Production control method and system of prebaked anode for electrolytic aluminum

Abstract

The invention discloses a production control method and system of a prebaked anode for electrolytic aluminum, and relates to the technical field of prebaked anode production control. The method comprises the following steps: firstly, determining a plurality of roasting furnaces in a roasting pre-state, constructing a sintering map corresponding to each roasting furnace, setting roasting parameters according to the sintering map, and starting roasting operation; establishing a digital twin model for each roasting furnace, simulating and generating a roasting temperature field model, further positioning a heating power non-uniform area of each roasting furnace, selecting one heating power non-uniform area to optimize all corresponding fuel nozzles, taking the optimized nozzles as reference objects, and synchronously optimizing control parameters of other fuel nozzles, so as to obtain a target object. Finally, the optimized roasting operation is simulated, temperature field changes are monitored, and the simulation process is regulated and controlled. According to the method, uneven heating power in the furnace can be accurately improved, the defects of anode cracks, deformation and the like are reduced, the product quality consistency is improved, the debugging period is shortened, and the production efficiency of the prebaked anode is indirectly improved.

Description

Technical Field

[0001] This invention relates to the field of prebaked anode production control technology, specifically to a production control method and system for prebaked anodes used in electrolytic aluminum. Background Technology

[0002] Prebaked anodes are a key consumable material in aluminum electrolysis production. Their quality directly affects the current efficiency, energy consumption level, and anode consumption rate of electrolytic aluminum production. Currently, the prebaked anode production process mainly includes raw material pretreatment, formula calculation, mixing, molding, and calcination. Each process is controlled relatively independently, which presents the following problems: Calcination is a key heat treatment process that determines the final performance of the anode. Existing technologies mainly rely on fixed heating curves and empirical adjustments based on limited temperature measurement points. They lack precise perception and control methods for the complex three-dimensional temperature field distribution inside the furnace. This leads to local over-burning or under-burning in the calcination furnace, which not only results in high energy consumption but is also the main source of defects such as cracks and deformation in the anode. How to solve the problem of uneven heat distribution is a key challenge in the current production control process of prebaked anodes. Summary of the Invention

[0003] The purpose of this invention is to provide a production control method and system for prebaked anodes used in electrolytic aluminum production, in order to address the shortcomings in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a production control method for prebaked anodes used in electrolytic aluminum, comprising the following steps: Step S1: Determine several calcining furnaces in the pre-calcination state, construct the sintering pattern corresponding to each calcining furnace, and set the calcination parameters corresponding to the calcining furnace based on the sintering pattern. Step S2: Start the roasting operation of the anode in each roasting furnace, build a corresponding digital twin model for each roasting furnace, and simulate the roasting temperature field model based on all the digital twin models; Step S3: Determine the thermal unevenness region of each roasting furnace based on the roasting temperature field model, select the thermal unevenness region of one roasting furnace, and use it to optimize the settings of all corresponding fuel nozzles. Step S4: Using the optimized fuel nozzle as a reference, optimize the control parameters of other fuel nozzles based on the reference, simulate the roasting operation of the anode in the roasting furnace after each optimization, monitor the changes in the corresponding temperature field, and adjust the roasting simulation process based on the monitoring results.

[0005] In a preferred embodiment, the process of identifying several calcining furnaces in the pre-calcination state and constructing the sintering pattern corresponding to each calcining furnace includes: The working status of several calcining furnaces is obtained, including the pre-calcination state, the calcination state, and the maintenance state. A number of calcining furnaces in the pre-calcination state are identified, and several calcination processes are obtained for each calcining furnace in the pre-calcination state. Each roasting process is taken as a roasting node, and all roasting nodes are mapped onto a preset blank map to construct the sintering map corresponding to each roasting furnace. The sintering map includes material map, geometric map and equipment map.

[0006] In a preferred embodiment, the process of initiating the calcination operation of the anode in each calcination furnace, constructing a corresponding digital twin model for each calcination furnace, and simulating the respective calcination temperature field model based on all the digital twin models includes: The roasting operation is divided into different types of roasting stages based on the roasting process. Temperature ranges for different roasting stages are set. Based on the roasting data of different roasting stages, stage twin sub-models for the corresponding roasting stages are constructed using digital twin technology. Integrate the stage twin models of the same roasting furnace at different roasting stages, perform data fusion on the different stage twin models, construct the corresponding digital twin model of the roasting furnace, and locate the runaway temperature region of the roasting furnace at different roasting stages, as well as the temperature value of each runaway temperature region. A calcination temperature monitoring node is created for each runaway temperature region for temperature analysis. A blank model architecture is set up based on the structure of the calcination furnace. The runaway temperature regions corresponding to all calcination temperature monitoring nodes are arranged according to their respective structural distribution on the calcination furnace to construct a calcination temperature field model.

[0007] In a preferred embodiment, the process of determining the thermal unevenness region of each roasting furnace based on the roasting temperature field model, and selecting the thermal unevenness region of one roasting furnace for optimizing the setting of all corresponding fuel nozzles includes: The roasting furnace is divided into several temperature recognition zones based on its structural regions. The real-time temperature values ​​of the different refined temperature regions included in the temperature recognition zone are recorded at each preset time interval under different roasting stages. Throughout the roasting process, a temperature field simulation snapshot is executed at a preset time interval to record the temperature statistical characteristic values ​​in each temperature identification zone, create structured data for each temperature identification zone, and store the structured data in the set time-series database. Based on the roasting curve of each roasting stage, the target temperature range of each temperature identification zone at the current time point is obtained, and the allowable temperature difference threshold is set for each temperature identification zone at different time points, thereby determining the stage compliance temperature range of each temperature identification zone. If a certain temperature identification zone is not within its respective stage compliant temperature range, the corresponding temperature identification zone is determined as a thermally uneven sub-region. All thermally uneven sub-regions in the same roasting furnace are integrated to determine the final thermally uneven region. Select one of several thermally uneven regions as the optimization target, and set the operating parameters of the fuel nozzles at all locations corresponding to the optimization target.

[0008] In a preferred embodiment, the roasting temperature field model consists of several temperature grids, and each refined temperature region on the roasting furnace is associated with a temperature grid on the corresponding roasting temperature field model.

[0009] In a preferred embodiment, the process of optimizing the control parameters of other fuel nozzles based on the optimized fuel nozzle as a reference includes: A graph sharing endpoint for reference objects is constructed to store all operating parameters of fuel nozzles that have been optimized. Each category of operating parameters is encapsulated into a graph item, and each graph item is associated with a deployment message node and a script container. The message node is used to determine whether the current map item has been requested to be reused by other fuel nozzles, generate different message identifiers based on the determination result, and set different container actions for the corresponding script container; The script container is used to perform different container actions on the fuel nozzles that need to be optimized, and to construct a private map corresponding to the fuel nozzle based on the execution results of all container actions of the same fuel nozzle, and to obtain the control parameters for the optimized settings of the fuel nozzle based on the private map.

[0010] In a preferred embodiment, the calcination operation of the calcining furnace on the anode is simulated after each optimized setting is completed, and the changes in the corresponding temperature field are monitored. The simulation process of adjusting the calcination based on the monitoring results specifically includes: Based on all the control parameters of the same calcining furnace, the calcining cycle segments are set when the calcining furnace simulates the calcining of the anode, and the calcining simulation parameters for each calcining cycle segment are set. The anode and the calcining furnace are taken as objects to be simulated. Their respective simulation entities are instantiated, a simulation environment is built, and their respective simulation entities are matched in the simulation environment. The different locations of the simulated entity are set up as monitoring points, and the temperature data of each monitoring point is collected to generate a real-time temperature field distribution map. The temperature field distribution map is used to record the temperature field data. The temperature field data is compared with the preset optimal roasting temperature field standard to analyze the surface temperature difference of the carbon block corresponding to the anode and the uniformity of the furnace temperature distribution corresponding to the roasting furnace, which is then used to characterize the changes in the temperature field and as a monitoring result. Based on the monitoring results, the roasting simulation parameters of the roasting furnace corresponding to different roasting cycle segments are adjusted until the temperature field distribution meets the optimal roasting standard and the performance indicators of the anode are up to standard. All simulation data are recorded as the execution dataset of the actual roasting process, and the actual roasting of the roasting furnace is completed based on the execution dataset.

[0011] The present invention also provides a production control system for prebaked anodes used in electrolytic aluminum, the system comprising: The calcination parameter setting module is used to determine several calcination furnaces in the pre-calcination state, construct the sintering pattern corresponding to each calcination furnace, and set the calcination parameters corresponding to the calcination furnace based on the sintering pattern. The temperature field model module is used to initiate the roasting operation of the anode in each roasting furnace, build a corresponding digital twin model for each roasting furnace, and simulate the roasting temperature field model based on all the digital twin models. The thermal unevenness positioning module determines the thermal unevenness region of each roasting furnace based on the roasting temperature field model, selects the thermal unevenness region of a roasting furnace, and uses it to optimize the settings of all corresponding fuel nozzles. The temperature field optimization module is used to use the optimized fuel nozzle as a reference object, optimize the control parameters of other fuel nozzles based on the reference object, simulate the roasting operation of the anode in the roasting furnace after each optimization setting, monitor the changes in the corresponding temperature field, and adjust the roasting simulation process based on the monitoring results.

[0012] In the above technical solution, the technical effects and advantages provided by the present invention are as follows: the thermal unevenness area corresponding to the calcining furnace is accurately located by using a digital twin model and a calcining temperature field model; the optimized fuel nozzle is used as a reference object to uniformly set the parameters of other fuel nozzles in the same thermal unevenness area, which solves the problem of poor parameter matching of traditional nozzles, which easily leads to uneven heat distribution in the calcining furnace; through real-time monitoring and dynamic control of the temperature field, local over-burning, under-burning and excessive core-surface temperature difference are avoided, thereby improving the calcining quality of the anode; through simulation optimization and parameter solidification, the debugging cycle is shortened to improve the production efficiency of prebaked anodes. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0014] Figure 1 This is a flowchart of a production control method for prebaked anodes used in electrolytic aluminum according to the present invention.

[0015] Figure 2 This is a system block diagram of a production control system for prebaked anodes used in electrolytic aluminum according to the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1, please refer to Figure 1 As shown in this embodiment, a production control method for prebaked anodes used in electrolytic aluminum includes the following steps: Step S1: Determine several calcining furnaces in the pre-calcination state, construct the sintering pattern corresponding to each calcining furnace, and set the calcination parameters corresponding to the calcining furnace based on the sintering pattern. Step S2: Start the roasting operation of the anode in each roasting furnace, build a corresponding digital twin model for each roasting furnace, and simulate the roasting temperature field model based on all the digital twin models; Step S3: Determine the thermal unevenness region of each roasting furnace based on the roasting temperature field model, select the thermal unevenness region of one roasting furnace, and use it to optimize the settings of all corresponding fuel nozzles. Step S4: Using the optimized fuel nozzle as a reference, optimize the control parameters of other fuel nozzles based on the reference, simulate the roasting operation of the anode in the roasting furnace after each optimization, monitor the changes in the corresponding temperature field, and adjust the roasting simulation process based on the monitoring results.

[0018] It should be further explained that, in the specific implementation process, the process of identifying several calcining furnaces in the pre-calcination state, constructing a sintering pattern for each calcining furnace, and setting the corresponding calcination parameters for the calcining furnace based on the sintering pattern includes: Obtain the furnace number corresponding to each of the several roasting furnaces, and obtain the working status of each roasting furnace in ascending order of the furnace number. The working status includes the pre-roasting status, roasting status and maintenance status. Among them, the pre-roasting state is the state in which the roasting furnace needs to be roasted and is in the queue; the working state is the state in which the roasting furnace is roasting; and the maintenance state indicates that there is an abnormality in the roasting furnace and the roasting furnace needs to be troubleshooted. A number of calcining furnaces in the pre-calcination state are identified, and a number of calcination processes corresponding to each calcining furnace in the pre-calcination state are obtained. Each calcination process is used as a calcination node, and all calcination nodes are mapped onto a preset blank map to construct the sintering map corresponding to each calcining furnace. The sintering diagrams include material diagrams, geometric diagrams, and equipment diagrams.

[0019] Based on the anode formulation and specifications, a pre-configured material database is called to generate the estimated thermophysical parameters of this batch of anodes, including specific heat capacity, thermal conductivity and volatile matter escape characteristic curves, and then a material spectrum is constructed. Based on the loading method, a three-dimensional geometric model of the furnace chamber corresponding to the roasting furnace is generated, clarifying the relative position and gap of the fuel flow channel, anode block and fire channel, etc., and then constructing a geometric map characterizing the three-dimensional structural features of the roasting furnace. By integrating historical performance data of the corresponding furnace chamber of the roasting furnace, known minor deformations, local insulation differences, and characteristic parameters of nozzles at different locations are identified, and then the corresponding equipment atlas is constructed. The sintering spectrum includes two status categories: historical spectrum and current spectrum. A baseline sintering curve is constructed based on the historical spectrum that successfully completed the entire sintering operation. The current spectrum is used to correct the baseline sintering curve based on the material spectrum to obtain the real-time sintering curve of each sintering furnace that performed the sintering operation.

[0020] Based on their respective real-time roasting curves, fuel flow rates are set for different roasting stages. Based on their respective geometric and equipment maps, the easily cooled and easily heated zones of the roasting furnace are located. Initial coefficients are set for the fuel nozzles and exhaust flues located in the easily cooled and easily heated zones. For example, the initial fuel flow distribution ratio for the easily cooled zone is set to 1.05, while that for the easily heated zone is set to 0.95.

[0021] It should be further explained that, in the specific implementation process, the process of initiating the anode roasting operation in each roasting furnace, constructing a corresponding digital twin model for each roasting furnace, and simulating the respective roasting temperature field model based on all the digital twin models includes: Start the roasting operation of the roasting furnace and divide the roasting operation into different types of roasting stages based on the roasting process. All types of roasting stages include the preheating stage, the volatile matter release and coking stage, the high-temperature sintering stage, and the cooling stage. Set the temperature range corresponding to each roasting stage of the roasting furnace; The temperature range during the preheating stage is from room temperature to about 300°C; the temperature range during the volatile matter release and coking stage is from about 300°C to about 800°C; the temperature range during the high-temperature sintering stage is from about 800°C to the target final temperature; and the temperature range during the cooling stage is from the target final temperature to the cooling exit temperature. The target final temperature is set to 1100°C to 1200°C, and the cooling exit temperature is set to less than 300°C.

[0022] Based on the roasting data of the roasting furnace at different roasting stages, a stage twin sub-model is constructed for the corresponding roasting stage using digital twin technology. The stage twin sub-model is used to determine the subordinate relationship between the real-time furnace temperature and temperature range of the roasting furnace at the corresponding roasting stage, thereby determining whether there are any abnormalities at the corresponding roasting stage. If so, an anomaly identifier for the corresponding roasting stage is generated and associated with the stage twin model; If not, no action will be taken.

[0023] The process involves integrating stage twin models of the same roasting furnace at different roasting stages, fusing data between the different stage twin models, and constructing a digital twin model of the corresponding roasting furnace. Based on the anomaly indicators of each roasting stage, the digital twin model locates the runaway temperature regions of the corresponding roasting furnace at different roasting stages, as well as the temperature values ​​corresponding to each runaway temperature region. The runaway temperature ranges under different temperature values ​​of different calcining furnaces are statistically analyzed, and a corresponding calcining temperature monitoring node is created for each runaway temperature range. Each calcining temperature monitoring node is used to perform temperature analysis on a runaway temperature range. Based on the structure of the roasting furnace, a blank model architecture is set up, and the runaway temperature regions corresponding to all roasting temperature monitoring nodes are arranged according to their respective structural distribution on the roasting furnace to construct the corresponding roasting temperature field model.

[0024] It should be further explained that, in the specific implementation process, the process of determining the thermal unevenness region of each roasting furnace based on the roasting temperature field model, and selecting the thermal unevenness region of one roasting furnace for optimizing the setting of all corresponding fuel nozzles includes: Each roasting furnace is divided into several temperature recognition zones based on its own structural region. The real-time temperature values ​​of each temperature recognition zone and the different refined temperature zone structures are recorded at each preset time interval under different roasting stages. Different refined temperature zone structures include longitudinal, transverse, and vertical structures; The longitudinal structure is set at several locations along the length of the fire channel of the roasting furnace, specifically divided into the inlet section, the middle section and the outlet section. The transverse structure is divided according to the material box number or anode distribution position of the several feeding boxes included in the roasting furnace. The vertical structure includes the upper anode, the middle anode and the lower anode, each set at its respective position.

[0025] The roasting temperature field model consists of several temperature grids, and each refined temperature region on the roasting furnace is associated with a temperature grid on the corresponding roasting temperature field model. Throughout the roasting process, the digital twin engine automatically executes a temperature field simulation snapshot at preset time intervals. For each snapshot of the roasting operation, it automatically extracts and records the temperature statistical feature values ​​in each temperature identification zone. The temperature statistical feature values ​​include the average temperature, the minimum / maximum temperature, and the temperature standard deviation. Create structured data for each temperature identification zone. The structured data is stored in the form of (furnace number, roasting stage, refined temperature zone structure, timestamp, temperature identification zone ID, temperature statistical feature value). Store the structured data of all temperature identification zones in the set time series database.

[0026] Based on the roasting curve of each roasting stage, the target temperature range of each temperature identification zone at the current time point is obtained, and the corresponding temperature difference allowable threshold is set for each temperature identification zone at different time points. Based on the target temperature range and the temperature difference allowable threshold, the stage compliance temperature range corresponding to each temperature identification zone is determined. If a certain temperature identification zone is not within the compliant temperature range of its respective stage, the corresponding temperature identification zone is determined as the thermal inhomogeneity sub-region corresponding to the roasting furnace. All thermal inhomogeneity sub-regions corresponding to each roasting furnace are integrated to determine the final thermal inhomogeneity region of the corresponding roasting furnace.

[0027] One of the thermally uneven regions corresponding to several roasting furnaces is selected as the optimization target. The working parameters of the fuel nozzles at all positions corresponding to the optimization target are set. The working parameters include fuel flow rate, fuel pressure, combustion air volume and air pressure, nozzle working angle and nozzle opening. By executing the operation of the fuel nozzles, the temperature of the corresponding thermally uneven region is optimized.

[0028] The different operating parameters are explained below: Fuel flow rate is used to determine the heat load of the corresponding input furnace chamber of the roasting furnace. It is the most critical parameter for adjusting furnace temperature and controlling output. During optimization, the heat distribution of the entire roasting furnace area is balanced by adjusting the nozzle flow rate in different areas of the roasting furnace. Fuel pressure is used to determine the fuel injection speed and range. The higher the pressure, the faster the fuel is injected, and the longer and more rigid the flame. If the pressure is too low, the flame will be soft and the range will be short, which may cause incomplete local combustion. Combustion air volume and air pressure are used to affect the air-fuel ratio in different areas of the calciner and the mixing intensity. The air-fuel ratio determines the excess air coefficient during combustion, which directly affects the combustion temperature, flue gas composition (O2, CO) and thermal efficiency. The effect of air pressure on mixing intensity characterizes the penetration, disturbance and mixing effect on fuel. The higher the effect of air pressure on mixing intensity, the more it contributes to complete combustion. The nozzle operating angle is the angle between the nozzle centerline and the furnace baseline (such as horizontal or vertical directions), which directly controls the direction of the flame and the initial release position of heat in the corresponding furnace chamber of the roasting furnace. The nozzle opening is used to determine whether the corresponding regulating valve on the fuel nozzle is stuck, has a dry stroke, or has a mechanical fault. If the nozzle opening does not conform to the preset opening range, there is a problem, and personnel will be arranged to carry out maintenance.

[0029] It should be further explained that, in the specific implementation process, the optimized fuel nozzle is used as a reference object, and the process of optimizing the control parameters of other fuel nozzles based on the reference object includes: A graph sharing terminal for reference objects is constructed. The graph sharing terminal is used to store all the working parameters corresponding to the fuel nozzles that have been optimized. Each category of working parameters is encapsulated into a graph item, and a corresponding message node and script container are deployed for each graph item. The message node is used to determine whether the current map item has been requested to be reused by other fuel nozzles. Based on the determination result, it generates different message identifiers and sets different container actions for the corresponding script container based on the type of the message identifier. The script container is used to perform different container actions on the fuel nozzles that need to be optimized, and to construct a private map corresponding to the fuel nozzle based on the execution results of all container actions of the same fuel nozzle, and to obtain the control parameters for the optimized settings of the fuel nozzle based on the private map.

[0030] It should be noted that the graph sharing terminal is essentially a structured database and semantic interaction interface that stores all optimized working parameters of the reference object. Its functions include parameter storage, graph item encapsulation, reuse request reception and response, and support for cross-region parameter sharing between multiple roasting furnaces and multiple fuel nozzles. The graph sharing terminal includes an ontology layer architecture and a relation layer architecture. The ontology layer architecture defines basic rules such as parameter categories, data formats and reuse permissions, while the relation layer establishes the association mapping between graph items and fuel nozzles and thermally uneven regions. The operating parameter categories refer to the classification of adjustable parameters during the operation of the fuel nozzle. Based on the requirements of the prebaked anode roasting process, they are specifically divided into five categories, including: fuel supply category: fuel flow rate (unit: m³ / h), fuel pressure (unit: MPa); combustion adaptation category: combustion air volume (unit: m³ / h); injection direction category: i.e., injection angle (unit: °), which defines the spatial direction of fuel injection (e.g., horizontal, upward / downward tilt) to adapt to the anode distribution in different areas of the furnace; opening control category: i.e., nozzle opening (unit: %), which controls the effective cross-sectional area of ​​the fuel injection channel; and timing adaptation category: including parameter adjustment response time (unit: ms) and continuous operation duration threshold (unit: h), which adapts to the dynamic temperature requirements of different stages of roasting. A graph item refers to an independent data unit formed by encapsulating each type of working parameter of the reference object according to parameter attributes + execution logic + adaptation conditions. It is the smallest unit for parameter reuse at the graph sharing end. Among them, parameter attributes are used to store specific parameter values ​​(such as fuel flow rate 42m³ / h), parameter accuracy level (such as ±0.1m³ / h), and parameter applicable temperature range (such as 300-800℃). Execution logic is used to record the hardware driving instructions corresponding to the parameter (such as adjusting the fuel flow rate to 42m³ / h). Adaptation conditions are used to clarify the scenario constraints for the reuse of graph items (such as the applicable roasting stage, the type of thermal unevenness region, furnace structure parameters, etc.). The message node is deployed in the semantic recognition and instruction generation unit of each graph item. It is essentially a lightweight communication component built on the OPCUA information model. It is responsible for realizing the reuse request interaction between the graph sharing end and the fuel nozzle to be optimized. Its functions include request parsing, adaptation judgment and identifier generation. It analyzes the nozzle equipment information and thermal region characteristics in the reuse request through semantic matching algorithm to determine whether the current graph item meets the reuse conditions and generates standardized message identifiers. The script container is a modular control unit that integrates parameter compilation, equipment adaptation and action execution functions. It corresponds one-to-one with each graph item and has built-in editable parameter adaptation scripts and hardware driver interfaces. Its function is to convert the parameter information in the graph item into hardware actions that the fuel nozzle to be optimized can directly execute. It supports action generation in two modes: direct reuse and adaptation adjustment. It also has parameter verification function and can automatically detect the rationality of the executed actions to avoid equipment failure.

[0031] The message identifiers generated by the message node based on the judgment result include three types, each type corresponding to a fixed container action instruction. The three types of message identifiers are as follows: Allow direct reuse of the identifier: This indicates that the scene of the nozzle to be optimized is a perfect match with the reference object (adaptation condition overlap ≥ 90%), and the script container executes the direct call action; Allow adaptation and reuse flag: indicates that the scene is partially matched (adaptation conditions overlap 60%-89%), and the script container performs fine-tuning of parameters; Reuse Prohibited Flag: Indicates a scenario mismatch (overlap of adaptation conditions < 60%). The script container will refuse to execute the action and provide the reason for the mismatch. The explanations for direct invocation actions, parameter fine-tuning actions, and actions that refuse to execute are as follows: Direct action invocation: The script container directly extracts the execution logic from the graph item without parameter adjustment, generates hardware driver instructions that are completely consistent with the reference object, and sends them to the fuel nozzle to be optimized; Parameter fine-tuning action: Based on the parameter attributes of the spectrum item, combined with the scene differences in the reuse request (such as furnace structure deviation, different anode distribution density), the parameters are fine-tuned (fine-tuning range ≤ ±15%) through preset adaptation algorithms (such as linear interpolation, proportional adjustment), and the adaptation execution instructions are recompiled to generate the adaptation execution instructions. Refusal to execute action: The script container does not generate drive instructions, and at the same time sends a refusal to reuse notification to the controller at the fuel nozzle to be optimized, explaining the specific reasons for the mismatch (such as incompatible thermal zone types or excessive differences in furnace parameters). The process of reusing nozzle parameters can improve the efficiency of parameter optimization, ensure the accuracy of adaptation, realize global coordination of nozzle parameters, optimize the thermal distribution in the furnace, reduce energy consumption and manual intervention, reduce operational errors, and comprehensively optimize the production control process of prebaked anodes.

[0032] It should be further explained that, in the specific implementation process, after simulating the roasting operation of the anode in the roasting furnace after each optimized setting, and monitoring the changes in the corresponding temperature field, the simulation process of adjusting the roasting based on the monitoring results specifically includes: Based on the optimized control parameters of all fuel nozzles in the same roasting furnace, the roasting cycle segments are set when the roasting furnace simulates roasting the anode. The fuel supply rate, furnace pressure and ventilation parameters of each roasting cycle segment are set as roasting simulation parameters. The anode and the calcining furnace are taken as the objects to be simulated; For each corresponding object to be simulated, a simulation entity is instantiated using digital twins, a simulation environment is built, and the simulation entities of the anode and the calcining furnace are mapped in the simulation environment. Monitoring points are set up at different locations of the simulated entity. Temperature data of each monitoring point is collected at preset time intervals, and a real-time temperature field distribution map is generated to record the rate of change of temperature gradient and temperature uniformity index. The rate of change of temperature gradient and the temperature uniformity index are used as temperature field data.

[0033] The temperature field data is compared with the preset optimal roasting temperature field standard, and the temperature difference of the carbon block core surface corresponding to the anode and the uniformity of the furnace temperature distribution corresponding to the roasting furnace are analyzed to characterize the changes in the temperature field. Based on the results of comparative analysis, the monitoring results of temperature field changes are obtained. Based on the monitoring results, the roasting simulation parameters of the roasting furnace corresponding to different roasting cycle segments are adjusted until the temperature field distribution meets the optimal roasting standard and the performance indicators of the anode are up to standard. Specifically, when there is a local temperature that is too low in the corresponding temperature field, the fuel supply pressure or opening of the nozzle in the corresponding local temperature low area is adjusted, and the auxiliary ventilation volume is increased. When the core surface temperature difference of the anode is too large, the temperature rise rate of the heating section is reduced and the duration of the constant temperature section is extended. When the overall furnace temperature exceeds the standard, the fuel supply of each nozzle is reduced or the staged cooling ventilation is activated to complete the handling measures for different abnormal problems in the temperature field.

[0034] All simulation data is recorded as the execution dataset for the actual roasting process, and the actual roasting in the roasting furnace is completed based on the execution dataset.

[0035] Example 2, please refer to Figure 2 As shown, the present invention also provides a production control system for prebaked anodes used in electrolytic aluminum, the system comprising: The calcination parameter setting module is used to determine several calcination furnaces in the pre-calcination state, construct the sintering pattern corresponding to each calcination furnace, and set the calcination parameters corresponding to the calcination furnace based on the sintering pattern. The temperature field model module is used to initiate the roasting operation of the anode in each roasting furnace, build a corresponding digital twin model for each roasting furnace, and simulate the roasting temperature field model based on all the digital twin models. The thermal unevenness positioning module determines the thermal unevenness region of each roasting furnace based on the roasting temperature field model, selects the thermal unevenness region of a roasting furnace, and uses it to optimize the settings of all corresponding fuel nozzles. The temperature field optimization module is used to use the optimized fuel nozzle as a reference object, optimize the control parameters of other fuel nozzles based on the reference object, simulate the roasting operation of the anode in the roasting furnace after each optimization setting, monitor the changes in the corresponding temperature field, and adjust the roasting simulation process based on the monitoring results.

[0036] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A production control method for prebaked anodes used in electrolytic aluminum, characterized in that, Includes the following steps: Step S1: Determine several calcining furnaces in the pre-calcination state, construct the sintering pattern corresponding to each calcining furnace, and set the calcination parameters corresponding to the calcining furnace based on the sintering pattern. Step S2: Start the roasting operation of the anode in each roasting furnace, build a corresponding digital twin model for each roasting furnace, and simulate the roasting temperature field model based on all the digital twin models; Step S3: Determine the thermal unevenness region of each roasting furnace based on the roasting temperature field model, select the thermal unevenness region of one roasting furnace, and use it to optimize the settings of all corresponding fuel nozzles. Step S4: Using the optimized fuel nozzle as a reference, optimize the control parameters of other fuel nozzles based on the reference, simulate the roasting operation of the anode in the roasting furnace after each optimization, monitor the changes in the corresponding temperature field, and adjust the roasting simulation process based on the monitoring results.

2. The production control method for prebaked anodes for electrolytic aluminum according to claim 1, characterized in that, The process of identifying several calcining furnaces in the pre-calcination state and constructing the sintering pattern corresponding to each calcining furnace includes: The working status of several calcining furnaces is obtained, including the pre-calcination state, the calcination state, and the maintenance state. A number of calcining furnaces in the pre-calcination state are identified, and several calcination processes are obtained for each calcining furnace in the pre-calcination state. Each roasting process is taken as a roasting node, and all roasting nodes are mapped onto a preset blank map to construct the sintering map corresponding to each roasting furnace. The sintering map includes material map, geometric map and equipment map.

3. The production control method for prebaked anodes for electrolytic aluminum according to claim 2, characterized in that, The process of initiating the anode roasting operation in each roasting furnace, constructing a corresponding digital twin model for each roasting furnace, and simulating the roasting temperature field model based on all the digital twin models includes: The roasting operation is divided into different types of roasting stages based on the roasting process. Temperature ranges for different roasting stages are set. Based on the roasting data of different roasting stages, stage twin sub-models for the corresponding roasting stages are constructed using digital twin technology. Integrate the stage twin models of the same roasting furnace at different roasting stages, perform data fusion on the different stage twin models, construct the corresponding digital twin model of the roasting furnace, and locate the runaway temperature region of the roasting furnace at different roasting stages, as well as the temperature value of each runaway temperature region. A calcination temperature monitoring node is created for each runaway temperature region for temperature analysis. A blank model architecture is set up based on the structure of the calcination furnace. The runaway temperature regions corresponding to all calcination temperature monitoring nodes are arranged according to their respective structural distribution on the calcination furnace to construct a calcination temperature field model.

4. The production control method for prebaked anodes for electrolytic aluminum according to claim 3, characterized in that, Based on the calcination temperature field model, the thermal unevenness region of each calcination furnace is determined. The process of selecting a thermal unevenness region of one calcination furnace for optimizing the settings of all corresponding fuel nozzles includes: The roasting furnace is divided into several temperature recognition zones based on its structural regions. The real-time temperature values ​​of the different refined temperature regions included in the temperature recognition zone are recorded at each preset time interval under different roasting stages. Throughout the roasting process, a temperature field simulation snapshot is executed at a preset time interval to record the temperature statistical characteristic values ​​in each temperature identification zone, create structured data for each temperature identification zone, and store the structured data in the set time-series database. Based on the roasting curve of each roasting stage, the target temperature range of each temperature identification zone at the current time point is obtained, and the allowable temperature difference threshold is set for each temperature identification zone at different time points, thereby determining the stage compliance temperature range of each temperature identification zone. If a certain temperature identification zone is not within its respective stage compliant temperature range, the corresponding temperature identification zone is determined as a thermally uneven sub-region. All thermally uneven sub-regions in the same roasting furnace are integrated to determine the final thermally uneven region. Select one of several thermally uneven regions as the optimization target, and set the operating parameters of the fuel nozzles at all locations corresponding to the optimization target.

5. The production control method for prebaked anodes for electrolytic aluminum according to claim 4, characterized in that, The roasting temperature field model consists of several temperature grids, which associate each refined temperature region on the roasting furnace with a temperature grid on the corresponding roasting temperature field model.

6. The production control method for prebaked anodes for electrolytic aluminum according to claim 5, characterized in that, The process of using the optimized fuel nozzle as a reference and optimizing the control parameters of other fuel nozzles based on the reference includes: A graph sharing endpoint for reference objects is constructed to store all operating parameters of fuel nozzles that have been optimized. Each category of operating parameters is encapsulated into a graph item, and each graph item is associated with a deployment message node and a script container. The message node is used to determine whether the current map item has been requested to be reused by other fuel nozzles, generate different message identifiers based on the determination result, and set different container actions for the corresponding script container; The script container is used to perform different container actions on the fuel nozzles that need to be optimized, and to construct a private map corresponding to the fuel nozzle based on the execution results of all container actions of the same fuel nozzle, and to obtain the control parameters for the optimized settings of the fuel nozzle based on the private map.

7. The production control method for prebaked anodes for electrolytic aluminum according to claim 6, characterized in that, After simulating the calcination operation of the anode in the calcination furnace after each optimized setting, and monitoring the changes in the corresponding temperature field, the simulation process of adjusting the calcination based on the monitoring results specifically includes: Based on all the control parameters of the same calcining furnace, the calcining cycle segments are set when the calcining furnace simulates the calcining of the anode, and the calcining simulation parameters for each calcining cycle segment are set. The anode and the calcining furnace are taken as objects to be simulated. Their respective simulation entities are instantiated, a simulation environment is built, and their respective simulation entities are matched in the simulation environment. The different locations of the simulated entity are set up as monitoring points, and the temperature data of each monitoring point is collected to generate a real-time temperature field distribution map. The temperature field distribution map is used to record the temperature field data. The temperature field data is compared with the preset optimal roasting temperature field standard to analyze the surface temperature difference of the carbon block corresponding to the anode and the uniformity of the furnace temperature distribution corresponding to the roasting furnace, which is then used to characterize the changes in the temperature field and as a monitoring result. Based on the monitoring results, the roasting simulation parameters of the roasting furnace corresponding to different roasting cycle segments are adjusted until the temperature field distribution meets the optimal roasting standard and the performance indicators of the anode are up to standard. All simulation data are recorded as the execution dataset of the actual roasting process, and the actual roasting of the roasting furnace is completed based on the execution dataset.

8. A production control system for prebaked anodes used in electrolytic aluminum production, for implementing the production control method for prebaked anodes according to any one of claims 1 to 7, characterized in that, The system includes: The calcination parameter setting module is used to determine several calcination furnaces in the pre-calcination state, construct the sintering pattern corresponding to each calcination furnace, and set the calcination parameters corresponding to the calcination furnace based on the sintering pattern. The temperature field model module is used to initiate the roasting operation of the anode in each roasting furnace, build a corresponding digital twin model for each roasting furnace, and simulate the roasting temperature field model based on all the digital twin models. The thermal unevenness positioning module determines the thermal unevenness region of each roasting furnace based on the roasting temperature field model, selects the thermal unevenness region of a roasting furnace, and uses it to optimize the settings of all corresponding fuel nozzles. The temperature field optimization module is used to use the optimized fuel nozzle as a reference object, optimize the control parameters of other fuel nozzles based on the reference object, simulate the roasting operation of the anode in the roasting furnace after each optimization setting, monitor the changes in the corresponding temperature field, and adjust the roasting simulation process based on the monitoring results.

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