A cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data

Abstract

The present invention relates to the field of data analysis, and particularly to a cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data. The present invention collects image data of electrolytic cells and fluctuation data of the liquid level of electrolytic cells; identifies low-temperature local areas based on the image data, and determines the electrolysis characteristics of the low-temperature local areas; analyzes the electrolysis stability characterization parameters of the electrolysis process based on the electrolysis characteristics in combination with the consumption of anodes, so as to divide the electrolysis state categories of the electrolysis process; in response to the division result of the electrolysis state category, determines the convection characteristics of each area on the surface of the electrolytic cell, and identifies the convection abnormal area based on the convection characteristics; obtains the fluctuation characteristics of the convection abnormal area, identifies the fluctuation abnormal stage, and determines the abnormal characterization value in combination with the temperature change characteristics in the fluctuation abnormal stage, so as to determine whether to issue a warning signal. The present invention can perform real-time detection on the aluminum electrolysis process on the premise of ensuring data reliability and improve the detection efficiency.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to a cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data. Background Art

[0002] In the process of the transformation of aluminum electrolysis enterprises towards digitalization and intelligentization, an operation and maintenance system that can closely connect production equipment, production processes, and management personnel is an important part of the digital transformation of aluminum electrolysis enterprises to achieve digital management and optimization of the production process and improve the competitiveness of enterprises.

[0003] Traditional operation and maintenance of aluminum electrolysis production equipment mainly rely on manual inspections and regular maintenance. This method has many drawbacks. Manual inspections are subjective and untimely, and it is easy to miss some potential fault hazards. Regular maintenance is often based on fixed time intervals or operating mileage, which may lead to over-maintenance or under-maintenance, increasing maintenance costs and equipment failure risks. Therefore, uploading the data of production equipment to the cloud to achieve remote monitoring and diagnosis, and at the same time, leveraging the powerful computing and storage capabilities of the cloud platform for data analysis and processing can not only reduce the informatization construction costs of enterprises but also improve the efficiency and accuracy of detection.

[0004] Chinese Patent Application Publication No.: CN115034306A discloses a method for aluminum electrolysis fault prediction and safety operation and maintenance based on an extension neural network. First, factor analysis and statistical methods are used to analyze the influencing factors of anode effects, cathode breakage, anode budding, cold baths and hot baths, and rolling aluminum. Then, a system identification method is used to propose a method for aluminum electrolysis fault modeling and diagnosis based on a model. A multi-level diagnosis method that combines model diagnosis and intelligent diagnosis is used to diagnose and predict the specific categories of faults. A test information of the aluminum electrolysis cell is constructed to establish a performance evaluation model of the aluminum electrolysis cell, and on this basis, the optimal test timing of the aluminum electrolysis cell is determined. A method based on an analytical model and data-driven is used to study the theory and method of abnormal working conditions or fault prediction and safe operation and maintenance of complex production processes to ensure the stable and safe production of the entire electrolysis and extend the life of the aluminum electrolysis cell.

[0005] However, the following problems still exist in the prior art.

[0006] When detecting abnormalities in the aluminum electrolysis process, it often relies on the experience of relevant operators, which is highly subjective, has low accuracy, and there is a lag in the discovery of potential abnormalities, making it impossible to handle faults in a timely manner. Summary of the Invention

[0007] To this end, the present invention provides a cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data to overcome the problems in the prior art that when detecting abnormalities in the aluminum electrolysis process, it often relies on the experience of relevant operators, has strong subjectivity and low accuracy, and there is a lag in the discovery of potential abnormalities, and the faults cannot be processed in time.

[0008] To achieve the above object, the present invention provides a cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data, which includes:

[0009] An acquisition and analysis module, which includes an infrared acquisition unit for acquiring image data of the electrolytic cell and a laser interference unit for acquiring fluctuation data of the liquid level of the electrolytic cell;

[0010] A feature recognition module, which is connected to the acquisition and analysis module, and is used to identify a low-temperature local area based on the image data and determine the electrolysis characteristics of the low-temperature local area, where the electrolysis characteristics include the range area of the low-temperature local area and the maximum temperature difference between the low-temperature local area and other local areas;

[0011] A feature analysis module, which is connected to the feature recognition module, and is used to analyze the electrolysis stability characterization parameters of the electrolysis process based on the electrolysis characteristics in combination with the consumption of the anode, so as to classify the electrolysis state categories of the electrolysis process;

[0012] A convection analysis module, which is connected to the feature analysis module, and in response to the classification result of the feature analysis module, is used to determine the convection characteristics of each area on the surface of the electrolytic cell and identify the convection abnormal area based on the convection characteristics;

[0013] A depth detection module, which is connected to the convection analysis module, is used to obtain the fluctuation characteristics of the convection abnormal area, identify the fluctuation abnormal stage, and determine the abnormal characterization value in combination with the temperature change characteristics in the fluctuation abnormal stage, so as to determine whether to issue a warning signal;

[0014] Wherein, the convection characteristics include the gradient temperature difference and the maximum temperature difference along the temperature flow direction, and the temperature change characteristics include the temperature change amount and the temperature change rate.

[0015] Furthermore, the feature analysis module is used to analyze the electrolysis stability characterization parameters of the electrolysis process based on the electrolysis characteristics in combination with the consumption of the anode, including,

[0016] Using the sum of the ratio of the range area of the low-temperature local area to the range area threshold and the ratio of the maximum temperature difference between the low-temperature local area and other local areas to the maximum temperature difference threshold as the first electrolysis stability feature;

[0017] Using the ratio of the consumption of the anode to the consumption threshold as the second electrolysis stability feature;

[0018] The first electrolysis stability feature and the second electrolysis stability feature are weighted and summed as the electrolysis stability characterization parameter.

[0019] Further, the feature analysis module is used to classify the electrolysis state categories of the electrolysis process, including

[0020] If the electrolysis stability characterization parameter is greater than or equal to the electrolysis stability characterization parameter threshold, the electrolysis process is classified into the weak stability state category.

[0021] Further, the convection analysis module responds to the classification result of the feature analysis module, including

[0022] If the electrolysis process is in the weak stability state category, the convection characteristics of each region on the surface of the electrolytic cell are determined, and the convection abnormal region is identified based on the convection characteristics.

[0023] Further, the convection analysis module is used to determine the convection characteristics of each region on the surface of the electrolytic cell, including

[0024] To determine the highest temperature point and the lowest temperature point of the electrolytic cell on the horizontal plane, and determine the direction corresponding to the lowest temperature point pointed by the highest temperature point as the temperature flow direction;

[0025] To divide several regions along the temperature flow direction and obtain the temperature values in each of the regions;

[0026] To solve the temperature difference between each region and its adjacent region, and determine the average temperature difference as the gradient temperature difference.

[0027] Further, the convection analysis module is used to identify the convection abnormal region based on the convection characteristics, including

[0028] If any region does not meet the convection reference conditions, the region is identified as the convection abnormal region;

[0029] Wherein, the convection reference conditions include that the gradient temperature difference along the temperature flow direction is greater than the gradient temperature difference threshold or / and the maximum temperature difference is greater than the maximum temperature difference threshold.

[0030] Further, the fluctuation characteristics are determined by calling the fluctuation data collected by the laser interference unit, and the fluctuation characteristics include the liquid level fluctuation amount and the fluctuation frequency of the electrolytic cell.

[0031] Further, the depth detection module is used to identify the fluctuation abnormal stage, including

[0032] If any moment meets the fluctuation abnormal conditions, the electrolysis process corresponding to the moment is identified as the fluctuation abnormal stage;

[0033] Among them, the abnormal fluctuation condition includes that the fluctuation amount of the electrolytic cell liquid level is greater than the fluctuation amount threshold and / or the fluctuation frequency is greater than the fluctuation frequency threshold.

[0034] Furthermore, the depth detection module is used to determine the abnormal characterization value, including,

[0035] using the sum of the ratio of the fluctuation amount of the electrolytic cell liquid level to the fluctuation amount threshold and the ratio of the fluctuation frequency to the fluctuation frequency threshold as the first abnormal feature;

[0036] using the sum of the ratio of the temperature change amount to the temperature change amount threshold and the ratio of the temperature change rate to the temperature change rate threshold as the second abnormal feature;

[0037] using the sum of the first abnormal feature and the second abnormal feature to determine the abnormal characterization value.

[0038] Furthermore, the depth detection module is used to determine whether to issue a warning signal, including,

[0039] If the abnormal characterization value is greater than or equal to the preset abnormal characterization threshold, it is determined to issue a warning signal.

[0040] Compared with the prior art, the present invention is provided with an acquisition and analysis module, including an infrared acquisition unit for acquiring image data of the electrolytic cell and a laser interference unit for acquiring fluctuation data of the electrolytic cell liquid level; a feature recognition module for identifying a low-temperature local area based on the image data and determining the electrolysis characteristics of the low-temperature local area; a feature analysis module for analyzing the electrolysis stability characterization parameters of the electrolysis process based on the electrolysis characteristics in combination with the consumption of the anode to classify the electrolysis state categories of the electrolysis process; a convection analysis module for determining the convection characteristics of each area on the surface of the electrolytic cell in response to the classification result of the feature analysis module, and identifying the convection abnormal area based on the convection characteristics; a depth detection module for obtaining the fluctuation characteristics of the convection abnormal area, identifying the abnormal fluctuation stage, and determining the abnormal characterization value in combination with the temperature change characteristics in the abnormal fluctuation stage to determine whether to issue a warning signal. The present invention can perform real-time detection of the aluminum electrolysis process on the premise of ensuring data reliability and improve the detection efficiency.

[0041] In particular, the present invention provides a feature analysis module, which analyzes the electrolysis stability characterization parameters of the electrolysis process based on the electrolysis characteristics and the consumption of the anode. In actual situations, the temperature distribution in the electrolytic cell should be relatively uniform to maintain a stable electrolysis process, enabling alumina to come into full contact with the electrolyte and participate in the electrochemical reaction. If the temperature in a local area is too low, it will disrupt the thermal balance, causing the temperature field in the electrolytic cell to be unevenly distributed. At the same time, low temperature will lead to a decrease in the viscosity of the electrolyte, poor fluidity, and a slow diffusion rate of alumina, making it difficult to quickly reach the electrode surface to participate in the reaction, resulting in a decrease in the rate of the electrochemical reaction and thus affecting the production efficiency of aluminum. Therefore, the present invention reflects the uniformity of the temperature distribution in the electrolytic cell through the area of the low-temperature local area, and further reflects the significant degree of the fluidity difference of the electrolyte in different areas through the maximum temperature difference between the low-temperature local area and other local areas. Moreover, the uneven temperature distribution will cause uneven consumption of the anode, reducing the stability of the electrolysis process. Furthermore, the stability of the electrolysis process is characterized by the electrolysis stability characterization parameters, providing data support for subsequent classification of the electrolysis state categories of the electrolysis process, and enabling real-time detection of the aluminum electrolysis process while ensuring data reliability, thereby improving the detection efficiency.

[0042] In particular, the present invention provides a convection analysis module, which identifies the convection anomaly area by determining the convection characteristics of each area on the surface of the electrolytic cell when the electrolysis process is in a weakly stable state. During the aluminum electrolysis process, normal convection has certain regularity in temperature distribution and change, which can ensure the stability of the aluminum electrolysis process. When the original convection balance is disrupted, it will affect the stability, energy consumption, and progress of the electrolysis reaction of the aluminum electrolysis process. Therefore, this application characterizes the attenuation uniformity of the temperature in the convection area based on the gradient temperature difference along the temperature flow direction, and clarifies the maximum range of temperature change in the convection area through the maximum temperature to reflect the temperature attenuation amount and the dispersion degree of the temperature distribution in the convection area. Furthermore, it analyzes and identifies the convection anomaly area with local overheating or overcooling. The present invention enables real-time detection of the aluminum electrolysis process while ensuring data reliability, thereby improving the detection efficiency.

[0043] In particular, the present invention provides a depth detection module, which conducts in-depth analysis of the convection anomaly area, identifies the abnormal fluctuation stage, and analyzes the abnormal fluctuation stage to determine whether there is an abnormality in the electrolysis process. During the aluminum electrolysis process, the change in the electrolyte temperature will cause the volume of the electrolyte to expand and contract thermally, thereby causing the liquid level of the electrolytic cell to fluctuate irregularly. By specifically analyzing the temperature change amount and temperature change rate of the electrolytic cell in the above situation, an abnormal characterization value is determined to characterize the degree of abnormality of the electrolysis process, so as to give an early warning in a timely manner. The present invention enables real-time detection of the aluminum electrolysis process while ensuring data reliability, thereby improving the detection efficiency. Description of the Drawings

[0044] Figure 1 It is a functional module diagram of a cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data in an invention embodiment;

[0045] Figure 2 It is a logical decision diagram for classifying the electrolysis state categories in the electrolysis process in an invention embodiment;

[0046] Figure 3 It is a logical decision diagram for identifying the convection abnormal area based on the convection characteristics in an invention embodiment;

[0047] Figure 4 It is a logical decision diagram for determining whether to issue a warning signal in an invention embodiment. Detailed implementation manners

[0048] In order to make the purpose and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0049] The preferred implementation manners of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these implementation manners are only used to explain the technical principle of the present invention and do not limit the protection scope of the present invention.

[0050] It should be noted that in the description of the present invention, the terms indicating the direction or positional relationship such as "upper", "lower", "inner", etc. are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.

[0051] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0052] Please refer to Figure 1 As shown, it is a functional module diagram of a cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data in an embodiment of the present invention. The cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data in an embodiment of the present invention includes:

[0053] An acquisition and analysis module, which includes an infrared acquisition unit for acquiring image data of an electrolytic cell and a laser interference unit for acquiring fluctuation data of the liquid level of the electrolytic cell;

[0054] A feature recognition module, which is connected to the acquisition and analysis module, is used to recognize the low-temperature local area based on the image data and determine the electrolysis features of the low-temperature local area. The electrolysis features include the range area of the low-temperature local area and the maximum temperature difference between the low-temperature local area and other local areas;

[0055] A feature analysis module, which is connected to the feature recognition module, is used to analyze the electrolysis stability characterization parameters of the electrolysis process based on the electrolysis features in combination with the consumption of the anode, so as to classify the electrolysis state categories of the electrolysis process;

[0056] A convection analysis module, which is connected to the feature analysis module, responds to the classification result of the feature analysis module, is used to determine the convection features of each area on the surface of the electrolytic cell, and identify the convection abnormal area based on the convection features;

[0057] A depth detection module, which is connected to the convection analysis module, is used to obtain the fluctuation features of the convection abnormal area, identify the fluctuation abnormal stage, and determine the abnormal characterization value in combination with the temperature change features within the fluctuation abnormal stage, so as to determine whether to issue a warning signal;

[0058] Wherein, the convection features include the gradient temperature difference and the maximum temperature difference along the temperature flow direction, and the temperature change features include the temperature change amount and the temperature change rate.

[0059] Specifically, the specific structure of the infrared acquisition unit is not limited, as long as it has the function of acquiring the image data of the electrolytic cell. For example, an infrared thermal imager with anti-corrosion performance is used to determine the layout distance of the infrared thermal imager according to the field of view angle and the size of the electrolytic cell, so as to ensure that the entire electrolytic cell can be covered. The bracket installation position is determined according to the layout distance. Among them, the bracket should be a stable bracket that can flexibly adjust the position and angle. The infrared thermal imager is installed on the bracket to collect the data of the electrolytic cell. Of course, other methods can also be used, which will not be elaborated here.

[0060] Specifically, the specific structure of the laser interference unit is not limited, as long as it has the function of collecting the fluctuation data of the liquid level of the electrolytic cell. In implementation, a laser interferometer can be used. For example, a Michelson interferometer, a Mach-Zehnder interferometer, etc. The laser interferometer is placed on a stable optical platform to reduce the interference of external vibrations and other factors. Optical elements such as reflectors or beam splitters are installed at appropriate positions above the electrolytic cell to ensure that the reflected light can smoothly return to the laser interferometer and interfere with the reference light. Utilizing the coherence of the laser, the laser is vertically irradiated onto the electrolyte surface through the laser interferometer. Since the fluctuations on the surface of the electrolytic cell will cause changes in the phase of the reflected light, the changes in the interference fringes between the reflected light and the reference light are detected. In implementation, the amount of phase change is determined as the amount of liquid level fluctuation of the electrolytic cell, and the number of phase changes per unit time is determined as the fluctuation frequency, which will not be elaborated here.

[0061] It can be understood that the low-temperature local area refers to the local area in the electrolytic cell where the temperature is significantly lower than the normal temperature. In actual situations, the temperature of the electrolytic cell during the aluminum electrolysis process is usually close to 1000 °C, while the low-temperature local area is about 20 °C - 50 °C lower than the average temperature of each local area. Therefore, in implementation, by obtaining the temperatures of each local area and solving for the average temperature, the local area where the temperature is lower than a predetermined proportion of the average temperature is determined as the low-temperature local area, where the predetermined proportion is selected within the range of [2%, 5%].

[0062] Specifically, the specific structures of the feature recognition module, the feature analysis module, the convection analysis module, and the depth detection module are not limited. It itself or each unit therein can be composed of logic components or a combination of logic components. The logic components include field programmable processors, computers, or microprocessors in computers.

[0063] Specifically, the feature analysis module is used to analyze the electrolysis stability characterization parameters of the electrolysis process based on the electrolysis characteristics in combination with the consumption of the anode, including,

[0064] using the sum of the ratio of the area of the low-temperature local area to the area threshold and the ratio of the maximum temperature difference between the low-temperature local area and other local areas to the maximum temperature difference threshold as the first electrolysis stability feature;

[0065] using the ratio of the consumption of the anode to the consumption threshold as the second electrolysis stability feature;

[0066] using the weighted sum of the first electrolysis stability feature and the second electrolysis stability feature as the electrolysis stability characterization parameter.

[0067] In this implementation, when performing the weighted sum, the weight of the first electrolysis stability feature is set to 0.6, and the weight of the second electrolysis stability feature is set to 0.4;

[0068] In this embodiment, the purpose of setting the range area threshold of the low-temperature local area, the maximum temperature difference threshold between the low-temperature local area and other local areas, and the consumption threshold of the anode is to characterize the uneven temperature distribution and low stability of the electrolysis process during electrolysis. Among them, the range area threshold of the low-temperature local area and the consumption threshold of the anode are determined based on the average value of the range area of the low-temperature local area and the average value of the anode consumption, respectively;

[0069] By obtaining a number of historical relevant data of the completed aluminum electrolysis process, calling the historical data of the range area of the low-temperature local area and the historical data of the anode consumption, and solving the average value of the range area of the low-temperature local area and the average value of the anode consumption. Since the purpose of setting the above two thresholds is to characterize the uneven temperature distribution and low stability of the electrolysis process during electrolysis, therefore, the range area threshold of the low-temperature local area is determined between 1.05 times and 1.1 times of the average value of the range area of the low-temperature local area, and the consumption threshold of the anode is determined between 1.15 times and 1.2 times of the average value of the anode consumption. The maximum temperature difference threshold between the low-temperature local area and other local areas is selected within the interval [10°C, 20°C].

[0070] Specifically, the present invention sets a feature analysis module to analyze the electrolysis stability characterization parameters of the electrolysis process based on the electrolysis characteristics and the anode consumption. In actual situations, the temperature distribution in the electrolytic cell should be relatively uniform to maintain a stable electrolysis process, enabling alumina to fully contact the electrolyte and participate in the electrochemical reaction. If the temperature of a local area is too low, it will break the thermal balance, resulting in an uneven temperature field distribution in the electrolytic cell. At the same time, low temperature will cause the viscosity of the electrolyte to decrease, its fluidity to become poor, and the diffusion rate of alumina to slow down, making it difficult to quickly reach the electrode surface to participate in the reaction, leading to a decrease in the electrochemical reaction rate and thus affecting the production efficiency of aluminum. Therefore, the present invention uses the range area of the low-temperature local area to reflect the uniformity of the temperature distribution in the electrolytic cell. If the area of the low-temperature area is large, it indicates that the electrolytic cell has excessive heat dissipation or insufficient local heating, meaning that the overall thermal stability of the electrolytic cell is poor and the thermal balance may be disrupted. The maximum temperature difference between the low-temperature local area and other local areas further reflects the obviousness of the fluidity difference of the electrolyte in different areas. Moreover, the uneven temperature distribution will cause uneven consumption of the anode, reducing the stability of the electrolysis process. Furthermore, the electrolysis stability characterization parameters are used to characterize the stability degree of the electrolysis process, providing data support for subsequent classification of the electrolysis state categories of the electrolysis process, and performing real-time detection of the aluminum electrolysis process while ensuring data reliability to improve the detection efficiency.

[0071] Specifically, please refer to Figure 2As shown, it is a logic decision diagram for classifying the electrolysis state categories of the electrolysis process in an embodiment of the present invention. The feature analysis module is used to classify the electrolysis state categories of the electrolysis process, including,

[0072] If the electrolysis stability characterization parameter is greater than or equal to the electrolysis stability characterization parameter threshold, then the electrolysis process is classified into the weak stability state category;

[0073] If the electrolysis stability characterization parameter is less than the electrolysis stability characterization parameter threshold, then the electrolysis process is classified into the strong stability state category.

[0074] The electrolysis stability characterization parameter threshold is selected within the range [1.65, 1.84].

[0075] Specifically, the convection analysis module responds to the classification result of the feature analysis module, including,

[0076] If the electrolysis process is in the weak stability state category, then determine the convection characteristics of each region on the surface of the electrolytic cell, and identify the convection abnormal region based on the convection characteristics.

[0077] Specifically, the convection analysis module is used to determine the convection characteristics of each region on the surface of the electrolytic cell, including,

[0078] Used to determine the highest temperature point and the lowest temperature point of the electrolytic cell on the horizontal plane, and determine the temperature flow direction by pointing the highest temperature point to the direction corresponding to the lowest temperature point;

[0079] Used to divide several regions along the temperature flow direction, and obtain the temperature values in each of the regions;

[0080] Used to solve the temperature difference between each region and its adjacent regions, and determine the average temperature difference as the gradient temperature difference.

[0081] Specifically, there is no limitation on the method of dividing the regions along the temperature flow direction, which can be determined according to the length of the electrolytic cell. In some possible implementations,

[0082] For small electrolytic cells with a length of less than 10m, generally 3 - 5 regions can be divided. For example, for an electrolytic cell with a length of 8m, it can be divided into one region every about 2m, which is convenient for targeted monitoring and regulation of relevant parameters, and also convenient for operators to carry out daily inspections and maintenance;

[0083] For medium-sized electrolytic cells with a length of 10m - 20m, generally 5 - 8 regions can be divided. For example, for an electrolytic cell with a length of 15m, it can be divided into one region every 2m - 3m, which is convenient for grasping the conditions of different parts during the electrolysis process, such as the anode region, the cathode region, and different electrolyte flow regions, etc., and is beneficial to optimizing the electrolysis process, improving production efficiency and product quality

[0084] For large electrolytic cells with a length exceeding 20 m, the number of generally divided regions may be more than 8, and even reach 10 - 20. For example, for a large electrolytic cell with a length of 23 m, according to the process requirements and equipment structure, regions are divided at intervals of about 2 m to better manage and control the electrolysis production process, ensure the stable operation of the electrolytic cell, reduce energy consumption, and increase production capacity. This will not be elaborated here.

[0085] Specifically, please refer to Figure 3 As shown, it is a logical decision diagram for the convection analysis module of the present invention to identify convection abnormal regions based on convection characteristics. The convection analysis module is used to identify convection abnormal regions based on convection characteristics, including,

[0086] If any region does not meet the convection reference conditions, then the region is identified as a convection abnormal region;

[0087] Among them, the convection reference conditions include that the gradient temperature difference along the temperature flow direction is greater than the gradient temperature difference threshold and / or the maximum temperature difference is greater than the maximum temperature difference threshold.

[0088] It can be understood that since the temperature distribution inside the electrolyte is relatively uniform, but there is still a certain temperature gradient to maintain convection. This is because the heat transfer inside the electrolyte is mainly through the migration and convection of ions, without particularly intense heat sources or heat sinks, and the temperature change is relatively gentle. Therefore, the gradient temperature difference threshold along the temperature flow direction is selected within the range of [8 °C / m, 12 °C / m];

[0089] At the same time, the electrolyte contains various components with complex proportions, and the physical and chemical properties of each component are different, which will make the heat transfer and distribution complex. For example, when the alumina content in the electrolyte is low and the content of other additives is high, there will be significant differences in the electrolysis reactions and heat generation in different regions. Therefore, the maximum temperature difference threshold is selected within the range of [25 °C, 30 °C].

[0090] Specifically, the present invention sets up a convection analysis module. In the case where the electrolysis process is in a weakly stable state, the convection characteristics of each region on the surface of the electrolytic cell are determined to identify the convection abnormal regions. During the aluminum electrolysis process, normal convection has certain regularity in temperature distribution and change, which can ensure the stability of the aluminum electrolysis process. When the original convection balance is broken, for example, eddy currents or reverse flows will cause insufficient mixing of the electrolyte in different regions, resulting in compositional differences and affecting the properties of the electrolyte itself, thereby affecting the stability and energy consumption of the aluminum electrolysis process. There are low-temperature regions in the electrolytic cell, which increase the viscosity of the electrolyte and make the flow worse, resulting in blocked fluid flow and affecting the progress of the electrolysis reaction. Therefore, this application characterizes the uniform degree of temperature decay in the convection region based on the gradient temperature difference along the temperature flow direction, that is, the temperature uniformity of heat transfer in the electrolytic cell, and determines the maximum range of temperature change in the convection region through the maximum temperature to reflect the temperature decay amount and the discrete degree of temperature distribution in the convection region. Furthermore, the convection abnormal regions with local overheating or overcooling are analyzed and identified. The present invention performs real-time detection of the aluminum electrolysis process on the premise of ensuring data reliability, improving the detection efficiency.

[0091] Specifically, the fluctuation characteristics are determined by calling the fluctuation data collected by the laser interference unit, and the fluctuation characteristics include the fluctuation amount and the fluctuation frequency of the electrolytic cell liquid surface.

[0092] Specifically, the depth detection module is used to identify the abnormal fluctuation stage, including

[0093] If the abnormal fluctuation condition is satisfied at any moment, the electrolysis process corresponding to that moment is identified as the abnormal fluctuation stage;

[0094] Among them, the abnormal fluctuation condition includes that the fluctuation amount of the electrolytic cell liquid surface is greater than the fluctuation amount threshold or / and the fluctuation frequency is greater than the fluctuation frequency threshold.

[0095] In this embodiment, the purpose of setting the fluctuation amount threshold and the fluctuation frequency threshold of the electrolytic cell liquid surface is to characterize the abnormal fluctuation situation of the electrolytic cell liquid surface during the electrolysis process. Among them, the fluctuation amount threshold of the electrolytic cell liquid surface and the fluctuation frequency are determined based on the average value of the fluctuation amount of the electrolytic cell liquid surface and the average value of the fluctuation frequency respectively;

[0096] By obtaining a number of historical relevant data of the completed aluminum electrolysis process, calling the historical data of the fluctuation amount of the electrolytic cell liquid surface and the historical data of the fluctuation frequency, solving the average value of the change amount of the electrolytic cell liquid surface fluctuation and the average value of the fluctuation frequency, and based on the purpose of setting the fluctuation amount threshold and the fluctuation frequency threshold of the electrolytic cell liquid surface, the fluctuation threshold of the electrolytic cell liquid surface is determined to be between 1.02 times and 1.04 times the average value of the fluctuation amount of the electrolytic cell liquid surface, and the fluctuation frequency threshold is determined to be between 1.01 times and 1.02 times the average value of the fluctuation frequency.

[0097] Specifically, the depth detection module is used to determine an abnormal characterization value, including

[0098] using the sum of the ratio of the liquid level fluctuation amount of the electrolytic cell to the fluctuation amount threshold and the ratio of the fluctuation frequency to the fluctuation frequency threshold as the first abnormal feature;

[0099] using the sum of the ratio of the temperature change amount to the temperature change amount threshold and the ratio of the temperature change rate to the temperature change rate threshold as the second abnormal feature;

[0100] using the sum of the first abnormal feature and the second abnormal feature to determine the abnormal characterization value.

[0101] In this embodiment, the corresponding temperature change amount threshold and temperature change rate threshold are determined through the average value of the temperature change amount and the average value of the temperature change rate. A number of historical relevant data of the aluminum electrolysis process completion are obtained, and the historical data of the temperature change amount and the historical data of the temperature change rate are called to solve the average value of the temperature change amount and the average value of the temperature change rate. Since the purpose of setting the temperature change amount threshold and the temperature change rate threshold is to characterize the situation of low stability in the electrolysis process, therefore, the temperature change amount threshold is determined between 1.03 times and 1.05 times of the average value of the temperature change amount, and the temperature change rate threshold is determined between 1.01 times and 1.12 times of the average value of the temperature change rate.

[0102] It can be understood that the temperature change amount refers to the increase amount or decrease amount of the temperature per unit time, and the temperature change rate refers to the increase rate or decrease rate of the temperature per unit time. Therefore, the temperature change amount and the temperature change rate are absolute positive values, which will not be elaborated here.

[0103] Specifically, please refer to Figure 4 as shown, which is the logic decision diagram for determining whether to send a warning signal in the embodiment of the present invention. The depth detection module is used to determine whether to send a warning signal, including

[0104] if the abnormal characterization value is greater than or equal to the preset abnormal characterization threshold, it is determined to send a warning signal;

[0105] if the abnormal characterization value is less than the preset abnormal characterization threshold, it is determined that there is no need to send a warning signal.

[0106] The preset abnormal characterization threshold is selected within the interval [4.25, 4.37].

[0107] Specifically, the present invention provides a depth detection module to perform depth analysis on the convection anomaly region, identify the abnormal fluctuation stage, and analyze the abnormal fluctuation stage to determine whether there is an abnormality in the electrolysis process. During the aluminum electrolysis process, the change in the electrolyte temperature will cause the volume of the electrolyte to expand and contract thermally, thereby causing irregular fluctuations in the liquid level of the electrolytic cell. By specifically analyzing the temperature change characteristics of the electrolytic cell in the above situation, including the temperature change amount and the temperature change rate, where,

[0108] In practice, the deviation degree of the temperature change amount and the temperature change rate is characterized by the second abnormal feature.

[0109] When the deviation degree of the temperature change amount is relatively stable and the deviation degree of the temperature change rate is relatively stable, it indicates that the electrolysis reaction is proceeding relatively smoothly, the oxidation-reduction reactions of the anode and cathode are in a normal state, and the aluminum production rate is relatively stable.

[0110] Correspondingly, when the deviation of the temperature change amount is too high and the deviation of the temperature change rate is too fast, regardless of whether it is heating or cooling, when the temperature change amount in the convection region is large, it will cause uneven changes in the conductivity of the electrolyte, resulting in uneven current distribution. In the region with a higher temperature and better conductivity, the current density will be relatively large; while in the region with a lower temperature and poorer conductivity, the current density will be smaller. This uneven current distribution will make the electrolysis reaction rates in different parts of the electrolytic cell inconsistent, affecting the quality and output of aluminum, and may also cause local overheating or overcooling, accelerating the damage of the equipment. At the same time, it will cause the conductivity of the electrolyte to change rapidly, further exacerbating the unevenness of the current distribution. Moreover, the rapid temperature change may also cause fluctuations in the electromagnetic force, because the current interacts with the magnetic field, and the change in the current distribution will cause a change in the electromagnetic force, thereby affecting the flow state of the aluminum liquid and the electrolyte, and further interfering with the stability of the electrolysis process.

[0111] An abnormal characterization value is determined through the above two representative features to characterize the abnormal degree of the electrolysis process for timely warning. The present invention performs real-time detection of the aluminum electrolysis process on the premise of ensuring data reliability, improving the detection efficiency.

[0112] So far, the technical solution of the present invention has been described in combination with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. A cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data, characterized in that: include: An acquisition and analysis module, which includes an infrared acquisition unit for acquiring image data of the electrolytic cell and a laser interference unit for acquiring fluctuation data of the liquid level of the electrolytic cell; a feature recognition module connected to the acquisition and analysis module, for identifying a low-temperature local area based on the image data, and determining an electrolytic feature of the low-temperature local area, wherein the electrolytic feature includes an area of ​​the low-temperature local area and a maximum temperature difference between the low-temperature local area and other local areas; A feature analysis module connected to the feature recognition module is used to analyze the electrolysis stability characterization parameters of the electrolysis process according to the electrolysis characteristics combined with the anode consumption, so as to classify the electrolysis state category of the electrolysis process; A convection analysis module, which is connected to the feature analysis module and is used to determine the convection characteristics of each area on the surface of the electrolytic cell in response to the division result of the feature analysis module, and to identify the convection abnormal area according to the convection characteristics; A depth detection module, which is connected to the convection analysis module, is used to obtain the fluctuation characteristics of the convection abnormal area, identify the fluctuation abnormal stage, and determine the abnormal characterization value in combination with the temperature change characteristics in the fluctuation abnormal stage to determine whether to issue an early warning signal; Wherein, the convection characteristics include the gradient temperature difference and the maximum temperature difference along the temperature flow direction, and the temperature change characteristics include the temperature change amount and the temperature change rate; The convection analysis module is used to determine the convection characteristics of each area on the surface of the electrolytic cell, including: Used to determine the highest temperature point and the lowest temperature point of the electrolytic cell on a horizontal plane, and determine the direction corresponding to the highest temperature point pointing to the lowest temperature point as the temperature flow direction; Dividing a plurality of regions along the temperature flow direction to obtain the temperature value in each of the regions; It is used to solve the temperature difference between each area and the adjacent area, and the average temperature difference is determined as the gradient temperature difference.

2. The cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data according to claim 1 is characterized in that: The characteristic analysis module is used to analyze the electrolysis stability characterization parameters of the electrolysis process according to the electrolysis characteristics combined with the anode consumption, including: The sum of the ratio of the range area of ​​the low temperature local area to the range area threshold and the ratio of the maximum temperature difference between the low temperature local area and other local areas to the maximum temperature difference threshold is used as the first electrolysis stability feature; for taking the ratio of the anode consumption to the consumption threshold as a second electrolysis stability characteristic; The first electrolytic stability characteristic and the second electrolytic stability characteristic are weightedly summed to obtain the electrolytic stability characterization parameter.

3. The cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data according to claim 1 is characterized in that: The feature analysis module is used to classify the electrolysis state categories of the electrolysis process, including: If the electrolysis stability characterization parameter is greater than or equal to the electrolysis stability characterization parameter threshold, the electrolysis process is classified as a weak stable state category.

4. The cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data according to claim 1 is characterized in that: The convection analysis module responds to the division result of the feature analysis module, including: If the electrolysis process is of the weakly stable state category, the convection characteristics of each area on the surface of the electrolytic cell are determined, and the convection abnormality area is identified based on the convection characteristics.

5. The cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data according to claim 1 is characterized in that: The convection analysis module is used to identify the convection abnormality area according to the convection characteristics, including: If there is any area that does not meet the convection benchmark condition, the area is identified as a convection anomaly area; The convection reference condition includes that the gradient temperature difference along the temperature flow direction is greater than the gradient temperature difference threshold and / or the maximum temperature difference is greater than the maximum temperature difference threshold.

6. The cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data according to claim 1 is characterized in that: The fluctuation characteristics are determined by calling the fluctuation data collected by the laser interference unit, and the fluctuation characteristics include the fluctuation amount and the fluctuation frequency of the liquid level of the electrolytic cell.

7. The cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data according to claim 6 is characterized in that: The depth detection module is used to identify the abnormal fluctuation stage. include, If the fluctuation abnormality condition is met at any time, the electrolysis process corresponding to the time is identified as the fluctuation abnormality stage; Among them, the abnormal fluctuation conditions include that the fluctuation amount of the electrolytic cell liquid level is greater than the fluctuation amount threshold and / or the fluctuation frequency is greater than the fluctuation frequency threshold.

8. The cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data according to claim 1 is characterized in that: The depth detection module is used to determine the abnormal characterization value, including: The sum of the ratio of the electrolytic tank liquid level fluctuation amount to the fluctuation amount threshold and the ratio of the fluctuation frequency to the fluctuation frequency threshold is used as the first abnormal feature; The sum of the ratio of the temperature change amount to the temperature change amount threshold and the ratio of the temperature change rate to the temperature change rate threshold is used as the second abnormal feature; Used to determine the sum of the first abnormal feature and the second abnormal feature as the abnormal characterization value.

9. The cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data according to claim 1 is characterized in that: The depth detection module is used to determine whether to issue a warning signal, including: If the abnormality characteristic value is greater than or equal to the preset abnormality characteristic threshold, it is determined that an early warning signal is issued.

Images