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

Abstract

The invention relates to the field of data analysis, in particular to a cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data. Identifying a low-temperature local area according to the image data, and determining electrolysis characteristics of the low-temperature local area; analyzing electrolysis stability characterization parameters of the electrolysis process according to the electrolysis characteristics in combination with the consumption of the anode so as to divide electrolysis state categories of the electrolysis process; responding to the classification result of the electrolysis state types to determine convection characteristics of all areas on the surface of the electrolytic cell, and identifying convection abnormal areas according to the convection characteristics; according to the method, the fluctuation characteristic of the abnormal convection area is obtained, the abnormal fluctuation stage is recognized, the abnormal characterization value is determined by combining the temperature change characteristic in the abnormal fluctuation stage, and whether an early warning signal is sent out or not is judged, the aluminum electrolysis process can be detected in real time on the premise that the data reliability is guaranteed, and the detection efficiency is improved.

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 aluminum electrolysis enterprises' transformation towards digitalization and intelligence, the 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, so as to realize the digital management and optimization of the production process and improve the competitiveness of enterprises; The operation and maintenance of traditional aluminum electrolysis production equipment mainly rely on manual inspections and regular maintenance. This method has many disadvantages. Manual inspections are subjective and untimely, and it is easy to miss some potential hidden dangers of failure; regular maintenance is often based on fixed time intervals or running mileage, which may lead to excessive or insufficient 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 using the powerful computing and storage capabilities of the cloud platform to perform data analysis and processing can not only reduce the company's information construction costs, but also improve the efficiency and accuracy of detection.

[0003] China's patent application publication number: CN115034306A, discloses a method for predicting aluminum electrolysis faults and safe operation and maintenance based on extension neural networks. First, factor analysis and statistical methods are used to analyze the influencing factors of anode effect, cathode damage, anode blisters, cold and hot tanks, and aluminum rolling; then a system identification method is used to propose a modeling and diagnosis method for aluminum electrolysis faults based on the model; a multi-level diagnosis method that integrates model diagnosis and intelligent diagnosis is used to diagnose and predict specific types of faults; the test information of aluminum electrolytic cells is constructed to establish an aluminum electrolytic cell performance evaluation model, and on this basis, the optimal test time of aluminum electrolytic cells is determined. The method based on analytical models and data-driven is used to study the abnormal working conditions or fault prediction and safe operation and maintenance theories and methods of complex production processes, to ensure the smooth and safe production of the entire electrolysis and extend the life of aluminum electrolytic cells.

[0004] However, there are still the following problems in the prior art: When detecting abnormalities in the aluminum electrolysis process, people often rely on the experience of relevant operators, which is highly subjective and has low accuracy. In addition, there is a lag in the discovery of potential abnormalities, and the fault cannot be handled in a timely manner. Summary of the invention

[0005] To this end, the present invention provides a cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data, so as to overcome the problems in the prior art that, when performing abnormality detection on the aluminum electrolysis process, the system often relies on the experience of relevant operators, which is highly subjective and has low accuracy. In addition, there is a lag in the discovery of potential abnormalities, and the fault cannot be handled in time.

[0006] 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: 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; 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.

[0007] Furthermore, 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.

[0008] Furthermore, 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.

[0009] Furthermore, 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.

[0010] Furthermore, 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.

[0011] Furthermore, 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.

[0012] Furthermore, 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 level.

[0013] Furthermore, the depth detection module is used to identify the abnormal fluctuation stage, including: 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.

[0014] Furthermore, 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.

[0015] Furthermore, 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.

[0016] Compared with the prior art, the present invention sets up 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 liquid surface of the electrolytic cell; a feature recognition module for identifying a low-temperature local area based on 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 combined with the consumption of the anode to classify the electrolysis state 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 fluctuation abnormal stage, and determining 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. The present invention can perform real-time detection of the aluminum electrolysis process on the premise of ensuring data reliability and improve detection efficiency.

[0017] In particular, the present invention sets a feature analysis module to analyze the electrolytic stability characterization parameters of the electrolytic process according to the electrolytic characteristics combined with the consumption of the anode. In actual situations, the temperature distribution in the electrolytic cell should be relatively uniform to maintain a stable electrolytic process so that the aluminum oxide is in full contact with the electrolyte and participates in the electrochemical reaction. If the temperature in a local area is low, the thermal equilibrium will be broken, so that the temperature field in the electrolytic cell is unevenly distributed. At the same time, low temperature will cause the viscosity of the electrolyte to decrease, the fluidity to deteriorate, and the diffusion rate of aluminum oxide to slow down, making it difficult to quickly reach the electrode surface to participate in the reaction, resulting in a decrease in the electrochemical reaction rate, thereby affecting the production efficiency of aluminum. Therefore, the present invention reflects the uniformity of the temperature distribution in the electrolytic cell through the range area of ​​the low-temperature local area, and further reflects the obvious degree of difference in the fluidity of the electrolyte in different areas through the maximum temperature difference between the low-temperature local area and other local areas. In addition, the uneven temperature distribution will lead to uneven consumption of the anode, reducing the stability of the electrolytic process. Then, the stability of the electrolytic process is characterized by the electrolytic stability characterization parameters, providing data support for the subsequent classification of the electrolytic state categories of the electrolytic process. On the premise of ensuring data reliability, the aluminum electrolysis process is detected in real time to improve the detection efficiency.

[0018] In particular, the present invention sets a convection analysis module. When the electrolysis process is in a weakly stable state, the convection characteristics of each area on the surface of the electrolytic cell are determined to identify the convection abnormal area. In the aluminum electrolysis process, normal convection has a certain regularity in temperature distribution and change, which can ensure the stability of the aluminum electrolysis process. When the original convection balance is broken, it will affect the stability of the aluminum electrolysis process, energy consumption and the progress of the electrolysis reaction. Therefore, the present application characterizes the uniformity of the attenuation of the temperature in the convection area according to 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 of the convection area and the discrete degree of temperature distribution. Then, the abnormal convection area with local overheating or overcooling is analyzed and identified. Under the premise of ensuring data reliability, the present invention performs real-time detection of the aluminum electrolysis process and improves detection efficiency.

[0019] In particular, the present invention sets up a deep detection module to perform in-depth analysis of the convection abnormality area, identify the fluctuation abnormality stage, and analyze the fluctuation abnormality stage to determine whether there is any abnormality in the electrolysis process. During the aluminum electrolysis process, the electrolyte temperature changes, which will cause the volume of the electrolyte to expand and contract, thereby causing irregular fluctuations in the liquid level of the electrolytic cell. By specifically analyzing the temperature change and the temperature change rate of the electrolytic cell under the above circumstances, the abnormal characterization value is determined to characterize the abnormal degree of the electrolysis process, so as to provide a timely warning. Under the premise of ensuring data reliability, the present invention performs real-time detection of the aluminum electrolysis process and improves detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a functional module diagram of a cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data according to an embodiment of the invention; Figure 2 A logic decision diagram for classifying the electrolysis state categories of the electrolysis process according to the embodiment of the invention; Figure 3 A logical decision diagram for identifying an abnormal convection area according to convection characteristics according to an embodiment of the invention; Figure 4 This is a logic decision diagram for determining whether to issue a warning signal according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the objects and advantages of the present invention more clearly understood, the present invention is 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.

[0022] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0023] It should be noted that in the description of the present invention, terms such as "upper", "lower", "inner", etc. indicating directions or positional relationships are based on the directions or positional relationships 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. Therefore, it cannot be understood as a limitation on the present invention.

[0024] 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 circumstances.

[0025] See also 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 according to an embodiment of the present invention. The cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data according to an embodiment of the present invention includes: 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; 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.

[0026] Specifically, there is no limitation on the specific structure of the infrared acquisition unit, which only needs to have the function of collecting image data of the electrolytic cell. For example, an infrared thermal imager with anti-corrosion performance can be used. The layout distance of the infrared thermal imager is determined according to the field angle and the size of the electrolytic cell to ensure that the entire electrolytic cell body can be covered. The installation position of the bracket is determined according to the layout distance. The bracket should be a strong bracket that can flexibly adjust the position and angle. The infrared thermal imager is installed on the bracket to collect the electrolytic cell data. Of course, other methods can also be used, which will not be repeated here.

[0027] Specifically, there is no limitation on the specific structure of the laser interference unit, which only needs to have the function of collecting the fluctuation data of the electrolytic cell liquid level. In implementation, a laser interferometer, such as a Michelson interferometer, a Mach-Zehnder interferometer, etc., can be used. The laser interferometer is placed on a stable optical platform to reduce interference from external vibrations and other factors. Optical elements such as reflectors or spectrometers 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. The laser coherence is used to irradiate the laser vertically onto the electrolyte surface through the laser interferometer. Since the fluctuation of the electrolytic cell surface will cause the phase of the reflected light to change, the interference fringes between the reflected light and the reference light are detected. In implementation, the phase change amount is determined as the electrolytic cell liquid level fluctuation amount, and the number of phase changes per unit time is determined as the fluctuation frequency. This will not be repeated.

[0028] It can be understood that the low-temperature local area refers to a local area in the electrolytic cell where the temperature is significantly lower than the normal temperature. In actual practice, the temperature of the electrolytic cell during aluminum electrolysis is usually close to 1000°C, and 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 temperature of each local area and solving the average temperature, the local area whose temperature is lower than the average temperature by a predetermined proportion is determined as the low-temperature local area, wherein the predetermined proportion is selected within the interval [2%, 5%].

[0029] Specifically, there is no limitation on the specific structures of the feature recognition module, feature parsing module, convection analysis module and depth detection module, and they themselves or each unit therein can be composed of logic components or a combination of logic components, and the logic components include a field programmable processor, a computer or a microprocessor in a computer.

[0030] Specifically, 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.

[0031] In this implementation, when performing weighted summation, the weight of the first electrolytic stability feature is set to 0.6, and the weight of the second electrolytic stability feature is set to 0.4; 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 anode consumption threshold is to characterize the situation of uneven temperature distribution and low stability of the electrolysis process, wherein the range area threshold of the low temperature local area and the anode consumption threshold are determined according to the range area mean of the low temperature local area and the anode consumption mean, respectively; By acquiring relevant historical data of several completed aluminum electrolysis processes, calling the historical data of the range area of ​​the low-temperature local area and the historical data of the anode consumption, the mean range area of ​​the low-temperature local area and the mean anode consumption are solved. Since the purpose of setting the above two thresholds is to characterize the uneven temperature distribution and low stability of the electrolysis process, the range area threshold of the low-temperature local area is determined between 1.05 and 1.1 times the mean range area of ​​the low-temperature local area, the anode consumption threshold is determined between 1.15 and 1.2 times the mean anode consumption, and the maximum temperature difference threshold between the low-temperature local area and other local areas is selected in the interval [10°C, 20°C].

[0032] Specifically, the present invention sets a feature analysis module to analyze the electrolytic stability characterization parameters of the electrolytic process according to the electrolytic characteristics combined with the consumption of the anode. In actual situations, the temperature distribution in the electrolytic cell should be relatively uniform to maintain a stable electrolytic process, so that the aluminum oxide is in full contact with the electrolyte and participates in the electrochemical reaction. If the temperature in a local area is low, the thermal equilibrium will be disrupted, causing the temperature field in the electrolytic cell to be unevenly distributed. At the same time, low temperature will cause the viscosity of the electrolyte to decrease, the fluidity to deteriorate, the diffusion rate of aluminum oxide to slow down, and it is difficult to quickly reach the electrode surface to participate in the reaction, resulting in a decrease in the electrochemical reaction rate, thereby affecting the production efficiency of aluminum. Therefore, the present invention uses the range area of ​​the low temperature local area to analyze the electrolytic stability of the aluminum oxide. It reflects the uniformity of temperature distribution in the electrolytic cell. If the low-temperature area is large, it means that the electrolytic cell has too fast heat dissipation or insufficient local heating, which means that the overall thermal stability of the electrolytic cell is poor and the thermal balance may be destroyed. The maximum temperature difference between the low-temperature local area and other local areas further reflects the obvious difference in the fluidity of the electrolyte in different areas. In addition, the uneven temperature distribution will lead to uneven consumption of the anode and reduce the stability of the electrolysis process. Then, the stability of the electrolysis process is characterized by the electrolysis stability characterization parameters, which provides data support for the subsequent classification of the electrolysis state category of the electrolysis process. On the premise of ensuring data reliability, the aluminum electrolysis process can be detected in real time to improve the detection efficiency.

[0033] Specifically, see Figure 2 As shown, it is a logical decision diagram for classifying the electrolysis state categories of the electrolysis process according to an embodiment of the present invention. 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; If the electrolysis stability characterization parameter is less than the electrolysis stability characterization parameter threshold, the electrolysis process is classified as a strong stable state category.

[0034] The threshold value of the electrolytic stability characterization parameter is selected in the interval [1.65,1.84].

[0035] Specifically, 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.

[0036] Specifically, 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.

[0037] 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, For small electrolytic cells with a length of less than 10m, they can generally be divided into 3-5 areas. For example, an electrolytic cell with a length of 8m can be divided into an area every 2m or so, which is convenient for targeted monitoring and regulation of relevant parameters and for operators to carry out daily inspection and maintenance. For medium-sized electrolytic cells with a length of 10m-20m, they can generally be divided into 5-8 areas. For example, for an electrolytic cell with a length of 15m, one area can be divided every 2m-3m, which is convenient for understanding the conditions of different parts during the electrolysis process, such as the anode area, cathode area, and different electrolyte flow areas, etc., which is conducive to optimizing the electrolysis process and improving production efficiency and product quality. For large electrolytic cells with a length of more than 20m, the number of divided areas may generally be more than 8, or even reach 10-20. For example, for a large electrolytic cell with a length of 23m, the areas are divided at intervals of about 2m according to process requirements and equipment structure, so as to better manage and control the electrolytic production process, ensure the stable operation of the electrolytic cell, reduce energy consumption, and increase production capacity. This will not be repeated here.

[0038] Specifically, see Figure 3 As shown, it is a logical decision diagram for identifying a convection abnormality area according to convection characteristics according to an embodiment of the present invention. The convection analysis module is used to identify a convection abnormality area according to 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.

[0039] It can be understood that although the temperature distribution inside the electrolyte is relatively uniform, there is still a certain temperature gradient to maintain convection. This is because the heat inside the electrolyte is mainly transferred by ion migration and convection, and there is no particularly drastic heat source or heat sink. The temperature change is relatively gentle. Therefore, the gradient temperature difference threshold along the temperature flow direction is selected in the range of [8℃ / m, 12℃ / m]; At the same time, the electrolyte contains multiple components in complex proportions. The different physical and chemical properties of each component will complicate heat transfer and distribution. For example, when the aluminum oxide content in the electrolyte is low and the content of other additives is high, the electrolysis reaction and heat generation in different areas will be quite different. Therefore, the maximum temperature difference threshold is selected in the range of [25°C, 30°C].

[0040] Specifically, the present invention sets a convection analysis module. When the electrolysis process is in a weakly stable state, the convection characteristics of each area on the surface of the electrolytic cell are determined to identify the convection abnormal area. In the aluminum electrolysis process, normal convection has a 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 current or reverse flow will lead to insufficient mixing of electrolytes in different areas, resulting in composition differences, and at the same time affect the properties of the electrolyte itself, thereby affecting the stability and energy consumption of the aluminum electrolysis process; the existence of a low temperature area in the electrolytic cell makes the electrolysis The viscosity of the fluid increases and the flow becomes worse, which hinders the flow of the fluid and affects the progress of the electrolysis reaction. Therefore, the present application characterizes the uniformity of the temperature attenuation in the convection area according to the gradient temperature difference along the temperature flow direction, that is, the temperature uniformity of the heat transfer in the electrolytic cell, and clarifies the maximum range of temperature change in the convection area through the maximum temperature to reflect the temperature attenuation in the convection area and the discrete degree of temperature distribution, and then analyzes and identifies the abnormal convection areas with local overheating or overcooling. Under the premise of ensuring data reliability, the present invention performs real-time detection of the aluminum electrolysis process and improves detection efficiency.

[0041] 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 level.

[0042] Specifically, the depth detection module is used to identify the abnormal fluctuation stage, including: 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.

[0043] In this embodiment, the purpose of setting the electrolytic tank liquid level fluctuation amount threshold and the fluctuation frequency threshold is to characterize the abnormal fluctuation of the electrolytic tank liquid level during the electrolysis process, wherein the electrolytic tank liquid level fluctuation amount threshold and the fluctuation frequency are determined based on the electrolytic tank liquid level fluctuation amount mean and the fluctuation frequency mean, respectively; By acquiring historical relevant data of several completed aluminum electrolysis processes, calling the historical data of the electrolytic cell liquid level fluctuation amount and the historical data of the fluctuation frequency, solving the average value of the electrolytic cell liquid level fluctuation change amount and the average value of the fluctuation frequency, based on the purpose of setting the electrolytic cell liquid level fluctuation amount threshold and the fluctuation frequency threshold, the electrolytic cell liquid level fluctuation threshold is determined between 1.02 times and 1.04 times the average value of the electrolytic cell liquid level fluctuation amount, and the fluctuation frequency threshold is determined between 1.01 times and 1.02 times the average value of the fluctuation frequency.

[0044] Specifically, 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.

[0045] In this embodiment, the corresponding temperature change threshold and temperature change rate threshold are determined by the mean temperature change value and the mean temperature change rate, and historical relevant data of several completed aluminum electrolysis processes are obtained. The historical data of temperature change value and the historical data of temperature change rate are called to solve the mean temperature change value and the mean temperature change rate. Since the purpose of setting the temperature change threshold and the temperature change rate threshold is to characterize the low stability of the electrolysis process, the temperature change threshold is determined between 1.03 times and 1.05 times the mean temperature change value, and the temperature change rate threshold and the mean temperature change rate are determined between 1.01 times and 1.12 times.

[0046] It can be understood that the temperature change refers to the amount of temperature increase or the amount of temperature decrease per unit time, and the temperature change rate refers to the rate of temperature increase or the rate of temperature decrease per unit time. Therefore, the temperature change and the temperature change rate are absolute positive values, which will not be repeated here.

[0047] Specifically, see Figure 4 As shown, it is a logic determination diagram for determining whether to issue a warning signal according to an embodiment of the present invention. 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; If the abnormality characteristic value is less than the preset abnormality characteristic threshold, it is determined that there is no need to issue a warning signal.

[0048] The preset anomaly characterization threshold is selected in the interval [4.25,4.37].

[0049] Specifically, the present invention sets a depth detection module to perform a depth analysis on the abnormal convection area, 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 electrolyte temperature changes, which will cause the volume of the electrolyte to expand and contract, thereby causing irregular fluctuations in the liquid level of the electrolytic cell. By specifically analyzing the temperature change characteristics of the electrolytic cell under the above conditions, including the temperature change amount and the temperature change rate, In implementation, the second abnormal feature is used to characterize the deviation of the temperature change amount and the temperature change rate. When the deviation of the temperature change is relatively stable and the deviation of the temperature change rate is relatively stable, it means that the electrolysis reaction is proceeding relatively smoothly, the redox reaction of the anode and cathode is in a normal state, and the generation rate of aluminum is relatively stable; Correspondingly, when the temperature change amount deviates too high, the temperature change rate deviates too fast, whether it is heating up or cooling down, when the temperature change amount in the convection area is large, it will cause uneven changes in the conductivity of the electrolyte, resulting in uneven current distribution. In areas with higher temperatures and better conductivity, the current density will be relatively large; while in areas with lower temperatures and poorer conductivity, the current density will be smaller. This uneven current distribution will cause inconsistent electrolytic reaction rates in various parts of the electrolytic cell, affecting the quality and output of aluminum, and may also cause local overheating or overcooling, accelerating equipment damage. At the same time, it will cause the conductivity of the electrolyte to change rapidly, further exacerbating the unevenness of the current distribution. Moreover, rapid temperature changes may also cause fluctuations in electromagnetic force. Because the current interacts with the magnetic field, changes in current distribution will cause changes in electromagnetic force, thereby affecting the flow state of aluminum liquid and electrolyte, further interfering with the stability of the electrolysis process.

[0050] By using the above two representative features, an abnormality representative value is determined to represent the abnormality degree of the electrolysis process, so as to give a timely warning. Under the premise of ensuring data reliability, the present invention performs real-time detection of the aluminum electrolysis process and improves detection efficiency.

[0051] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying 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 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; 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.

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 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.

6. The cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data according to claim 5 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.

7. 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.

8. The cross-platform production equipment operation and maintenance system based on aluminum electrolysis production data according to claim 7 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.

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 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.

10. 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.

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