Aluminum electrolysis anode effect prediction method, device, equipment, medium and product
By dividing the aluminum electrolytic cell into multiple regions and using linear fitting method to predict changes in regional current and resistance, the problem of difficulty in predicting the anode effect in a timely manner is solved, and early warning and treatment of the anode effect is achieved, power loss and emissions are reduced, and the stability and efficiency of the electrolytic cell are improved.
Patent Information
- Application Number
- CN202510065356.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-15
- Publication Date
- 2025-03-14
AI Technical Summary
During aluminum electrolysis, the anode effect leads to arc formation, increased electrical energy loss, reduced current efficiency, and accompanied by perfluorocarbon emissions, causing production and environmental damage. The prior art is difficult to predict the occurrence of anode effect in a timely manner, resulting in hysteresis treatment.
By dividing the aluminum electrolytic cell into multiple regions according to the number of feeding ports, each region includes at least one anode, circulating through each region, obtaining linear changes in the region current and resistance, using a linear fitting method to predict whether anode effect will occur in the future, and sending out early warning information in a timely manner.
In advance prediction of the anode effect is achieved, the power loss and perfluorocarbon emissions are reduced, and the stability and current efficiency of the electrolytic cell are improved.
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Figure CN119615291A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of electrolysis technology, and in particular to a method, device, equipment, medium and product for predicting anode effect of aluminum electrolysis. Background Art
[0002] In the industrial production process of producing metallic aluminum using the cryolite-alumina molten salt electrolysis method, the anode effect is a special phenomenon that commonly occurs in this process. Once it occurs, many small arcs will appear on the anode, the cell voltage will rise sharply, the power loss will increase, the current efficiency will decrease, and perfluorocarbon strong greenhouse gases will be emitted, causing serious damage to normal production and the environment.
[0003] The occurrence of the anode effect is related to the aluminum oxide concentration and current density in the electrolyte. The aluminum oxide concentration in various places in the electrolytic cell and the current density of each anode carbon block are different. Therefore, the anode effect first occurs in the local part of the anode or on the individual anode carbon blocks with higher anode current density, which is called the local anode effect. At this time, the anode bubble resistance increases, which, on the one hand, causes the current in the local area to decrease first, and on the other hand, it also causes the current density of other anode carbon blocks to increase, thereby inducing the appearance of the anode effect of the anode carbon blocks with higher current density, until the anode effect occurs in the entire cell. Therefore, the current change in the local area can more finely show which area first experiences the local anode effect, and the appearance of the local anode effect and the anode effect of individual anode carbon blocks is the prelude to the appearance of the anode effect of the entire cell.
[0004] In the related art, the judgment of anode effect mainly adopts the method of tracking cell voltage. When the cell voltage is detected to increase to greater than or equal to 8V several times within a certain period of time, it is determined that the anode effect of the entire cell has occurred and the process of switching to the effect extinction is started. This method can only judge the anode effect after it has occurred in the entire cell for several seconds. At this time, it has already caused the emission of perfluorocarbon greenhouse gases, the loss of electricity and the reduction of current efficiency, and there is a significant hysteresis problem. Summary of the invention
[0005] The purpose of this application is to provide a method, device, equipment, medium and product for predicting the anode effect of aluminum electrolysis, which can provide timely anode effect prompts, reduce power losses and perfluorocarbon strong greenhouse gas emissions.
[0006] To achieve the above objectives, this application provides the following solutions:
[0007] In a first aspect, the present application provides a method for predicting an aluminum electrolysis anode effect, comprising:
[0008] Dividing the target aluminum electrolysis cell into a plurality of regions according to the number of feeding ports, each region including at least one anode;
[0009] Each area is traversed in turn in a loop manner to obtain the linear change of the regional current of the current area traversed. If it is predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional current, the loop is stopped and an anode effect warning message is issued. If it is not predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional current, the linear change of the regional resistance of the current area traversed is obtained. If it is predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional resistance, the loop is stopped and an anode effect warning message is issued. If it is not predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional resistance, the next area is traversed until the traversal of each area is completed.
[0010] Optionally, each area is traversed in turn in a loop, and a linear change of regional current in the current area traversed is obtained. If it is predicted that an anode effect will occur within a first set time period in the future based on the linear change of regional current, the loop is stopped and an anode effect warning message is issued. If it is not predicted that an anode effect will occur within the first set time period in the future, a linear change of regional resistance in the current area traversed is obtained. If it is predicted that an anode effect will occur within the first set time period in the future based on the linear change of regional resistance, the loop is stopped and an anode effect warning message is issued. If it is not predicted that an anode effect will occur within the first set time period in the future, the next area is traversed until the traversal of each area is completed, specifically including:
[0011] Collecting multiple regional currents and cell voltages in the current region within a second set time period to obtain a regional current time series; the cell voltage is the cell voltage of the target aluminum electrolytic cell;
[0012] Using a linear fitting method to fit the regional current time series, a regional current linear model of the current region is obtained;
[0013] Determine whether the slope of the regional current linear model of the current region is less than or equal to a first set value, and obtain a first determination result;
[0014] If the first judgment result is yes, it is determined that the anode effect will occur within the first set time period in the future, the cycle is stopped and an anode effect warning message is issued; the anode effect warning message includes the number of the current area;
[0015] If the first judgment result is determined to be no, then a regional resistance time series consisting of multiple regional resistances in the current region within the second set time period is determined, and each regional resistance is calculated and determined based on the regional current and cell voltage collected at the same time within the second set time period; the cell voltage is the cell voltage of the target aluminum electrolytic cell;
[0016] Using a linear fitting method to fit the regional resistance time series, a regional resistance linear model of the current region is obtained;
[0017] Determine whether the absolute value of the slope of the regional resistance linear model of the current region is greater than or equal to a second set value, and obtain a second determination result;
[0018] If the second judgment result is yes, it is determined that an anode effect will occur within the first set time period in the future, the circulation is stopped and an anode effect warning message is issued;
[0019] If the second judgment result is determined to be no, it is determined whether the traversal of each area is completed. If the traversal is not completed, the next area is updated as the current area, and the step of collecting multiple regional currents of the current area within the second set time period to obtain the regional current time series is returned until the traversal is completed;
[0020] If, after the traversal is completed, the absolute value of the slope of the regional resistance linear model of each region is smaller than the second set value, it is determined that the anode effect will not occur in the future first set time period.
[0021] Optionally, the calculation formula of the regional resistance is: R j =(VV ext ) / I j ;
[0022] Among them, R j represents the region resistance of the jth region, V represents the cell voltage, V ext is a constant, I j represents the regional current of the jth region.
[0023] Optionally, the first set time period in the future ranges from 1 to 300 seconds, and the second set time period ranges from 30 to 120 seconds.
[0024] Optionally, the first setting value is -0.03, and the second setting value is 0.01.
[0025] Optionally, the target aluminum electrolysis cell is divided into a plurality of regions according to the number of feeding ports, each region including at least one anode, specifically including:
[0026] The target aluminum electrolysis cell is divided into M areas, and the number of anodes in the jth area is N j , 1≤M≤10, N j is the number of anodes in the jth region, and N is the total number of anodes.
[0027] In a second aspect, the present application provides an aluminum electrolysis anode effect prediction device, the aluminum electrolysis anode effect prediction device comprising:
[0028] A region division module, used to divide the target aluminum electrolytic cell into multiple regions according to the number of feeding ports, each region including at least one anode;
[0029] The anode effect prediction module is used to traverse each area in turn in a loop, obtain the linear change of the regional current of the current area traversed, and if it is predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional current, the loop is stopped and an anode effect warning message is issued; if it is not predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional current, the linear change of the regional resistance of the current area traversed is obtained; if it is predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional resistance, the loop is stopped and an anode effect warning message is issued; if it is not predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional resistance, the next area is traversed until the traversal of each area is completed.
[0030] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the above-described aluminum electrolysis anode effect prediction methods.
[0031] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for predicting the anode effect of aluminum electrolysis.
[0032] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned aluminum electrolysis anode effect prediction methods.
[0033] According to the specific embodiments provided in this application, this application discloses the following technical effects:
[0034] The present application provides a method, device, equipment, medium and product for predicting the anode effect of aluminum electrolysis. The target aluminum electrolytic cell is divided into zones, and each zone is traversed in turn in a loop. Whether the anode effect will occur is predicted based on the linear change of current or the linear change of resistance in each zone. The occurrence of the anode effect is predicted in advance and the location where the local anode effect will first occur is predicted, so that the aluminum oxide in the corresponding area can be replenished in time to reduce the probability of the occurrence of the anode effect of the entire cell, reduce power loss and perfluorocarbon strong greenhouse gas emissions, and improve the stability of the electrolytic cell and the current efficiency. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0036] Figure 1 A schematic flow chart of a method for predicting an anode effect of aluminum electrolysis provided in one embodiment of the present application;
[0037] Figure 2 A schematic diagram of a cycle process in a method for predicting an anode effect of aluminum electrolysis provided in one embodiment of the present application;
[0038] Figure 3 A schematic diagram of the structure of a device for predicting anode effect of aluminum electrolysis provided in one embodiment of the present application;
[0039] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0040] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0041] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.
[0042] This application provides a method for predicting the anode effect of aluminum electrolysis. Figure 1 As shown, the aluminum electrolysis anode effect prediction method includes:
[0043] Step 100: Divide the target aluminum electrolysis cell into multiple areas according to the number of feeding ports, and each area includes at least one anode.
[0044] Step 200: Traverse each area in turn in a loop manner to obtain the linear change of the regional current of the current area traversed. If it is predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional current, the loop is stopped and an anode effect warning message is issued. If it is not predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional current, the linear change of the regional resistance of the current area traversed is obtained. If it is predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional resistance, the loop is stopped and an anode effect warning message is issued. If it is not predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional resistance, the next area is traversed until the traversal of each area is completed.
[0045] In an exemplary embodiment, step 100 specifically includes: dividing the target aluminum electrolysis cell into M regions, the number of anodes in the jth region is N j , 1≤M≤10, The number of regions is equal to the number of feeding ports, M is also the number of feeding ports, N j is the number of anodes in the jth region, and N is the total number of anodes.
[0046] In an exemplary embodiment, when it is determined according to the relevant judgment strategy that the electrolytic cell is currently in the anode effect state, the prediction work described in step 200 is not performed. If the relevant judgment strategy does not determine that the electrolytic cell is in the anode effect state, step 200 of the present application is executed.
[0047] In an exemplary embodiment, multiple regional current values of each region are collected by a regional current collection device, and multiple slot voltages collected by a voltage collection device are simultaneously acquired. The regional current collection device and the voltage collection device can realize synchronous online collection.
[0048] like Figure 2 As shown, step 200 specifically includes steps 201 to 214.
[0049] Step 201: Determine the initial region as the first region.
[0050] Step 202: Collect multiple regional currents in the current region within the second set time period to obtain a regional current time series. Figure 2 The jth region in .
[0051] The regional current acquisition device uses continuous acquisition to collect data, and the acquisition cycle is optional from 1 second to 30 seconds. The second set time period is optional from 30 seconds to 120 seconds. This time period is obtained after a large number of experiments and analyses. When this time period is set, the entire prediction and rapid judgment results are more timely and accurate.
[0052] Step 203: Obtain the slot voltage collected by the voltage collection device. In the second set time period, multiple slot voltage values are obtained by synchronous online collection together with the regional current. The slot voltage collection device uses a continuous collection method to collect data, and synchronizes the collection with the regional current collection device. The collection cycle is 1 to 30 seconds. The set time period is 30 seconds to 120 seconds, which is a time period obtained after a large number of experiments and analyses. When this time period is set, the entire prediction and rapid judgment results are more timely and accurate.
[0053] Step 204: fitting the regional current time series using a linear fitting method to obtain a regional current linear model of the current region, thereby determining a temporal variation pattern of a plurality of regional current values collected in the current region within a second set time period.
[0054] The independent variable of the regional current linear model is time in seconds, and the dependent variable is the regional current in kA.
[0055] The slope of the regional current linear model within the second set time is obtained according to the regional current linear model.
[0056] Step 205: Obtain the slope of the regional current linear model of the current region.
[0057] Step 206: Determine whether the slope of the regional current linear model of the current region is less than or equal to a first set value, and obtain a first determination result.
[0058] If the first judgment result is yes, step 207 is executed.
[0059] Step 207: Determine that an anode effect will occur within a first set time period in the future, stop the cycle and issue an anode effect warning message; the anode effect warning message includes the number of the current area.
[0060] Anode effect warning information is used to remind staff to handle it to reduce the probability of anode effect in the whole tank. When the effect warning is generated according to the trend of regional current change, a material unloading instruction is issued to the corresponding area to replenish alumina in the area in time to eliminate the occurrence of anode effect.
[0061] If the first determination result is no, step 208 is executed.
[0062] Step 208: Determine a regional resistance time series consisting of a plurality of regional resistances in the current region within the second set time period, wherein each regional resistance is calculated and determined based on the regional current and slot voltage collected at the same time.
[0063] Step 209: fitting the regional resistance time series using a linear fitting method to obtain a regional resistance linear model of the current region, thereby determining a change pattern of multiple regional resistances of the current region within a second set time period over time.
[0064] The multiple regional resistances continuously calculated in the current region within the second set time period are sorted according to the acquisition time to obtain a sorted regional resistance time series.
[0065] The regional resistance time series is linearly fitted using a linear fitting method to obtain a regional resistance linear model. The independent variable of the regional resistance linear model is time, in seconds, and the dependent variable is regional resistance, in μΩ;
[0066] The absolute value of the slope of the regional resistance linear model within the second set time is obtained.
[0067] Step 210: Obtain the absolute value of the slope of the regional resistance linear model of the current region.
[0068] Step 211: Determine whether the absolute value of the slope of the regional resistance linear model of the current region is greater than or equal to a second set value, and obtain a second determination result.
[0069] If the second determination result is yes, step 207 is executed.
[0070] If the second determination result is no, step 212 is executed.
[0071] Step 212: Determine that the anode effect may not occur within the first set time period in the future, and continue to execute 213.
[0072] Step 213: Determine whether the traversal of each area is completed, that is, determine whether the area number value of the next area (j+1th area) is less than or equal to (not exceeding) the total number M of areas divided by the electrolytic cell. If the traversal is not completed, update the next area to the current area, and return to the step of collecting multiple area currents of the current area within the second set time period to obtain the area current time series until the traversal is completed.
[0073] Step 214: If, after the traversal is completed, the absolute value of the slope of the regional resistance linear model of each region is less than the second set value, it is determined that the anode effect will not occur in the future first set time period.
[0074] Prior to the implementation of this application, through on-site testing and research, it was found that in the normal production process of aluminum electrolysis, both the regional current and the regional resistance showed a pattern of regular fluctuation over time; and before the anode effect of the entire tank occurs, there are always individual areas where the regional current value drops rapidly or the regional resistance rises rapidly. Therefore, the changing trends of the regional current and regional resistance are the most intuitive information for predicting the occurrence of the anode effect in the production process of aluminum electrolysis.
[0075] In an exemplary embodiment, the calculation formula of the regional resistance is: R j =(VV ext ) / I j (1)
[0076] Among them, R j represents the region resistance of the jth region, V represents the cell voltage, V ext is a constant, V ext =1.65V, I j Represents the regional current of the jth region, 1≤j≤M.
[0077] The range of the first future set time period is 1 to 300 seconds.
[0078] The first set value is a negative value, specifically -0.03, and the second set value is a positive value, specifically 0.01.
[0079] The present application predicts and quickly determines the anode effect, timely prevents and extinguishes the anode effect in advance, and reduces the effect coefficient, thereby solving the problem in the related technology that the anode effect has already occurred for a long time when it is detected, resulting in reduced stability of the electrolytic cell, large-scale emission of perfluorocarbon strong greenhouse gases, power loss and reduced current efficiency.
[0080] Example 1: The cell voltage and regional current of the 1001# aluminum electrolytic cell of a 400kA aluminum electrolytic plant can be collected online synchronously. The regional current is collected by an optical fiber ring current sensor, and the cell voltage is collected by a voltage collection device. The two collectors can realize synchronous online collection. The collection cycle is 20 seconds.
[0081] The aluminum electrolytic cell has 6 discharge ports, which are numbered starting from the aluminum discharge end. The discharge port closest to the aluminum discharge end is numbered 1#, and the discharge port closest to the flue end is numbered 6. Therefore, the electrolytic cell can be divided into 6 areas according to the number of discharge ports, with discharge port 1# corresponding to area 1, and so on.
[0082] The real-time regional current value I1 of area 1 was collected by the optical fiber ring current sensor at 8:23:20 on August 3, 2024, which was 72.7 kA. At this time, the factory slot control machine did not issue an anode effect warning for the slot.
[0083] It is determined that the regional current values I1 of the first region within 60 seconds (the second set time period, △T) are 70.1kA, 70.5kA, 71.6kA and 72.7kA respectively, and the acquisition times correspond to 8:22:20, 8:22:40, 8:23:0 and 8:23:20 respectively. The linear fitting method is used to fit the time series of multiple regional current values I1 in the first region, and the regional current linear model I1=69.89+0.0445T is obtained, where T is time (unit: second).
[0084] According to the linear model of the regional current in the first region at this time, its slope k 1I It is 0.0445, which is greater than the first set value (-0.03), and the first judgment result is obtained.
[0085] The cell voltage of the first region and the regional resistance R1 corresponding to the regional current acquisition time are calculated according to formula (1) based on the cell voltage V measured in real time. The cell voltages corresponding to 8:22:20, 8:22:40, 8:23:00 and 8:23:20 are 3981mV, 4010mV, 4040mV and 4123mV respectively. The corresponding regional resistances R1 (retain two decimal places) calculated according to formula (1) are 33.25, 33.48, 33.38 and 34.02μΩ respectively. The linear fitting method is used to fit the multiple regional resistance R1 time series of the first region, and the regional resistance linear model R1=33.201+0.01105T of the first region is obtained, where T is time (unit: second).
[0086] According to the linear model of the regional resistance of the first region at this time, its slope k 1R It is 0.01105, which is greater than the second set value (0.01), and the second judgment result is obtained. At 8:23:20, it is determined that the cell will have an anode effect within the next 1 to 300 seconds.
[0087] In fact, compared with the records of the factory tank control machine, the anode effect occurred in the tank after 20 seconds, proving that the occurrence of the anode effect was successfully predicted by this method.
[0088] Embodiment 2 is different from Embodiment 1 only in that the real-time collection of regional current and the corresponding slot voltage are collected for region 6. The regional current values I6 of region 6 within 60 seconds (second set time period, △T) are determined to be 56.2kA, 55.5kA, 54.7kA and 53.9kA, respectively. The collection times correspond to 8:21:20, 8:21:40, 8:22 and 8:22:20 on August 3, 2024, respectively. The linear fitting method is used to fit the time series of multiple regional current values I6 of region 6, and the regional current linear model I6=56.23-0.0385T is obtained, where T is time (unit: second).
[0089] According to the linear model of the regional current in the sixth region within the set time period (60 seconds), its slope k is 6I It is -0.0385, which is less than the first set value (-0.03), and the second judgment result is obtained.
[0090] Therefore, the second judgment result indicates that the prediction method predicts that the cell will have an anode effect in the next 1 to 300 seconds at 8:22:20. In fact, by comparing the prediction results of Example 1, it can be seen that the same method based on the regional current and regional resistance change trends of the first region also predicts that the cell will have an anode effect in the next 1 to 300 seconds before the anode effect occurs, and compared with the prediction of the first region based on the regional current change trend of the sixth region, the accurate prediction result is completed 60 seconds in advance.
[0091] Embodiment 3 is different from Embodiment 2 only in that the synchronous online acquisition of regional current and slot voltage for the first region is performed between 6:59 and 7:00 on August 3, 2024. The regional current values I1 collected from the first region by the optical fiber ring current sensor within 60 seconds (set time period, △T) are: 62.27kA, 61.7kA, 61.3kA and 61.3kA, respectively, and the acquisition times correspond to 6:59, 6:59:20, 6:59:40 and 7:00, respectively. The linear fitting method is used to fit the time series of multiple regional current values I1 of the first region, and the regional current linear model I1=62.09-0.0155T is obtained, where T is time (unit: second).
[0092] According to the linear model of the regional current in the first region, its slope k 1I It is -0.015, which is greater than the second set value (-0.03), and the second judgment result is obtained.
[0093] Continue to calculate the slot voltage of the first region and the regional resistance R1 corresponding to the regional current collection time according to formula (1) based on the real-time measured slot voltage V. The slot voltages collected at 6:59, 6:59:20, 6:59:40 and 7:00 are 3958mV, 3954mV, 3964mV and 3958mV respectively. The corresponding regional resistances R1 (retain two decimal places) calculated according to formula (1) are 37.11μΩ, 37.34μΩ, 37.75μΩ and 37.65μΩ respectively. The linear fitting method is used to fit the multiple regional resistance R1 time series of the first region to obtain the regional resistance linear model R1=37.158+0.01015T of the first region, where T is time (unit: second).
[0094] According to the linear model of the regional resistance of the first region at this time, its slope k 1R The value is 0.009, which is less than the second set value (0.01), and a third judgment result is obtained, which determines that the cell may not have an anode effect in the next 1 to 300 seconds.
[0095] Thereafter, the second to sixth regions are judged according to the above logic cycle, and it is determined that the cell may not have an anode effect in the next 1 to 300 seconds, and it is determined that the cell will not have an anode effect in the next 1 to 300 seconds.
[0096] Experiments show that the occurrence of the anode effect of the whole tank of aluminum electrolysis must be started by the local effect. At this time, the aluminum oxide concentration in the local area is too low, the resistance of the local area increases, and the current in the local area decreases, which leads to an increase in the current density in other areas and induces the occurrence of local anode effects in other areas until the anode effect of the entire electrolytic cell occurs. This application utilizes the characteristics that the local effect will appear in the local area before the anode effect of the whole tank occurs, resulting in an increase in local resistance and a decrease in current. At the same time, combined with the characteristics that the local current will show a significant sudden rise or drop when the anode effect of the whole tank occurs, it is proposed to measure the regional current online, analyze the change pattern of the regional current and the regional resistance within a time window of 30 seconds to 120 seconds, and realize the rapid prediction of the anode effect. Once this feature is found, it can be judged that the anode effect of the whole tank is about to occur, and early warning information is given in the early stage before the anode effect of the whole tank occurs, prompting the effect prevention treatment or the effect extinguishing treatment, thereby greatly reducing the effect coefficient and shortening the duration of the effect, significantly reducing the emission of perfluorocarbon strong greenhouse gases, current loss and other hazards caused by the anode effect, which has very important application value and provides new technologies and methods for green and low-carbon production of aluminum electrolysis.
[0097] Based on the same inventive concept, the embodiment of the present application also provides an aluminum electrolysis anode effect prediction device for implementing the aluminum electrolysis anode effect prediction method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the aluminum electrolysis anode effect prediction device provided below can refer to the limitations of the aluminum electrolysis anode effect prediction method above, and will not be repeated here.
[0098] In an exemplary embodiment, the present application provides an aluminum electrolysis anode effect prediction device comprising:
[0099] The area division module is used to divide the target aluminum electrolytic cell into multiple areas according to the number of feeding ports, and each area includes at least one anode.
[0100] The anode effect prediction module is used to traverse each area in turn in a loop, obtain the linear change of the regional current of the current area traversed, and if it is predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional current, the loop is stopped and an anode effect warning message is issued; if it is not predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional current, the linear change of the regional resistance of the current area traversed is obtained; if it is predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional resistance, the loop is stopped and an anode effect warning message is issued; if it is not predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional resistance, the next area is traversed until the traversal of each area is completed.
[0101] In an exemplary embodiment, Figure 3 As shown, the present application provides an aluminum electrolysis anode effect prediction device comprising:
[0102] The regional current and slot voltage acquisition module 301 is used to acquire the regional current value collected by the regional current acquisition device and the slot voltage collected by the voltage acquisition device; within the second set time period, multiple regional current values of the region and the slot voltage corresponding to each regional current at the same time are acquired.
[0103] The regional current linear fitting module 302 is used to fit multiple regional current value time series of the region using a linear fitting method to obtain a regional current linear model when the current judgment strategy does not determine that the electrolytic cell is in the anode effect state.
[0104] The regional current slope acquisition module 303 is used to acquire the slope of the regional current linear model.
[0105] The first judgment module 304 judges the comparison between the slope of the regional current linear model of the region and the first set value to obtain a first judgment result.
[0106] The anode effect prediction module 305 is used to determine that the anode effect will occur within the next 1 to 300 seconds when the first judgment result indicates that the slope of the regional current linear model of the region is less than or equal to a first set value.
[0107] The regional resistance acquisition module 306 is used to calculate the regional resistance according to formula (1) when the first judgment result indicates that the slope of the regional current linear model of the region is greater than a first set value.
[0108] The regional resistance linear fitting module 307 is used to fit multiple regional resistances in the region in time series using a linear fitting method based on the calculated regional resistance to obtain a regional resistance linear model when the first judgment result indicates that the slope of the regional current linear model of the region is greater than a first set value.
[0109] The regional resistance slope acquisition module 308 is used to acquire the absolute value of the slope of the regional resistance linear model.
[0110] The second judgment module 309 judges the comparison between the absolute value of the slope of the regional resistance linear model of the region and the second set value to obtain a second judgment result.
[0111] The anode effect prediction module 305 is further configured to determine that the anode effect will occur within 1 to 300 seconds in the future when the second judgment result indicates that the absolute value of the slope of the regional resistance linear model of the region is greater than or equal to a second set value.
[0112] The anode effect may not occur prediction module 310 is used for the second judgment result indicating that the absolute value of the slope of the regional resistance linear model of the region is less than the second set value, and it is determined that the anode effect may not occur in the electrolytic cell within the next 1 to 300 seconds.
[0113] The third judgment module 311 is used to judge the comparison between the feed port number corresponding to the next area and the total number of feed ports (i.e. the total number of electrolytic cell areas) when the second judgment result indicates that the absolute value of the slope of the regional resistance linear model of the area is less than the second set value, and obtain the third judgment result.
[0114] The loop module 312 is used to detect and calculate the regional current and regional resistance of the next area when the third judgment result indicates that the feeding port number corresponding to the next area is less than or equal to the total number of feeding ports, and judge according to the above logic, and the loop is repeated until all areas of the slot are detected and judged.
[0115] The anode effect non-occurrence determination module 313 is used to express that the circulation module indicates that the feed port number corresponding to the next area is greater than the total number of feed ports, that is, when the result of diagnosing all areas of the cell is that the anode effect may not occur in the electrolytic cell within the next 1 to 300 seconds, it is determined that the anode effect will not occur in the electrolytic cell within the next 1 to 300 seconds.
[0116] The aluminum electrolysis anode effect prediction also includes: an early warning information generation module, which is used to generate early warning information when it is determined that the aluminum electrolysis cell is about to have an anode effect, so as to remind the staff to handle it. The handling measure is to issue a material unloading instruction to the corresponding area when an effect warning is generated according to the regional current or regional resistance change trend, and replenish the alumina in the area in time to eliminate the occurrence of the anode effect.
[0117] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store aluminum electrolysis anode effect prediction data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for predicting the anode effect of aluminum electrolysis is implemented.
[0118] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.
[0119] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0120] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0121] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0122] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0123] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a data processing logic of a programmable logic device, etc., but is not limited thereto.
[0124] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0125] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A method for predicting the anode effect of aluminum electrolysis, characterized in that: The aluminum electrolysis anode effect prediction method comprises: Dividing the target aluminum electrolysis cell into a plurality of regions according to the number of feeding ports, each region including at least one anode; Each area is traversed in turn in a loop manner to obtain the linear change of the regional current of the current area traversed. If it is predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional current, the loop is stopped and an anode effect warning message is issued. If it is not predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional current, the linear change of the regional resistance of the current area traversed is obtained. If it is predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional resistance, the loop is stopped and an anode effect warning message is issued. If it is not predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional resistance, the next area is traversed until the traversal of each area is completed.
2. The method for predicting the anode effect of aluminum electrolysis according to claim 1, characterized in that: Traversing each area in turn in a loop, obtaining a linear change in regional current of the current area traversed, if it is predicted that an anode effect will occur within a first set time period in the future based on the linear change in regional current, then stopping the loop and issuing an anode effect warning message, if it is not predicted that an anode effect will occur within the first set time period in the future, then obtaining a linear change in regional resistance of the current area traversed, if it is predicted that an anode effect will occur within the first set time period in the future based on the linear change in regional resistance, then stopping the loop and issuing an anode effect warning message, if it is not predicted that an anode effect will occur within the first set time period in the future, then traversing the next area until the traversal of each area is completed, specifically including: Collecting multiple regional currents and cell voltages in the current region within a second set time period to obtain a regional current time series; the cell voltage is the cell voltage of the target aluminum electrolytic cell; Using a linear fitting method to fit the regional current time series, a regional current linear model of the current region is obtained; Determine whether the slope of the regional current linear model of the current region is less than or equal to a first set value, and obtain a first determination result; If the first judgment result is yes, it is determined that the anode effect will occur within the first set time period in the future, the cycle is stopped and an anode effect warning message is issued; the anode effect warning message includes the number of the current area; If the first judgment result is determined to be no, then a regional resistance time series consisting of multiple regional resistances in the current region within the second set time period is determined, and each regional resistance is calculated and determined based on the regional current and cell voltage collected at the same time within the second set time period; the cell voltage is the cell voltage of the target aluminum electrolytic cell; Using a linear fitting method to fit the regional resistance time series, a regional resistance linear model of the current region is obtained; Determine whether the absolute value of the slope of the regional resistance linear model of the current region is greater than or equal to a second set value, and obtain a second determination result; If the second judgment result is yes, it is determined that an anode effect will occur within the first set time period in the future, the circulation is stopped and an anode effect warning message is issued; If the second judgment result is determined to be no, it is determined whether the traversal of each area is completed. If the traversal is not completed, the next area is updated as the current area, and the step of collecting multiple regional currents of the current area within the second set time period to obtain the regional current time series is returned until the traversal is completed; If, after the traversal is completed, the absolute value of the slope of the regional resistance linear model of each region is smaller than the second set value, it is determined that the anode effect will not occur in the future first set time period.
3. The method for predicting the anode effect of aluminum electrolysis according to claim 2, characterized in that: The calculation formula of the regional resistance is: R j =(VV ext ) / I j ; Among them, R j represents the region resistance of the jth region, V represents the cell voltage, V ext is a constant, I j represents the regional current of the jth region.
4. The method for predicting the anode effect of aluminum electrolysis according to claim 2, characterized in that: The first set time period in the future ranges from 1 to 300 seconds, and the second set time period ranges from 30 to 120 seconds.
5. The method for predicting the anode effect of aluminum electrolysis according to claim 2, characterized in that: The first setting value is -0.03, and the second setting value is 0.
01.
6. The method for predicting the anode effect of aluminum electrolysis according to claim 1, characterized in that: The target aluminum electrolysis cell is divided into multiple areas according to the number of feeding ports, each area includes at least one anode, specifically including: The target aluminum electrolytic cell is divided into M areas, and the number of anodes in the jth area is N j , 1≤M≤10, N j is the number of anodes in the jth region, and N is the total number of anodes.
7. A device for predicting anode effect of aluminum electrolysis, characterized in that: The aluminum electrolysis anode effect prediction device comprises: A region division module, used to divide the target aluminum electrolytic cell into multiple regions according to the number of feeding ports, each region including at least one anode; The anode effect prediction module is used to traverse each area in turn in a loop, obtain the linear change of the regional current of the current area traversed, and if it is predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional current, the loop is stopped and an anode effect warning message is issued; if it is not predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional current, the linear change of the regional resistance of the current area traversed is obtained; if it is predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional resistance, the loop is stopped and an anode effect warning message is issued; if it is not predicted that the anode effect will occur within the first set time period in the future based on the linear change of the regional resistance, the next area is traversed until the traversal of each area is completed.
8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aluminum electrolysis anode effect prediction method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the anode effect of aluminum electrolysis according to any one of claims 1 to 6 is implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting the anode effect of aluminum electrolysis according to any one of claims 1 to 6 is implemented.