Method, system and equipment for dynamically adjusting anode size and pole changing period based on current change and medium

Abstract

The invention relates to a method, a system and equipment for dynamically adjusting the anode size and the electrode changing period based on current change and a medium, and belongs to the technical field of electrolytic aluminum production. The method comprises the following steps: acquiring the running current of an electrolytic bath for electrolytic aluminum, and determining the current variation according to the running current and the rated current of the electrolytic bath; based on the current variable quantity, determining a current change level corresponding to the current operation condition of the electrolytic cell; calling a pre-established parameter correlation model, and generating an anode size adjustment parameter and a pole changing period adjustment parameter according to the current change level; and carrying out collaborative optimization on the anode size adjustment parameter and the pole change period adjustment parameter according to the anode gross consumption constraint condition and the pole change period constraint condition, and executing anode management operation according to an optimization result. The anode size and the anode changing period can be dynamically adjusted according to the current change of the electrolytic cell, and the production stability of electrolytic aluminum is improved.

Description

Technical Field

[0001] This invention relates to a method, system, equipment, and medium for dynamic adjustment of anode size and switching cycle based on current variation, belonging to the field of electrolytic aluminum production technology. Background Technology

[0002] In aluminum electrolysis, the electrolytic cell uses direct current to carry out the electrolytic reaction of alumina. Prebaked anode carbon blocks, as key conductive components and reaction participants, have their consumption behavior determining the efficiency and economy of the production process. Currently, anode management generally adopts a static design mode based on the rated current of the electrolytic cell, that is, setting fixed anode geometry and fixed anode replacement cycles for a specific cell type. This mode assumes that the electrolytic cell always operates stably under the rated current. However, in actual production, due to factors such as grid peak shaving, equipment maintenance, or production plan adjustments, the operating current of the electrolytic cell often fluctuates actively or passively. When the operating current is higher than the rated value, the electrochemical reaction rate of the anode intensifies, and the consumption per unit time increases significantly; when the operating current is lower than the rated value, the proportion of chemical oxidation reaction at the anode increases relatively, leading to increased non-productive consumption. These two situations cause the actual anode consumption pattern to deviate from the trajectory expected by the original static design.

[0003] Because existing technologies use fixed anode dimensions and switching cycles, they cannot respond to dynamic changes in operating current, resulting in technical drawbacks: on the one hand, when the current increases, the anode may be consumed too quickly, leading to excessively thin residual electrodes that need to be replaced prematurely; on the other hand, when the current decreases, the anode experiences increased ineffective losses due to excessive exposure and oxidation. This static management method makes it difficult to control anode consumption as current fluctuates, causing a mismatch between the switching cycle and the actual consumption rate, ultimately hindering the improvement of electrolytic cell production stability and operational economy. Summary of the Invention

[0004] To address the problem that existing electrolytic aluminum production uses fixed anode size and switching cycle, which cannot respond to dynamic changes in operating current, this invention proposes a method, system, equipment, and medium for dynamically adjusting anode size and switching cycle based on current changes. This invention can dynamically adjust the anode size and switching cycle according to changes in the electrolytic cell current, thereby improving the stability of electrolytic aluminum production.

[0005] The method for dynamically adjusting anode size and switching cycle based on current changes includes the following steps: S1. Obtain the operating current of the electrolytic cell for aluminum electrolysis, and determine the current change based on the operating current and the rated current of the electrolytic cell. ; S2. Based on current change Determine the current change level corresponding to the current operating condition of the electrolytic cell; S3. Call the pre-established parameter association model to generate anode size adjustment parameters and switching cycle adjustment parameters according to the current change level; S4. The anode size adjustment parameters and the switching cycle adjustment parameters are optimized in a coordinated manner using the anode loss constraint and the switching cycle constraint, and the anode management operation is executed based on the optimization results.

[0006] Further, step S1 involves obtaining the operating current of the electrolytic cell for electrolytic aluminum production and determining the current change based on the operating current and the rated current of the electrolytic cell. Specifically, this includes: S11. The operating current value of the electrolytic cell is collected in real time according to the preset acquisition frequency through the cell control system of the electrolytic cell; S12. Calculate the average value of the operating current within the preset statistical period as the current evaluation current; S13. Calculate the difference between the current being evaluated and the rated current of the electrolyzer to obtain the change in current. .

[0007] Furthermore, step S2 is based on the change in current. Determine the current variation level corresponding to the current operating condition of the electrolyzer, specifically including: S21. Calculate the change in current. The ratio of the absolute value of the current to the rated current value yields the current deviation rate. ; S22. Current deviation rate Compare with a preset first threshold and a second threshold; the first threshold is less than the second threshold. S23. When the current deviation rate When the value is less than or equal to the first threshold, it is determined to be a first-level change; S24. When the current deviation rate When the value is greater than the first threshold and less than or equal to the second threshold, it is determined to be a second-level change; S25. When the current deviation rate When the value exceeds the second threshold, it is determined to be a third-level change.

[0008] Further, step S3 calls a pre-established parameter correlation model to generate anode size adjustment parameters and switching cycle adjustment parameters based on the current change level, specifically including: S31. The parameter correlation model has at least one set of correction coefficients pre-set to be associated with different current change levels; the correction coefficients include height correction coefficient, cross-sectional area correction coefficient and period correction coefficient; S32. Call the corresponding correction coefficient group associated with the current change level; S33. Use the current change, rated current value, reference anode height of the electrolytic cell under rated current, reference anode cross-sectional area, reference switching cycle, and the set of correction coefficients as the input set of the parameter association model; S34. Parameter correlation model based on current change The proportional relationship with the rated current value is used, and correction factors are applied to adjust the reference anode height, reference anode cross-sectional area, and reference switching cycle to generate anode size adjustment parameters and switching cycle adjustment parameters.

[0009] Furthermore, the parameter correlation model in step S34 is based on the change in current. The proportional relationship with the rated current value is established, and correction factors are applied to adjust the reference anode height, reference anode cross-sectional area, and reference switching cycle, generating anode size adjustment parameters and switching cycle adjustment parameters, specifically including: S341. The parameter correlation model uses the proportional relationship between the current change and the rated current value, along with a height correction coefficient, to perform a linear calculation on the reference anode height, obtaining the adjusted anode height as the anode height adjustment parameter; the anode height adjustment parameter model is as follows: ; S342. The parameter correlation model uses the proportional relationship between the current change and the rated current value, as well as the cross-sectional area correction coefficient, to perform linear calculations on the reference anode cross-sectional area, obtaining the adjusted anode cross-sectional area as the anode cross-sectional area adjustment parameter; the anode cross-sectional area adjustment parameter model is as follows: ; S343. The parameter correlation model uses the proportional relationship between the current change and the rated current value, along with the period correction coefficient, to linearly calculate the reference switching period, obtaining the adjusted switching days as the switching period adjustment parameter; the switching period adjustment parameter model is as follows: ; In the formula, , , These represent the adjusted anode height, anode cross-sectional area, and switching cycle, respectively. , , These represent the reference anode height, reference anode cross-sectional area, and reference commutation period under rated current, respectively. , , These represent the height correction factor, cross-sectional area correction factor, and period correction factor, respectively.

[0010] Furthermore, step S4 involves co-optimizing the anode size adjustment parameters and the switching cycle adjustment parameters using anode loss constraints and switching cycle constraints, and performing anode management operations based on the optimization results, specifically including: S41. Calculate the mass of a single anode carbon block based on the anode height adjustment parameters and anode cross-sectional area adjustment parameters, combined with the anode pseudo density; S42. Based on the anode switching cycle adjustment parameters and the aluminum production per unit time of the electrolytic cell, calculate the anode consumption per ton of aluminum under the combination of anode size adjustment parameters and anode switching cycle adjustment parameters; S43. Determine whether the aluminum anode consumption per ton exceeds the preset anode gross consumption assessment threshold, and determine whether the electrode replacement cycle adjustment parameter is shorter than the preset minimum allowable electrode replacement cycle; if either determination result is yes, adjust the correction coefficient in the parameter association model, and regenerate the anode size adjustment parameter and electrode replacement cycle adjustment parameter based on the adjusted correction coefficient, until the aluminum anode consumption per ton does not exceed the preset anode gross consumption assessment threshold and the electrode replacement cycle adjustment parameter is not shorter than the preset minimum allowable electrode replacement cycle.

[0011] Furthermore, step S41, based on the anode height adjustment parameters and the anode cross-sectional area adjustment parameters, and in conjunction with the anode pseudo density, calculates the mass of a single anode carbon block, specifically including: S411. Based on the anode height adjustment parameters and anode cross-sectional area adjustment parameters, calculate the volume of a single anode carbon block according to the geometric volume calculation formula; S412. Multiply the volume of a single anode carbon block by the anode pseudo density value to calculate the estimated mass of a single anode carbon block; Step S42 calculates the anode consumption per ton of aluminum under the combination of anode size adjustment parameters and anode cycle adjustment parameters, based on the anode switching cycle adjustment parameters and the aluminum production per unit time of the electrolytic cell. Specifically, this includes: S421. Calculate the total aluminum production of the electrolytic cell within a complete anode replacement cycle based on the mass of a single anode, the electrode replacement cycle adjustment parameters, and the average daily aluminum production. S422. Divide the mass of a single anode by the total aluminum production during the cycle to calculate the expected anode consumption per ton of aluminum under this specific anode size and switching cycle combination.

[0012] The method for dynamically adjusting anode size and switching cycle based on current variation described in this invention can continuously acquire historical operating current data, actual anode parameters, switching cycle and corresponding production index data of the electrolytic cell, and use machine learning algorithms to periodically train and update the parameter correlation model.

[0013] This invention provides a dynamic adjustment system for anode size and switching cycle based on current variation, used to execute the dynamic adjustment method for anode size and switching cycle based on current variation. The system includes: The current monitoring and processing module is used to acquire the operating current of the electrolytic cell and determine the current change based on the operating current and the rated current of the electrolytic cell. The operating condition level determination module is used to determine the current change level corresponding to the current change of the electrolytic cell based on the current change. The parameter generation module is used to call a pre-established parameter association model to generate anode size adjustment parameters and switching cycle adjustment parameters based on the current change level. The optimization and execution module is used to collaboratively optimize the anode size adjustment parameters and the switching cycle adjustment parameters based on the anode loss constraint and the switching cycle constraint, and to execute anode management operations based on the optimization results.

[0014] This invention provides an electronic device, comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the dynamic adjustment method for anode size and switching cycle based on current variation.

[0015] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for dynamically adjusting the anode size and switching cycle based on current changes.

[0016] The beneficial effects of this invention are: (1) The method for dynamic adjustment of anode size and switching cycle based on current change provided by the present invention obtains the operating current of the electrolytic cell in real time and determines the amount and level of change of the current relative to the rated value, and then calls the pre-established parameter association model to dynamically generate matching anode size adjustment parameters and switching cycle adjustment parameters, and finally performs collaborative optimization and execution under the dual constraints of anode consumption and switching cycle. (2) The present invention can realize the adaptive adjustment of the anode management parameters to the operating current, so that the anode size can match the actual consumption rate under different current conditions, thereby reducing the excessive electrochemical consumption or non-productive oxidation loss of the anode caused by the current deviating from the rated value, and optimizing the gross consumption of anode per ton of aluminum. (3) The electrode switching cycle determined by the present invention is matched with the anode consumption process through collaborative optimization, which can avoid the problem of frequent electrode switching caused by premature anode depletion when the current increases and the problem of excessive anode aging caused by excessive cycle when the current decreases, thus ensuring the planning of electrode switching operation and the stability of the electrolytic cell operation rhythm. (4) The present invention improves the operational stability of the electrolytic cell under fluctuating production conditions by dynamically balancing the supply and consumption of anodes. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the dynamic adjustment method for anode size and switching cycle based on current variation in Example 1. Figure 2 This is a schematic diagram of the system structure for dynamically adjusting anode size and switching cycle based on current variation in Example 4. Figure 3 This is a schematic diagram of the electronic device structure in Example 5. Detailed Implementation

[0018] The present invention will be further described in detail below with reference to specific embodiments, but the scope of protection of the present invention is not limited to the content described.

[0019] Example 1: A method for dynamic adjustment of anode size and commutation cycle based on current variation (see...) Figure 1 The specific steps are as follows: S1. Obtain the operating current of the electrolytic cell for aluminum electrolysis, and determine the current change based on the operating current and the rated current of the electrolytic cell. Specifically, this includes: S11. The operating current value of the electrolytic cell is collected in real time according to the preset acquisition frequency through the cell control system. The cell control system, as the nerve center of the electrolytic cell, continuously monitors the cell voltage and series current. This method uses its data interface to read the instantaneous value of the operating current of the electrolytic cell at a fixed frequency of once per hour and records it in kiloamperes. S12. To eliminate the interference of instantaneous fluctuations during the production process on the evaluation, the average value of the operating current within a preset statistical period (a continuous complete statistical period) is calculated as the evaluation current characterizing the current stable production condition; the purpose is to preprocess the raw current data to extract the operating current that can represent the stable production state.

[0020] In this embodiment, a statistical analysis period of seven consecutive natural days, totaling 168 hours, can be set. The system automatically sums all 168 instantaneous current values ​​obtained in step S11 within this period, then divides by 168 to calculate the average value, which is the current under the current operating condition. , can be represented as ; In the formula, Indicates a point in time Instantaneous values ​​of the electrolytic cell operating current collected; subscript It is a time index, representing sampling points over a consecutive 168 hours; S13. Calculate the algebraic difference between the current being evaluated and the rated current of the electrolyzer to obtain the precise change in current. The rated current value is the standard operating current designed for the electrolytic cell. The change in current can be calculated by subtracting this rated current value from the current evaluation current calculated in step S12. .

[0021] S2. Based on current change Determine the current variation level corresponding to the current operating condition of the electrolytic cell; specifically including: S21. Calculate the change in current. The ratio of the absolute value of the current to the rated current value yields the current deviation rate. Current deviation rate It can be represented as: ; In the formula: , indicating the reference current With rated current The difference.

[0022] S22. Current deviation rate The threshold is compared with a preset first threshold and a preset second threshold; the first threshold is less than the second threshold; the preset first threshold and second threshold can be set based on production management experience and current fluctuation impact analysis, the first threshold can be 1%, and the second threshold can be 5%; S23. When the current deviation rate When the value is less than or equal to the first threshold, it is determined to be a first-level change; S24. When the current deviation rate When the value is greater than the first threshold and less than or equal to the second threshold, it is determined to be a second-level change; S25. When the current deviation rate When the value exceeds the second threshold, it is determined to be a third-level change; Minor changes: This level corresponds to routine fine-tuning conditions in production, such as daily load shaving or equipment parameter adjustments, and its impact is generally considered acceptable normal fluctuation; moderate variation: This level corresponds to moderate adjustments with some duration and impact, such as planned, phased power rationing or the restart of large equipment after maintenance; significant changes: This level corresponds to significant, unconventional, and drastic load fluctuations, such as production restrictions implemented due to emergencies or full-load startup of the entire series after major overhauls.

[0023] Transforming continuous current changes into discrete operating condition levels provides a basis for subsequent differentiated parameter adjustments.

[0024] S3. The pre-established parameter correlation model is invoked to generate anode size adjustment parameters and switching cycle adjustment parameters based on the current change level. The pre-established parameter correlation model is a quantitative relationship model trained based on historical production data of the target electrolyzer series. Training and analysis are performed based on historical production data of the target electrolyzer operation, including anode consumption data, switching records, and final production economic indicators corresponding to different current fluctuation periods. Specifically, this includes: S31. The parameter correlation model has at least one set of correction coefficients pre-set to be associated with different current change levels; the correction coefficients include height correction coefficient, cross-sectional area correction coefficient and period correction coefficient; through statistical regression or machine learning algorithms, the system learns the quantitative relationship between the degree of current change and the anode size and the adjustment range of the switching cycle, and solidifies this relationship into multiple sets of correction coefficients, which are classified and stored according to the different current change levels. That is, for the first level change, the second level change and the third level change, the model has at least one set of correction coefficient combinations that are uniquely corresponding to them, thereby forming a hierarchical parameter adjustment strategy library.

[0025] S32. Based on the current change level, call the corresponding correction coefficient group associated with that level, including height correction coefficient, cross-sectional area correction coefficient, and period correction coefficient; when the current current change level is determined, for example, if it is determined to be a second level change, the model is automatically triggered to call the specific correction coefficient associated with the second level change from the level strategy library established in step S31 according to the level identifier.

[0026] S33. Change in current Rated current value Reference anode height of the electrolytic cell under rated current Reference anode cross-sectional area Reference switching cycle , and the set of corrected coefficients called as the input set of the parameter association model; S34. Parameter correlation model based on current change The proportional relationship with the rated current value is used, and correction factors are applied to adjust the reference anode height, reference anode cross-sectional area, and reference switching cycle to generate anode size adjustment parameters and switching cycle adjustment parameters; the switching cycle can be expressed as: ; In the formula: a carbon anode block is moved from an initial height Consumed to the extreme height The total number of days required is the polarization cycle. When production requires a switching cycle When the height remains constant, the required anode height is... With the rate of anode consumption It changes with the times. Generally speaking, the current efficiency in actual production at a company... It can also be considered as a fixed value, namely the anode height. With anode current density It is a linear correlation. Specifically, it includes: S341. The parameter correlation model uses the proportional relationship between the current change and the rated current value, along with a height correction coefficient, to linearly calculate the reference anode height, obtaining the adjusted anode height as the anode height adjustment parameter. When the current changes, the anode height adjustment parameter model is as follows: ; S342. The parameter correlation model uses the proportional relationship between the current change and the rated current value, along with the cross-sectional area correction coefficient, to perform linear calculations on the reference anode cross-sectional area, obtaining the adjusted anode cross-sectional area as the anode cross-sectional area adjustment parameter. When the current changes, the anode cross-sectional area adjustment parameter model is as follows: ; S343. The parameter correlation model uses the proportional relationship between the current change and the rated current value, along with the period correction coefficient, to linearly calculate the reference commutation period, obtaining the adjusted commutation days as the commutation period adjustment parameter; when the current changes, the commutation period adjustment parameter model is as follows: ; In the formula, , , These represent the adjusted anode height, anode cross-sectional area, and switching cycle, respectively. , , These represent the reference anode height, reference anode cross-sectional area, and reference commutation period under rated current, respectively. , , These represent the height correction factor, cross-sectional area correction factor, and period correction factor, respectively. Their specific values ​​need to be obtained through training with historical production data, reflecting the sensitivity of each parameter to changes in current under specific production conditions of the enterprise.

[0027] This embodiment can define storable, callable, and calculable coefficients and rules based on electrochemical consumption processes and production management experience, thereby enabling the rapid and automatic generation of matching anode size and switching cycle adjustment values ​​based on real-time determined current operating conditions.

[0028] S4. The anode size adjustment parameters and the switching cycle adjustment parameters are synergistically optimized based on anode gross consumption constraints and switching cycle constraints, and anode management operations are executed according to the optimization results. The mass of a single anode carbon block is calculated based on the anode size adjustment parameters, and the expected anode gross consumption per ton of aluminum is estimated under this parameter combination, combined with the switching cycle adjustment parameters and the aluminum production capacity of the electrolytic cell. Simultaneously, the switching cycle adjustment parameters are evaluated to determine if they meet the minimum cycle required for production organization. The estimated gross consumption is compared with the preset upper limit of gross consumption assessment, and the evaluation cycle is compared with the preset minimum allowable cycle. If any indicator does not meet the constraints, the correction coefficients used in the parameter association model are automatically fine-tuned, and the process returns to step S3 to regenerate new adjustment parameters. This process is repeated until the generated anode size adjustment parameters and switching cycle adjustment parameters simultaneously meet all constraints. Finally, the optimized anode size parameters are sent to the anode assembly or carbon block supply system to guide customized anodes; the optimized switching cycle parameters are issued to the electrolysis workshop production management system for the formulation or revision of anode replacement operation plans. Specifically, this includes: S41. Based on the anode height adjustment parameters and anode cross-sectional area adjustment parameters, and combined with the anode pseudo density (which is the equivalent density after considering the porosity of the carbon block) obtained from the anode carbon block material technical standard, calculate the mass of a single anode carbon block; specifically including: S411. Based on the anode height adjustment parameters and anode cross-sectional area adjustment parameters, calculate the volume of a single anode carbon block according to the geometric volume calculation formula (multiply the adjusted anode height by the cross-sectional area); S412. Multiply the volume of a single anode carbon block by the anode pseudo density value to calculate the estimated mass of a single anode carbon block.

[0029] S42. Based on the anode switching cycle adjustment parameters and the aluminum production per unit time of the electrolytic cell, calculate the anode consumption per ton of aluminum under the combination of anode size adjustment parameters and anode switching cycle adjustment parameters; specifically including: S421. Based on the mass of a single anode, the adjustment parameters of the anode replacement cycle, and the average daily aluminum production, calculate the total aluminum production of the electrolytic cell within a complete anode replacement cycle, which is the product of the aluminum production per unit time and the number of days in the anode replacement cycle. S422. Divide the mass of a single anode by the total aluminum production during the cycle to calculate the expected anode consumption per ton of aluminum under this specific anode size and switching cycle combination; where the consumption rate is calculated using the following formula: ; In the formula: 8.054 is the preset comprehensive calculation coefficient, which is equal to the electrochemical equivalent of aluminum multiplied by 24 hours; It represents the anode consumption rate, expressed in cm / d, and defines the amount of anode height that is reduced daily due to the reaction. Indicates the anode current density. ,in For operating current, This represents the anode cross-sectional area. It represents the current efficiency, reflecting the effective utilization rate of current for aluminum deposition during the production process; Indicates the net consumption of the anode; This represents the anodic pseudo-density; when the current changes, it mainly affects the current efficiency. and anode current density The changes.

[0030] S43. Determine whether the aluminum anode consumption per ton exceeds the preset anode gross consumption assessment threshold, and determine whether the electrode replacement cycle adjustment parameter is shorter than the preset minimum allowable electrode replacement cycle; if either determination result is yes, return to the correction coefficient in the parameter association model in step S3, and regenerate the anode size adjustment parameter and electrode replacement cycle adjustment parameter based on the adjusted correction coefficient, until the aluminum anode consumption per ton does not exceed the preset anode gross consumption assessment threshold and the electrode replacement cycle adjustment parameter is not shorter than the preset minimum allowable electrode replacement cycle.

[0031] Two key constraints are preset: the upper limit of the allowable anode consumption per ton of aluminum as specified in the process evaluation standards, and the shortest allowable anode replacement cycle set in the production requirements to ensure operational safety and stability. The expected anode consumption per ton of aluminum calculated in step S42 is compared with the upper limit of the consumption, and the anode replacement cycle adjustment parameter is compared with the shortest allowable cycle. If the expected consumption exceeds the upper limit or the proposed cycle is shorter than the lower limit, the current parameter combination is deemed infeasible. In this case, the parameters are not directly output; instead, the optimization program is automatically initiated: based on the specific unmet constraints, the correction coefficients used in the parameter association model are adjusted in reverse according to preset rules. For example, if the consumption exceeds the limit, the correction coefficient used for calculating the height is reduced proportionally or the correction coefficient used for calculating the cycle is increased; if the cycle is too short, the correction coefficient used for calculating the cross-sectional area is increased or the correction coefficient used for calculating the cycle is decreased. After the coefficients are adjusted, the system returns and re-executes step S3 to generate a new set of adjustment parameters based on the new coefficients. Then, step S4 is executed again for a new round of verification until the calculated anode consumption per ton of aluminum is lower than or equal to the gross consumption assessment threshold, and the anode replacement cycle adjustment parameter is longer than or equal to the shortest allowable anode replacement cycle. The final parameter set generated at this point is the optimal solution that simultaneously satisfies both technical feasibility and economic requirements. The anode gross consumption constraint can be expressed as: ; In the formula: The density is the anode. This represents the average daily aluminum production of the electrolytic cell; This represents the anode consumption constraint, which is the total mass of anodes consumed to produce one unit of aluminum.

[0032] This embodiment introduces a dual constraint verification based on anode gross loss and the shortest switching cycle, along with a coefficient iterative optimization mechanism linked to them. This method not only ensures the theoretical rationality of the parameters generated by the model, but also forces it to pass the test of actual production economic indicators and operational feasibility. As a result, it outputs a set of immediately executable solutions that achieve the best balance between reducing consumption costs and ensuring stable production, thus ensuring the reliability of the output results of the entire dynamic adjustment process.

[0033] The method described in this embodiment can continuously acquire historical operating current data, actual anode parameters, switching cycles, and corresponding production indicator data of the electrolytic cell. Machine learning algorithms are used to periodically train and update the parameter association model. This embodiment, while executing daily anode management decisions as described in the previous embodiment, simultaneously initiates data accumulation and model learning processes. Multi-dimensional historical data is continuously and automatically collected from the production database to form a training sample set, mainly including: long-term operating current sequences of the electrolytic cell, actual anode height, cross-sectional area, and other dimensional parameters, actual switching cycles, and corresponding key production indicators such as net anode consumption per ton of aluminum, current efficiency, and cell voltage stability. Then, a model training task is initiated at set periodic nodes, such as quarterly. Using the accumulated historical dataset, with current change levels and current change amounts as input features, and actual anode size parameters and switching cycles that have been verified as optimal or near-optimal as target output labels, a regression-based machine learning algorithm is used to refit and calculate the correction coefficients within the parameter association model. The goal of the training process is to minimize the difference between the parameters predicted by the model based on the current input and the parameters that produce the best overall effect from the historical data. When the performance evaluation index of the newly trained model is better than that of the existing model, the old model is replaced by the new model and its updated correction coefficients. This ensures that the dynamic adjustment strategy of anode size and switching cycle can continuously improve and optimize itself over time, and maintain the accuracy of its decision-making in the long term.

[0034] Therefore, this embodiment acquires the operating current of the electrolytic cell in real time and determines its change and level relative to the rated value. Then, it dynamically generates matching anode size adjustment parameters and switching cycle adjustment parameters by calling a pre-established parameter association model. Finally, it performs collaborative optimization and execution under the dual constraints of anode consumption and switching cycle. This technical solution achieves adaptive adjustment of anode management parameters to the operating current, enabling the anode size to match the actual consumption rate under different current conditions. This reduces excessive electrochemical consumption or non-productive oxidation loss of the anode caused by current deviation from the rated value, thus optimizing the anode consumption per ton of aluminum. Simultaneously, the switching cycle determined through collaborative optimization matches the anode consumption process, avoiding frequent switching due to premature anode depletion when the current increases and excessive anode aging due to excessively long cycles when the current decreases. This ensures the planning of switching operations and the stability of the electrolytic cell's operating rhythm. This invention improves the operational stability of the electrolytic cell under fluctuating production conditions through the dynamic balance of anode supply and consumption.

[0035] Example 2: This example, within the framework of the dynamic adjustment method constructed in Example 1, demonstrates how, under a specific operating condition where the electrolytic cell operating current remains stable, the logic of this invention is applied to adjust the anode size, thereby optimizing production indicators. A company faces the need to further improve the utilization rate of anode carbon blocks and reduce the net anode consumption per ton of aluminum under normal operating conditions with stable current in its electrolytic aluminum production. This embodiment adopts the dynamic adjustment method of anode size and switching cycle based on current changes in Example 1, and calls the pre-established parameter correlation model. The results are shown in Table 1. Table 1 Comparison of the Optimization Effect of Anode Carbon Block Width in a Certain Company ; As shown in Table 1, after the width adjustment, the anode cross-sectional area of ​​the electrolytic cells in both the 230kA and 350kA series decreased accordingly. According to the current density calculation formula, under the premise of constant operating current, the reduction in cross-sectional area leads to an increase in anode current density of approximately 0.011 A / cm². 2 Based on the positive correlation between anode consumption rate and current density, an increase in current density leads to a corresponding increase in the daily anode consumption rate of approximately 0.021 cm / d. The total anode consumption height will increase by approximately 7 mm per cycle, meaning that within the same anode replacement cycle, the anode carbon block is utilized more fully, and the residual anode height is further reduced. This directly reduces the net mass of anode carbon blocks consumed per ton of aluminum produced, achieving the core objective of reducing net anode consumption per ton of aluminum.

[0036] Example 3: This example, within the framework of the dynamic adjustment method constructed in Example 1, demonstrates another application scenario of the present invention to optimize economic indicators by adjusting the anode size dimension. This example focuses on reducing anode loss by optimizing the structural dimensions of the carbon bowl used to connect the guide rod on the anode carbon block, rather than directly changing the working surface size that carries the current, under the condition that the electrolytic cell operating current remains stable. The specific implementation method is as follows: To continuously reduce anode loss, a company applied the method of this invention. A pre-established parameter correlation model was invoked, and based on the optimization objective and economic constraints, the model output adjustment parameters for the local structural dimensions of the anode carbon block, i.e., the carbon bowl. The results are shown in Table 2. Table 2 Statistical Table of Charcoal Block and Charcoal Bowl Size Optimization ; As shown in Table 2, for the 230kA and 350kA series electrolytic cells, the optimized scheme slightly increases the diameter of the carbon bowl from 210mm to 215mm, while reducing the depth from 115mm to 110mm. This combined adjustment directly alters the volume and mass of the carbon bowl. Since the total anode mass consists of the mass of the carbon blocks on the working surface and the mass of the carbon bowl, by redesigning the carbon bowl while keeping the working surface dimensions unchanged, the overall mass of a single anode carbon block is reduced, thus lowering the anode's individual weight. According to the definition of anode gross consumption, under the premise of maintaining a constant aluminum production in the electrolytic cell, reducing the anode's individual weight directly reduces the total weight of anode carbon blocks consumed per ton of aluminum produced, thereby achieving the economic goal of reducing anode gross consumption.

[0037] Example 4: Dynamic adjustment system for anode size and commutation cycle based on current variation (see Example 4) Figure 2 The system is used to execute the dynamic adjustment method for anode size and switching cycle based on current variation as described in Example 1, and the system includes: The current monitoring and processing module is used to acquire the operating current of the electrolytic cell and determine the current change based on the operating current and the rated current of the electrolytic cell. The operating condition level determination module is used to determine the current change level corresponding to the current change of the electrolytic cell based on the current change. The parameter generation module is used to call a pre-established parameter association model to generate anode size adjustment parameters and switching cycle adjustment parameters based on the current change level. The optimization and execution module is used to collaboratively optimize the anode size adjustment parameters and the switching cycle adjustment parameters based on the anode loss constraint and the switching cycle constraint, and to execute anode management operations based on the optimization results.

[0038] Example 5: An electronic device (see Figure 3 ),include: At least one processor; and, A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the dynamic adjustment method for anode size and switching cycle based on current variation as described in Embodiment 1.

[0039] The electronic device can be either or both of the first device and the second device, or a standalone device independent of them, which can communicate with the first device and the second device to receive the acquired input signals from them.

[0040] like Figure 3 As shown, the electronic device includes one or more processors and memory. The processor may be a central processing unit (CPU) or other processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement the current-varying anode size and switching cycle dynamic adjustment method of Embodiment 1 described above, and / or other desired functions.

[0041] The electronic device may also include input and output devices, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). Furthermore, the input device may include, for example, a keyboard, a mouse, etc. The output device can output various information to the outside, including determined distance information, direction information, etc. The output device may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0042] Example 6: A computer program product or computer-readable storage medium storing a program for implementing a method for dynamically adjusting anode size and switching cycle based on current variation, wherein when the computer program is executed by a processor, it implements the method for dynamically adjusting anode size and switching cycle based on current variation as described in Example 1.

[0043] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0044] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0045] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0046] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details of the invention described above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the specific details described above.

[0047] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0048] The block diagrams of devices, apparatuses, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0049] The methods and apparatus of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0050] It should also be noted that in the apparatus, device, and method of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of the present invention.

Claims

1. A method for dynamically adjusting anode size and commutation cycle based on current variation, characterized in that, The specific steps are as follows: S1. Obtain the operating current of the electrolytic cell for aluminum electrolysis, and determine the current change based on the operating current and the rated current of the electrolytic cell. ; S2. Based on current change Determine the current change level corresponding to the current operating condition of the electrolytic cell; S3. Call the pre-established parameter association model to generate anode size adjustment parameters and switching cycle adjustment parameters according to the current change level; S4. The anode size adjustment parameters and the switching cycle adjustment parameters are optimized in a coordinated manner using the anode loss constraint and the switching cycle constraint, and the anode management operation is executed based on the optimization results.

2. The method for dynamically adjusting anode size and switching cycle based on current variation according to claim 1, characterized in that: Step S1 involves obtaining the operating current of the electrolytic cell for aluminum electrolysis and determining the current change based on the operating current and the rated current of the electrolytic cell. Specifically, this includes: S11. The operating current value of the electrolytic cell is collected in real time according to the preset acquisition frequency through the cell control system of the electrolytic cell; S12. Calculate the average value of the operating current within the preset statistical period as the current evaluation current; S13. Calculate the difference between the current being evaluated and the rated current of the electrolyzer to obtain the change in current. .

3. The method for dynamically adjusting anode size and switching cycle based on current variation according to claim 1, characterized in that: Step S2 is based on the change in current. Determine the current variation level corresponding to the current operating condition of the electrolyzer, specifically including: S21. Calculate the change in current. The ratio of the absolute value of the current to the rated current value yields the current deviation rate. ; S22. Current deviation rate Compare with a preset first threshold and a second threshold; the first threshold is less than the second threshold. S23. When the current deviation rate When the value is less than or equal to the first threshold, it is determined to be a first-level change; S24. When the current deviation rate When the value is greater than the first threshold and less than or equal to the second threshold, it is determined to be a second-level change; S25. When the current deviation rate When the value exceeds the second threshold, it is determined to be a third-level change.

4. The method for dynamically adjusting anode size and switching cycle based on current variation according to claim 1, characterized in that: Step S3 calls the pre-established parameter correlation model to generate anode size adjustment parameters and switching cycle adjustment parameters based on the current change level, specifically including: S31. The parameter correlation model has at least one set of correction coefficients pre-set to be associated with different current change levels; the correction coefficients include height correction coefficient, cross-sectional area correction coefficient and period correction coefficient; S32. Call the corresponding correction coefficient group associated with the current change level; S33. Use the current change, rated current value, reference anode height of the electrolytic cell under rated current, reference anode cross-sectional area, reference switching cycle, and the set of correction coefficients as the input set of the parameter association model; S34. Parameter correlation model based on current change The proportional relationship with the rated current value is used, and correction factors are applied to adjust the reference anode height, reference anode cross-sectional area, and reference switching cycle to generate anode size adjustment parameters and switching cycle adjustment parameters.

5. The method for dynamically adjusting anode size and switching cycle based on current variation according to claim 4, characterized in that: Step S34: Parameter correlation model based on current change. The proportional relationship with the rated current value is established, and correction factors are applied to adjust the reference anode height, reference anode cross-sectional area, and reference switching cycle, generating anode size adjustment parameters and switching cycle adjustment parameters, specifically including: S341. The parameter correlation model uses the proportional relationship between the current change and the rated current value, along with a height correction coefficient, to perform a linear calculation on the reference anode height, obtaining the adjusted anode height as the anode height adjustment parameter; the anode height adjustment parameter model is as follows: ； S342. The parameter correlation model uses the proportional relationship between the current change and the rated current value, as well as the cross-sectional area correction coefficient, to perform linear calculations on the reference anode cross-sectional area, obtaining the adjusted anode cross-sectional area as the anode cross-sectional area adjustment parameter; the anode cross-sectional area adjustment parameter model is as follows: ； S343. The parameter correlation model uses the proportional relationship between the current change and the rated current value, along with the period correction coefficient, to linearly calculate the reference switching period, obtaining the adjusted switching days as the switching period adjustment parameter; the switching period adjustment parameter model is as follows: ； In the formula, , , These represent the adjusted anode height, anode cross-sectional area, and switching cycle, respectively. , , These represent the reference anode height, reference anode cross-sectional area, and reference commutation period under rated current, respectively. , , These represent the height correction factor, cross-sectional area correction factor, and period correction factor, respectively.

6. The method for dynamically adjusting anode size and switching cycle based on current variation according to claim 1, characterized in that: Step S4 performs coordinated optimization of the anode size adjustment parameters and the switching cycle adjustment parameters using anode loss constraints and switching cycle constraints, and executes anode management operations based on the optimization results, specifically including: S41. Calculate the mass of a single anode carbon block based on the anode height adjustment parameters and anode cross-sectional area adjustment parameters, combined with the anode pseudo density; S42. Based on the anode switching cycle adjustment parameters and the aluminum production per unit time of the electrolytic cell, calculate the anode consumption per ton of aluminum under the combination of anode size adjustment parameters and anode switching cycle adjustment parameters; S43. Determine whether the aluminum anode consumption per ton exceeds the preset anode gross consumption assessment threshold, and determine whether the electrode replacement cycle adjustment parameter is shorter than the preset minimum allowable electrode replacement cycle; if either determination result is yes, adjust the correction coefficient in the parameter association model, and regenerate the anode size adjustment parameter and electrode replacement cycle adjustment parameter based on the adjusted correction coefficient, until the aluminum anode consumption per ton does not exceed the preset anode gross consumption assessment threshold and the electrode replacement cycle adjustment parameter is not shorter than the preset minimum allowable electrode replacement cycle.

7. The method for dynamically adjusting anode size and switching cycle based on current variation according to claim 6, characterized in that: Step S41 calculates the mass of a single anode carbon block based on the anode height adjustment parameters and anode cross-sectional area adjustment parameters, combined with the anode pseudo density. Specifically, this includes: S411. Based on the anode height adjustment parameters and anode cross-sectional area adjustment parameters, calculate the volume of a single anode carbon block according to the geometric volume calculation formula; S412. Multiply the volume of a single anode carbon block by the anode pseudo density value to calculate the estimated mass of a single anode carbon block; Step S42 calculates the anode consumption per ton of aluminum under the combination of anode size adjustment parameters and anode cycle adjustment parameters, based on the anode switching cycle adjustment parameters and the aluminum production per unit time of the electrolytic cell. Specifically, this includes: S421. Calculate the total aluminum production of the electrolytic cell within a complete anode replacement cycle based on the mass of a single anode, the electrode replacement cycle adjustment parameters, and the average daily aluminum production. S422. Divide the mass of a single anode by the total aluminum production during the cycle to calculate the expected anode consumption per ton of aluminum under this specific anode size and switching cycle combination.

8. A dynamic adjustment system for anode size and commutation cycle based on current variation, characterized in that, The system is used to perform the dynamic adjustment method for anode size and switching cycle based on current variation as described in any one of claims 1 to 7, the system comprising: The current monitoring and processing module is used to acquire the operating current of the electrolytic cell and determine the current change based on the operating current and the rated current of the electrolytic cell. The operating condition level determination module is used to determine the current change level corresponding to the current change of the electrolytic cell based on the current change. The parameter generation module is used to call a pre-established parameter association model to generate anode size adjustment parameters and switching cycle adjustment parameters based on the current change level. The optimization and execution module is used to collaboratively optimize the anode size adjustment parameters and the switching cycle adjustment parameters based on the anode loss constraint and the switching cycle constraint, and to execute anode management operations based on the optimization results.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the dynamic adjustment method for anode size and switching cycle based on current variation as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the dynamic adjustment method for anode size and switching cycle based on current variation as described in any one of claims 1 to 7.

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