Electrolytic cell management method, device, equipment, medium and product

Abstract

The application discloses an electrolytic cell management and control method, device, equipment, medium and product, relates to the technical field of electrolytic cells, and comprises the following steps: acquiring a plurality of management and control indexes of an electrolytic cell, wherein the management and control indexes comprise a molecular ratio, an electrolyte temperature, a voltage deviation, a polar distance, an electrolyte level, an aluminum amount in the cell, an overheating degree, a noise value and an alumina discharge amount; comparing the plurality of management and control indexes with a plurality of corresponding preset index range thresholds respectively to obtain a plurality of comparison results; in response to an abnormal management and control index existing in the plurality of comparison results, determining an abnormal type according to the abnormal management and control index; and adjusting the operation parameters of the electrolytic cell according to the abnormal type. The application solves the technical problem that the traditional electrolytic cell management and control mode is mostly adjusted after the formation of results, the adjustment is not timely, the adjustment is difficult, the recovery period is long, and accurate regulation and control is difficult to realize.

Description

Technical Field

[0001] This application relates to the field of electrolytic cell technology, and in particular to an electrolytic cell control method, apparatus, equipment, medium and product. Background Technology

[0002] Aluminum electrolysis production relies on aluminum electrolysis cells to melt and electrolyze alumina to produce primary aluminum. The operating conditions of the electrolysis cells directly determine the primary aluminum output, product quality, power consumption, and equipment operation safety. Process control is the core management link in electrolysis production.

[0003] In aluminum electrolysis production, electrolytic cells often experience abnormal operation due to mismatched process conditions and untimely process adjustments. These abnormalities manifest as frequent effects, voltage fluctuations, abnormal temperatures, abnormal electrolyte levels, and decreased efficiency. Traditional methods typically involve adjustments only after results have been achieved, leading to delayed, difficult, and lengthy recovery periods, hindering precise control and long-term stable operation of the electrolytic cell. Summary of the Invention

[0004] The purpose of this application is to provide an electrolytic cell control method, apparatus, equipment, medium, and product that can solve the technical problems of traditional electrolytic cell control methods, which mostly involve adjustments after the results are formed, resulting in untimely adjustments, high adjustment difficulty, long recovery cycles, and difficulty in achieving precise control.

[0005] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for controlling an electrolytic cell, including: Multiple control indicators of the electrolytic cell are acquired in real time, including molecular ratio, electrolyte temperature, voltage deviation, electrode distance, electrolyte level, aluminum content in the cell, superheat, noise level, and alumina feed rate. Multiple control indicators are compared with multiple corresponding preset indicator range thresholds to obtain multiple comparison results; In response to the presence of abnormal control indicators in multiple comparison results, the type of abnormality is determined based on these indicators. Based on the type of anomaly, the operating parameters of the electrolytic cell are adjusted to obtain the adjustment results.

[0006] Optionally, the anomaly type can be determined based on the anomaly control indicators, specifically including: obtaining continuous indicator data of the anomaly control indicators within a preset time period; obtaining the on-site cell condition of the electrolytic cell; and determining the anomaly type based on the on-site cell condition and the continuous indicator data.

[0007] Optionally, the on-site cell condition of the electrolytic cell is obtained, specifically including: obtaining real-time image data of the electrolytic cell; inputting the real-time image data into a preset cell condition recognition model, and outputting the on-site cell condition, wherein the on-site cell condition includes flame color, shell surface condition, material blockage condition, carbon-slag separation condition, and electrolyte working condition.

[0008] Optionally, the anomaly type can be determined based on the on-site tank conditions and continuous index data. Specifically, this includes: determining the initial judgment result based on the continuous index data; and verifying the initial judgment result based on the on-site tank conditions to obtain the anomaly type.

[0009] Optionally, the operating parameters of the electrolytic cell are adjusted according to the type of anomaly to obtain the adjustment result. Specifically, this includes: in response to the anomaly type being a hot / cold cell anomaly, obtaining the aluminum level; comparing the aluminum level with a preset aluminum level standard range to obtain the comparison result; and adjusting the cell voltage of the electrolytic cell according to the comparison result until the adjustment result indicates that the anomaly has been eliminated.

[0010] Optionally, the operating parameters of the electrolyzer can be adjusted according to the type of anomaly. Specifically, in response to an anomaly of molecular ratio anomaly, the amount of fluoride salt added can be adjusted according to a preset adjustment strategy until the adjustment results indicate that the molecular ratio anomaly has been eliminated.

[0011] Secondly, this application provides an electrolytic cell control device, comprising: The acquisition module is used to acquire multiple control indicators of the electrolytic cell in real time, including molecular ratio, electrolyte temperature, voltage deviation, electrode distance, electrolyte level, aluminum content in the cell, superheat, noise level, and alumina feed rate. The comparison module is used to compare the multiple control indicators with multiple corresponding preset indicator range thresholds to obtain multiple comparison results. The determination module is used to determine the anomaly type based on the abnormal control indicators found in the multiple comparison results. The adjustment module is used to adjust the operating parameters of the electrolytic cell according to the anomaly type.

[0012] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the electrolytic cell control method described in any one of the above.

[0013] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the electrolytic cell control method described in any one of the above descriptions.

[0014] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the electrolytic cell control method described in any one of the above descriptions.

[0015] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides an electrolytic cell control method, device, equipment, medium, and product. The method collects multi-dimensional control indicators in real time, including molecular ratio, electrolyte temperature, voltage deviation, electrode distance, electrolyte level, aluminum content in the cell, superheat, noise level, and alumina feed amount. Simultaneously, each indicator is compared with a dedicated preset threshold range to generate quantitative comparison results. Once an abnormal control indicator is identified, the abnormality type can be locked. Then, based on the corresponding abnormality type, an appropriate adjustment strategy is matched to optimize and adjust the electrolytic cell operating parameters and output adjustment feedback results. Compared to the traditional control model that waits for obvious consequences after the cell condition deteriorates before taking action, this solution relies on real-time acquisition of all parameters and threshold quantification to identify anomalies in advance. It can detect the root cause of parameter deviations early without waiting for serious faults such as frequent effects or large voltage fluctuations, thus solving the problem of delayed adjustments in traditional control. Multi-indicator linkage judgment can accurately distinguish between single parameter imbalance and multi-parameter coordinated instability, avoiding misjudgments and omissions caused by subjective human judgment, and significantly reducing the difficulty of cell adjustment analysis. At the same time, it matches targeted and quantitative adjustment methods according to the clear anomaly type, abandoning experience-based blind adjustments and adopting a small-scale, gradual, and economical adjustment logic to shorten the recovery cycle of abnormal cell conditions. It can truly achieve precise control of all operating conditions of the electrolytic cell, continuously maintain the steady-state operation of the cell, and simultaneously achieve comprehensive technical benefits such as improving current efficiency, reducing electricity consumption per ton of aluminum, reducing manual labor intensity, and avoiding equipment safety hazards. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is an application environment diagram of an electrolytic cell control method according to an embodiment of this application; Figure 2 A schematic flowchart illustrating an electrolytic cell control method according to an embodiment of this application; Figure 3 A flowchart illustrating an electrolytic cell control method provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electrolytic cell control device provided in one embodiment of this application; Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] The electrolytic cell control method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server.

[0021] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, and IoT devices. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0022] In one exemplary embodiment, such as Figure 2 As shown, an electrolytic cell control method is provided. This method is executed by a computer device, specifically a terminal or server, or both. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 204. Wherein: Step S201: Real-time acquisition of multiple control indicators of the electrolytic cell, including molecular ratio, electrolyte temperature, voltage deviation, electrode distance, electrolyte level, amount of aluminum in the cell, superheat, noise level, and alumina feed rate.

[0023] In this embodiment, step S201 is the real-time acquisition of all dimensions of electrolytic cell operating data, adapted to a 400kA large-scale aluminum electrolytic cell production system. Continuous data acquisition is conducted for nine control parameters across three low-temperature, one-high-temperature, five-stability, and nine-key control parameters. The acquisition logic and acquisition method for each indicator are as follows: Molecular ratio: The electrolyte in the tank is sampled and tested regularly to detect the content of fluorine, sodium and aluminum elements in the electrolyte, and the molar number of sodium fluoride and aluminum fluoride is converted. The real-time molecular ratio data is obtained by calculating the molar ratio. Electrolyte temperature: The real-time temperature of the molten electrolyte in the cell is continuously collected by thermocouples arranged in the electrolyte area of ​​the electrolytic cell, forming a 24-hour continuous temperature data stream; Voltage deviation: The actual operating voltage of a single tank is collected in real time, and the difference is calculated with the preset reference voltage of the same tank to obtain the instantaneous voltage deviation value. The continuous deviation data for 24 hours is statistically analyzed and the daily average deviation value is calculated as a control indicator. Polar distance: The vertical distance between the bottom surface of the anode and the surface of the molten aluminum is measured at multiple points daily using a dedicated polar distance measuring tool. The arithmetic mean of the multiple measurements taken daily is used as the real-time polar distance index. Electrolyte level: The height of the molten electrolyte in the electrolytic cell is measured on-site daily, and the measured value is recorded as a control indicator; Aluminum content in the tank: The total weight of aluminum stored in the tank in real time is calculated by combining the daily measured height of the molten aluminum with the results of periodic inventory and calibration. Superheat: The measured electrolyte temperature and the corresponding primary crystallization temperature of the electrolyte are collected simultaneously, and the superheat index is calculated in real time by the difference between the two. Noise level: The voltage fluctuation noise data of the tank is collected continuously for 24 hours, and the daily average voltage fluctuation is used as the noise control indicator. Alumina feed rate: The total weight of alumina fed into the electrolytic cell feeding system is calculated on a daily basis to form the daily feed rate index.

[0024] Understandably, by using the above-mentioned multi-channel, high-frequency, and standardized data collection methods, real-time values ​​of nine control indicators can be obtained simultaneously, fully covering the four major operating dimensions of electrolytic cell thermal balance, material balance, electrical balance, and material supply. This provides a complete, objective, and continuous data foundation for subsequent quantitative benchmarking and cell condition judgment, overcoming the information deficiencies of traditional cell adjustment that rely solely on single-point, intermittent data or manual visual observation.

[0025] Step S202: Compare multiple control indicators with multiple corresponding preset indicator range thresholds to obtain multiple comparison results.

[0026] In this embodiment of the application, step S202 is a standardized quantitative benchmarking and judgment step, in which the optimal range threshold for each control indicator (i.e., a five-stable standard system of three lows and one high) is established in advance for the 400kA aluminum electrolytic cell.

[0027] For example, the preset threshold ranges are as follows: molecular ratio 2.35~2.45, electrolyte temperature 935℃~945℃, voltage deviation 15mV~23mV, electrode distance 4.5cm~5.0cm, electrolyte level 18cm~20cm, aluminum content in the tank 33±0.5t, superheat 8℃~12℃, noise level 19mV~21mV, and daily alumina feed rate 5550kg~5650kg.

[0028] For each real-time control indicator acquired by S201, it is independently compared with the preset threshold range corresponding to that indicator, and a single comparison result is generated one by one: if the real-time indicator value falls within the corresponding preset threshold range, the indicator is determined to be normal and qualified, and a normal comparison result is output; if the real-time indicator value is lower than the corresponding lower threshold limit, a low indicator comparison result is output; if the real-time indicator value is higher than the corresponding upper threshold limit, a high indicator comparison result is output.

[0029] After each of the nine control indicators was independently compared, a complete set of comparison results was compiled, including all indicators in the states of being qualified, high, or low. This enabled the quantitative and graded judgment of parameters, abandoning the traditional method of relying on the subjective experience of operators to roughly judge the tank condition, and achieving standardized, data-driven, and quantifiable judgment of the tank condition.

[0030] Step S203: In response to the presence of abnormal control indicators among multiple comparison results, determine the type of abnormality based on the abnormal control indicators.

[0031] In this embodiment, step S203 is the anomaly tracing and tank condition classification and judgment step. When any one or more of the anomaly control indicators marked as too high or too low exist in the comparison results obtained in S202, the anomaly type identification logic is activated. Combining the linkage characteristics of multiple anomaly indicators and the actual working conditions of the tank surface for dual verification, the logic accurately distinguishes between two major categories of anomalies: single parameter imbalance and multi-parameter coordinated imbalance, and further subdivides them into specific anomaly types: Single parameter imbalance type: Only one of the indicators, such as molecular ratio, electrode distance, or alumina feeding amount, deviates from the threshold. This is classified as a single fault type, such as abnormal molecular ratio, electrode distance too small / too large, or feeding imbalance. Multi-parameter coordinated imbalance type: Differentiating core operating condition anomalies in hot and cold baths based on the combined characteristics of multiple abnormal indicators. Cold tank anomaly judgment: If multiple abnormal indicators are present at the same time, such as electrolyte temperature <935℃, voltage deviation >23mV, noise value >21mV, alumina feed amount >5650kg, electrolyte level <18cm, and aluminum content increase, and are accompanied by on-site characteristics such as increased material blockage and frequent anode effects, it is judged to be a cold tank. Anomaly detection of hot tub: If multiple abnormal indicators are present simultaneously, such as electrolyte temperature >945℃, voltage deviation <15mV, noise level <19mV, alumina feed amount <5550kg, electrolyte level >20cm, and aluminum content decrease, along with on-site characteristics such as shell collapse, fire, and weak flame, it is determined to be a hot tub.

[0032] Understandably, this step relies on multi-indicator linkage logic to pinpoint the root cause of anomalies, effectively distinguishing between single parameter deviations and systemic tank condition imbalances. This avoids the problems of misjudging or missing tank conditions by relying solely on single symptoms in traditional control, providing a clear target for subsequent precise and economical parameter adjustments.

[0033] Step S204: Adjust the operating parameters of the electrolytic cell according to the type of abnormality to obtain the adjustment result.

[0034] In this embodiment, step S204 is the process adjustment execution step, which follows the core principle of adjusting by the most economical means. For different anomaly types identified in S203, a specific quantitative adjustment strategy is matched. The entire adjustment process adopts the principle of small, multiple, and gradual adjustments. After the parameter adjustment is completed, the adjustment range, adjustment timing, and real-time indicator values ​​are recorded to form a complete adjustment result. For abnormalities in the cold bath: if the amount of aluminum in the bath is within the standard range or exceeds 33.5t, prioritize increasing the amount of aluminum discharged to enhance heat dissipation of the bath and suppress heat loss by relying on the aluminum liquid layer; if the amount of aluminum in the bath is lower than the standard lower limit of 32.5t, increase the bath voltage by 20mV in a single adjustment and continuously monitor the changes in the indicators. For abnormal hot tub conditions: If the amount of aluminum in the tub is within the standard range, the tub voltage should be lowered by no more than 10mV at a time to reduce ineffective heat loss in stages; if the amount of aluminum in the tub is lower than the lower limit of 32.5t, the voltage should be increased by 20mV at a time to balance heat input. For abnormally high / low molecular ratios: the amount of aluminum fluoride and sodium fluoride to be added is accurately calculated based on the deviation of the test results, and the molecular ratio is corrected to the standard range of 2.35~2.45 by adding fluoride salts. For single parameter anomalies such as too small / too large electrode gap or unbalanced feeding: the electrode gap is finely adjusted by the anode lifting mechanism and the feeding frequency and single feeding amount of the feeding system are corrected to achieve the correction.

[0035] After all adjustment operations are completed, real-time changes in nine key control indicators are continuously tracked and collected. The differences between the indicators before and after adjustment, as well as the trend of improvement in tank conditions, are recorded. This results in a complete set of adjustment information, including the adjustment plan, adjustment range, and post-adjustment indicator values, forming a closed-loop control system of data collection, benchmarking, judgment, adjustment, and tracking. Compared to traditional post-event tank adjustment methods, this step, with its targeted matching of abnormal types and quantitative adjustment methods, can shorten the tank condition recovery cycle, reduce energy consumption fluctuations during the adjustment process, and achieve low-cost, high-efficiency, and precise control.

[0036] Based on the description of the above embodiments, the method provided by the present invention collects multi-dimensional control indicators in real time, including molecular ratio, electrolyte temperature, voltage deviation, electrode distance, electrolyte level, aluminum content in the cell, superheat, noise level, and alumina feed amount. Simultaneously, each indicator is compared with a dedicated preset threshold range to generate quantitative comparison results. Once an abnormal control indicator is identified, the abnormality type can be locked. Then, according to the corresponding abnormality type, an appropriate adjustment strategy is matched to complete the optimization and adjustment of the electrolytic cell operating parameters and output the adjustment feedback results. Compared to the traditional control model that waits for obvious consequences after the cell condition deteriorates before taking action, this solution relies on real-time acquisition of all parameters and threshold quantification to identify anomalies in advance. It can detect the root cause of parameter deviations early without waiting for serious faults such as frequent effects or large voltage fluctuations, thus solving the problem of delayed adjustments in traditional control. Multi-indicator linkage judgment can accurately distinguish between single parameter imbalance and multi-parameter coordinated instability, avoiding misjudgments and omissions caused by subjective human judgment, and significantly reducing the difficulty of cell adjustment analysis. At the same time, it matches targeted and quantitative adjustment methods according to the clear anomaly type, abandoning experience-based blind adjustments and adopting a small-scale, gradual, and economical adjustment logic to shorten the recovery cycle of abnormal cell conditions. It can truly achieve precise control of all operating conditions of the electrolytic cell, continuously maintain the steady-state operation of the cell, and simultaneously achieve comprehensive technical benefits such as improving current efficiency, reducing electricity consumption per ton of aluminum, reducing manual labor intensity, and avoiding equipment safety hazards.

[0037] Optionally, in step S202, the anomaly type is determined based on the anomaly control indicators, specifically including the following steps: Step S2031: Obtain continuous indicator data of the abnormal control indicators within a preset time period.

[0038] In this embodiment, step S2031 is used to remove instantaneous data interference, identify long-term parameter offset trends based on continuous time-series data, and avoid misjudgments caused by single instantaneous acquisition errors. The preset time period is preferably a weekly or monthly continuous acquisition cycle. The system retrieves the full-time-series historical data continuously stored in step S201 and extracts the continuous numerical sequences corresponding to the abnormal control indicators that have been determined to be too high or too low.

[0039] For example, if the electrolyte temperature and voltage deviation are determined to be abnormal, the average electrolyte temperature for the past 7 consecutive 24 hours and the average voltage deviation for the past 7 days are retrieved to form a time-series trend curve; for daily measured indicators such as molecular ratio and aluminum content in the tank, the continuous records of laboratory tests and inventory for the past 30 days are retrieved.

[0040] Understandably, by extracting continuous indicator data within a preset time period, it is possible to intuitively identify whether anomalies are short-term instantaneous fluctuations or long-term persistent imbalances, distinguish between occasional equipment data collection interference and actual tank condition deterioration, provide long-term data support for subsequent anomaly root cause location, and make up for the shortcomings of traditional methods that rely solely on single-day, single-point data to make one-sided judgments on tank conditions.

[0041] Step S2032: Obtain the on-site cell condition of the electrolytic cell.

[0042] In this embodiment, step S2032 introduces the on-site apparent working condition as a supplementary verification dimension for data judgment, realizing two-way verification between data quantification indicators and the physical on-site condition, and eliminating the limitations of judgment based on a single data dimension. The on-site tank condition can be collected and recorded synchronously by on-site personnel during inspections, covering the following on-site characteristic information: Combustion status of the tank surface: color of the anode flame, intensity of the flame, whether there is continuous shell collapse or large-area fire phenomenon on the shell surface; Operating conditions of the feeding mechanism: whether there is blockage or leakage at each feeding port, and the integrity of the crust formation in the feeding area; Electrolyte stratification status: the separation effect of carbon slag in the tank, and the amount of carbon slag accumulation; Abnormal production phenomena: frequency of anode effect occurrence and duration of effect.

[0043] The above-mentioned on-site tank condition information and the data of the nine major control indicators are archived simultaneously to form a dual information database of numerical data and on-site appearance, which solves the problem that relying solely on the background data cannot identify human and on-site interference factors such as equipment operation and shell management.

[0044] Step S2033: Determine the anomaly type based on the on-site tank conditions and continuous index data.

[0045] In this embodiment, step S2033 is a multi-source information fusion judgment step. It uses the continuous time-series trend of the abnormal indicators obtained in S2031 as the core judgment basis, and the on-site tank conditions collected in S2032 as verification and correction conditions. It also links and matches preset hot / cold tank and single-parameter imbalance judgment rules to accurately pinpoint the specific abnormal type of the electrolytic cell. This is divided into two types of judgment logic: Systemic tank condition imbalance assessment (cold tank / hot tank): The abnormal indicators were retrieved over several consecutive days and cross-validated with the actual tank conditions. Cold tank determination: Continuous time-series data showed electrolyte temperature consistently below 935℃, daily average voltage deviation consistently above 23mV, noise levels consistently above 21mV, daily alumina feed consistently above 5650kg, and electrolyte levels consistently below 18cm. Simultaneously, on-site tank conditions showed frequent material blockage at the feed inlet and frequent anode effects. Therefore, the overall anomaly type was determined to be a cold tank. Hot tank determination: Continuous time-series data showed electrolyte temperature consistently above 945℃, daily average voltage deviation consistently below 15mV, noise levels consistently below 19mV, daily alumina feed consistently below 5550kg, and electrolyte levels consistently above 20cm. Coupled with on-site characteristics such as shell collapse, weak flames, and large-area fires, the overall anomaly type was determined to be a hot tank.

[0046] Single parameter imbalance judgment: If only one control indicator deviates from the threshold for multiple consecutive periods, while the time series data of other indicators are all within the standard range, and there are no batch appearance characteristics of hot and cold tanks on site, then the single abnormality type is directly determined based on the abnormal indicator name, including abnormal molecular ratio, small / large electrode distance, alumina feeding imbalance, abnormal electrolyte level, and unbalanced aluminum content in the tank.

[0047] Understandably, this step eliminates instantaneous interference by using continuous time-series data and then corrects data misjudgments based on the on-site tank conditions. This achieves multi-dimensional information coupling to accurately distinguish various anomalies, eliminating the misjudgments and omissions caused by relying solely on single-day, single-point data and purely manual visual observation in traditional process control. This ensures that subsequent adjustment operations are targeted and accurate, providing a reliable basis for parameter adjustments using economic means.

[0048] For example, using weekly and monthly continuous data trend analysis, if the material quantity is gradually increasing while the electrolyte is gradually decreasing, and combined with the operating curve showing excessive over-processing, excessive anode effect, and excessive material blockage on site, it is judged that the electrolytic cell is too cold. If the material quantity is gradually decreasing while the electrolyte is gradually increasing, and combined with the operating curve showing excessive under-processing, and on site there are signs of flames and shell collapse on the electrolytic cell shell, it is judged that the electrolytic cell is too hot.

[0049] Optionally, in step S2032, obtaining the on-site cell condition of the electrolytic cell specifically includes the following steps: Step S2032a: Obtain real-time image data of the electrolytic cell.

[0050] In this embodiment of the application, step S2032a is the process of collecting visual information on the on-site working conditions of the electrolytic cell. Industrial high-definition video acquisition devices are deployed on the shell surface of the aluminum electrolytic cell, the anode combustion area, each feeding port, the electrolyte observation area, and the carbon slag retrieval observation point. The video acquisition devices continuously and uninterruptedly collect real-time images of the upper area of ​​the electrolytic cell to obtain real-time image data covering the key observation areas of the entire cell.

[0051] The collected image data comprehensively includes the anode flame imaging area, the tank surface crust coverage area, the structural areas of each alumina feed port, and the stratified observation area of ​​carbon slag and electrolyte in the tank. The image acquisition frequency is synchronized with the data acquisition sequence of the S201 control indicators, ensuring that the visual information of the images and the numerical information of the nine process parameters are matched and consistent in time dimension, avoiding the time difference between the data and the on-site working conditions that could lead to judgment distortion. After the acquisition is completed, the original image data is cached in real time and synchronously transmitted to the background processing unit, providing original visual materials for subsequent intelligent recognition by the model. This replaces the traditional manual on-site inspection and subjective recording acquisition mode, eliminating the defects of insufficient frequency of manual inspection, observation bias, and recording lag.

[0052] Step S2032b: Input the real-time image data into the preset tank condition recognition model and output the on-site tank condition, which includes the flame color, shell surface condition, material blockage condition, carbon slag separation condition and electrolyte working condition.

[0053] In this embodiment, step S2032b is a visual image intelligent analysis step. A standardized tank condition recognition model is pre-trained based on a large number of on-site image samples of hot and cold electrolytic cells and various abnormal operating conditions. The model has built-in multi-dimensional image feature extraction and classification judgment logic. The real-time image data output from S2032a is used as the input source and imported into the preset tank condition recognition model. The model automatically extracts the pixel features, morphological features, and color features of each observation area in the image, and completes the automatic recognition and classification output of five types of on-site tank conditions. The recognition and judgment rules for each type of tank condition are explained in conjunction with the process standards of this invention as follows: Flame color recognition: The model extracts the color values ​​of the flame pixels in the anode combustion area, distinguishes between normal golden yellow flames, pale and dim flames corresponding to cold slots, and weak light red flames corresponding to hot slots, and outputs the flame color judgment result as an auxiliary criterion for hot and cold slots. Shell surface condition identification: Identify the integrity of the crust on the tank surface, whether there is a large area of ​​crust collapse, continuous exposure of molten electrolyte, frequent fire on the shell surface, etc., and output two types of shell surface condition results: complete / collapsed and fire on the shell surface, and match the typical appearance characteristics of the hot tub. Material blockage status recognition: Contour recognition is performed on the imaging area of ​​each material outlet to distinguish between three states: unobstructed material outlet, material accumulation and blockage, and material leakage. The blockage judgment result is output, corresponding to the typical blockage process manifestations in the cold tank. Carbon slag separation state identification: Identify the thickness of carbon slag accumulation on the surface of the electrolyte and the clarity of the separation between carbon slag and molten electrolyte, and output three states: good carbon slag separation, excessive carbon slag accumulation, and poor separation, to help judge the abnormal electrolyte fluidity caused by molecular ratio and temperature imbalance. Electrolyte working status identification: Combining electrolyte surface fluctuations and melting uniformity within the image to further corroborate the electrode distance and thermal equilibrium matching status.

[0054] After the model completes the simultaneous recognition of five types of features, it integrates and outputs a structured on-site tank condition dataset. This dataset will form a two-way verification basis with the continuous time-series data of the abnormal control indicators obtained in step S2031, realizing the fusion judgment of quantitative process parameters and machine vision recognition of the on-site appearance status. This completely avoids the subjective bias, missed detection, and recording lag problems caused by traditional manual observation, and improves the accuracy and objectivity of the subsequent abnormal type judgment in S2033. It provides complete and objective on-site working condition support for the present invention to pre-judge and accurately distinguish abnormal types such as cold tanks, hot tanks, and single parameter imbalances.

[0055] Optionally, in step S2033, the anomaly type is determined based on the on-site tank conditions and continuous index data, specifically including: Step S2033a: Determine the initial judgment result based on the continuous index data.

[0056] In this embodiment of the application, step S2033a uses the continuous time series data of the abnormal control indicators obtained in S2031 for a preset period as the basis for judgment, and completes the preliminary qualitative assessment of the tank condition without regard to the on-site appearance information, thus forming an initial judgment result.

[0057] The system retrieves the multi-day continuous average and trend curves of abnormal indicators and matches them with preset parameters to trigger imbalance logic: If multiple sets of indicators deviate synchronously from the threshold range for a long period of time, an initial judgment of systemic imbalance is generated according to the parameter combination characteristics: if the electrolyte temperature is <935℃, the daily average voltage deviation is >23mV, the noise value is >21mV, the alumina feed rate is >5650kg, and the electrolyte level is <18cm for several consecutive days (e.g., 3 days), the initial judgment result is a cold tank; if the electrolyte temperature is >945℃, the daily average voltage deviation is <15mV, the noise value is <19mV, the alumina feed rate is <5550kg, and the electrolyte level is >20cm for several consecutive days, the initial judgment result is a hot tank.

[0058] If only one indicator continuously deviates from the corresponding threshold, while the other eight indicators remain within the standard range throughout the process, then the initial judgment result of a single imbalance will be output based on the name of the abnormal indicator, including abnormal molecular ratio, small electrode distance, large electrode distance, material imbalance, abnormal electrolyte level, deviation of aluminum content in the tank, and abnormal superheat.

[0059] If the continuous data only shows a small, instantaneous deviation on a single day, and the trend over multiple days does not show a sustained deviation, then the initial judgment is that it is an instantaneous data acquisition disturbance, and does not belong to an abnormality in the production process.

[0060] Understandably, this step relies on long-term quantitative data to avoid single-point instantaneous acquisition errors, and prioritizes locking in potential anomalies from the dimensions of thermal balance, material balance, and electrical balance data to obtain objective initial judgment results without human subjective interference, providing a basic judgment baseline for subsequent on-site working condition verification.

[0061] Step S2033b: Verify the initial judgment result based on the on-site tank conditions to obtain the anomaly type.

[0062] In this embodiment of the application, step S2033b introduces the structured on-site tank condition data output by machine vision recognition or manual recognition in S2032, performs positive verification and reverse correction on the initial judgment result generated in S2033a, eliminates equipment and operation interference factors that cannot be identified by pure numerical data, and finally outputs a unique and accurate anomaly type.

[0063] The on-site tank condition includes five categories of visual recognition results: flame color, shell surface condition, material port blockage condition, carbon-slag separation condition, and electrolyte working condition. Cross-validation was performed in two scenarios: The verification logic for the initial determination of systemic hot and cold baths is as follows: When the initial determination result is a cold bath, the on-site bath condition is retrieved for verification: If the image recognition result shows that the flame is pale and dim, there is a large area of ​​material blockage at the discharge port, and there is no large area of ​​shell collapse and fire, then the on-site bath condition matches the data characteristics of the cold bath, the verification passes, and the final anomaly type is determined to be a cold bath; if the visual recognition shows that the shell surface is continuously collapsing and emitting fire, and the flame is weak and red, which is completely contrary to the appearance characteristics of the cold bath, then the continuous index deviation is determined to be caused by equipment acquisition failure or human operation error, the initial determination is corrected, and the process imbalance anomaly is eliminated. When the initial determination result is a hot bath, the on-site bath condition is retrieved for verification: If the image recognition result shows that the flame is weak, the bath surface frequently collapses, there is a large area of ​​fire, and the discharge port is unobstructed, then the data matches the on-site working conditions, the verification passes, and the final anomaly type is determined to be a hot bath; if the visual recognition shows that the discharge port is severely blocked and the anode effect occurs frequently, then it conflicts with the appearance characteristics of the hot bath, the initial determination is corrected, and the process anomaly determination is eliminated.

[0064] Verification logic for initial judgment of single parameter imbalance: When the initial judgment is that a single parameter such as molecular ratio, electrode distance, or feed rate is imbalanced, it is corroborated by the carbon-slag separation state, electrolyte level fluctuation, and feed port state. For example, if the initial judgment is that the molecular ratio is too low, and the visual recognition shows that the carbon-slag layering is blurred and a large amount of carbon-slag is accumulated, then the on-site working condition corroborates the judgment, and the final anomaly type remains the molecular ratio anomaly. If the carbon-slag separation is clear and other visual features are not abnormal, then the judgment index shift is due to laboratory or measuring equipment error, and the process anomaly mark is canceled.

[0065] Understandably, after on-site verification and correction of the tank condition, a unique and definite anomaly type is output. This not only avoids the shortcomings of pure numerical data in distinguishing between equipment failure and actual tank condition imbalance, but also eliminates the subjective bias caused by relying solely on manual visual judgment. It realizes bidirectional coupling judgment between time-series quantitative data and machine vision operating conditions, providing a reliable and unambiguous basis for anomaly classification for subsequent S204 matching economic and directional quantitative adjustment schemes.

[0066] Optionally, in step S204, the operating parameters of the electrolytic cell are adjusted according to the type of abnormality to obtain the adjustment result, specifically including: Step S2041: In response to the anomaly type being hot / cold bath anomaly, obtain the aluminum level.

[0067] In this embodiment, when the anomaly type output by S2033 is determined to be either a cold tank anomaly or a hot tank anomaly, the system triggers the aluminum level data acquisition process. The aluminum level mentioned here is a measured parameter directly corresponding to the amount of aluminum in the tank. The total weight of aluminum in the tank in real time is calculated by daily on-site measurement of the aluminum liquid level in the electrolytic cell, combined with periodic inventory and calibration data of the electrolytic cell. This application presets a standard aluminum content of 33t, with an allowable fluctuation range of 33±0.5t. The corresponding measured aluminum level height corresponds one-to-one with this stored aluminum weight.

[0068] It should be noted that the aluminum level is the core intermediate judgment parameter for regulating the thermal balance of the hot and cold baths. The aluminum liquid layer plays a key role in regulating the heat input and heat loss in the bath. A higher aluminum level will improve the heat dissipation capacity of the bath, while a lower aluminum level will result in a weaker heat storage capacity of the bath and an unstable thermal balance. Therefore, this step prioritizes retrieving the real-time aluminum level value as a prerequisite for subsequent step adjustment of the bath voltage, to avoid over-adjustment of the bath and repeated fluctuations in the operating conditions caused by directly adjusting the voltage without the aluminum liquid heat dissipation base.

[0069] Step S2042: Compare the aluminum level with the preset aluminum level standard range to obtain the comparison result.

[0070] In this embodiment, a standard range for aluminum levels adapted to a 400kA electrolytic cell is pre-configured, corresponding to an aluminum content of 32.5t to 33.5t in the cell. The aluminum content in the cell after conversion from the real-time aluminum level obtained in step S2041 is quantitatively compared with this preset standard range, generating three types of comparison results: If the real-time aluminum content in the tank falls within the range of 32.5t to 33.5t, the output comparison result is: aluminum level is qualified; if the real-time aluminum content in the tank is >33.5t, the output comparison result is: aluminum level is too high; if the real-time aluminum content in the tank is <32.5t, the output comparison result is: aluminum level is too low.

[0071] Understandably, this step completes the standardized and quantitative classification of aluminum levels, distinguishes the different working conditions of the aluminum liquid layer heat dissipation substrate, and provides a clear quantitative basis for matching differentiated voltage regulation logic to cold and hot baths respectively, abandoning the crude operation mode of arbitrarily raising and lowering voltage based solely on experience in traditional bath adjustment.

[0072] Step S2043: Based on the comparison results, adjust the cell voltage of the electrolytic cell until the adjustment results indicate that the abnormality has been eliminated.

[0073] In this embodiment, step S2043 follows the principle of small-scale, multiple, gradual, and economically optimal tank adjustment. It combines the results of comparing cold and hot tank anomalies with aluminum levels to perform differentiated tank voltage adjustment. After adjustment, it continuously collects nine control indicators and monitors the tank conditions on-site, cyclically verifying until the anomalies return to the standard range and the on-site tank conditions match normal operating conditions. It then determines that the anomaly has been eliminated and outputs the complete adjustment results, explaining the voltage adjustment logic in two categories of anomalies: I. Pressure regulation rules when the abnormality type is cold bath If the S2042 comparison result is that the aluminum level is qualified or the aluminum level is too high (aluminum quantity ≥33t): prioritize increasing the aluminum output to enhance the heat dissipation of the tank and improve the cold environment inside the tank by reducing the aluminum level; if the improvement in the tank condition is insufficient after increasing the aluminum output, then adopt the voltage increase scheme, with a single voltage increase of 20mV, and only one increase operation is performed per day. If the comparison result of S2042 shows that the aluminum level is low (aluminum amount <32.5t): directly increase the tank voltage by 20mV in a single operation to supplement the heat gain in the tank and make up for the continuous cooling trend caused by insufficient heat dissipation of the aluminum liquid layer.

[0074] II. Pressure Regulation Rules When the Abnormal Type is Hot Bath If the comparison result of S2042 shows that the aluminum level is qualified (32.5t≤aluminum quantity≤33.5t): adopt a step-down voltage reduction strategy, with the single tank voltage reduction not exceeding 10mV, and gradually reduced over multiple days to reduce ineffective heat loss in the tank. If the S2042 comparison result shows that the aluminum level is too low (aluminum amount <32.5t): the voltage should not be reduced directly and drastically. Instead, the cell voltage should be increased by 20mV at a time to avoid the thermal balance collapse caused by the voltage reduction when the aluminum level is too low.

[0075] After all voltage regulation operations are completed, the system continuously collects nine control indicators in real time: molecular ratio, electrolyte temperature, voltage deviation, electrode distance, electrolyte level, aluminum content in the cell, superheat, noise level, and alumina feed rate. Simultaneously, it retrieves the on-site cell conditions identified by machine vision for cyclical verification. When all control indicators return to their respective preset standard ranges, and the on-site cell conditions such as flame color, shell surface, feed port, and carbon-slag separation match the characteristics of a normal electrolytic cell, it is determined that the cell condition abnormality has been completely eliminated. The abnormality type, initial aluminum level, voltage regulation range, regulation time, and all parameter values ​​before and after regulation are recorded to form a complete and traceable regulation result archived and stored.

[0076] Understandably, this step uses a combination of pre-classification of aluminum levels and differentiated step-by-step voltage regulation to achieve targeted and low-cost process correction for hot and cold cells. This solves the shortcomings of traditional cell adjustment and pressure regulation, such as the lack of quantitative standards for the range of adjustment, long recovery period of cell condition after adjustment, and large fluctuations in energy consumption, thus ensuring that the electrolytic cell smoothly returns to the steady-state production range.

[0077] Optionally, in step S204, the operating parameters of the electrolyzer are adjusted according to the type of abnormality. Specifically, in response to the abnormality type being a molecular ratio abnormality, the amount of fluoride salt added is adjusted according to a preset adjustment strategy until the adjustment results indicate that the molecular ratio abnormality has been eliminated.

[0078] In this embodiment of the application, when the abnormality type finally output by step S2033 is abnormal molecular ratio, the quantitative addition adjustment process of fluoride salt is executed, the total amount of fluoride salt added is accurately corrected according to the preset standardized adjustment strategy, and the molecular ratio index is continuously tracked until the monitoring results show that the molecular ratio returns to the standard range and the abnormal molecular ratio is completely eliminated.

[0079] For example, this application pre-sets the optimal control range of molecular ratio for a 400kA aluminum electrolytic cell to be 2.35 to 2.45, where molecular ratio = number of moles of sodium fluoride in the cell / number of moles of aluminum fluoride. After the threshold judgment in step S202, if the measured value of molecular ratio is less than 2.35, it is considered an abnormality of low molecular ratio; if the measured value is greater than 2.45, it is considered an abnormality of high molecular ratio. The two types of molecular ratio abnormalities are configured with exclusive preset fluoride salt adjustment strategies.

[0080] When an abnormally low molecular ratio is identified, the root cause is a relative excess of aluminum fluoride and an insufficient amount of sodium fluoride in the tank. The corresponding preset adjustment strategy is to increase the amount of sodium fluoride or decrease the amount of aluminum fluoride: The system retrieves the difference between the actual molecular ratio obtained from the current electrolyte test and the lower limit of the range of 2.35, accurately calculates the required amount of sodium fluoride to be added in tons based on the fluoride molar conversion formula, or determines the reduction range of aluminum fluoride, and issues an instruction to the automatic fluoride feeding system to complete the quantitative replenishment operation, thereby reducing the difference in the molar number of aluminum fluoride and sodium fluoride and increasing the molecular ratio in the tank.

[0081] When an abnormally high molecular ratio is identified, the root cause is a relative excess of sodium fluoride and insufficient aluminum fluoride in the tank. The corresponding preset adjustment strategy is to increase the amount of aluminum fluoride added: the system calculates the deviation between the measured molecular ratio and the upper limit of the range of 2.45, and calculates the precise weight of aluminum fluoride to be added by combining the total amount of electrolyte in the tank. Aluminum fluoride is then added quantitatively in multiple batches through the fluoride salt feeding mechanism to increase the molar proportion of aluminum fluoride in the system and gradually reduce the overall molecular ratio value.

[0082] Optionally, after completing a single fluoride salt addition adjustment operation, the system establishes a continuous tracking and verification mechanism: Electrolyte samples are re-collected in the tank according to a predetermined testing cycle to detect the fluoride, sodium, and aluminum content within the electrolyte, and the real-time molecular ratio index is calculated again; the updated molecular ratio value is continuously compared with the standard range of 2.35–2.45. If the molecular ratio still does not fall within the preset range, the corresponding adjustment strategy for increasing or decreasing the amount of fluoride salt is repeated, and the total amount of fluoride salt added is cyclically corrected; when the molecular ratio obtained from two consecutive tests is stably maintained within the range of 2.35–2.45, and other related thermal balance and material balance indicators show no derivative deviations, the molecular ratio anomaly is determined to be eliminated.

[0083] Optionally, the system synchronously records data throughout the entire adjustment process to form a complete archive of adjustment results. The recorded information includes the initial molecular ratio deviation value, abnormality type, type of fluoride salt to be adjusted, single dosage weight, adjustment execution time, molecular ratio change curves tracked by multiple rounds of testing, and abnormality elimination judgment node.

[0084] Understandably, this adjustment method abandons the traditional control model that relies on manual experience to roughly add fluoride salts. Instead, it uses test data to quantitatively calculate the amount of fluoride salt to achieve precise molecular ratio and small-scale, gradual correction. This avoids the chain imbalance of electrolyte viscosity, conductivity, and alumina solubility caused by excessive addition of fluoride salts, shortens the molecular ratio recovery cycle, stabilizes the material balance system of the electrolyzer, and ensures that the electrolyzer is in a low-consumption and stable operating state for a long time.

[0085] Optionally, refer to Figure 3 In some embodiments of the present invention, the electrolytic cell control method is implemented as follows: 1. Standards Establishment First, establish data standards for normal production of electrolytic cells, collect corresponding production data, and standardize the data collection process, data processing, and data visualization to provide a data foundation for subsequent analysis.

[0086] 2. Data Analysis Data analysis was conducted based on the "three lows and one high" control strategy and the "five stable target states": Three lows and one high: low molecular weight ratio, low electrolyte temperature, low voltage deviation, and high electrode distance; Five stabilizations: stable upper level, stable aluminum quantity, stable superheat, stable noise level, and stable alumina feed rate.

[0087] 3. Confirm the condition of the cells (confirm the condition of the aluminum electrolysis cells on site) By combining the data with the actual working conditions on site, various apparent phenomena are identified: cold tank / hot tank, shell collapse and fire, aluminum level and electrolyte level, excessive carbon slag / poor separation, material blockage / leakage, and a comprehensive judgment of the tank condition is completed.

[0088] 4. Adjust using the most economical means The process adjustment is carried out in accordance with the principle of fine-tuning and gradual progress: the adjustment is carried out based on the standard of three highs and one low, the adjustment process adheres to the five stability indicators, and economic evaluation methods are introduced to complete the optimization of electrolytic cell parameters.

[0089] It should be noted that the specific implementation of the above process has been described in the foregoing embodiments, and will not be repeated here.

[0090] Based on the same inventive concept, this application also provides an electrolytic cell control device for implementing the electrolytic cell control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the electrolytic cell control device provided below can be found in the limitations of the electrolytic cell control method described above, and will not be repeated here.

[0091] In one exemplary embodiment, such as Figure 4 As shown, an electrolytic cell control device 40 is provided, comprising: an acquisition module 401, used to acquire multiple control indicators of the electrolytic cell in real time, wherein the control indicators include molecular ratio, electrolyte temperature, voltage deviation, electrode distance, electrolyte level, aluminum content in the cell, superheat, noise level, and alumina feed rate; a comparison module 402, used to compare the multiple control indicators with multiple corresponding preset indicator range thresholds to obtain multiple comparison results; a determination module 403, used to determine the abnormality type based on the abnormal control indicators in response to the presence of abnormal control indicators in the multiple comparison results; and an adjustment module 404, used to adjust the operating parameters of the electrolytic cell according to the abnormality type.

[0092] Optionally, the determining module 403 is also used to: obtain continuous indicator data of the abnormal control indicators within a preset time period; obtain the on-site cell condition of the electrolytic cell; and determine the abnormality type based on the on-site cell condition and the continuous indicator data.

[0093] Optionally, the determining module 403 is also used to: acquire real-time image data of the electrolytic cell; input the real-time image data into a preset cell condition recognition model and output the on-site cell condition, wherein the on-site cell condition includes flame color, shell surface condition, material blockage condition, carbon-slag separation condition and electrolyte working condition.

[0094] Optionally, the determining module 403 is also used to: determine the initial judgment result based on continuous index data; and verify the initial judgment result based on the on-site tank conditions to obtain the anomaly type.

[0095] Optionally, the adjustment module 404 is further configured to: in response to an anomaly type of hot / cold cell anomaly, obtain the aluminum level; compare the aluminum level with a preset aluminum level standard range to obtain a comparison result; and adjust the cell voltage of the electrolytic cell according to the comparison result until the adjustment result indicates that the anomaly has been eliminated.

[0096] Optionally, the adjustment module 404 is also configured to: in response to an anomaly type of molecular ratio anomaly, adjust the amount of fluoride salt added according to a preset adjustment strategy until the adjustment results indicate that the molecular ratio anomaly has been eliminated.

[0097] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 5 As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores control instructions. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements an electrolytic cell control method.

[0098] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0099] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0100] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0101] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0102] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0103] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0104] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0105] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for controlling an electrolytic cell, characterized in that, The electrolytic cell control method includes: Multiple control indicators of the electrolytic cell are acquired in real time, including molecular ratio, electrolyte temperature, voltage deviation, electrode distance, electrolyte level, aluminum content in the cell, superheat, noise level, and alumina feed rate. The multiple control indicators are compared with multiple corresponding preset indicator range thresholds to obtain multiple comparison results; In response to the presence of anomaly control indicators among the multiple comparison results, the anomaly type is determined based on the anomaly control indicators; Based on the type of anomaly, the operating parameters of the electrolytic cell are adjusted to obtain the adjustment result.

2. The electrolytic cell control method according to claim 1, characterized in that, Based on the aforementioned anomaly control indicators, the anomaly type is determined, specifically including: Obtain continuous indicator data of the anomaly control indicators within a preset time period; Obtain the on-site cell condition of the electrolytic cell; Based on the on-site tank conditions and the continuous index data, the anomaly type is determined.

3. The electrolytic cell control method according to claim 2, characterized in that, The process of obtaining the on-site cell condition of the electrolytic cell specifically includes: Acquire real-time image data of the electrolytic cell; The real-time image data is input into a preset tank condition recognition model, and the on-site tank condition is output. The on-site tank condition includes flame color, shell surface condition, material blockage condition, carbon and slag separation condition, and electrolyte working condition.

4. The electrolytic cell control method according to claim 2, characterized in that, The step of determining the anomaly type based on the on-site tank conditions and the continuous indicator data specifically includes: Based on the continuous index data, determine the initial judgment result; The initial judgment result is verified based on the on-site tank conditions to obtain the anomaly type.

5. The electrolytic cell control method according to claim 1, characterized in that, The step of adjusting the operating parameters of the electrolytic cell according to the type of abnormality to obtain the adjustment result specifically includes: In response to the anomaly type being hot / cold bath anomaly, the aluminum level is obtained; The aluminum level is compared with a preset aluminum level standard range to obtain the comparison result; Based on the comparison results, the cell voltage of the electrolytic cell is adjusted until the adjustment results indicate that the abnormality has been eliminated.

6. The electrolytic cell control method according to claim 1, characterized in that, The adjustment of the operating parameters of the electrolytic cell according to the type of abnormality specifically includes: In response to the abnormality being a molecular ratio abnormality, the amount of fluoride added is adjusted according to a preset adjustment strategy until the adjustment result indicates that the molecular ratio abnormality has been eliminated.

7. An electrolytic cell control device, characterized in that, The electrolytic cell control device includes: The acquisition module is used to acquire multiple control indicators of the electrolytic cell in real time, including molecular ratio, electrolyte temperature, voltage deviation, electrode distance, electrolyte level, aluminum content in the cell, superheat, noise level, and alumina feed rate. The comparison module is used to compare the multiple control indicators with multiple corresponding preset indicator range thresholds to obtain multiple comparison results; The determination module is used to determine the anomaly type based on the anomaly control indicators in response to the presence of anomaly control indicators among the multiple comparison results. The adjustment module is used to adjust the operating parameters of the electrolytic cell according to the type of abnormality.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the electrolytic cell control method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the electrolytic cell control method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the electrolytic cell control method according to any one of claims 1-6.

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