A dynamic oxygen supply method and device for vanadium extraction in a converter

Abstract

The application provides a converter vanadium extraction dynamic oxygen supply method and device, and belongs to the technical field of steel metallurgy. The method comprises the following steps: based on real-time component content data and real-time bath temperature of vanadium-containing molten iron to be blown in the converter, a real-time vanadium oxidation critical temperature is calculated through a target vanadium oxidation critical temperature model; the target vanadium oxidation critical temperature model is obtained by fitting historical vanadium extraction production data corresponding to multiple historical production heats of the converter; a bath temperature regulation interval is determined based on the real-time vanadium oxidation critical temperature, target oxygen lance position parameters and oxygen supply intensity target parameters are determined based on the size relationship between the real-time bath temperature and the bath temperature regulation interval; and oxygen supply control is performed on the oxygen lance actuator based on the target oxygen lance position parameters and the oxygen supply intensity target parameters. The application can realize dynamic oxygen supply, adapt to bath temperature fluctuations and improve vanadium resource utilization.

Description

Technical Field

[0001] This application belongs to the field of iron and steel metallurgy technology, and more specifically, relates to a dynamic oxygen supply method and apparatus for vanadium extraction in a converter. Background Technology

[0002] Vanadium extraction in converters is a core process for the comprehensive utilization of vanadium-containing molten iron. It utilizes the principle of selective oxidation reaction to preferentially oxidize vanadium in the molten iron into the slag to form vanadium slag, while retaining carbon in the molten iron to meet the requirements of subsequent steelmaking. It is the main way to produce vanadium products in industry and directly determines the utilization rate of vanadium resources and the comprehensive economic benefits of steel products.

[0003] Currently, the vanadium extraction process in converters using fixed-parameter oxygen supply is widely adopted in industrial production. This means that operators preset the oxygen lance position and oxygen supply intensity based on historical production experience, maintaining these parameters constant throughout the blowing process. However, this fixed oxygen supply mode cannot respond to dynamic changes in the ferroelectric properties of molten iron, making it difficult to stably control the molten pool temperature within a reasonable range. When the molten pool temperature is too low, the vanadium oxidation reaction kinetics are insufficient, resulting in incomplete vanadium oxidation and a significant decrease in vanadium recovery. When the molten pool temperature is too high, the carbon oxidation reaction rate increases dramatically, not only causing excessively low carbon content in the semi-steel, increasing energy consumption and costs in subsequent steelmaking processes, but also leading to excessive phosphorus reversion. Furthermore, the vanadium slag grade fluctuates significantly, reducing the value of subsequent deep processing of the vanadium slag. Summary of the Invention

[0004] The purpose of this application is to provide a dynamic oxygen supply method and apparatus for vanadium extraction in a converter, to solve the problem that a fixed oxygen supply mode cannot adapt to fluctuations in the molten pool temperature, resulting in low vanadium resource utilization and unstable quality. To solve the above problems, the technical solution provided by this application is as follows: Firstly, a dynamic oxygen supply method for vanadium extraction in a converter is provided, including: Based on the real-time composition data and real-time molten pool temperature of the vanadium-containing molten iron to be blown in the converter, the real-time critical temperature for vanadium oxidation is calculated by the target vanadium oxidation critical temperature model. The target vanadium oxidation critical temperature model is obtained by fitting the historical vanadium extraction production data corresponding to each of the multiple historical production furnaces of the converter. The molten pool temperature control range is determined based on the real-time critical temperature of vanadium oxidation, and the target oxygen lance position parameters and target oxygen supply intensity parameters are determined based on the relationship between the real-time molten pool temperature and the molten pool temperature control range. The oxygen supply is controlled by the oxygen lance actuator based on the target oxygen lance position parameters and the target oxygen supply intensity parameters.

[0005] Secondly, a dynamic oxygen supply device for vanadium extraction in a converter is provided, comprising: The critical temperature dynamic calculation module is used to calculate the real-time critical temperature of vanadium oxidation based on the real-time composition content data and real-time molten pool temperature of the vanadium-containing molten iron to be blown in the converter, through the target vanadium oxidation critical temperature model. The target vanadium oxidation critical temperature model is obtained by fitting the historical vanadium extraction production data corresponding to multiple historical production furnaces of the converter. The oxygen supply parameter determination module is used to determine the molten pool temperature control range based on the real-time critical temperature of vanadium oxidation, and to determine the target oxygen lance position parameters and target oxygen supply intensity parameters based on the relationship between the real-time molten pool temperature and the molten pool temperature control range. The oxygen supply control module is used to control the oxygen supply to the oxygen lance actuator based on the target oxygen lance position parameters and the target oxygen supply intensity parameters.

[0006] Thirdly, embodiments of this application also provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement a converter vanadium extraction dynamic oxygen supply method provided by any possible implementation of the first aspect.

[0007] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements a dynamic oxygen supply method for vanadium extraction in a converter provided by any possible implementation of the first aspect.

[0008] The beneficial effects of the technical solution provided in this application are as follows: The present application provides a method and apparatus for dynamic oxygen supply in a converter for vanadium extraction, which, compared with related technologies, offers the following advantages: This application embodiment establishes a critical temperature model for vanadium oxidation that reflects the intrinsic relationship between molten iron composition and temperature through fitting analysis of a large amount of historical production data. This model can calculate the vanadium and carbon oxidation equilibrium point temperature corresponding to the current furnace batch of molten iron in real time, and use this as a benchmark to determine the optimal molten pool temperature control range. Based on this, this application embodiment automatically matches the corresponding oxygen lance position and oxygen supply intensity according to the real-time detected molten pool temperature. When the temperature is too low, the oxygen supply intensity is increased to accelerate the temperature rise and ensure the vanadium oxidation reaction proceeds fully; when the temperature is too high, the oxygen supply intensity is decreased to slow down the temperature rise and inhibit excessive carbon oxidation and reverse phosphorus dissolution.

[0009] The embodiments of this application can automatically adapt to the differences in the characteristics of molten iron from different furnaces, and can stably control the molten pool temperature within the optimal range for preferential vanadium oxidation. This not only significantly improves the vanadium recovery rate, but also ensures that the carbon and phosphorus content of the semi-steel meets the standards, while reducing the fluctuation range of vanadium slag grade, effectively improving the comprehensive utilization value of vanadium resources and the economic efficiency of subsequent production processes. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0011] Figure 1 A schematic flowchart of a dynamic oxygen supply method for vanadium extraction in a converter, provided for an embodiment of this application; Figure 2 A structural block diagram of a converter vanadium extraction dynamic oxygen supply device provided in this application embodiment; Figure 3 A schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] The embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions of the embodiments of this application.

[0013] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this application mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element establish a connection relationship through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” can be implemented as “A,” or as “B,” or as “A and B.” When describing multiple (two or more) items, if the relationship between the multiple items is not explicitly defined, the multiple items can refer to one, several or all of the multiple items. For example, the description of "parameter A includes A1, A2, A3" can be implemented as parameter A includes A1 or A2 or A3, or it can be implemented as parameter A includes at least two of the three items A1, A2 and A3.

[0014] It is understood that in the embodiments of this application, data such as user information are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0015] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0016] This application provides a dynamic oxygen supply method for vanadium extraction in a converter, which can be executed by electronic equipment, such as... Figure 1 As shown, the method may include: S101: Based on the real-time composition data and real-time molten pool temperature of the vanadium-containing molten iron to be blown in the converter, the real-time critical temperature of vanadium oxidation is calculated by the target vanadium oxidation critical temperature model; the target vanadium oxidation critical temperature model is obtained by fitting the historical vanadium extraction production data corresponding to each of the multiple historical production furnaces of the converter.

[0017] In this embodiment, the real-time composition data includes real-time silicon content and real-time titanium content; based on the real-time composition data and real-time molten pool temperature of the vanadium-containing molten iron to be smelted in the converter, the real-time critical temperature for vanadium oxidation is calculated using a target vanadium oxidation critical temperature model, including: Based on the real-time silicon content, real-time titanium content, and real-time molten pool temperature of the vanadium-containing molten iron to be smelted, the real-time critical temperature for vanadium oxidation is calculated using the target critical temperature model for vanadium oxidation. The target vanadium oxidation critical temperature model is as follows:

[0018] in, This refers to the real-time critical temperature for vanadium oxidation. , b and c These are the model fitting coefficients. d For constant terms; Real-time silicon content, For real-time titanium content, This represents the real-time molten pool temperature.

[0019] In this embodiment, real-time component content data refers to the elemental content of molten iron detected in real time during the blowing process, including silicon and titanium content, used to characterize the thermal properties of molten iron, for example, silicon content 0.40% and titanium content 0.12%. Real-time molten pool temperature refers to the real-time temperature within the molten pool during blowing, reflecting the thermal state of the reaction, for example, 1280℃. The target vanadium oxidation critical temperature model refers to a mathematical model obtained by fitting historical data, used to calculate the vanadium-carbon oxidation equilibrium temperature; inputting silicon and titanium content and temperature will output the critical temperature. The molten pool temperature control range refers to the temperature range set based on the critical temperature to ensure preferential vanadium oxidation, for example, 1250-1280℃. The target oxygen lance position parameter refers to the target height of the oxygen lance, used to control the oxygen supply effect, for example, 1.5m. The target oxygen supply intensity parameter refers to the oxygen supply volume per unit time, used to control the reaction rate, for example, 2.6m³ / (t). (min). The parameters in the target vanadium oxidation critical temperature model are dimensionless and calculated solely based on their numerical values. The fitting process using historical data also only fits the dependencies between the numerical values ​​themselves.

[0020] For example, this embodiment can use a laser-induced breakdown spectrophotometry (LIBS) online detection system to detect the real-time silicon content (0.40%), titanium content (0.12%), and molten pool temperature (1280℃) of the vanadium-containing molten iron to be smelted, with silicon content accuracy ±0.01%, titanium content accuracy ±0.01%, and temperature accuracy ±5℃. This embodiment can retrieve a pre-fitted target vanadium oxidation critical temperature model, which is obtained by fitting historical production data from over 1000 heats of the same specification using multiple linear regression. The collected real-time silicon, titanium content, and temperature are input into the model to calculate the current real-time vanadium oxidation critical temperature. For example, the model fitting coefficients are a=100, b=50, c=0.9, and d=180. Substituting these values ​​into the calculation yields 0.4%×100+0.12%×50+1280×0.9+180=1332.46.

[0021] This embodiment uses the real-time critical temperature for vanadium oxidation as a benchmark, setting a lower limit of 120°C and an upper limit of 90°C to determine the molten pool temperature control range, ensuring that the range covers the optimal temperature range for rapid vanadium oxidation. If the real-time molten pool temperature is lower than the lower limit of the range, the target oxygen lance position is determined to be 1.2-1.4m, and the oxygen supply intensity is determined to be 2.8-3.5m³ / (t). (min); when the temperature is within the specified range, determine the gun position to be 1.4-1.6m and the oxygen supply intensity to be 2.3-3.0m³ / (t). (min); if the temperature is higher than the upper limit of the range, determine the gun position to be 1.6-1.8m and the oxygen supply intensity to be 2.0-2.7m³ / (t). This embodiment can send the target parameters to the oxygen lance actuator, with a lance position adjustment accuracy of ±0.05m and an oxygen supply intensity adjustment accuracy of ±0.1m³ / (t). The response time is ≤5 seconds and is continuously dynamically adjusted until the blowing process is completed.

[0022] This embodiment achieves precise calculation of critical temperature through data-driven modeling and dynamically matches oxygen supply parameters, solving the problem that traditional fixed oxygen supply cannot adapt to fluctuations in molten iron. This embodiment can stably control the molten pool temperature within the vanadium preferential oxidation range, significantly improving vanadium recovery rate, ensuring that the carbon and phosphorus content of semi-steel meets the standards, reducing fluctuations in vanadium slag grade, reducing smelting energy consumption, and exhibiting strong process adaptability, which is conducive to stable industrial-scale application.

[0023] In this embodiment, the historical vanadium extraction production data includes the iron composition data, molten pool temperature data, and smelting index data corresponding to the historical production furnaces; the iron composition data includes carbon content data and vanadium content data. The target vanadium oxidation critical temperature model was obtained by fitting historical vanadium extraction production data corresponding to multiple historical production furnaces of the converter in the following manner: Based on smelting index data, multiple historical production furnaces of the converter were divided into multiple subsets of historical production furnaces. The initial vanadium oxidation critical temperature model corresponding to the historical production furnace for each historical production furnace in each historical production furnace subset is obtained by iteratively fitting the historical vanadium extraction production data corresponding to each historical production furnace subset using a multiple linear regression algorithm. The target vanadium oxidation critical temperature model is determined based on the initial vanadium oxidation critical temperature models corresponding to each of the multiple historical production furnace subsets.

[0024] In this embodiment, the historical vanadium extraction production data corresponding to each historical production furnace in each historical production furnace subset is iteratively fitted using a multiple linear regression algorithm, including: For each historical production furnace, a carbon content variation curve is obtained based on the carbon content data corresponding to that historical production furnace, and a vanadium content variation curve is obtained based on the vanadium content data corresponding to that historical production furnace. The first derivatives of the carbon content variation curve and the vanadium content variation curve are obtained to obtain the carbon oxidation rate curve and the vanadium oxidation rate curve, respectively. Based on the intersection of the carbon oxidation rate curve and the vanadium oxidation rate curve, the critical temperature for vanadium oxidation corresponding to that historical production furnace is determined. The critical vanadium oxidation temperature corresponding to each historical production furnace in each historical production furnace subset is used as the output label dataset corresponding to that historical production furnace subset. Use the historical vanadium extraction production data corresponding to each historical production furnace in each historical production furnace subset as the input dataset for that historical production furnace subset. Multiple linear regression fitting is performed on the input dataset and output label dataset corresponding to each subset of historical production furnaces.

[0025] In this embodiment, the target vanadium oxidation critical temperature model is determined based on the initial vanadium oxidation critical temperature models corresponding to each of multiple historical production furnace subsets, including: Obtain the validation dataset, and validate the initial vanadium oxidation critical temperature model for each of the multiple historical production furnace subsets based on the validation dataset, and obtain the fitting degree and prediction error of each initial vanadium oxidation critical temperature model. Multiple qualified models that meet the preset goodness-of-fit threshold and the preset error threshold are selected, and the target vanadium oxidation critical temperature model is determined based on the multiple qualified models.

[0026] In this embodiment, historical vanadium production data refers to the complete process data retained during the converter's past production, used for model training. It covers all smelting-related information for each heat, such as production data from over 1000 consecutive heats of a 120t converter. Historical production heats refer to the complete process record of a single smelting operation in the converter's past, serving as the basic unit for data statistics, such as heats numbered 001 to 1000 within a certain batch. Smelting index data refers to quantitative data reflecting the final effect after a single heat smelting, used for heat division and model verification, such as vanadium recovery rate, semi-steel carbon content, phosphorus content, and vanadium slag grade. Carbon content data refers to the record of carbon content in molten iron, including time-series variation data, used to derive the oxidation rate, such as the carbon content value collected every 30 seconds during the entire blowing process. Vanadium content data refers to the record of vanadium content in molten iron, including time-series variation data, used to derive the oxidation rate, such as the vanadium content value collected every 30 seconds during the entire blowing process. The historical production furnace subset refers to the grouping of furnaces according to smelting indicators, used for regional model fitting, such as the set of qualified furnaces with a vanadium recovery rate of over 85%. Iterative fitting refers to the process of repeatedly optimizing model parameters to improve fitting accuracy, such as adjusting regression coefficients multiple times until the error is minimized. The carbon / vanadium content change curve refers to the trajectory of carbon / vanadium content changing with blowing time, used for rate derivation, such as a curve plotted with time on the horizontal axis and content on the vertical axis.

[0027] First-order derivatives are used to obtain the oxidation rate; for example, differentiating the content curve yields the instantaneous oxidation rate. The carbon / vanadium oxidation rate curve refers to the trajectory of the carbon / vanadium oxidation rate over time, used to determine the critical temperature; for example, a curve showing the rate change with the blowing process. The output label dataset refers to the target data for model training, i.e., the critical temperatures of each furnace, such as the critical temperature values ​​obtained by inverting from each qualified furnace. The input dataset refers to the feature data for model training, i.e., historical production data for each furnace, such as silicon, titanium, carbon content, and molten pool temperature data. The validation dataset refers to independent data used to test the model's accuracy, eliminating training interference; for example, randomly selecting data from 200 furnaces that did not participate in training. The goodness of fit refers to the degree of agreement between the model's calculated values ​​and the actual values, measuring model quality; for example, a goodness of fit R² ≥ 0.92. The prediction error refers to the difference between the model's calculated values ​​and the actual values, measuring model accuracy; for example, an average error ≤ 5℃. The preset goodness of fit threshold refers to the minimum goodness of fit standard for selecting qualified models; for example, a preset R² ≥ 0.92. The preset error threshold refers to the highest standard of prediction error for selecting qualified models, such as a preset average error ≤ 5℃. A qualified model refers to an initial model that simultaneously meets the requirements for goodness of fit and error, and is used to construct the final model, such as an initial model corresponding to the high-silicon-titanium content range.

[0028] For example, this embodiment can extract historical production heat data from a production database of converters of the same specification, covering more than 1000 consecutive heats. The data types strictly correspond to three categories: molten iron composition data, molten pool temperature data, and smelting index data. The molten iron composition data includes carbon content data and vanadium content data, along with auxiliary component data such as silicon and titanium content. The molten pool temperature data is a time-series temperature record of the entire blowing process, collected at 30-second intervals with an accuracy of ±5℃. The smelting index data includes four core indicators: vanadium recovery rate, semi-steel carbon content, semi-steel phosphorus content, and vanadium slag grade. The collected raw data is preprocessed to remove abnormal data, missing data, and extreme values ​​caused by equipment malfunctions or operational errors, retaining valid heat data to ensure data authenticity and validity. After preprocessing, the number of valid heats is no less than 850.

[0029] This embodiment can use smelting index data as the basis for setting qualified furnace batch standards: vanadium recovery rate ≥80%, semi-steel carbon content 3.0%-3.5%, semi-steel phosphorus content ≤0.08%, and vanadium slag grade 10%-15%. Furnaces meeting these standards are classified as qualified subsets, and the remaining furnace batches are classified as abnormal subsets. This embodiment can further refine the qualified subsets according to the range of silicon content, titanium content, and initial temperature. The silicon content ranges are set as 0.3%-0.35%, 0.35%-0.4%, and 0.4%-0.5%, the titanium content ranges are set as 0.05%-0.1%, 0.1%-0.15%, and 0.15%-0.2%, and the initial temperature ranges are set as 1200-1250℃, 1250-1300℃, and 1300-1350℃. Each refined subset contains no less than 50 furnace data, forming multiple historical production furnace batch subsets.

[0030] This embodiment extracts time-series carbon and vanadium content data for each historical production furnace, plotting carbon and vanadium content variation curves with blowing time on the horizontal axis and content values ​​on the vertical axis. This embodiment uses numerical differentiation to perform first-order derivative processing on the two curves, calculating the instantaneous oxidation rate at each time point, generating carbon oxidation rate curves and vanadium oxidation rate curves respectively. This embodiment can determine the intersection point of the two rate curves; the real-time temperature of the molten pool corresponding to this intersection point is the critical vanadium oxidation temperature for that furnace, completing the single-furnace critical temperature inversion with an inversion accuracy controlled within ±3℃.

[0031] This embodiment can summarize the critical vanadium oxidation temperatures obtained from the inversion of each furnace in the subset of historical production furnaces and organize them into an output label dataset. This embodiment can also summarize the iron composition data (carbon, vanadium, silicon and titanium content) and molten pool temperature data corresponding to each furnace in the subset and organize them into an input dataset to ensure that the input data corresponds one-to-one with the output data.

[0032] This embodiment employs a multiple linear regression algorithm to iteratively fit the input dataset and output label dataset corresponding to each subset of historical production furnaces. Initially, the regression coefficients are set to an initial range: silicon content coefficient 80-120, titanium content coefficient 40-60, temperature coefficient 0.85-0.95, and constant term 150-200. This embodiment gradually adjusts the regression coefficients through iterative optimization. Each iteration calculates the model fit and prediction error. The iteration terminates when the fit improves by less than 0.01 and the prediction error decreases by less than 0.5℃ after three consecutive iterations, yielding the initial vanadium oxidation critical temperature model for that subset. Each initial model has a fit of no less than 0.90.

[0033] In this embodiment, 200 furnaces of data that were not used in model training can be randomly selected from the preprocessed valid furnace batches as a validation dataset. This dataset covers various compositions and temperature ranges, and the data is evenly distributed. This embodiment can input the validation dataset into each initial vanadium oxidation critical temperature model and calculate the goodness of fit and prediction error of each model. This embodiment can preset a goodness of fit threshold of 0.92 and a prediction error threshold of 5℃, selecting qualified models with a goodness of fit ≥ 0.92 and a prediction error ≤ 5℃, and eliminating substandard initial models, ensuring that the number of qualified models is no less than 3.

[0034] This embodiment can construct a target vanadium oxidation critical temperature model by using a model fusion method on multiple qualified models selected through screening. For example, based on the verification accuracy of each qualified model in different composition and temperature ranges, different weights are assigned, with higher accuracy resulting in greater weights, and the weight values ​​ranging from 0.2 to 0.5. This embodiment can also perform weighted calculations on the regression coefficients of each qualified model to obtain the regression coefficients of the target model, ultimately determining the target vanadium oxidation critical temperature model. The overall model fit is ≥0.92, the average prediction error is ≤4℃, and it is suitable for smelting requirements under all working conditions.

[0035] This embodiment divides the furnace batches into subsets based on smelting indices, fits an initial model to each region, obtains the critical temperature through inversion, and then verifies and selects qualified models before merging them to make the target model adaptable to different molten iron conditions. The model has high fitting accuracy and small prediction error, accurately reflecting the correlation between molten iron composition, temperature, and critical temperature, providing a reliable basis for oxygen supply control, effectively avoiding the problem of poor adaptability of traditional models, ensuring the stability of the vanadium extraction process, helping to improve vanadium recovery rate, stabilize vanadium slag grade, and adapt to the needs of industrial-scale production.

[0036] S102: Determine the molten pool temperature control range based on the real-time critical temperature of vanadium oxidation, and determine the target oxygen lance position parameters and target oxygen supply intensity parameters based on the relationship between the real-time molten pool temperature and the molten pool temperature control range.

[0037] In this embodiment, determining the molten pool temperature control range based on the real-time vanadium oxidation critical temperature includes: Based on the real-time critical temperature of vanadium oxidation, the lower limit of the molten pool temperature control range is determined to be the difference between the real-time critical temperature of vanadium oxidation and the first preset temperature threshold, and the upper limit is the difference between the real-time critical temperature of vanadium oxidation and the second preset temperature threshold, wherein the second preset temperature threshold is less than the first preset temperature threshold. Based on the lower and upper limits, the temperature control range of the molten pool is obtained.

[0038] In this embodiment, the target oxygen lance position parameters and target oxygen supply intensity parameters are determined based on the relationship between the real-time molten pool temperature and the molten pool temperature control range, including: When the real-time molten pool temperature is lower than the lower limit of the molten pool temperature control range, the target oxygen lance position parameter is determined as the first lance position, and the target oxygen supply intensity parameter is determined as the first oxygen supply intensity. When the real-time molten pool temperature is within the molten pool temperature control range, the target oxygen lance position is determined as the second lance position, and the target oxygen supply intensity parameter is determined as the second oxygen supply intensity. When the real-time molten pool temperature is higher than the upper limit of the molten pool temperature control range, the target oxygen lance position is determined as the third lance position, and the target oxygen supply intensity parameter is determined as the third oxygen supply intensity. The third gun position is greater than the second gun position, and the second gun position is greater than the first gun position; the first oxygen supply intensity is greater than the second oxygen supply intensity, and the second oxygen supply intensity is greater than the third oxygen supply intensity.

[0039] In this embodiment, the molten pool temperature control range refers to the temperature range calculated based on the real-time critical temperature for vanadium oxidation, used to guide the matching of oxygen supply parameters and ensure preferential oxidation of vanadium, for example, 1250-1280℃. The first preset temperature threshold refers to a fixed temperature value at the lower limit of the calculated control range, with an industrial verification adaptation range of 110-130℃, for example, 120℃. The second preset temperature threshold refers to a fixed temperature value at the upper limit of the calculated control range, less than the first threshold, with an adaptation range of 80-100℃, for example, 90℃. The first lance position refers to the height of the low-level oxygen lance when the molten pool temperature is low, used to enhance oxygen supply and temperature rise, for example, 1.2-1.4m. The second lance position refers to the height of the middle-level oxygen lance when the molten pool temperature is normal, used to stabilize the oxidation reaction, for example, 1.4-1.6m. The third lance position refers to the height of the high-level oxygen lance when the molten pool temperature is high, used to weaken oxygen supply and temperature control, for example, 1.6-1.8m. The first oxygen supply intensity refers to the high-intensity oxygen supply value under low-temperature conditions, with an adaptation range of 2.8-3.5m³ / (t). min), for example 3.0 m³ / (t The second oxygen supply intensity refers to the medium oxygen supply value under normal operating conditions, with an applicable range of 2.3-3.0 m³ / (t). min), for example 2.6 m³ / (t The third oxygen supply intensity refers to the low-intensity oxygen supply value under high-temperature conditions, with an applicable range of 2.0-2.7 m³ / (t). min), for example 2.4 m³ / (t min).

[0040] For example, this embodiment can use an online detection system to collect the real-time silicon content, real-time titanium content, and real-time molten pool temperature of the vanadium-containing molten iron to be smelted. The detection frequency is once every 20 seconds, the detection accuracy of silicon and titanium content is ±0.01%, and the detection accuracy of molten pool temperature is ±5℃. This embodiment can input the above real-time data into a pre-trained target vanadium oxidation critical temperature model. The model is fitted with industrial data from more than 1000 heats and can quickly output the real-time vanadium oxidation critical temperature. For example, the real-time vanadium oxidation critical temperature calculated for a certain heat is 1370℃.

[0041] Both the first and second preset temperature thresholds were determined based on multiple rounds of industrial trials and production data statistics. This embodiment selects typical production data from 500 furnaces with different compositions and temperature ranges, and combines this data with core smelting indicators such as vanadium recovery rate and semi-steel carbon content to repeatedly verify the impact of different threshold combinations on the molten pool temperature control. Ultimately, the first preset temperature threshold was fixed at 120℃, and the second preset temperature threshold was fixed at 90℃, ensuring that the calculated control range accurately covers the optimal kinetic range for rapid vanadium oxidation and slow carbon oxidation.

[0042] This embodiment uses the real-time critical temperature for vanadium oxidation as a benchmark, substituting it with preset temperature thresholds to calculate the upper and lower limits of the temperature control range. Subtracting the first preset temperature threshold from the real-time critical temperature for vanadium oxidation yields the lower limit of the molten pool temperature control range. This embodiment also subtracts a second preset temperature threshold from the real-time critical temperature for vanadium oxidation to obtain the upper limit of the molten pool temperature control range. For example, when the real-time critical temperature for vanadium oxidation is 1370℃, the lower limit is 1370℃ - 120℃ = 1250℃, and the upper limit is 1370℃ - 90℃ = 1280℃, thus determining the molten pool temperature control range as 1250-1280℃.

[0043] During the blowing process, the real-time molten pool temperature is continuously collected by an online temperature measuring device inside the furnace. This data is transmitted in real-time to the smelting control system, providing accurate data support for range determination. For example, different molten pool temperatures such as 1240℃, 1265℃, and 1290℃ are collected in real-time. This embodiment can compare the real-time molten pool temperature with the numerical relationship between the molten pool temperature and the molten pool temperature control range in real time, accurately matching the target oxygen lance position parameters and oxygen supply intensity target parameters under three operating conditions: When the real-time molten pool temperature is below 1250℃, it is determined to be a low-temperature operating condition, and the first gun position is set at 1.2-1.4m, and the first oxygen supply intensity is set at 2.8-3.5m³ / (t). (min), this parameter can enhance oxygen impact, accelerate the strong exothermic oxidation reaction of silicon and titanium, and rapidly increase the temperature of the molten pool; When the real-time molten pool temperature is within the range of 1250-1280℃, it is considered a normal operating condition. The second lance position is determined to be 1.4-1.6m, and the second oxygen supply intensity to be 2.3-3.0m³ / (t). This parameter (min) can maintain a stable oxidation rate and ensure that vanadium continues to be preferentially oxidized. When the real-time molten pool temperature exceeds 1280℃, it is determined to be a high-temperature operating condition. The third lance position is then set at 1.6-1.8m, and the third oxygen supply intensity is set at 2.0-2.7m³ / (t). This parameter (min) can weaken oxygen impact, slow down the exothermic oxidation, and inhibit excessive carbon oxidation.

[0044] In this embodiment, the matched target oxygen lance position parameters and oxygen supply intensity target parameters can be sent to the oxygen lance actuator. The oxygen lance position adjustment accuracy is ±0.05m, and the oxygen supply intensity adjustment accuracy is ±0.1m³ / (t) The parameter adjustment response time is no more than 5 seconds. After execution, the real-time molten pool temperature is continuously fed back, forming a closed-loop control until the blowing is completed. No manual intervention is required throughout the process, ensuring accurate and timely control.

[0045] This embodiment dynamically sets the temperature range based on the real-time critical temperature for vanadium oxidation, precisely matching oxygen supply parameters according to different operating conditions, and can quickly respond to fluctuations in the molten pool temperature. This embodiment can effectively avoid the problems of insufficient vanadium oxidation when the temperature is too low and excessive carbon oxidation when the temperature is too high, stabilizing the molten pool in the optimal range for preferential vanadium oxidation, improving vanadium recovery rate, stabilizing vanadium slag grade and semi-steel quality, reducing fluctuations in smelting indicators, adapting to dynamic industrial operating conditions, and ensuring stable and efficient operation of vanadium extraction production.

[0046] S103: Control the oxygen supply of the oxygen lance actuator based on the target oxygen lance position parameters and the target oxygen supply intensity parameters.

[0047] In this embodiment, the target oxygen lance position parameter refers to the set height that the oxygen lance needs to reach to match the oxygen supply impact effect, for example, 1.5m. The target oxygen supply intensity parameter refers to the set value of oxygen supply per unit time to control the oxidation reaction rate, for example, 2.6m³ / (t). The oxygen lance actuator is the device responsible for raising and lowering the oxygen lance and regulating the oxygen flow rate; it is the unit responsible for executing the oxygen supply action. Oxygen supply control refers to adjusting the oxygen lance's movement and oxygen supply according to target parameters to achieve precise control of the smelting process.

[0048] For example, in this embodiment, after determining the target oxygen lance position parameters and the target oxygen supply intensity parameters, these are transmitted in real time to the oxygen lance actuator via an industrial control bus. Upon receiving the position parameters, the oxygen lance actuator drives the lifting mechanism to precisely move the oxygen lance to the target height, with an adjustment accuracy controlled within ±0.05m, for example, from 1.3m to 1.5m. This embodiment can also synchronously control the oxygen flow regulating device to adjust the oxygen output according to the target oxygen supply intensity parameters, with an adjustment accuracy of ±0.1m³ / (t). For example, stabilizing the oxygen supply intensity at 2.6 m³ / (t) (min). The oxygen lance actuator transmits real-time data on the actual lance position and oxygen supply to the control system, compares it with the target parameters, and automatically fine-tunes when deviations occur. During the blowing process, the command issuance, execution, and feedback process is continuously cyclical, dynamically matching the real-time status of the molten pool until the blowing ends, ensuring that the oxygen supply accurately adapts to the smelting needs throughout the entire process.

[0049] This embodiment can achieve precise closed-loop execution of oxygen supply parameters, with fast control response and high precision. It can adapt to changes in smelting conditions in real time, stabilize the reaction state of the molten pool, avoid index fluctuations caused by oxygen supply deviations, ensure vanadium oxidation efficiency and semi-steel quality, and improve production stability and smelting economy.

[0050] Based on the same principle as the converter vanadium extraction dynamic oxygen supply method provided in the embodiments of this application, the embodiments of this application also provide a converter vanadium extraction dynamic oxygen supply device, such as... Figure 2 As shown, the converter vanadium extraction dynamic oxygen supply device 20 may specifically include: a critical temperature dynamic calculation module 21, an oxygen supply parameter determination module 22, and an oxygen supply control module 23. The critical temperature dynamic calculation module 21 is used to calculate the real-time vanadium oxidation critical temperature based on the real-time composition data and real-time molten pool temperature of the vanadium-containing molten iron to be blown in the converter, through a target vanadium oxidation critical temperature model. The target vanadium oxidation critical temperature model is obtained by fitting the historical vanadium extraction production data corresponding to multiple historical production furnaces of the converter. The oxygen supply parameter determination module 22 is used to determine the molten pool temperature control range based on the real-time vanadium oxidation critical temperature, and to determine the target oxygen lance position parameters and target oxygen supply intensity parameters based on the relationship between the real-time molten pool temperature and the molten pool temperature control range. The oxygen supply control module 23 is used to control the oxygen supply of the oxygen lance actuator based on the target oxygen lance position parameters and the target oxygen supply intensity parameters.

[0051] In one embodiment of this application, historical vanadium extraction production data includes iron composition data, molten pool temperature data, and smelting index data corresponding to historical production furnaces; the iron composition data includes carbon content data and vanadium content data; the critical temperature model fitting module is used for: Based on smelting index data, multiple historical production furnaces of the converter were divided into multiple subsets of historical production furnaces. The initial vanadium oxidation critical temperature model corresponding to the historical production furnace for each historical production furnace in each historical production furnace subset is obtained by iteratively fitting the historical vanadium extraction production data corresponding to each historical production furnace subset using a multiple linear regression algorithm. The target vanadium oxidation critical temperature model is determined based on the initial vanadium oxidation critical temperature models corresponding to each of the multiple historical production furnace subsets.

[0052] In one embodiment of this application, the critical temperature model fitting module is specifically used for: For each historical production furnace, a carbon content variation curve is obtained based on the carbon content data corresponding to that historical production furnace, and a vanadium content variation curve is obtained based on the vanadium content data corresponding to that historical production furnace. The first derivatives of the carbon content variation curve and the vanadium content variation curve are obtained to obtain the carbon oxidation rate curve and the vanadium oxidation rate curve, respectively. Based on the intersection of the carbon oxidation rate curve and the vanadium oxidation rate curve, the critical temperature for vanadium oxidation corresponding to that historical production furnace is determined. The critical vanadium oxidation temperature corresponding to each historical production furnace in each historical production furnace subset is used as the output label dataset corresponding to that historical production furnace subset. Use the historical vanadium extraction production data corresponding to each historical production furnace in each historical production furnace subset as the input dataset for that historical production furnace subset. Multiple linear regression fitting is performed on the input dataset and output label dataset corresponding to each subset of historical production furnaces.

[0053] In one embodiment of this application, the critical temperature model fitting module is further configured to: obtain a verification dataset; verify the initial vanadium oxidation critical temperature models corresponding to each of the multiple historical production furnace subsets based on the verification dataset, and obtain the fitting degree and prediction error of each initial vanadium oxidation critical temperature model; select multiple qualified models whose fitting degree meets a preset fitting degree threshold and whose prediction error meets a preset error threshold; and determine the target vanadium oxidation critical temperature model based on the multiple qualified models.

[0054] In one embodiment of this application, the real-time component content data includes real-time silicon content and real-time titanium content; the critical temperature dynamic calculation module 21 is specifically used to: calculate the real-time vanadium oxidation critical temperature based on the real-time silicon content, real-time titanium content, and real-time molten pool temperature of the vanadium-containing molten iron to be smelted, using a target vanadium oxidation critical temperature model; the target vanadium oxidation critical temperature model is:

[0055] in, This refers to the real-time critical temperature for vanadium oxidation. , b and c These are the model fitting coefficients. d For constant terms; Real-time silicon content, For real-time titanium content, This represents the real-time molten pool temperature.

[0056] In one embodiment of this application, the oxygen supply parameter determination module 22 is specifically used to: determine the lower limit of the molten pool temperature control range based on the real-time vanadium oxidation critical temperature as the difference between the real-time vanadium oxidation critical temperature and a first preset temperature threshold, and the upper limit as the difference between the real-time vanadium oxidation critical temperature and a second preset temperature threshold, wherein the second preset temperature threshold is less than the first preset temperature threshold; and obtain the molten pool temperature control range based on the lower limit and the upper limit.

[0057] In one embodiment of this application, the oxygen supply parameter determination module 22 is further configured to: When the real-time molten pool temperature is lower than the lower limit of the molten pool temperature control range, the target oxygen lance position parameter is determined as the first lance position, and the target oxygen supply intensity parameter is determined as the first oxygen supply intensity. When the real-time molten pool temperature is within the molten pool temperature control range, the target oxygen lance position is determined as the second lance position, and the target oxygen supply intensity parameter is determined as the second oxygen supply intensity. When the real-time molten pool temperature is higher than the upper limit of the molten pool temperature control range, the target oxygen lance position is determined as the third lance position, and the target oxygen supply intensity parameter is determined as the third oxygen supply intensity. The third gun position is greater than the second gun position, and the second gun position is greater than the first gun position; the first oxygen supply intensity is greater than the second oxygen supply intensity, and the second oxygen supply intensity is greater than the third oxygen supply intensity.

[0058] The apparatus in this application embodiment can execute the method provided in this application embodiment. The implementation principle is similar. The actions performed by each module in the apparatus of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the apparatus, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0059] Figure 3 A schematic diagram of the structure of an electronic device to which this application embodiment applies is shown, such as... Figure 3 As shown, the electronic device can be used to implement the methods provided in any embodiment of this application.

[0060] like Figure 3 As shown, the electronic device 300 may primarily include at least one processor 301. Figure 3The diagram shows components such as a memory 302, a communication module 303, and an input / output interface 304. Optionally, these components can be connected and communicate with each other via a bus 305. It should be noted that... Figure 3 The structure of the electronic device 300 shown is merely illustrative and does not constitute a limitation on the electronic devices to which the methods provided in the embodiments of this application are applicable.

[0061] The memory 302 can be used to store operating systems and applications, etc. The applications can include computer programs that implement the methods shown in the embodiments of this application when invoked by the processor 301, and can also include programs for implementing other functions or services. The memory 302 can be ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices that can store information and computer programs, or it can be EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0062] Processor 301 is connected to memory 302 via bus 305 and implements corresponding functions by calling the application programs stored in memory 302. Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0063] Electronic device 300 can connect to a network via communication module 303 (which may include, but is not limited to, components such as a network interface) to communicate with other devices (such as user terminals or servers) through the network and achieve data interaction, such as sending data to or receiving data from other devices. Communication module 303 may include wired network interfaces and / or wireless network interfaces, meaning the communication module may include at least one of wired or wireless communication modules.

[0064] The electronic device 300 can connect to necessary input / output devices, such as a keyboard or display device, via the input / output interface 304. The electronic device 300 itself may have a display device, and other external display devices can also be connected via the input / output interface 304. Optionally, a storage device, such as a hard drive, can also be connected via the input / output interface 304 to store data from the electronic device 300, retrieve data from the storage device, or store data from the storage device in the memory 302. It is understood that the input / output interface 304 can be a wired interface or a wireless interface. Depending on the actual application scenario, the device connected to the input / output interface 304 can be a component of the electronic device 300 or an external device connected to the electronic device 300 when needed.

[0065] The bus 305 used to connect the components may include a path for transmitting information between the components. The bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Depending on its function, the bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0066] Optionally, for the solution provided in the embodiments of this application, the memory 302 can be used to store a computer program that executes the solution of this application, and the processor 301 runs the computer program. When the processor 301 runs the computer program, it implements the operation of the method or apparatus provided in the embodiments of this application.

[0067] Based on the same principle as the method provided in the embodiments of this application, the embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the corresponding content of the aforementioned method embodiments.

[0068] It should be noted that the terms "first," "second," "third," "fourth," "1," "2," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the figures or text.

[0069] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0070] It should be understood that although arrows indicate various operation steps in the flowcharts of this application's embodiments, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of this application's embodiments, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all steps in each flowchart, based on the actual implementation scenario, may include multiple sub-steps or multiple stages. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and this application's embodiments do not limit this.

[0071] The above description is only an optional implementation method for some implementation scenarios of this application. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this application without departing from the technical concept of this application also fall within the protection scope of the embodiments of this application.

Claims

1. A dynamic oxygen supply method for vanadium extraction in a converter, characterized in that, include: Based on the real-time composition data and real-time molten pool temperature of the vanadium-containing molten iron to be smelted in the converter, the real-time critical temperature for vanadium oxidation is calculated by the target vanadium oxidation critical temperature model; the target vanadium oxidation critical temperature model is obtained by fitting the historical vanadium extraction production data corresponding to each of the multiple historical production furnaces of the converter. Based on the real-time critical temperature of vanadium oxidation, the temperature control range of the molten pool is determined, and based on the relationship between the real-time molten pool temperature and the temperature control range of the molten pool, the target oxygen lance position parameters and the target oxygen supply intensity parameters are determined. The oxygen supply is controlled by the oxygen lance actuator based on the target oxygen lance position parameters and the target oxygen supply intensity parameters.

2. The dynamic oxygen supply method for vanadium extraction in a converter as described in claim 1, characterized in that, The historical vanadium extraction production data includes data on the composition of molten iron, the temperature of the molten pool, and smelting indicators corresponding to each historical production furnace; the molten iron composition data includes carbon content data and vanadium content data. The target vanadium oxidation critical temperature model was obtained by fitting historical vanadium extraction production data corresponding to multiple historical production furnaces of the converter in the following manner: Based on the smelting index data, the converter is divided into multiple historical production furnaces to obtain multiple subsets of historical production furnaces. The initial vanadium oxidation critical temperature model corresponding to the historical production furnace for each historical production furnace in each historical production furnace subset is obtained by iteratively fitting the historical vanadium extraction production data corresponding to each historical production furnace subset using a multiple linear regression algorithm. The target vanadium oxidation critical temperature model is determined based on the initial vanadium oxidation critical temperature model corresponding to each of the multiple historical production furnace subsets.

3. The dynamic oxygen supply method for vanadium extraction in a converter as described in claim 2, characterized in that, The step of iteratively fitting the historical vanadium extraction production data corresponding to each historical production furnace in each subset of historical production furnaces using a multiple linear regression algorithm includes: For each historical production furnace, a carbon content variation curve is obtained based on the carbon content data corresponding to that historical production furnace, and a vanadium content variation curve is obtained based on the vanadium content data corresponding to that historical production furnace. The first derivatives of the carbon content variation curve and the vanadium content variation curve are obtained to obtain the carbon oxidation rate curve and the vanadium oxidation rate curve, respectively. Based on the intersection of the carbon oxidation rate curve and the vanadium oxidation rate curve, the critical vanadium oxidation temperature corresponding to that historical production furnace is determined. The critical vanadium oxidation temperature corresponding to each historical production furnace in each historical production furnace subset is used as the output label dataset corresponding to that historical production furnace subset. Use the historical vanadium extraction production data corresponding to each historical production furnace in each historical production furnace subset as the input dataset for that historical production furnace subset. Perform multiple linear regression fitting on the input dataset and the output label dataset corresponding to each subset of historical production furnaces.

4. The dynamic oxygen supply method for vanadium extraction in a converter as described in claim 3, characterized in that, The determination of the target vanadium oxidation critical temperature model based on the initial vanadium oxidation critical temperature models corresponding to each of the multiple historical production furnace subsets includes: Obtain a validation dataset, and validate the initial vanadium oxidation critical temperature model corresponding to each of the multiple historical production furnace subsets based on the validation dataset, and obtain the fitting degree and prediction error of each initial vanadium oxidation critical temperature model. Multiple qualified models that meet the preset fit threshold and the preset error threshold are selected, and the target vanadium oxidation critical temperature model is determined based on the multiple qualified models.

5. The dynamic oxygen supply method for vanadium extraction in a converter as described in claim 1, characterized in that, The real-time component content data includes real-time silicon content and real-time titanium content; The real-time critical temperature for vanadium oxidation is calculated using a target vanadium oxidation critical temperature model based on real-time composition data and real-time molten pool temperature of the vanadium-containing molten iron to be smelted in the converter. This includes: Based on the real-time silicon content, real-time titanium content, and real-time molten pool temperature of the vanadium-containing molten iron to be smelted, the real-time critical temperature for vanadium oxidation is calculated using the target critical temperature model for vanadium oxidation. The target vanadium oxidation critical temperature model is as follows: in, This refers to the real-time critical temperature for vanadium oxidation. , b and c These are the model fitting coefficients. d For constant terms; Real-time silicon content, For real-time titanium content, This represents the real-time molten pool temperature.

6. The dynamic oxygen supply method for vanadium extraction in a converter as described in claim 1, characterized in that, The determination of the molten pool temperature control range based on the real-time vanadium oxidation critical temperature includes: Based on the real-time critical temperature of vanadium oxidation, the lower limit of the molten pool temperature control range is determined to be the difference between the real-time critical temperature of vanadium oxidation and the first preset temperature threshold, and the upper limit is the difference between the real-time critical temperature of vanadium oxidation and the second preset temperature threshold, wherein the second preset temperature threshold is less than the first preset temperature threshold. Based on the lower limit and the upper limit, the temperature control range of the molten pool is obtained.

7. The dynamic oxygen supply method for vanadium extraction in a converter as described in claim 1, characterized in that, The determination of the target oxygen lance position parameters and target oxygen supply intensity parameters based on the relationship between the real-time molten pool temperature and the molten pool temperature control range includes: When the real-time molten pool temperature is lower than the lower limit of the molten pool temperature control range, the target oxygen lance position parameter is determined as the first lance position, and the target oxygen supply intensity parameter is determined as the first oxygen supply intensity. When the real-time molten pool temperature is within the molten pool temperature control range, the target oxygen lance position is determined as the second lance position, and the target oxygen supply intensity parameter is determined as the second oxygen supply intensity. When the real-time molten pool temperature is higher than the upper limit of the molten pool temperature control range, the target oxygen lance position is determined as the third lance position, and the target oxygen supply intensity parameter is determined as the third oxygen supply intensity. The third gun position is larger than the second gun position, and the second gun position is larger than the first gun position; the first oxygen supply intensity is larger than the second oxygen supply intensity, and the second oxygen supply intensity is larger than the third oxygen supply intensity.

8. A dynamic oxygen supply device for vanadium extraction in a converter, characterized in that, include: The critical temperature dynamic calculation module is used to calculate the real-time critical temperature of vanadium oxidation based on the real-time composition content data and real-time molten pool temperature of the vanadium-containing molten iron to be blown in the converter, through a target vanadium oxidation critical temperature model; the target vanadium oxidation critical temperature model is obtained by fitting the historical vanadium extraction production data corresponding to multiple historical production furnaces of the converter. The oxygen supply parameter determination module is used to determine the molten pool temperature control range based on the real-time vanadium oxidation critical temperature, and to determine the target oxygen lance position parameters and target oxygen supply intensity parameters based on the relationship between the real-time molten pool temperature and the molten pool temperature control range. The oxygen supply control module is used to control the oxygen supply of the oxygen lance actuator based on the target oxygen lance position parameters and the target oxygen supply intensity parameters.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes a dynamic oxygen supply method for vanadium extraction in a converter as described in any one of claims 1 to 7 when running the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the dynamic oxygen supply method for vanadium extraction in a converter as described in any one of claims 1 to 7.

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