Real-time control method and system for carbon spraying and oxygen blowing process of electric arc furnace

Abstract

The invention belongs to the technical field of metallurgical industry, and discloses a real-time control method and system for a carbon spraying and oxygen blowing process of an electric arc furnace. The method includes the following steps that at least three high-temperature-resistant distance measuring sensors are evenly installed on the top of the electric arc furnace, and the distance between the top of the electric arc furnace and foamed slag is measured; calculating an average distance value, and calculating the height of the foaming slag according to the hearth height. A telescopic air gun is used for blowing away local foaming slag, and the height of the molten steel level is measured. The thickness of the foam slag is calculated and compared with a preset threshold value, and the carbon spraying and oxygen blowing technological parameters are adjusted accordingly. And establishing a correlation model of the carbon spraying and oxygen blowing process parameters and the thickness of the foam slag, predicting the thickness change trend of the foam slag, and performing pre-adjustment. The system comprises a distance measuring sensor, a telescopic air gun, a data acquisition module, a calculation module, a prediction module and a control module. The method and system can monitor and control the thickness of the foamed slag in real time, effectively prevent the slag flowing phenomenon and the difficulty of electrode submerged arc, and improve the smelting efficiency and quality of the electric arc furnace.

Description

Technical Field

[0001] The invention relates to the technical field of metallurgical industry, and in particular to a real-time control method and system for a carbon injection and oxygen blowing process of an electric arc furnace. Background Art

[0002] In the electric arc furnace steelmaking process, carbon injection and oxygen blowing operations are crucial to the regulation of the smelting process. The formation and control of foamy slag is the core link of the process, which directly affects the smelting efficiency, energy consumption and equipment safety. The height of foamy slag not only determines the arc stability and thermal efficiency, but is also closely related to the purity of molten steel. However, in actual production, the precise measurement and control of the height of foamy slag has always been a technical problem that has plagued the steel industry.

[0003] Traditional foam slag height measurement and carbon injection and oxygen blowing process control methods mainly rely on manual observation and empirical judgment, and have problems such as low measurement accuracy, poor real-time performance, and process control lag. These methods make it difficult to adjust the carbon injection and oxygen blowing operating parameters in a timely manner, which may lead to slag flow accidents or arc burial difficulties. In addition, the existing technology lacks the ability to predict the trend of foam slag height changes, which limits the continuous optimization of the process.

[0004] To solve these problems, a method and system is urgently needed to measure the height of foamy slag in the electric arc furnace in real time, accurately and automatically, and optimize the carbon injection and oxygen blowing process accordingly. This method should be able to improve the controllability and safety of the smelting process, optimize production efficiency, reduce energy consumption and equipment loss, and have the ability to predict based on historical data.

[0005] The present invention proposes a novel real-time control method and system for carbon injection and oxygen blowing process of electric arc furnace. The method uses multiple high temperature resistant ranging sensors to monitor the height of foam slag and molten steel in real time, and combines machine learning algorithms to establish a correlation model between process parameters and foam slag height. This innovative method not only solves the measurement and control problems in the prior art, but also realizes the intelligent and refined management of carbon injection and oxygen blowing process, providing a new technical path for further optimization of electric arc furnace steelmaking process. Summary of the invention

[0006] The purpose of the present invention is to provide a real-time control method and system for carbon injection and oxygen blowing process in an electric arc furnace, which can accurately measure the height of foam slag in the electric arc furnace, and further measure the height of molten steel and calculate the thickness of foam slag, thereby realizing real-time optimization control of the carbon injection and oxygen blowing process.

[0007] The technical solution of the present invention is as follows: A real-time control method for an electric arc furnace carbon injection and oxygen blowing process, comprising the following steps:

[0008] At least three high temperature resistant distance measuring sensors are evenly installed on the top of the electric arc furnace; the high temperature resistant distance measuring sensors emit lasers to the surface of the foamed slag and receive return signals to measure the distance between the top of the furnace and the foamed slag, and obtain distance values ​​D1, D2, D3...Dn respectively; the average distance value Davg measured by the high temperature resistant distance measuring sensors is calculated, wherein Davg=(D1+D2+D3+...Dn) / n;

[0009] According to the furnace height H and the average distance value Davg, the foam slag height Hslag = H-Davg is calculated;

[0010] Measure the height of the steel liquid level Hsteel;

[0011] Based on the foam slag height Hslag and the molten steel level height Hsteel, the foam slag thickness Tfoam = Hslag-Hsteel is calculated;

[0012] The foamed slag thickness Tfoam is compared with a preset foamed slag thickness threshold, and the carbon injection and oxygen blowing process parameters are adjusted according to the comparison result.

[0013] Furthermore, the method also includes the following steps: collecting and storing carbon injection and oxygen blowing process parameters and corresponding foamed slag thickness data; using a machine learning algorithm to train the data and establish a correlation model between the carbon injection and oxygen blowing process parameters and the foamed slag thickness; using the correlation model to predict the change trend of the foamed slag thickness based on the current carbon injection and oxygen blowing process parameters; based on the prediction results, pre-adjusting the carbon injection and oxygen blowing process parameters to optimize the foamed slag thickness control.

[0014] The carbon injection and oxygen blowing process parameters are adjusted according to the comparison results as follows: when Tfoam is higher than a preset threshold, the foam slag height is judged to be too high, and the oxygen blowing flow rate and the carbon injection flow rate are reduced; when Tfoam is lower than a preset threshold, the foam slag height is judged to be too low, and the oxygen blowing flow rate and the carbon injection flow rate are increased.

[0015] The preset threshold is the foam slag thickness of the lowest energy consumption furnace among the historical furnaces in which the endpoint composition and temperature of the molten steel meet the process requirements.

[0016] The specific method of measuring the height of the molten steel level is as follows: a plurality of retractable air guns are installed on the top of the electric arc furnace, close to the position of the high-temperature resistant distance measuring sensor; the retractable air guns are retracted outside the furnace in a non-working state; when the height of the molten steel level needs to be measured, the retractable air guns are extended into the furnace and close to the foam slag, and an inert gas is introduced to blow away the foam slag at this position, so that the surface of the molten steel is exposed; the blowing angle of the retractable air gun is adjusted so that the position where the surface of the molten steel is exposed is located below the distance measuring center of one of the high-temperature resistant distance measuring sensors; the high-temperature resistant distance measuring sensor measures the distance between the furnace top and the surface of the molten steel by emitting a laser to the surface of the molten steel and receiving a return signal, and records it as the molten steel surface distance value Dsteel; according to the height H of the furnace and the molten steel surface distance value Dsteel, the molten steel level height Hsteel=H-Dsteel is calculated.

[0017] The inert gas is argon or nitrogen.

[0018] The high temperature resistant distance measuring sensor is a laser distance measuring sensor or an ultrasonic distance measuring sensor.

[0019] A real-time control system for an electric arc furnace carbon injection and oxygen blowing process, comprising:

[0020] n high temperature resistant distance measuring sensors installed on the top of the electric arc furnace are used to measure the distance between the top of the furnace and the foamy slag;

[0021] n retractable air guns, used to blow away local foam slag to expose the surface of molten steel;

[0022] Data acquisition module, used to collect measurement data of high temperature resistant distance measuring sensor and steel liquid level data;

[0023] A calculation module, used to calculate the thickness of foam slag;

[0024] Prediction module, used to predict the change trend of foam slag thickness based on historical data;

[0025] The control module is used to adjust the carbon injection and oxygen blowing process parameters according to the actual value and predicted value of the foam slag thickness.

[0026] The prediction module includes: a data storage unit for storing carbon injection and oxygen blowing process parameters and corresponding historical data of foamed slag thickness; a machine learning unit for training a correlation model between the carbon injection and oxygen blowing process parameters and the foamed slag thickness; and a prediction unit for predicting a change trend of the foamed slag thickness based on the current carbon injection and oxygen blowing process parameters and the correlation model.

[0027] Beneficial effects of the present invention: The present invention realizes the process control of carbon spraying and oxygen blowing in an electric arc furnace, and starts from the thickness direction of the foamed slag, significantly improves the smelting efficiency, product quality and production safety, and reduces energy consumption and raw material waste. The automation of the entire measurement, prediction and control process minimizes manual intervention, which not only improves production efficiency, but also reduces the labor intensity and safety risks of operators. The control system can automatically adjust the carbon spraying and oxygen blowing process parameters according to the real-time measurement and prediction results, so that the thickness of the foamed slag is always kept within the optimal range. This precise control not only optimizes the stability and thermal efficiency of the arc, but also significantly improves the purity of the molten steel. At the same time, by avoiding excessive carbon spraying and oxygen blowing, the system effectively reduces energy consumption and raw material waste, and improves resource utilization efficiency. In addition, the early warning function of the system can timely discover and handle potential abnormal situations, greatly improving the safety of the production process. Accurate real-time measurement of the height of the foamed slag and the height of the molten steel level is achieved, and the control accuracy of the carbon spraying and oxygen blowing process of the electric arc furnace is greatly improved. By evenly installing at least three high-temperature resistant ranging sensors on the furnace top, the present invention overcomes the errors that may be caused by single-point measurement and ensures the accuracy and representativeness of the foamed slag height measurement. At the same time, the innovative method of using a retractable air gun to blow away local foam slag has achieved accurate measurement of the steel liquid level. This measurement method can not only obtain accurate data without affecting normal production, but also adapt to the complex internal environment of the electric arc furnace, providing a reliable data basis for subsequent process control. The establishment of this precise measurement system enables electric arc furnace operators to grasp the situation in the furnace in real time, providing a direct and reliable basis for optimizing the carbon injection and oxygen blowing process.

[0028] By introducing machine learning algorithms, a correlation model between carbon injection and oxygen blowing process parameters and foam slag thickness was established, realizing the prediction of foam slag thickness and intelligent optimization and adjustment of process parameters. This innovative approach transforms traditional passive control into active prediction and optimization. The system can learn the complex relationship between process parameters and foam slag thickness based on historical data, and on this basis predict the future trend of foam slag thickness changes. This predictive capability enables the system to adjust carbon injection and oxygen blowing process parameters in advance to prevent problems before they occur, effectively avoiding problems such as slag flow caused by excessively thick foam slag or difficulty in electrode arc burial caused by excessively thin foam slag. At the same time, the continuous optimization characteristics of the machine learning model enable the system to continuously adapt to changes in production conditions and maintain the best control effect, which greatly improves the stability and efficiency of the arc furnace smelting process. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The present invention is a flow chart of a real-time control method for an electric arc furnace carbon injection and oxygen blowing process.

[0030] Figure 2 The present invention is a device diagram of a real-time control method for an electric arc furnace carbon injection and oxygen blowing process.

[0031] Figure 3 This is a schematic diagram of a real-time control system for an electric arc furnace carbon injection and oxygen blowing process according to the present invention.

[0032] Numbers in the figure: 1. Oxygen blowing gun; 2. Distance measuring sensor; 3. Air gun; 4. Molten steel; 5. Foam slag; 6. Carbon powder gun. DETAILED DESCRIPTION

[0033] Example

[0034] The present invention provides a real-time control method for an electric arc furnace carbon injection and oxygen blowing process, comprising the following steps:

[0035] Installation of distance measuring sensors: Three high temperature resistant distance measuring sensors are evenly installed on the top of the electric arc furnace. These distance measuring sensors measure the distance between the furnace top and the foam slag by emitting laser to the foam slag surface and receiving the return signal, and obtain the distance values ​​D1, D2, and D3 respectively.

[0036] Calculate the average distance value: Calculate the average distance value Davg measured by the three distance measuring sensors, where Davg = (D1+D2+D3) / 3.

[0037] Calculate the height of the foamed slag: According to the height H of the furnace and the average distance value Davg, the height of the foamed slag Hslag = H-Davg is calculated.

[0038] Measure the height of the molten steel level: measure the height of the molten steel level Hsteel.

[0039] Three retractable air guns are respectively installed on the top of the electric arc furnace, near the positions of the three distance measuring sensors, and the air guns are retracted outside the furnace in a non-working state.

[0040] When it is necessary to measure the height of the molten steel, the air gun is extended into the furnace and close to the foamy slag, and an inert gas is introduced to blow away the foamy slag at this position, so that the surface of the molten steel is exposed.

[0041] The blowing angle of the air gun is adjusted so that the position where the molten steel is exposed is located below the distance measuring center of one of the distance measuring sensors.

[0042] The distance sensor measures the distance between the furnace top and the surface of the molten steel by emitting laser to the surface of the molten steel and receiving the return signal, and records it as the distance value Dsteel.

[0043] According to the furnace height H and the distance value Dsteel from the molten steel surface, the molten steel level height Hsteel=H-Dsteel is calculated.

[0044] Calculation of foam slag thickness: Based on the foam slag height Hslag and the molten steel level height Hsteel, calculate the foam slag thickness Tfoam = Hslag - Hsteel.

[0045] Adjusting the carbon injection and oxygen blowing process parameters: comparing the foam slag thickness Tfoam with a preset foam slag thickness threshold, and adjusting the carbon injection and oxygen blowing process parameters according to the comparison result:

[0046] When Tfoam is higher than the preset threshold, it is judged that the foam slag height is too high and slag flow may occur, so the oxygen blowing and carbon injection operations should be reduced;

[0047] When Tfoam is lower than the preset threshold, it is judged that the foam slag height is too low, which may cause difficulty in electrode arc immersion and increase oxygen blowing and carbon injection operations.

[0048] Data collection and model training: Collect and store carbon injection and oxygen blowing process parameters and corresponding foam slag thickness data; use machine learning algorithms to train the data and establish a correlation model between carbon injection and oxygen blowing process parameters and foam slag thickness. The specific implementation steps and the resulting correlation model are described in detail as follows:

[0049] Implementation steps

[0050] Data collection: Collect various parameter data from the real-time control system of the carbon injection and oxygen blowing process of the electric arc furnace, including: carbon injection flow rate, oxygen blowing flow rate and other operating parameters; real-time measurement of foam slag thickness data; other environmental variables and process conditions (such as temperature, pressure) and other data. These data are collected in real time in the data acquisition module and stored in the historical database. The time span of the data should be long enough to cover different working conditions and process parameter combinations to ensure the versatility of the model.

[0051] Data preprocessing: Clean and preprocess the collected data, including: processing missing values ​​and outliers; normalizing or standardizing data of different dimensions to facilitate model training; selecting key feature variables according to actual needs, such as carbon injection flow rate, oxygen blowing flow rate, etc.

[0052] Select machine learning algorithm: In this embodiment, two machine learning algorithms are used to establish the association model: neural network and random forest. Neural network model: A multilayer perceptron neural network is used to form a model through an input layer, a hidden layer, and an output layer. The input layer receives different process parameters, such as carbon injection flow rate and oxygen blowing flow rate. The number of hidden layers and the number of nodes in each layer can be adjusted according to the training effect. The output layer predicts the thickness of the foam slag. Random forest model: Using a random forest regression model, the data is randomly divided into multiple decision trees, and the prediction accuracy is improved by integrating the results of multiple decision trees. Random forests are more robust to data outliers and nonlinear relationships.

[0053] Model training and validation: 80% of the historical data is used to train the model, and 20% of the data is used for model validation. The model parameters are adjusted by cross-validation, such as the learning rate, number of hidden layers and number of nodes of the neural network, or the number of trees and maximum depth of the random forest. The mean square error (MSE) is used as an evaluation indicator to compare the prediction effects of the model on the training set and the validation set, and the model with the lowest error is selected as the final association model.

[0054] Model evaluation and selection: Compare the prediction accuracy of the neural network and random forest models, and select the model with lower error based on the experimental results. Assume that the model obtained after training has higher prediction accuracy, and the root mean square error of the model fluctuates within the range of ±1mm (the specific value is determined according to the actual training results), which meets the process control accuracy requirements.

[0055] The final model selected is the random forest model, and its output is the predicted value of foam slag thickness. The following specific correlations are obtained through model training:

[0056] When the oxygen blowing flow rate increases and the carbon injection flow rate remains stable, the thickness of the foamed slag does not change significantly;

[0057] When the carbon injection flow rate increases and the oxygen blowing flow rate remains unchanged, the thickness of the foamed slag does not change significantly;

[0058] When both increase to a certain extent, the foamy slag thickness reaches an optimal control value (eg, a preset foamy slag thickness threshold).

[0059] Real-time prediction and control: Based on this model, the thickness of foam slag is predicted in the control module. The control module predicts the trend of foam slag thickness in the future through the correlation model based on the current carbon injection and oxygen blowing process parameters. If the predicted foam slag thickness exceeds the preset range, the system will issue an adjustment instruction in advance to appropriately increase or decrease the carbon injection or oxygen blowing flow rate to achieve optimal control of the foam slag thickness.

[0060] Prediction and optimization: using the correlation model, predict the change trend of foam slag thickness according to the current carbon injection and oxygen blowing process parameters; based on the prediction results, pre-adjust the carbon injection and oxygen blowing process parameters to optimize the foam slag thickness control.

[0061] Corresponding to the above method embodiments, the present invention also provides a real-time control system for an electric arc furnace carbon injection and oxygen blowing process, comprising:

[0062] Three high temperature resistant distance measuring sensors installed on the top of the electric arc furnace are used to measure the distance between the top and the foamy slag;

[0063] Three retractable air guns for blowing away local foamy slag to expose the molten steel surface;

[0064] Data acquisition module, used to collect measurement data of the distance sensor and steel liquid level data;

[0065] A calculation module, used to calculate the thickness of foam slag;

[0066] Prediction module, used to predict the trend of foam slag thickness based on historical data;

[0067] The control module is used to adjust the carbon injection and oxygen blowing process parameters according to the actual value and predicted value of the foam slag thickness.

[0068] In the present invention, the high temperature resistant distance measuring sensor may be a laser distance measuring sensor or an ultrasonic distance measuring sensor. The inert gas may be argon or nitrogen.

[0069] like Figure 1 , Figure 2 and Figure 3 As shown, the present invention provides a real-time control method and system for carbon injection and oxygen blowing process of an electric arc furnace. The method and system can measure the foam slag height in real time and optimize the carbon injection and oxygen blowing process parameters accordingly, thereby improving the smelting efficiency and product quality of the electric arc furnace.

[0070] like Figure 2 As shown, at least three high temperature resistant distance measuring sensors 2 are evenly installed on the top of the electric arc furnace. Take three high temperature resistant distance measuring sensors 2 as an example; these distance measuring sensors 2 can be laser distance measuring sensors or ultrasonic distance measuring sensors, which can work stably in a high temperature environment. The distance measuring sensor 2 measures the distance between the furnace top and the foam slag 5 by emitting a laser or ultrasonic signal to the surface of the foam slag 5 and receiving a return signal, and obtains distance values ​​D1, D2, and D3 respectively.

[0071] The system also includes at least three retractable air guns 3 installed on the furnace roof. Three air guns are set corresponding to the number of high temperature resistant distance measuring sensors 2. These air guns 3 are retracted outside the furnace in a non-working state, which does not affect the normal smelting operation. When it is necessary to measure the height of the molten steel 4 surface, the air gun 3 is extended into the furnace and close to the foam slag 5, and an inert gas (such as argon or nitrogen) is introduced to blow away the foam slag 5 at this position, so that the surface of the molten steel 4 is exposed.

[0072] In addition, the system is also equipped with an oxygen blowing gun 1 and a carbon powder gun 6 for performing carbon injection and oxygen blowing operations. The operating parameters of these devices will be adjusted in real time based on the measurement and prediction results.

[0073] like Figure 1 As shown, the real-time control method of the electric arc furnace carbon injection and oxygen blowing process of the present invention comprises the following steps:

[0074] 1. Measuring the height of foam slag: The system first calculates the average distance value Davg = (D1 + D2 + D3) / 3 measured by the three distance measuring sensors 2. Then, according to the height H of the furnace and the average distance value Davg, the height of foam slag Hslag = H-Davg is calculated.

[0075] 2. Measure the height of the molten steel surface: After starting the air gun 3 to blow away the local foam slag 5, use one of the distance sensors 2 to measure the distance Dsteel between the furnace top and the surface of the molten steel 4. According to the furnace height H, the height of the molten steel surface Hsteel = H-Dsteel is calculated.

[0076] 3. Calculate the foam slag thickness: Based on the foam slag height Hslag and the molten steel level height Hsteel, calculate the foam slag thickness Tfoam = Hslag - Hsteel.

[0077] 4. Data collection and model training: The system continuously collects and stores carbon injection and oxygen blowing process parameters (such as carbon powder injection amount, oxygen flow rate, etc.) and corresponding foam slag thickness data. Use machine learning algorithms (such as neural networks or random forests) to train these data and establish a correlation model between carbon injection and oxygen blowing process parameters and foam slag thickness.

[0078] 5. Prediction and optimization: Using the trained model, the change trend of foam slag thickness is predicted according to the current carbon injection and oxygen blowing process parameters. Based on the prediction results and the actual measured foam slag thickness, the system optimizes and adjusts the carbon injection and oxygen blowing process parameters.

[0079] 6. Adjust the carbon injection and oxygen blowing process parameters: The system compares the measured and predicted foam slag thickness Tfoam with the preset foam slag thickness threshold. When Tfoam is higher than the preset upper threshold, the system determines that the foam slag height is too high and slag flow may occur, so the oxygen blowing amount of the oxygen blowing gun 1 and the carbon spraying amount of the carbon powder gun 6 are reduced. When Tfoam is lower than the preset lower threshold, the system determines that the foam slag height is too low, which may cause difficulty in electrode arc burial, so oxygen blowing and carbon spraying operations are increased.

[0080] like Figure 3 As shown, the real-time control system for the electric arc furnace carbon injection and oxygen blowing process of the present invention includes the following modules:

[0081] 1. Data acquisition module: responsible for collecting the measurement data of the distance sensor 2 and the steel liquid level data.

[0082] 2. Calculation module: used to calculate the foam slag height, molten steel level height and foam slag thickness.

[0083] 3. Prediction module: Train a machine learning model based on historical data and use the model to predict the changing trend of foam slag thickness.

[0084] 4. Control module: according to the actual value and predicted value of the foam slag thickness, combined with the preset threshold value, adjust the operating parameters of the oxygen blowing gun 1 and the carbon powder gun 6.

[0085] 5. Data storage module: stores historical production data and provides data support for the training and optimization of machine learning models.

[0086] 6. Human-computer interaction interface: allows operators to monitor the system operation status and perform manual intervention when necessary.

[0087] Through this method and system, the present invention realizes real-time and intelligent control of the carbon injection and oxygen blowing process of the electric arc furnace. It can not only accurately measure the thickness of the foamed slag, but also predict its change trend, thereby realizing the forward-looking adjustment of the process parameters. This control method significantly improves the smelting efficiency and product quality, while reducing energy consumption and raw material waste, and provides an effective technical means for the optimization of the electric arc furnace steelmaking process.

[0088] All technical parameters and steps in this embodiment can be adjusted and optimized according to actual needs.

[0089] The method of the present invention can measure the thickness of foam slag in the electric arc furnace in real time and accurately, providing reliable data support for the optimization of the smelting process, thereby improving the smelting efficiency and reducing resource waste. By reasonably adjusting the smelting operation, the problem caused by too much or too little foam slag can be effectively avoided.

Claims

1. A real-time control method for carbon injection and oxygen blowing process of an electric arc furnace, characterized in that: The following steps are involved: At least three high temperature resistant distance measuring sensors are evenly installed on the top of the electric arc furnace; the high temperature resistant distance measuring sensors emit lasers to the surface of the foamed slag and receive return signals to measure the distance between the top of the furnace and the foamed slag, and obtain distance values ​​D1, D2, D3...Dn respectively; the average distance value Davg measured by the high temperature resistant distance measuring sensors is calculated, wherein Davg=(D1+D2+D3+...Dn) / n; According to the furnace height H and the average distance value Davg, the foam slag height Hslag = H-Davg is calculated; Measure the height of the steel liquid level Hsteel; Based on the foam slag height Hslag and the molten steel level height Hsteel, the foam slag thickness Tfoam = Hslag-Hsteel is calculated; The foamed slag thickness Tfoam is compared with a preset foamed slag thickness threshold, and the carbon injection and oxygen blowing process parameters are adjusted according to the comparison result.

2. The real-time control method for carbon injection and oxygen blowing process of an electric arc furnace according to claim 1, characterized in that: The following steps are also included: Collect and store carbon injection and oxygen blowing process parameters and corresponding foam slag thickness data; The data are trained using a machine learning algorithm to establish a correlation model between carbon injection and oxygen blowing process parameters and foam slag thickness; and the correlation model is used to predict a change trend of foam slag thickness according to current carbon injection and oxygen blowing process parameters; Based on the prediction results, the carbon injection and oxygen blowing process parameters are pre-adjusted to optimize the foamy slag thickness control.

3. The real-time control method for carbon injection and oxygen blowing process of an electric arc furnace according to claim 1 or 2, characterized in that: The carbon injection and oxygen blowing process parameters are adjusted according to the comparison results as follows: when Tfoam is higher than a preset threshold, the foam slag height is judged to be too high, and the oxygen blowing flow rate and the carbon injection flow rate are reduced; when Tfoam is lower than a preset threshold, the foam slag height is judged to be too low, and the oxygen blowing flow rate and the carbon injection flow rate are increased.

4. The real-time control method for carbon injection and oxygen blowing process of an electric arc furnace according to claim 3 is characterized in that: The preset threshold is the foam slag thickness of the lowest energy consumption furnace among the historical furnaces in which the endpoint composition and temperature of the molten steel meet the process requirements.

5. The real-time control method for carbon injection and oxygen blowing process of an electric arc furnace according to claim 1 or 2, characterized in that: The specific method of measuring the height of the molten steel level is as follows: a plurality of retractable air guns are installed on the top of the electric arc furnace, close to the position of the high-temperature resistant distance measuring sensor; the retractable air guns are retracted outside the furnace in a non-working state; when the height of the molten steel level needs to be measured, the retractable air guns are extended into the furnace and close to the foam slag, and an inert gas is introduced to blow away the foam slag at this position, so that the surface of the molten steel is exposed; the blowing angle of the retractable air gun is adjusted so that the position where the surface of the molten steel is exposed is located below the distance measuring center of one of the high-temperature resistant distance measuring sensors; the high-temperature resistant distance measuring sensor measures the distance between the furnace top and the surface of the molten steel by emitting a laser to the surface of the molten steel and receiving a return signal, and records it as the molten steel surface distance value Dsteel; according to the height H of the furnace and the molten steel surface distance value Dsteel, the molten steel level height Hsteel=H-Dsteel is calculated.

6. The real-time control method for carbon injection and oxygen blowing process of an electric arc furnace according to claim 1 or 2, characterized in that: The inert gas is argon or nitrogen.

7. The real-time control method for carbon injection and oxygen blowing process of an electric arc furnace according to claim 1 or 2, characterized in that: The high temperature resistant distance measuring sensor is a laser distance measuring sensor or an ultrasonic distance measuring sensor.

8. A real-time control system for carbon injection and oxygen blowing process of electric arc furnace, characterized in that: include: n high temperature resistant distance measuring sensors installed on the top of the electric arc furnace are used to measure the distance between the top of the furnace and the foamy slag; n retractable air guns, used to blow away local foam slag to expose the surface of molten steel; Data acquisition module, used to collect measurement data of high temperature resistant distance measuring sensor and steel liquid level data; A calculation module, used to calculate the thickness of foam slag; Prediction module, used to predict the change trend of foam slag thickness based on historical data; The control module is used to adjust the carbon injection and oxygen blowing process parameters according to the actual value and predicted value of the foam slag thickness.

9. The real-time control system for carbon injection and oxygen blowing process of electric arc furnace according to claim 8, characterized in that: The prediction module comprises: A data storage unit, used for storing carbon injection and oxygen blowing process parameters and corresponding foam slag thickness historical data; A machine learning unit for training the correlation model between carbon injection and oxygen blowing process parameters and foamy slag thickness; The prediction unit is used to predict the change trend of the foam slag thickness according to the current carbon injection and oxygen blowing process parameters and the correlation model.

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