to obtain comprehensive index coefficients for electrolytic cells at each numbered position;

[0019] S6. Substitute the anode quality and index coefficient, post-intervention coefficient, and electrolytic cell comprehensive index coefficient into the basic calculation model for correction to obtain the final calculation model. The specific calculation formula is as follows:

[0020]

[0021] Among them, H 高度 H is the height of the single anode real-time model, in mm; 初始 is the initial measured height of the anode; ∫i(t)dt is the real-time anode power consumption, where the current unit is A and the time unit is s; F 法拉第常数 is the Faraday constant; nc 碳耗系数 is the carbon consumption coefficient; η 电效 is the anode operating current efficiency, which is the value corresponding to the single cell power consumption minus aluminum production; ρ 阳极体积密度 is the initial measured mass-to-volume ratio of the anode in g / mm 3 ;s 阳极水平截面面积 The cross-sectional area value is obtained by measuring the anode size in mm. 2 ;k 阳极截面系数 It is the correction coefficient for the change in cross-sectional area caused by mechanical loss of the anode. Through the coefficient setting, the coefficient is 1 when post-intervention anode is used; K1 is the anode quality and index coefficient, and its value range is (-20%, 20%); K2 is the post-intervention coefficient, and its value range is (-40%, 0); K3 is the comprehensive index coefficient of the electrolytic cell, and its value range is (-30%, 30%); the total value range of the K1, K2, and K3 coefficients is (-60%, 50%).

[0022] As a further improvement of the above solution:

[0023] S3 specifically includes the following steps:

[0024] S31. Integrate the anode information and anode treatment information into a database, perform a fuzzy clustering algorithm based on the measured values, and set the number of clusters corresponding to the correlation coefficient.

[0025] S32. After inputting all sample data based on the number of associated clusters, the environmental impact factor data is accurately calculated and combined with the anode height output value. The membership degree and cluster center are continuously iterated until the membership degree is basically stable. The samples are classified according to the size of the membership value. The specific calculation formula is as follows:

[0026] K1=N-(z1n1+z2·n2+z3·n3+…+z n ·n n ) / 100

[0027] n1+n2+n3+…+n n =100%

[0028] Wherein, N is the indicator constant obtained by iterative settlement of the database, and its value range is (-40%, 40%); z is the value corresponding to each indicator; n is the coefficient corresponding to the indicator;

[0029] S33. Subsequently, the anode information can be automatically classified after collection, and the anode economic benefits and the location of the tank can be analyzed in combination with the tank situation, and the anode usage results can be obtained in combination with any environmental influencing factors.

[0030] The specific calculation formula in step S4 is as follows:

[0031] K2=(H1m1+H2·m2+H3·m3+…+H n ·m n ) / (-100)

[0032] Among them, H is the corresponding coefficient of the specific post-intervention means, the unit is %; m is the quantitative indicator of post-intervention treatment.

[0033] S5 specifically includes the following steps:

[0034] S51. Analyze the comprehensive index of the electrolytic cell: According to the influence of environmental overlap, the singular value decomposition (SVD) method is adopted to decompose the matrix A into the matrix U, the matrix S and the matrix V;

[0035] S52, completing the original data matrix A by using the matrix U, matrix S and matrix V of S41 according to the actual information collection data;

[0036] S53. After obtaining the various related influencing factors of the electrolytic cell environment, the total influencing coefficient is split to obtain the influencing coefficient corresponding to the single environmental influencing factor, which is then included in the anode state model to ensure the accuracy of the anode height calculation result. The sum of the electrolytic cell comprehensive index coefficients of each numbered anode in the electrolytic cell is calculated as follows:

[0037]

[0038] The method further includes the following steps: S7, setting the seasonal ambient temperature change, the north-south climate difference, the difference in anode shape, the difference in cathode voltage drop, the difference in electrolysis temperature, and the difference in abnormal tank as correction coefficients K. n , K n Substitute the final calculation model in S6 to further modify the calculation structure. The specific calculation formula is as follows:

[0039]

[0040] Among them, K n is the corresponding correction coefficient of the historical data of the tank condition, and the value range is (-10%, 10%).

[0041] The following steps are also included:

[0042] S8. Collect data on the residual pole after each pole change, including the length, width, height, and weight of the residual pole. Combine the model results with the actual data for comparison, and continuously optimize the correlation coefficient through algorithm optimization and self-learning.

[0043] A method for diagnosing anode faults in an aluminum anode electrolysis process, further comprising the following steps after the above method: comparing the predicted value of the real-time height of a single anode calculated using the final calculation model data obtained in step S6 or S7 with the actual data, determining anode defects for anodes with abnormal data, and taking corresponding measures to prevent the electrolytic cell from being continuously affected.

[0044] This includes diagnosing and determining abnormal anode consumption rates, using the final calculation model data to calculate the predicted value of the single anode real-time height, taking the derivative of each anode's slot entry time to obtain the real-time consumption rate of each single anode, combining the model database to determine abnormalities in the real-time consumption rate of a single anode, and adjusting the real-time consumption rate by manipulating the anode pitch. The specific calculation formula is as follows:

[0045]

[0046] Wherein, v real-time consumption rate is expressed as the real-time height consumption rate of a single anode, in mm / s; H 高度 Expressed as the height of the single anode real-time model in mm; t 入槽时间 It is expressed as the time for a single anode to enter the electrolytic cell, in seconds.

[0047] A method for optimizing and controlling an aluminum electrolysis process, after the above method, further comprises the following steps: programming using the calculation model obtained in step S6 or S7 as the underlying logic, completing an online database combined with an online calculation model to realize data sharing and online monitoring of real-time data.

[0048] Compared with the prior art, the advantages of the present invention are:

[0049] The present invention proposes a simulation model for calculating the real-time height of a single anode in aluminum electrolysis. By decomposing all relevant factors of anode consumption in the electrolytic cell, the main influencing factors of the anode in the electrolytic cell are dynamically separated, and combined with post-anode intervention measures, the horizontal dimension error is eliminated, thereby accurately estimating the real-time consumption height of a single anode and realizing the digitization of the results.

[0050] Furthermore, by collecting the technical indicator data of the anode itself, on the one hand, the impact of the heterogeneity and differences of each anode can be taken into account, and on the other hand, it provides research data and research basis for the impact of various anode indicators on anode consumption, and feeds this data back to the anode production end for adjustment and optimization, especially for the design of special anode sizes for corresponding types of electrolytic cells to reduce the negative impact of anode quality on anode consumption.

[0051] Furthermore, based on the overall environmental impact of the electrolytic cell, the comprehensive indicator factors of the electrolytic cell are decomposed to obtain the results of the uneven anode consumption caused by other environments such as anode position, electric field, thermal field, magnetic field, magnetic fluid performance, electrolyte system, and raw material conditions. This method can overcome the inadequacy of only analyzing electric field data, and can also provide data support for the study of the relationship between electric field, thermal field, and magnetic field.

[0052] Furthermore, the present invention also proposes a method for diagnosing anode faults in the aluminum anode electrolysis process. The predicted value of the real-time height of a single anode calculated using the final calculation model data is compared with the actual data, and anode defects are determined for anodes with abnormal data, so that corresponding measures can be taken to prevent the electrolytic cell from being continuously affected.

[0053] Furthermore, the present invention also proposes an optimization control method for the aluminum electrolysis cell process, which uses a calculation model as the underlying logic for programming, completes the online database combined with the online calculation model to realize data sharing and online monitoring of real-time data. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of the model calculation of the real-time model height of a single anode in the embodiment. (Not shown in the embodiment) DETAILED DESCRIPTION

[0055] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] Example 1:

[0057] Figure 1 An embodiment of a method for constructing a simulation model for calculating the real-time height of a single anode of aluminum electrolysis according to the present invention is shown, comprising the following steps:

[0058] S1. Collecting real-time current data: Numbering the anodes at various locations in the electrolytic cell and collecting real-time current data for each anode;

[0059] S2. Establish a real-time height model for a single anode in an aluminum electrolytic cell, and process the influencing factors in combination with the historical database: collect the real-time current data of each anode in the electrolytic cell through existing equipment, and then integrate the current flow through each guide rod. Combine the empirical data of each cell with the corresponding digitized post-intervention anode data to establish a model for each anode in each numbered position of each cell. The model calculation formula is as follows:

[0060] The real-time current data of each anode in the electrolytic cell is collected through existing equipment, and then the current flowing through each guide rod is obtained by integration. Then, the empirical data of each cell is combined with the corresponding digital post-intervention anode data to establish a model for each anode position in each cell. The specific formula is as follows:

[0061]

[0062] Formula Notes:

[0063] (1).H 高度 : represents the height of the single anode real-time model, in mm;

[0064] (2).H 初始 : represents the initial measured height of the anode, in mm;

[0065] (3)∫i(t)dt: real-time anode power consumption by integration method, including real-time current / A and time / s;

[0066] (4).F 法拉第常数 : Expressed as Faraday constant F = eNA = 96485 C / mol;

[0067] (5).nc 碳耗系数 : Expressed as carbon consumption coefficient, according to the electrolytic aluminum reaction equation, the carbon consumption coefficient is ≤4;

[0068] (6).Mc 摩尔质量 : Expressed as the molar mass of C, which is 12.0107 g / mol;

[0069] (7).ρ 阳极体积密度 : Expressed as the initial measured mass-to-volume ratio of the anode, in g / mm 3 ;

[0070] (8).s 阳极水平截面面积 : Indicates the cross-sectional area value obtained by measuring the anode size, in mm 2 ;

[0071] (9).k 阳极截面系数: The cross-sectional area changes due to mechanical loss of the anode, which is set by a coefficient. When the anode is intervened after use, the coefficient is 1;

[0072] (10).η 电效 : Anode operating current efficiency, %;

[0073] Among them, H 初始 、s 阳极水平截面面积 The database is accumulated for the measured value, where k 阳极截面系数 F is the setting coefficient for horizontal dimension change due to mechanical wear and tear; 法拉第常数 、nc 碳耗系数 、Mc 摩尔质量 is a fixed coefficient, where nc 碳耗系数 Related to CO concentration caused by ineffective carbon consumption; ρ 阳极体积密度 is the batch sampling data; η 电效 is the value corresponding to the power consumption of a single cell minus the aluminum production; ∫i(t)dt is the current passing through the anode.

[0074] Among them, the anode has a selective burning phenomenon in the bonding phase due to the different reactivity of the binder coke and the aggregate. When the bonding phase reacts to a certain extent, the bonding force between the aggregates is greatly weakened, and the petroleum coke particles fall off under the action of mechanical force, thermal stress and electromagnetic force, resulting in changes in the cross-sectional size of the anode such as "slimming" and "shrinkage". Post-intervention treatment can solve this problem. When using post-intervention anode, k 阳极截面系数 is 1.

[0075] S3. Analyze the anode-related influence coefficients: Combine the anode number with the anode physical and chemical indicators. The anode physical and chemical indicators include apparent density, true density, compressive strength, CO2 reactivity residual rate, flexural strength, room temperature resistivity, thermal expansion coefficient, air reactivity residual rate, impermeability, V content, Na content, etc. Collect data for each anode and upload it to the database. The collected data mainly includes size and physical and chemical indicator data, and form a three-dimensional graph. Bind the anode number to ensure that after the anode is installed in the cell, the changes of the anode in the electrolytic cell are reflected in real time through a mathematical model combined with other relevant coefficients. The specific calculation formula is as follows:

[0076] K1=N-(z1n1+z2·n2+z3·n3+…+z n ·n n ) / 100

[0077] n1+n2+n3+…+n n =100%

[0078] Among them, N is the index constant obtained by iterative settlement of the database, and its value range is (-40%, 40%); z is the value corresponding to each index; n is the coefficient corresponding to the index; K1 is the anode quality and the index coefficient has a value range of (-20%, 20%);

[0079] The specific steps include:

[0080] S31: Due to the differences between anodes caused by the anode production process and the heterogeneity of the anode itself, there are large similarities and differences after the anode information is collected. The anode information itself and the anode processing information are integrated into the database. The relevant technology needs to combine the measured values ​​to perform a fuzzy clustering algorithm and set the number of clusters corresponding to the correlation coefficient. The measured values ​​of the anode information itself include apparent density, true density, compressive strength, CO2 reactivity residual rate, flexural strength, room temperature resistivity, thermal expansion coefficient, air reactivity residual rate, impermeability, V content, Na content and other indicators.

[0081] S32: After all sample data are input according to the number of associated clusters, the environmental influencing factor data is obtained with the exact value and combined with the anode height output value, and the membership degree and cluster center are continuously iterated to calculate until the membership degree is basically stable, and the samples are classified according to the size of the membership degree value;

[0082] S33: Subsequently, the anode information can be automatically classified after collection, and the economic benefits of the anode and the location of the tank can be analyzed in combination with the tank situation, and the use results of the anode can be obtained in combination with any environmental influencing factors.

[0083] Combined with the anode number and anode physical and chemical indicators, the anode physical and chemical indicators include apparent density, true density, compressive strength, CO2 reactivity residual rate, flexural strength, room temperature resistivity, thermal expansion coefficient, air reactivity residual rate, impermeability, V content, Na content, etc. Data collection is performed on each anode and uploaded to the database. The collected data mainly includes dimensions and physical and chemical indicators, and a three-dimensional graph is generated and bound to the anode number to ensure that after the anode is installed in the cell, the changes of the anode in the electrolytic cell are reflected in real time through mathematical models combined with other relevant coefficients;

[0084] S4. Analyze the post-intervention coefficient: To ensure the accuracy of the mathematical model, the anode post-intervention treatment can effectively solve the problems of anode surface slagging and anode oxidation, avoiding the model only measuring the height without considering the reduction of the anode horizontal size. When the anode post-intervention treatment is not performed, the horizontal cross-sectional size of the anode needs to be set with a corresponding setting coefficient; the post-intervention coefficient is the positive impact of the intervention measures before and after the pre-baked anode is taken out of the baking furnace and before and after the anode is placed in the tank on the use of the pre-baked anode in the electrolytic cell. The specific calculation formula is as follows:

[0085] K2=(H1m1+H2·m2+H3·m3+…+H n ·mn ) / (-100)

[0086] H is the corresponding coefficient of the specific post-intervention means, in %;

[0087] m is the quantitative index of post-intervention treatment;

[0088] K2 is the post-intervention coefficient, and its value range is (-40%, 0).

[0089] S5. Analyze the comprehensive index coefficient of the electrolytic cell: Combine the comprehensive index coefficients of the electrolytic cell type capacity, busbar configuration and magnetic field distribution, cathode and lining configuration, magnetic fluid stability, thermal balance and thermal field distribution, raw material conditions, etc., including the current data of the anode monitoring in step S1, and collect and upload them to the database. The comprehensive index coefficient of the entire electrolytic cell is set to ktotal, and the sum of the comprehensive index coefficients of each anode in the electrolytic cell is calculated as follows:

[0090]

[0091] where k 热场系数 、k 磁场系数 、k 电场系数 、k 电解质体系 、k 磁流体稳定性系数 、k 原材料系数 、k i The above are all calculated by existing formulas; K3 is the comprehensive index coefficient of the electrolytic cell, and its value range is (-30%, 30%), %; n is the position number of the electrolytic cell;

[0092] The specific steps include:

[0093] S51: Comprehensive indicators of electrolytic cells, such as cell capacity, busbar configuration and magnetic field distribution, cathode and lining configuration, magnetic fluid stability, thermal balance and thermal field distribution, electrolyte system, and raw material conditions, all vary. Furthermore, the production environment is constantly changing, making data collection difficult. Furthermore, data is also affected by human factors, such as changes in thermal, electrical, and magnetic fields during pole reversal or extraction. Therefore, singular value decomposition (SVD) is employed to account for the overlapping effects of these environments. SVD decomposes matrix A into three matrices: U, S, and V.

[0094] S52: The above three matrices are used to complete the original data matrix A based on the actual information collection data. Assuming A is an m*n order matrix of the magnetic field coefficient-thermal field coefficient, after obtaining the corresponding output results, the eigenvector is used to simplify the entire matrix to obtain the actual influence coefficient of the magnetic field coefficient-thermal field coefficient. Other related systems such as the anode itself or the comprehensive index conditions of the electrolytic cell are quickly obtained through the eigenvector of the singular value decomposition to reduce the amount of data processing;

[0095] S53: After obtaining the various related influencing factors of the electrolytic cell environment, the total influencing coefficient is split to obtain the influencing coefficient corresponding to the single environmental influencing factor, which is then included in the anode state model to achieve a high degree of accuracy in the calculation results.

[0096] S6. Substitute the above-mentioned influencing factor coefficients into the basic formula in step S1 to obtain the final calculation model of the single anode real-time model height. The real-time value is obtained by calculation. If the error is large, it is corrected. The specific calculation formula is as follows:

[0097]

[0098] S7. Other correction coefficients can be further analyzed: seasonal ambient temperature changes, north-south climate differences, anode shape differences, cathode voltage drop differences, electrolysis temperature differences, and abnormal cell differences are set as correction coefficients Kn, and Kn is substituted into the final calculation model in S6 to further correct the calculation structure. The specific calculation formula is as follows:

[0099]

[0100] Among them, Kn is the corresponding correction coefficient of the historical data of the tank condition, and the value range is (-10%, 10%).

[0101] The above K coefficient calculation method is handled accordingly according to the actual situation. The total value range of K1, K2, and K3 coefficients is (-60%, 50%).

[0102] S8: Data on the residual poles after each pole change is collected and stored in a database. The model results are compared with the actual data. Through algorithm optimization and self-learning, the correlation coefficient is continuously optimized to ensure that the accuracy of real-time data reaches 99.9% and the accuracy of predicted result data reaches 99.5%.

[0103] Taking a 500kA aluminum electrolytic cell containing 48 anodes as an example, the actual and predicted anode height data for three anodes from the same cell (cell number 1419) are used. The correlation coefficients for K1 (anode quality and index coefficient) and K2 (post-intervention coefficient) are obtained from the anode information database and the anode treatment database. The data for these coefficients are shown in Table 1. The correlation coefficients for the magnetic, thermal, and electric fields corresponding to the position of cell 1419, K3 (electrolytic cell comprehensive index coefficient), are shown in Table 2. Three anodes were selected from the database as samples, and the process and result data were derived. Details are shown in Table 3.

[0104] By processing the comprehensive index coefficient of the electrolytic cell, it can be found that after splitting the overall impact into the impact of each position, the overall situation presents a normal distribution. The horizontal axis of the normal distribution is then split into 24 equal parts (for example, when the difference between the AB surfaces is large, it needs to be split into 48 equal parts. The difference between the AB surfaces of this cell is small). Then, the environmental impact coefficient (K3) corresponding to the position is obtained by combining the constant with the corresponding area of ​​the 24 equal parts.

[0105] During the electrolysis process, the current distribution corresponding to each anode is different. From the data in Table 1, it can be found that under the same time, the current passing through the anode is different. Sample 2 is located in the center of the electrolytic cell and has not been specially treated, while sample 3 is located at the outer part of the electrolytic cell and has been post-treated. Therefore, treating each anode as having the same current intensity will lead to a large error in the result. By collecting the current and the anode data itself, combined with the correlation coefficient of the anode cell environment obtained by database analysis, the predicted height value is obtained.

[0106] Table 1 Sources of K1 and K2 coefficients in the anode information database

[0107]

[0108] Table 2 Corresponding coefficients of 1419 electrolytic cell position numbers

[0109]

[0110] Table 3 500KA aluminum electrolytic cell anode model data sheet

[0111]

[0112] Example 2:

[0113] A 400kA aluminum electrolytic cell, containing 40 anodes, was used to obtain actual and predicted anode height data for five anodes from the same cell (cell number 324). The K1 and K2 correlation coefficients were obtained from the anode information database and the anode treatment database. The K3 correlation coefficient for the magnetic, thermal, and electric fields corresponding to the cell position was also obtained. The relevant model data is shown in Table 4. Five anodes were selected from the database as samples, and the process and result data were derived. See Table 4 for details.

[0114] From the data in the table, we can find that due to the differences in anode production, there are differences in anode K1 (anode quality and index coefficient), and different anode post-intervention treatment methods and different processing volumes will lead to different anode K2 (post-intervention coefficient), while the influence of K3 (comprehensive index coefficient of the electrolytic cell) at the same position remains unchanged, and the current passing through the same position and the same cell entry time is not completely consistent. However, the calculation model can accurately obtain the accurate value of the real-time height of a single anode.

[0115] This embodiment fully considers various influencing factors and combines them with an online prediction and simulation method for the real-time status of a single anode in an electrolytic cell. Based on the model and actual conditions, the model's correlation coefficient is supplemented to obtain the actual anode consumption. This predicted value is highly accurate and closely aligned with actual conditions, offering advantages such as simple calculation, good adaptability, and accurate and reliable performance. By comparing the actual value measured after removing and cooling the anode and comparing it with the theoretical value, the prediction accuracy can reach over 99%.

[0116] Table 4 400KA aluminum electrolytic cell anode model data sheet

[0117]

[0118]

[0119] Example 3:

[0120] A 400kA aluminum electrolytic cell, containing 40 anodes, was used to obtain actual and predicted anode height data for three anodes from the same cell (cell number 412). The K1 and K2 correlation coefficients were obtained from the anode information database and the anode treatment database. The K3 correlation coefficient, corresponding to the magnetic, thermal, and electric fields at the cell position, was also obtained. The relevant model data is shown in Table 4. Five anodes were selected from the database as samples, and the process and result data were derived, as detailed in Table 5.

[0121] The anodes of Samples 1 and 2 are slotted anodes, so the Kn value is added for correction. The data in the table shows that due to the slotting of the anode, the correction factor Kn is introduced to obtain the anode height data calculated by the model and the actual anode height data with high accuracy.

[0122] Table 5

[0123]

[0124] Example 4:

[0125] By analyzing the cumulative current flux of each anode at the same entry time, it was found that the value of ∫i(t)dt / C of a certain anode differed by more than 10% from the ∫i(t)dt / C values ​​of other anodes at the same entry time. Through calculation, the real-time height data of this single anode was more than 3% higher than the real-time height data of other anodes at the same entry time, and the real-time consumption rate of the single anode decreased, the current flow was abnormal, and the real-time consumption rate of nearby single anodes increased. Therefore, it was judged that the anode had defects, and the abnormal situation was the "long bag" of the anode. Through on-site inspection, it was found that the electrolyte at the bottom of the anode had a long bag. After the anode was de-agglomerated and installed in the electrolytic cell for 24 hours, the anode returned to normal.

[0126] Although the present invention has been disclosed above with reference to preferred embodiments, this is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, utilize the technical content disclosed above to make many possible changes and modifications to the technical solution of the present invention, or modify it into an equivalent embodiment with equivalent changes. Therefore, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for constructing a simulation model for real-time height calculation of a single anode for aluminum electrolysis, characterized in that: The following steps are involved: S1. Number the anodes at various positions in the electrolytic cell and collect real-time current data of each anode; S2, collecting the height, horizontal cross-sectional area, volume density, current efficiency data and cumulative current flux data of the residual poles at each numbered position during normal pole replacement, obtaining a database of the corresponding relationship between the single anode cumulative current flux and the single anode height, and establishing a basic calculation model; S3, collecting the anode physical and chemical index data of each numbered anode, and deriving the anode quality and index coefficient; S4. Collect the corresponding coefficients of the post-intervention measures and the quantitative indicators of the post-intervention treatment for each numbered anode, and derive the post-intervention coefficient; S5. Collect comprehensive index coefficients including electrolytic cell capacity, busbar configuration and magnetic field distribution, cathode and lining configuration, magnetic fluid stability, thermal balance and thermal field distribution, and raw material conditions, and combine empirical data to obtain comprehensive index coefficients of electrolytic cells at each numbered position; S6. Substitute the anode quality and index coefficient, post-intervention coefficient, and electrolytic cell comprehensive index coefficient into the basic calculation model for correction to obtain the final calculation model. The specific calculation formula is as follows: Among them, H 高度 H is the height of the single anode real-time model, in mm; 初始 is the initial measured height of the anode; ∫i(t)dt is the real-time anode power consumption, where the current unit is A and the time unit is s; F 法拉第常数 is the Faraday constant; nc 碳耗系数 is the carbon consumption coefficient; η 电效 is the anode operating current efficiency, which is the corresponding value of single cell power consumption minus aluminum production; 阳极体积密度 is the initial measured mass-to-volume ratio of the anode in g / mm 3 ;s 阳极水平截面面积 The cross-sectional area value is obtained by measuring the anode size in mm 2 ;k 阳极截面系数 It is the correction coefficient for the change of cross-sectional area caused by mechanical loss of anode. It is set by coefficient. When post-intervention anode is used, the coefficient is 1; K1 is the anode quality and index coefficient, and its value range is (-20%, 20%); K2 is the post-intervention coefficient, and its value range is (-40%, 0); K3 is the comprehensive index coefficient of electrolytic cell, and its value range is (-30%, 30%); the total value range of K1, K2 and K3 coefficients is (-60%, 50%).

2. The method for constructing a simulation model for real-time height calculation of a single anode for aluminum electrolysis according to claim 1, characterized in that: S3 specifically includes the following steps: S31, integrating the anode information and the anode processing information into a database, combining the measured values ​​with a fuzzy clustering algorithm, and setting the number of clusters corresponding to the correlation coefficient; S32. After all sample data are input according to the number of associated clusters, the environmental influencing factor data is obtained with exact values ​​and combined with the output value of the anode height, and the membership and cluster center are continuously iterated to calculate until the membership is basically stable. The samples are classified according to the size of the membership value. The specific calculation formula is as follows: K1=N-(z1n1+z2·n2+z3·n3+…+z n ·n n ) / 100 <h2 style=";text-align:left;direction:ltr">n1+n2+n3+…+n<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> <100% Among them, N is the indicator constant obtained by iterative settlement of the database, and its value range is (-40%, 40%); z is the value corresponding to each indicator; n is the coefficient corresponding to the indicator; S33. Subsequently, the anode information can be automatically classified after collection, and the economic benefits of the anode and the location of the tank can be analyzed in combination with the tank situation, and the use results of the anode can be obtained in combination with any environmental influencing factors.

3. The method for constructing a simulation model for real-time height calculation of a single anode for aluminum electrolysis according to claim 1, characterized in that: The specific calculation formula in step S4 is as follows: <h2 style=";text-align:left;direction:ltr">K2 = (H1m1+H2m2+H3m3+…+H<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ·m<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ) / (-100) Among them, H is the corresponding coefficient of the specific post-intervention measure, the unit is %; m is the quantitative index of post-intervention treatment, and n is the electrolytic cell location number.

4. The method for constructing a simulation model for real-time height calculation of a single anode for aluminum electrolysis according to claim 1, characterized in that: S5 specifically includes the following steps: S51. Analyze the comprehensive index of the electrolytic cell: According to the influence of environmental overlap, the singular value decomposition (SVD) method is adopted to decompose the matrix A into the matrix U, the matrix S and the matrix V; S52, completing the original data matrix A by using the matrix U, matrix S and matrix V of S41 according to the actual information collection data; S53. After obtaining the related influencing factors of the electrolytic cell environment, the total influencing coefficient is split to obtain the influencing coefficient corresponding to the single environmental influencing factor, which is then included in the anode state model to complete the accuracy of the anode height calculation result; the sum of the comprehensive index coefficients of the electrolytic cell for each numbered anode in the electrolytic cell is calculated as follows: k 热场系数 +k 磁场系数 +k 电场系数 +k 电解质体系 +k 磁流体稳定性系数 +k 原材料系数 +…+k i =k 总 5. The method for constructing a simulation model for real-time height calculation of a single anode for aluminum electrolysis according to claim 1, characterized in that: The following steps are also included: S7, set the correction coefficient K for seasonal ambient temperature changes, north-south climate differences, anode shape differences, cathode voltage drop differences, electrolysis temperature differences, and abnormal cell differences n , K n Substitute the final calculation model in S6 to further modify the calculation structure. The specific calculation formula is as follows: Among them, K n It is the corresponding correction coefficient of the historical data of the tank condition, and its value range is (-10%, 10%).

6. The method for constructing a simulation model for real-time height calculation of a single anode for aluminum electrolysis according to claim 5, characterized in that: The following steps are also included: S8. Collect data on the residual pole after each pole change, including the length, width, height, and weight of the residual pole. Compare the model results with the actual data, and continuously optimize the correlation coefficient through algorithm optimization and self-learning.

7. A method for diagnosing anode faults during aluminum anode electrolysis, characterized in that: The method of claims 1-6 further comprises the following steps: comparing the predicted value of the real-time height of a single anode calculated using the final calculation model data obtained in step S6 or S7 with the actual data, determining anode defects for anodes with abnormal data, and taking corresponding measures to prevent the electrolytic cell from being continuously affected.

8. The anode fault diagnosis method according to claim 7, characterized in that: It includes diagnosing and judging the abnormal anode consumption rate, calculating the predicted value of the single anode real-time height through the final calculation model data, derivation of each anode entry time to obtain the real-time consumption rate of each single anode, judging the abnormality of the real-time consumption rate of a single anode in combination with the model database, and adjusting the real-time consumption rate by operating the anode pole distance. The specific calculation formula is as follows: Wherein, v real-time consumption rate is expressed as the real-time height consumption rate of a single anode, in mm / s; H 高度 Expressed as the height of the single anode real-time model in mm; t 入槽时间 It is expressed as the time for a single anode to enter the electrolytic cell, in seconds.

9. A method for optimizing and controlling an aluminum electrolysis process, characterized in that: After the test method of claims 1-6, the following steps are further included: programming is performed using the calculation model obtained in step S6 or S7 as the underlying logic, completing the online database combined with the online calculation model to realize data sharing and online monitoring of real-time data.

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