Electric furnace oxygen blowing system and intelligent oxygen supply adjusting method thereof

Abstract

The application discloses an oxygen blowing system of an electric furnace and an intelligent oxygen supply adjusting method thereof, and belongs to the technical field of electric furnace smelting technology.The oxygen blowing system of the electric furnace comprises a furnace body, a working condition sensing module, an oxygen blowing execution module, an intelligent adjusting module and a safety protection module, wherein: the output end of the oxygen blowing execution module and the working condition sensing module are arranged on the inner side of the furnace mouth of the furnace body, the output end of the working condition sensing module is electrically connected with the intelligent adjusting module, the output end of the intelligent adjusting module is electrically connected with the oxygen blowing execution module, the input end of the oxygen blowing execution module is connected with an oxygen source module through an oxygen supply pipeline, and the safety protection module is integrated on the oxygen supply pipeline.The intelligent adjusting module is used for calculating optimal oxygen supply pressure, flow and injection angle, and outputting adjusting instructions.The oxygen blowing system of the electric furnace realizes the automation and intelligence of oxygen blowing of the electric furnace, improves smelting efficiency and the quality of the steel billet, reduces raw material loss and labor cost, and is suitable for the process requirements of different steel grades and different smelting stages.

Description

Technical Field

[0001] This invention belongs to the field of electric furnace smelting technology, and relates to electric furnace oxygen blowing systems, and more particularly to intelligent adjustable electric furnace oxygen blowing systems and their adjustment methods. Background Technology

[0002] In electric arc furnace (EAF) smelting, oxygen blowing is a key process for improving smelting efficiency, reducing energy consumption, and enhancing steel quality. Its core function is to supply oxygen into the EAF to promote complete combustion of fuel, accelerate the melting process of the charge, and remove impurities through oxidation. Currently, mainstream EAF oxygen blowing systems mainly consist of core components such as oxygen source devices, oxygen delivery pipelines, oxygen lances, and control valves. Oxygen blowing operations are typically carried out according to pre-set fixed oxygen supply pressure and flow rate.

[0003] Long-term practice has shown that existing electric arc furnace oxygen blowing systems have the following significant problems: First, significant adjustment lag. Current systems mostly rely on manual adjustment of valve openings based on experience, making it difficult to respond in real time to dynamic changes in furnace temperature, pressure, and molten steel composition, leading to either excessive or insufficient oxygen supply. Excessive oxygen supply not only wastes oxygen resources and increases smelting costs but may also exacerbate furnace wear; while insufficient oxygen supply reduces smelting efficiency, prolongs smelting time, and adversely affects the stability of molten steel quality. Second, poor oxygen blowing uniformity. Existing oxygen lances mostly use a single nozzle structure, resulting in a limited and concentrated oxygen injection range. This leads to over-oxidation in some areas of the furnace and insufficient oxidation in others, further affecting the uniformity of molten steel composition. Third, insufficient safety. When abnormal pressure fluctuations occur in the oxygen supply pipeline, existing systems lack rapid-response pressure relief and protection measures, easily leading to safety hazards such as oxygen leakage or pipeline rupture. Summary of the Invention

[0004] The technical problem to be solved by this invention is: in order to solve the problems of obvious adjustment lag, poor oxygen blowing uniformity and insufficient safety of existing electric furnace oxygen blowing systems, this invention proposes an electric furnace oxygen blowing system and its intelligent oxygen supply adjustment method. With the help of a closed-loop design of "sensing-adjustment-execution-protection", it can respond to the furnace working conditions in real time, accurately control the oxygen supply parameters, improve the oxygen blowing uniformity, and have safety protection functions.

[0005] The technical solution adopted by this invention to solve its technical problem is: an electric furnace oxygen blowing system, comprising: Furnace body; The working condition sensing module is used to collect furnace temperature, pressure, and molten steel composition in real time. The intelligent adjustment module is used to receive data collected by the working condition sensing module, calculate the optimal oxygen supply pressure, flow rate and injection angle, and output adjustment commands to the oxygen blowing execution module. An oxygen blowing execution module, the input end of which is connected to an oxygen source module through an oxygen supply pipeline, and the oxygen blowing execution module performs oxygen injection operations according to the output adjustment command; A safety protection module, integrated on the oxygen delivery pipeline, is used to trigger a rapid pressure relief action and send an abnormal alarm signal to the intelligent adjustment module when the pressure in the oxygen delivery pipeline exceeds a preset threshold. Wherein: the output end of the oxygen blowing execution module and the working condition sensing module are located inside the furnace opening of the furnace body; the output end of the working condition sensing module is electrically connected to the intelligent adjustment module; and the output end of the intelligent adjustment module is electrically connected to the oxygen blowing execution module.

[0006] Therefore, the electric furnace oxygen blowing system, through the coordinated operation of the working condition sensing, intelligent adjustment, oxygen blowing execution, and safety protection modules, achieves real-time monitoring, intelligent control, precise execution, and a safe closed loop throughout the entire process of electric furnace oxygen blowing, fundamentally solving the problems of traditional oxygen blowing systems such as reliance on manual experience, delayed adjustment, uneven oxygen supply, and prominent safety hazards.

[0007] Furthermore, the operating condition sensing module includes a temperature sensor, a pressure sensor, and a composition sensor. The temperature sensor is a thermocouple type sensor, the pressure sensor uses a high-temperature pressure transmitter, and the composition sensor uses an infrared spectral sensor. The data output terminals of the temperature sensor, pressure sensor, and composition sensor are electrically connected to the multi-channel analog input interface of the intelligent adjustment module. The multi-channel analog input interface of the intelligent adjustment module supports synchronous sampling and anti-interference filtering. Thus, the operating condition sensing module collects core parameters such as furnace temperature, pressure, and molten steel composition in real time. The monitored data directly reflects the actual smelting state inside the furnace, eliminating the subjectivity and lag of traditional experience-based judgments, and providing accurate and real-time data support for subsequent oxygen blowing parameter adjustment.

[0008] Furthermore, the component sensor includes a detection beam and an infrared detector. The detection beam is emitted through a collimating optical system, penetrates the gaseous reaction layer at the furnace opening, and is received by the infrared detector. The infrared detector converts the received spectral signal into an electrical signal, which is then sent to the intelligent adjustment module for component inversion calculation after high-speed analog-to-digital conversion. The inversion principle is as follows: Let c be the concentration of the target element in the molten steel, A be the degree of absorption of the target element in the molten steel by a specific wavelength of infrared light, and L be the optical path length of the specific wavelength of infrared light passing through the gaseous reaction layer. Then: , where ε is the molar absorptivity of the target element in the molten steel at this specific wavelength. Thus, the optical detection structure is used to achieve non-contact detection. Compared with the traditional contact and semi-quantitative composition detection methods, it neither interferes with the smelting reaction in the furnace nor directly damages the sensor in high-temperature, dusty, and corrosive environments, significantly improving the stability and service life of the sensor under the high-temperature conditions of the electric furnace. Moreover, the optical detection structure only needs to detect the infrared light absorbance A, and can directly and quantitatively convert the true concentration c of the target element in the molten steel, realizing digital and precise detection of the composition concentration, abandoning the subjectivity and errors of traditional empirical estimation and semi-quantitative detection, and being more suitable for linkage with the intelligent adjustment module.

[0009] Furthermore, the composition sensor further includes a reference beam, which is a wavelength beam that is not absorbed. After passing through the gaseous reaction layer, the reference beam enters the infrared detector synchronously with the detection beam. Thus, a dual-beam synchronous calibration detection mechanism is formed based on the original single-beam infrared detection. Compared with the single-beam detection structure, the composition detection of the present invention can directly cancel the signal loss caused by environmental interference by performing differential / ratio operations on the signals of the detection beam and the reference beam, eliminating the influence of harsh conditions in the furnace such as dust, stray light, and temperature changes on the detection results in principle, and significantly improving the detection stability.

[0010] Furthermore, the outer shells of the temperature sensor, the pressure sensor, and the composition sensor are all made of high-temperature-resistant stainless steel, and their interiors are filled with heat-insulating ceramic wool. The light source and detector of the composition sensor are provided with micro cooling channels. Thus, a triple protection structure of a high-temperature-resistant stainless steel outer shell, internal heat-insulating ceramic wool filling, and micro cooling channels for core components is adopted to form a composite protection of passive heat insulation, active cooling, and electromagnetic shielding, enabling the internal components to still maintain a suitable working environment under the dual harsh interference of strong radiant heat and high-frequency electromagnetic pulses in the furnace, effectively reducing the detection errors caused by high temperature and electromagnetic interference, significantly improving the long-term operation reliability and measurement accuracy, and meeting the on-line real-time detection requirements under the harsh conditions of electric furnace smelting.

[0011] Furthermore, the sampling frequency of the composition sensor is 5 Hz, the sampling frequencies of the temperature sensor and the pressure sensor are both 10 Hz, and the temperature sensor, the pressure sensor, and the composition sensor are synchronously collected through a unified clock signal. Thus, the multi-source sensing data is strictly aligned on the time axis, ensuring the timing consistency of the carbon and oxygen content data with the furnace temperature and pressure data, and providing synchronous input for the parameter matching model of the intelligent adjustment module.

[0012] Furthermore, the condition sensing module has two or more copies of the same type of sensor. This allows the system to compare the output signals of multiple similar sensors in real time, automatically identify faulty sensors and issue alarms, and seamlessly switch to normal sensor operation without downtime for maintenance or manual intervention. This ensures continuous detection, facilitates rapid fault location by maintenance personnel, and reduces on-site maintenance difficulty and downtime costs. The use of two or more copies of the same type of sensor greatly enhances the module's fault tolerance; even if the performance of some sensors degrades, the overall module can still output stable and accurate data, effectively extending the fault-free operating time of the condition sensing module and adapting to the long-term continuous operation requirements of industrial sites.

[0013] Furthermore, the intelligent adjustment module includes a central controller, a variable frequency drive, and an electronically controlled valve group. The input terminal of the central controller is electrically connected to the output terminals of each sensor in the working condition sensing module, and the output terminal of the central controller is electrically connected to the control signal input terminals of the variable frequency drive and the electronically controlled valve group. The variable frequency drive and the electronically controlled valve group work together according to the principle of "pressure priority, flow correction": when the flow rate needs to be adjusted, the pressure is first adjusted to the reference pressure required for the corresponding flow rate by the variable frequency drive, and then the flow rate is finely adjusted by the electronically controlled valve group to avoid flow instability caused by pressure fluctuations. Thus, the intelligent adjustment module constructs a fully automatic control link of real-time acquisition, centralized calculation, and precise execution, which does not require manual intervention or adjustment throughout the process. It fundamentally replaces manual experience judgment and on-site operation, realizes the automation and intelligent control of the entire process of electric furnace oxygen blowing, reduces manual operation errors and labor intensity, and improves both the real-time response of the control and the matching accuracy of oxygen supply parameters.

[0014] Furthermore, the central controller includes a data processing unit, which uses a Kalman filter algorithm to denoise the digital signal, thereby eliminating abnormal data caused by fluctuations in the furnace airflow and electromagnetic interference; the data processing unit uses a moving average method to smooth the processed signal to obtain stable operating characteristic values. Therefore, through a two-stage collaborative data processing mechanism of real-time noise reduction using the Kalman filter algorithm and subsequent smoothing using the moving average method, the processed data exhibits smaller errors, higher consistency, and stronger long-term stability, maximizing the reproduction of real operating conditions. Compared to unfiltered, single-processing methods, the data processing unit of the central controller in this invention filters out non-real operating condition signals at the source, retaining effective data that most closely reflects the actual smelting state. This completely solves the problems of signal distortion and drift in harsh industrial environments, ensuring the authenticity and reliability of the original data and preventing frequent jumps in subsequent control commands due to minor signal fluctuations. It provides stable and usable core data for the central controller's decision-making. Furthermore, the Kalman filter and moving average method have low computational load and fast response speed, enabling real-time synchronous processing of multiple operating condition digital signals without data processing delay. This ensures zero lag between data processing and control command output, perfectly adapting to the online, real-time, and continuous intelligent control requirements of electric furnace smelting.

[0015] Furthermore, the central controller also includes an adaptive model, which is constructed based on a BP neural network algorithm. The training samples for the adaptive model are built using historical datasets from multiple sets of different steel grades and smelting stages. The training samples cover measured oxygen blowing response data under different smelting temperature ranges, different carbon content gradients, and different pressures. The input parameters of the adaptive model include temperature, pressure, carbon content, and oxygen content. The output parameters of the adaptive model include the target oxygen supply flow rate Q and the target oxygen supply pressure P. Therefore, relying on this adaptive model, it can adaptively match the oxygen blowing conditions of different steel grades and different smelting stages, achieving intelligent and precise output of oxygen supply flow rate and pressure, effectively avoiding errors and lags caused by manually setting parameters. Through multi-dimensional measured data training, it significantly improves the response speed and adjustment accuracy of oxygen blowing control, stabilizes temperature, carbon content, and oxygen content indicators during the smelting process, reduces smelting parameter fluctuations, improves billet smelting quality and production stability, and optimizes oxygen supply energy consumption, achieving refined and efficient control of the smelting oxygen blowing process.

[0016] Furthermore, the central controller also includes a collaborative decision-making unit, whose signal terminal is interactively connected to the signal terminal of the adaptive model. The collaborative decision-making unit is used to dynamically generate adjustment weight coefficients for the adaptive model based on real-time operating condition characteristic values ​​and preset process objective functions. Thus, the collaborative decision-making unit can dynamically adjust the weights of each input parameter of the adaptive model according to the real-time smelting status and process requirements, abandoning the rigid setting of traditional fixed weights, and tilting the model calculation towards the current core control objectives (such as temperature control, carbon reduction, energy saving, and efficiency improvement). From the algorithm level, the influence of key parameters is strengthened and secondary interference is weakened, making the output of the adaptive model more in line with the actual control requirements. This achieves full self-optimization of oxygen blowing control without human intervention, greatly improving the intelligent decision-making level and robustness of the electric furnace oxygen blowing system, stabilizing smelting quality, and reducing production energy consumption.

[0017] Furthermore, the output of the central controller is electrically connected to an industrial touch screen human-machine interface terminal; thereby, the industrial touch screen human-machine interface terminal displays furnace operating data, current oxygen supply parameters, historical adjustment curves, and closed-loop adjustment operation status in real time, realizing human-machine interaction visualization and operation traceability.

[0018] Furthermore, the oxygen blowing execution module includes an oxygen blowing lance and a multi-nozzle oxygen blowing head. The oxygen blowing lance is made of high-temperature resistant stainless steel, and its input end is connected to the output end of a frequency converter. The input end of the multi-nozzle oxygen blowing head is fixedly connected to the output end of the oxygen blowing lance. The multi-nozzle oxygen blowing head has multiple nozzles evenly distributed circumferentially along the axis of the oxygen blowing lance, and the spray direction of the nozzles forms an angle of 30°-40° with the axis of the oxygen blowing lance. Thus, the multi-nozzle structure can synchronously and evenly spray oxygen outward from multiple points, forming an annular dispersion area of ​​oxygen in the furnace, greatly expanding the contact area between oxygen and molten steel and furnace charge. Compared with the traditional single-hole direct injection oxygen lance, the multi-nozzle structure avoids the problems of local over-blowing, intense central reaction, and insufficient edge reaction caused by single-hole direct injection, ensuring the overall uniformity of the smelting reaction in the furnace.

[0019] Furthermore, the oxygen source module includes an industrial liquid oxygen storage tank, a vaporizer, and a pressure stabilizing tank. The output end of the industrial liquid oxygen storage tank is sequentially connected to the vaporizer and the pressure stabilizing tank, and the output end of the pressure stabilizing tank is connected to the variable frequency drive pipeline. Thus, the gas supply structure forms a three-stage continuous oxygen supply link of storage-vaporization-pressure stabilization, providing a constant pressure and stable flow rate of gaseous oxygen output. It can work efficiently with the intelligent adjustment module and the oxygen blowing execution module to avoid unstable oxygen blowing flow, over-blowing, or under-blowing problems caused by fluctuations in gas source pressure, thereby improving the oxygen blowing control accuracy and ensuring the quality of steel smelting and production safety.

[0020] Furthermore, the safety protection module includes a pressure monitor, a pressure relief valve, and an alarm device. The pressure monitor is installed on the oxygen supply pipeline near the oxygen source module, the pressure relief valve is installed in parallel with the oxygen supply pipeline, and the alarm device is an audible and visual alarm. The pressure monitor, pressure relief valve, and alarm device are all electrically connected to the central controller. This forms an integrated safety closed loop of real-time monitoring, automatic judgment, rapid pressure relief, and audible and visual alarm. The pressure monitor is positioned near the oxygen source to prevent direct impact from high-pressure oxygen and damage to subsequent control and execution components, enabling early detection and protection against overpressure. The pressure relief valve, connected in parallel with the oxygen supply pipeline, prevents sudden interruption of oxygen blowing operations due to pressure relief, ensuring continuous and stable smelting. The audible and visual alarm provides a prominent signal with a wide transmission range, allowing on-site personnel to detect potential hazards remotely and take timely emergency measures. Furthermore, through the control of the central controller, the system automatically collects pressure data, automatically judges the situation, automatically drives pressure relief, and automatically triggers alarms, all without manual operation and with no response delay, achieving true intelligent and automated safety protection.

[0021] The above-mentioned intelligent oxygen supply regulation method for the electric furnace oxygen blowing system includes the following steps: S1. System parameter setting: Before smelting begins, the system parameters are set, including the steel grade, the liquid oxygen storage tank capacity of the oxygen source module, the vaporization capacity of the vaporizer, the preset output pressure of the pressure stabilizing tank, and the preset operating condition standard of the PLC controller of the intelligent adjustment module. S2. Model calibration: Initialize the system and load the process parameter template for the corresponding steel grade, and simultaneously calibrate the zero point and range of each sensor; S3. Data Acquisition: At the start of smelting, the working condition sensing module synchronously collects data on temperature, pressure and carbon / oxygen content in the electric furnace through a unified clock signal, and transmits the data to the data processing unit of the central controller. S4. Data preprocessing: The data processing unit performs multi-step processing on the raw signals collected by the sensors and outputs the current operating condition characteristic values ​​to the collaborative decision-making unit; S5. Deviation Analysis and Classification: Based on the parameter settings in step S1, the collaborative decision-making unit retrieves the preset standard interval of the corresponding stage of the adaptive model in step S2, calculates the deviation value between the current working condition characteristic value and the preset standard interval in real time, and classifies the deviation type. S6. Deviation Adjustment Decision: The collaborative decision-making unit executes a graded adjustment strategy based on the deviation type classification results; S7. Execution command generation: The collaborative decision-making unit outputs adjustment commands to the frequency converter and the electronic control valve group, so that the real-time operating data continuously approaches the preset standard range; S8. Adjustment Feedback: After the adjustment command is executed by the frequency converter and the electronic control valve group, the central controller waits for two sets of sampled data and compares the deviation between the adjusted operating condition data and the target value; it then makes another adjustment based on the type of deviation until the operating condition data is stable within the standard range. S9. Safety monitoring: After the adjustment command is executed by the frequency converter and the electric control valve group, the central controller monitors the pressure of the oxygen supply pipeline and the valve opening status of the pressure relief valve in real time. If the pipeline pressure exceeds the safety threshold, the closed-loop adjustment is immediately suspended, and the pressure relief and alarm operations of the safety protection module are executed first. After the pressure returns to normal, the adjustment process is restarted to avoid conflict between the adjustment action and the safety protection. S10. Model Training and Parameter Update: Based on the preset BP neural network structure, the working condition feature value output in step S4 is used as input, and the historical best oxygen blowing flow rate, nozzle angle and oxygen supply sequence are used as labels to carry out online incremental training.

[0022] Furthermore, the central controller divides the smelting process into stages based on the smelting time, including the initial smelting stage, the middle smelting stage, and the later smelting stage. The preset operating condition standard in step S5 is dynamically updated according to the smelting progress. Thus, the dynamic updating of the preset operating condition standard enables the operating condition control parameters to match the process requirements of the corresponding smelting stage in real time, significantly improving the control accuracy and adaptability of the smelting process. By implementing segmented and refined management of the entire smelting process, the problem that the traditional unified control mode cannot adapt to the process differences of different smelting stages is solved, and the control logic is more in line with the actual smelting rules. Compared with the fixed operating condition standard, this dynamic adjustment method can effectively optimize smelting energy consumption, reduce raw material loss, improve the quality stability and yield of smelted products, reduce manual intervention, and improve the efficiency and automation level of smelting operations.

[0023] Furthermore, the deviation type classification in step S5 includes first-level deviation, second-level deviation, and third-level deviation, wherein: When the deviation value is ≤5%, it is judged as a first-level deviation, and a fine-tuning strategy is implemented: pressure fine-tuning is performed only through the frequency converter to ensure parameter stability; When the deviation value is in the range of 5% to 15%, it is judged as a level 2 deviation, and the pressure-flow coordinated regulation strategy is executed: start the variable frequency speed controller and the electric control valve group, and adjust the oxygen supply pressure and the opening range of the electric control valve group respectively, with an adjustment step of 5% / time; When the deviation value is greater than 15%, it is judged as a level three deviation and the emergency adjustment mechanism is triggered: the frequency converter and the electronic control valve group adjust at the maximum response rate, while shortening the data sampling period to 5Hz to accelerate the closed-loop feedback speed.

[0024] Furthermore, in step S7, when adjusting the oxygen supply parameters, the central controller simultaneously calculates the diffusion coverage area of ​​oxygen in the furnace; thus, when the pressure inside the furnace is too low, the oxygen supply pressure is appropriately increased to ensure that the oxygen spray covers the entire surface of the furnace charge; when the local temperature inside the furnace is too high, the opening degree of the electronically controlled valve group is finely adjusted to reduce the oxygen flow in the local area and avoid excessive oxidation.

[0025] Furthermore, step S8 also includes a fault-tolerant adjustment failure process. When the number of consecutive adjustments exceeds a preset value, and the deviation of the current operating data still exceeds the secondary adjustment threshold, the central controller determines that the adjustment has failed and immediately activates the backup adjustment scheme. The backup adjustment scheme is as follows: if it is determined that the problem is caused by insufficient oxygen supply, the oxygen supply pressure is automatically increased to the safety threshold and the flow rate is increased to the emergency parameter; if it is determined that the problem is caused by excessive oxygen supply, the pressure and flow rate are automatically reduced to the minimum value, and an "abnormal adjustment" alarm is triggered to prompt the staff to check the equipment malfunction.

[0026] The beneficial effects of this invention are: 1. The electric furnace oxygen blowing system of the present invention forms a complete closed-loop control system through the collaborative operation of working condition sensing, intelligent adjustment, oxygen blowing execution, and safety protection modules. It realizes full-process automation and intelligence of electric furnace oxygen blowing from working condition detection, parameter control, oxygen injection to safety protection. It not only greatly improves smelting efficiency and billet quality, but also optimizes oxygen supply energy consumption, reduces raw material loss and labor costs, and improves the reliability and stability of long-term system operation, adapting to the process requirements of different steel grades and different smelting stages. 2. The electric furnace oxygen blowing system of the present invention adopts a multi-nozzle annular injection design to make the oxygen in the furnace uniformly distributed, avoiding the problems of local overblowing and insufficient edge reaction caused by traditional single-hole direct injection, ensuring the overall uniformity of the smelting reaction in the furnace and reducing the fluctuation of smelting parameters.

[0027] 3. The electric furnace oxygen blowing system of the present invention adopts a three-stage oxygen supply link of "industrial liquid oxygen storage tank-vaporizer-pressure stabilizer tank", which can provide gaseous oxygen with constant pressure and stable flow rate, avoiding the problems of unstable oxygen blowing flow, over-blowing or under-blowing caused by gas source pressure fluctuation. It works efficiently with the intelligent adjustment module and the oxygen blowing execution module to further improve the oxygen blowing control accuracy and ensure smelting quality.

[0028] 4. The intelligent oxygen supply regulation method of the present invention follows the core process of "setting-calibration-collection-processing-regulation-feedback-optimization". Through a refined, segmented and intelligent regulation strategy, it further leverages the synergistic advantages of each module of the system and solves the problems of poor adaptability, low regulation accuracy and insufficient fault tolerance of traditional regulation methods.

[0029] 5. The intelligent oxygen supply regulation method of the present invention divides the smelting process into initial, middle and late stages according to the smelting time. The preset working condition standard is dynamically updated with the smelting progress, so that the working condition control parameters can match the process requirements of the corresponding stage in real time. This solves the problem that the traditional unified control mode cannot adapt to the process differences of different smelting stages. The control logic is more in line with the actual smelting law, and the control accuracy and adaptability of the smelting process are improved.

[0030] 6. The intelligent oxygen supply regulation method of the present invention adjusts the oxygen supply in stages according to the deviation between the characteristic value of the working condition and the preset standard range, adapting to different deviation scenarios, avoiding over-adjustment or lag, and improving the efficiency and accuracy of regulation; and adopts dynamic feedback to form a closed-loop mechanism of "adjustment-feedback-re-adjustment" to ensure that the regulation effect continues to meet the standard; at the same time, it calculates the oxygen diffusion coverage area and adjusts the oxygen supply parameters in a targeted manner to avoid problems such as local overblowing and excessive oxidation, further optimizing the smelting effect.

[0031] 7. The intelligent oxygen supply regulation method of the present invention sets up a fault-tolerant handling mechanism for regulation failure. When the number of consecutive adjustments exceeds the preset value and the deviation still cannot be controlled within a reasonable range, the backup regulation scheme is automatically activated. The parameters are adjusted according to the cause of the deviation (insufficient / excessive oxygen supply), and an alarm is triggered to prompt the staff to troubleshoot the fault. This avoids smelting interruption or product quality decline due to regulation failure, ensures the continuity and stability of smelting operations, and reduces production losses. Attached Figure Description

[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0033] Figure 1 This is a schematic diagram of the electric furnace oxygen blowing system in Example 1.

[0034] Figure 2 This is a simplified diagram of the architecture of the electric furnace oxygen blowing system in Example 1.

[0035] Figure 3 This is a schematic diagram of the structure of the multi-nozzle oxygen blowing head in Embodiment 1.

[0036] Figure 4 yes Figure 2 Flowchart of oxygen supply regulation for the oxygen blowing system of a medium-sized electric furnace.

[0037] Figure 5 yes Figure 1 Control logic flowchart of the intelligent oxygen supply regulation method.

[0038] Figure 6 This is a flowchart of the regulation logic of the intelligent oxygen supply regulation method in Example 2.

[0039] Figure 7 This is a feedback logic flowchart of the intelligent oxygen supply regulation method in Example 3.

[0040] Figure 8 This is a fault-tolerant logic flowchart of the linkage between the intelligent adjustment module and the safety control module in Example 4.

[0041] In the diagram: 1. Furnace body; 2. Operating condition sensing module; 21. Temperature sensor; 22. Pressure sensor; 23. Composition sensor; 3. Oxygen blowing execution module; 31. Oxygen blowing gun rod; 32. Multi-nozzle oxygen blowing head; 4. Oxygen source module; 41. Industrial liquid oxygen storage tank; 42. Vaporizer; 43. Pressure stabilizing tank; 5. Intelligent adjustment module; 51. Central controller; 52. Variable frequency speed controller; 53. Electrically controlled valve group; 6. Safety protection module; 61. Pressure monitor; 62. Pressure relief valve; 63. Alarm device. Detailed Implementation

[0042] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0043] Example 1: like Figure 1 , Figure 2 As shown, an electric furnace oxygen blowing system includes a furnace body 1, a working condition sensing module 2, an oxygen blowing execution module 3, an intelligent adjustment module 5, and a safety protection module 6, wherein: The output end of the oxygen blowing execution module 3 and the working condition sensing module 2 are located inside the furnace mouth of the furnace body 1. The output end of the working condition sensing module 2 is electrically connected to the intelligent adjustment module 5. The working condition sensing module 2 is used to collect the furnace temperature, pressure, and molten steel composition in real time. The oxygen blowing execution module 3 executes oxygen injection operation according to the output adjustment command. The output end of the intelligent adjustment module 5 is electrically connected to the oxygen blowing execution module 3. The intelligent adjustment module 5 is used to receive the data collected by the working condition sensing module 2, calculate the optimal oxygen supply pressure, flow rate, and injection angle, and output adjustment commands to the oxygen blowing execution module 3. The input end of the oxygen blowing execution module 3 is connected to the oxygen source module 4 through the oxygen supply pipeline. The safety protection module 6 is integrated on the oxygen supply pipeline. The safety protection module 6 is used to trigger a rapid pressure relief action and send an abnormal alarm signal to the intelligent adjustment module 5 when the pressure in the oxygen supply pipeline exceeds a preset threshold.

[0044] Therefore, the electric furnace oxygen blowing system, through the coordinated operation of six modules including working condition sensing, intelligent adjustment, oxygen blowing execution, and safety protection, achieves real-time monitoring, intelligent control, precise execution, and a safe closed loop throughout the entire process of electric furnace oxygen blowing. This fundamentally solves the problems of traditional oxygen blowing systems, such as reliance on manual experience, delayed adjustment, uneven oxygen supply, and prominent safety hazards.

[0045] Reference Figure 2The operating condition sensing module 2 includes a temperature sensor 21, a pressure sensor 22, and a composition sensor 23. The temperature sensor 21 is a thermocouple-type sensor with a measurement range of 0-1800℃; the pressure sensor 22 uses a high-temperature pressure transmitter; and the composition sensor 23 uses an infrared spectral sensor. The data output terminals of the temperature sensor 21, pressure sensor 22, and composition sensor 23 are electrically connected to the multi-channel analog input interface of the intelligent adjustment module 5. The multi-channel analog input interface of the intelligent adjustment module 5 supports synchronous sampling and anti-interference filtering. Thus, the operating condition sensing module 2 collects core parameters such as furnace temperature, pressure, and molten steel composition in real time. The monitored data directly reflects the actual smelting state inside the furnace, eliminating the subjectivity and lag of traditional experience-based judgments, and providing accurate and real-time data support for subsequent oxygen blowing parameter adjustments.

[0046] in: The sampling frequency of the composition sensor 23 is 5Hz, while the sampling frequencies of the temperature sensor 21 and the pressure sensor 22 are both 10Hz. The temperature sensor 21, pressure sensor 22, and composition sensor 23 are synchronously acquired through a unified clock signal. This ensures that the multi-source sensor data are strictly aligned on the time axis, guaranteeing the temporal consistency between the carbon and oxygen content data and the furnace temperature and pressure data, and providing synchronous input for the parameter matching model of the intelligent adjustment module 5.

[0047] The composition sensor 23 includes a detection beam and an infrared detector. The detection beam is emitted through a collimating optical system, penetrates the gaseous reaction layer at the furnace opening, and is received by the infrared detector. The infrared detector converts the received spectral signal into an electrical signal, which is then sent to the intelligent adjustment module 5 for composition inversion calculation after high-speed analog-to-digital conversion. Thus, the optical detection structure achieves non-contact detection. Compared with traditional contact and semi-quantitative composition detection methods, it does not interfere with the smelting reaction in the furnace and avoids direct damage to the sensor caused by high temperature, dust, and corrosive environment, greatly improving the stability and service life of the sensor under the high temperature conditions of the electric furnace. Moreover, the optical detection structure only needs to detect the infrared light absorbance A to directly and quantitatively calculate the true concentration c of the target element in the molten steel, realizing digital and accurate detection of composition concentration, eliminating the subjectivity and error of traditional empirical estimation and semi-quantitative detection, and is more suitable for linkage with the intelligent adjustment module 5.

[0048] Considering the impact of harsh furnace conditions such as dust, stray light, and temperature changes on the detection results, in addition to a customized filter set, the component sensor 23 adopts a "dual-beam contrast detection" technology. That is, the component sensor 23 also includes a reference beam emitted simultaneously with the detection beam. The reference beam is a wavelength beam that is not absorbed by carbon and oxygen. After passing through the gaseous reaction layer, the reference beam enters the infrared detector synchronously with the detection beam. Thus, a dual-beam synchronous calibration detection mechanism is formed on the basis of the original single-beam infrared detection. Compared with the single-beam detection structure, the component detection of this invention can directly offset the signal loss caused by environmental interference by performing differential / ratio calculations on the signals of the detection beam and the reference beam, which greatly improves the detection stability.

[0049] To adapt to the high-temperature operating conditions of the electric furnace, the housings of temperature sensor 21, pressure sensor 22, and composition sensor 23 are all made of high-temperature resistant stainless steel, such as 310S; and their interiors are filled with heat-insulating ceramic cotton. The light source and detector of composition sensor 23 are equipped with miniature cooling channels. Thus, the triple protection structure of high-temperature resistant stainless steel housing, internal heat-insulating ceramic cotton filling, and miniature cooling channels for core components forms a composite protection of passive heat insulation, active cooling, and electromagnetic shielding. This ensures that the internal components of the entire operating condition sensing module 2 can still maintain a suitable working environment under the dual severe interference of strong radiant heat and high-frequency electromagnetic pulses in the furnace, effectively reducing detection errors caused by high temperature and electromagnetic interference. The long-term operational reliability and measurement accuracy are significantly improved, meeting the online real-time detection requirements under the harsh operating conditions of electric furnace smelting.

[0050] Furthermore, the operating condition sensing module 2 has two or more copies of the same type of sensor. This allows the system to compare the output signals of multiple similar sensors in real time, automatically identify faulty sensors and issue alarms, and seamlessly switch to normal sensor operation without downtime for maintenance or manual intervention. This ensures continuous detection and facilitates quick fault location by maintenance personnel, reducing on-site maintenance difficulty and downtime costs. Moreover, the dual or multiple copies of the same type of sensor greatly enhance the module's fault tolerance. Even if the performance of some sensors degrades, the overall module can still output accurate data stably, effectively extending the fault-free operating time of the operating condition sensing module 2 and adapting to the long-term continuous operation requirements of industrial sites.

[0051] Reference Figure 2The intelligent adjustment module 5 includes a central controller 51, a variable frequency drive 52, and an electric valve group 53. The central controller is a PLC controller, model S7-1200. The input terminal of the central controller 51 is electrically connected to the output terminals of each sensor in the working condition sensing module 2, and the output terminal of the central controller 51 is electrically connected to the control signal input terminals of the variable frequency drive 52 and the electric valve group 53. The variable frequency drive 52 and the electric valve group 53 work together to follow the principle of "pressure priority, flow correction": when the flow needs to be adjusted, the pressure is first adjusted to the reference pressure required for the corresponding flow through the variable frequency drive 52, and then the flow is finely adjusted through the electric valve group 53 to avoid flow instability caused by pressure fluctuations. The intelligent adjustment module 5 constructs a fully automatic control link of real-time acquisition-centralized calculation-precise execution, which does not require manual intervention or adjustment throughout the process. It fundamentally replaces manual experience judgment and on-site operation, realizes the automation and intelligent control of the entire process of electric furnace oxygen blowing, reduces manual operation errors and labor intensity, and improves both the real-time control response and the matching accuracy of oxygen supply parameters.

[0052] The central controller 51 includes a data processing unit. The data processing unit uses a Kalman filter algorithm to reduce noise in the digital signal, which can eliminate abnormal data caused by fluctuations in the furnace airflow and electromagnetic interference. The data processing unit uses a moving average method to smooth the processed signal in order to obtain stable operating characteristic values. Therefore, through a two-stage collaborative data processing mechanism of real-time noise reduction using the Kalman filter algorithm and subsequent smoothing using the moving average method, the processed data has smaller errors, higher consistency, and stronger long-term stability, maximizing the restoration of the real working conditions. Compared with no filtering and single processing methods, the data processing unit of the central controller 51 of this invention filters out non-real-condition signals from the source, retaining effective data that is closest to the actual smelting state. It completely solves the problem of signal distortion and drift in harsh industrial environments, ensuring the authenticity and reliability of the original data and avoiding frequent jumps in subsequent control commands due to slight signal fluctuations, providing stable and usable core data for the central controller 51's decision-making. In addition, the Kalman filter and moving average method have low computational load and fast response speed, and can process multiple working condition digital signals synchronously in real time without data processing delay, ensuring zero lag between data processing and control command output, perfectly adapting to the online, real-time, and continuous intelligent control requirements of electric furnace smelting.

[0053] The central controller 51 also includes an adaptive model, built on a BP neural network algorithm. The training samples for the adaptive model are constructed from historical datasets of different steel grades and smelting stages. The training samples cover measured oxygen blowing response data under different smelting temperature ranges, carbon content gradients, and pressures. The input parameters of the adaptive model include temperature, pressure, carbon content, and oxygen content. The output parameters include the target oxygen supply flow rate Q and the target oxygen supply pressure P. Therefore, relying on this adaptive model, it can adaptively match oxygen blowing conditions for different steel grades and smelting stages, achieving intelligent and precise output of oxygen supply flow rate and pressure, effectively avoiding errors and lags caused by manually set parameters. Through multi-dimensional measured data training, it significantly improves the response speed and adjustment accuracy of oxygen blowing control, stabilizes temperature, carbon content, and oxygen content indicators during the smelting process, reduces smelting parameter fluctuations, improves billet smelting quality and production stability, and optimizes oxygen supply energy consumption, achieving refined and efficient control of the smelting oxygen blowing process.

[0054] The central controller 51 also includes a collaborative decision-making unit, whose signal terminals are interconnected with those of the adaptive model. The collaborative decision-making unit dynamically generates adjustment weight coefficients for the adaptive model based on real-time operating condition characteristics and preset process objective functions. Thus, the collaborative decision-making unit can dynamically adjust the weights of each input parameter of the adaptive model according to the real-time smelting status and process requirements, abandoning the rigid setting of traditional fixed weights. This allows the model calculation to be tilted towards the current core control objectives (such as temperature control, carbon reduction, energy saving, and efficiency improvement), strengthening the influence of key parameters and weakening secondary interferences at the algorithm level. This makes the output of the adaptive model more in line with the actual control requirements, thereby achieving full self-optimization of oxygen blowing control without human intervention, significantly improving the intelligent decision-making level and robustness of the electric furnace oxygen blowing system, stabilizing smelting quality, and reducing production energy consumption.

[0055] In addition, the output of the central controller 51 is electrically connected to an industrial touch screen human-machine interface terminal; thus, the industrial touch screen human-machine interface terminal displays furnace operating data, current oxygen supply parameters, historical adjustment curves, and closed-loop adjustment operation status in real time, realizing human-machine interaction visualization and operation traceability.

[0056] Reference Figure 3The oxygen blowing execution module 3 includes an oxygen blowing lance 31 and a multi-nozzle oxygen blowing head 32. The oxygen blowing lance 31 is made of high-temperature resistant stainless steel, and in this embodiment, model 310S is used. The input end of the oxygen blowing lance 31 is connected to the output end of the frequency converter 52 via a pipeline. The input end of the multi-nozzle oxygen blowing head 32 is fixedly connected to the output end of the oxygen blowing lance 31. The multi-nozzle oxygen blowing head 32 has multiple nozzles evenly distributed around the axis of the oxygen blowing lance 31. The spray direction of the nozzles forms an angle of 30°-40° with the axis of the oxygen blowing lance 31. Thus, the multi-nozzle structure can spray oxygen synchronously and evenly outward from multiple paths, so that oxygen forms an annular dispersion area in the furnace, which greatly expands the contact area between oxygen and molten steel and furnace charge. Compared with the traditional single-hole direct injection oxygen lance, the multi-nozzle structure avoids the problems of local over-blowing, violent central reaction, and insufficient edge reaction caused by single-hole direct injection, and ensures that the overall smelting reaction in the furnace is uniform.

[0057] Reference Figure 1 , Figure 2 The oxygen source module 4 includes an industrial liquid oxygen storage tank 41, a vaporizer 42, and a pressure stabilizing tank 43. The output end of the industrial liquid oxygen storage tank 41 is connected to the vaporizer 42 and the pressure stabilizing tank 43 in sequence. The output end of the pressure stabilizing tank 43 is connected to the pipeline of the variable frequency speed controller 52. Thus, the gas supply structure forms a three-stage continuous oxygen supply link of storage-vaporization-pressure stabilization, providing a constant pressure and stable flow rate of gaseous oxygen output. It can work efficiently with the intelligent adjustment module 5 and the oxygen blowing execution module 3 to avoid unstable oxygen blowing flow, over-blowing or under-blowing problems caused by fluctuations in gas source pressure, thereby improving the oxygen blowing control accuracy and ensuring the quality of steel smelting and production safety.

[0058] Reference Figure 2 The safety protection module 6 includes a pressure monitor 61, a pressure relief valve 62, and an alarm device 63. The pressure monitor is installed at the end of the oxygen supply pipeline between the frequency converter 52 and the oxygen blowing gun rod 31, which is close to the oxygen source. The pressure relief valve is installed in parallel on the oxygen supply pipeline between the frequency converter 52 and the oxygen blowing gun rod 31. The alarm device is an audible and visual alarm. The pressure monitor, pressure relief valve, and alarm device are all electrically connected to the central controller 51. This creates an integrated safety closed loop encompassing real-time monitoring, automatic judgment, rapid pressure relief, and audible and visual alarms. Pressure monitors are positioned close to the oxygen source to prevent direct impact from high-pressure oxygen, which could damage subsequent control and execution components, enabling early detection and protection against overpressure. Pressure relief valves are connected in parallel to the oxygen pipeline to prevent sudden interruptions in oxygen blowing operations due to pressure relief, ensuring continuous and stable smelting. Audible and visual alarms provide prominent signals with a wide transmission range, allowing on-site personnel to detect potential hazards remotely and take timely emergency measures. Furthermore, the system is controlled by the central controller 51, enabling automatic pressure collection, judgment, pressure relief, and alarm triggering without manual intervention or delay, achieving true intelligent and automated safety protection.

[0059] The working principle of the above-mentioned electric furnace oxygen blowing system is as follows: Reference Figure 4 The operating condition sensing module 2 collects real-time data on furnace temperature, pressure, and molten steel composition (mainly carbon and oxygen content) in the furnace body 1. After converting the analog signals into digital signals, the data is transmitted to the central controller 51 of the intelligent adjustment module 5. The central controller 51 has a built-in preset operating condition-oxygen supply parameter matching model and a multi-dimensional collaborative control algorithm. By comparing the real-time collected data with preset standard data, it calculates the optimal oxygen supply pressure and flow parameters. Subsequently, the central controller sends control commands to the frequency converter and the electric control valve group to adjust the output pressure of the oxygen source module and the flow rate of the oxygen delivery pipeline. The oxygen blowing execution module sprays oxygen evenly into the furnace through a multi-nozzle oxygen blowing head to achieve precise and uniform oxygen blowing. The safety protection module monitors the pressure of the oxygen delivery pipeline in real time. When an abnormal pressure occurs, it automatically performs pressure relief and alarm operations to ensure the safe operation of the system.

[0060] Reference Figure 5 The above-mentioned intelligent oxygen supply regulation method for the electric furnace oxygen blowing system specifically includes the following steps: S1. System parameter setting: Before smelting begins, the system parameters are set, including the steel grade, the liquid oxygen storage tank capacity of oxygen source module 4, the vaporization capacity of vaporizer 42, the preset output pressure value of pressure stabilizing tank 43, and the preset operating condition standard of PLC controller of intelligent adjustment module 5. S2. Model calibration: Initialize the system and load the process parameter template for the corresponding steel grade, and simultaneously calibrate the zero point and range of each sensor; S3. Data Acquisition: At the start of smelting, the working condition sensing module 2 synchronously collects data on temperature, pressure and carbon / oxygen content in the electric furnace through a unified clock signal, and transmits the data to the data processing unit of the central controller 51. S4. Data preprocessing: The data processing unit performs multi-step processing on the raw signals collected by the sensors and outputs the current operating condition characteristic values ​​to the collaborative decision-making unit; S5. Deviation Analysis and Classification: Based on the parameter settings in step S1, the collaborative decision-making unit retrieves the preset standard interval of the corresponding stage of the adaptive model in step S2, calculates the deviation value between the current working condition characteristic value and the preset standard interval in real time, and classifies the deviation type.

[0061] S6. Deviation Adjustment Decision: The collaborative decision-making unit executes the adjustment strategy based on the deviation results; S7. Execution command generation: The collaborative decision-making unit outputs adjustment commands to the frequency converter 52 and the electronic control valve group, so that the real-time operating data continuously approaches the preset standard range; S8. Adjustment Feedback: After the adjustment command is executed by the frequency converter 52 and the electronic control valve group 53, the central controller 51 waits for two sets of sampled data and compares the deviation between the adjusted operating condition data and the target value; it then makes another adjustment based on the type of deviation until the operating condition data is stable within the standard range. S9. Safety monitoring: After the adjustment command is executed by the frequency converter 52 and the electric control valve group 53, the central controller 51 monitors the pressure of the oxygen supply pipeline and the valve opening status of the pressure relief valve in real time. If the pipeline pressure exceeds the safety threshold, the closed-loop adjustment is immediately suspended, and the pressure relief and alarm operations of the safety protection module are executed first. After the pressure returns to normal, the adjustment process is restarted to avoid conflict between the adjustment action and the safety protection. S10. Model Training and Parameter Update: Based on the preset BP neural network structure, the working condition feature value output in step S4 is used as input, and the historical best oxygen blowing flow rate, nozzle angle and oxygen supply sequence are used as labels to carry out online incremental training.

[0062] Example 2: As smelting progresses, the composition of the molten steel and the thermal state within the furnace continuously evolve dynamically, and the oxygen supply demand also changes accordingly. To more accurately match real-time operating conditions, refer to... Figure 6 Based on Example 1, this embodiment introduces a dynamic standard update mechanism in step S5 and performs categorized and graded processing on the deviation results, as detailed below: The central controller 51 divides the smelting process into stages based on the smelting time. The smelting stages include the initial smelting stage, the middle smelting stage, and the later smelting stage. The preset operating condition standards are dynamically updated according to the smelting progress. Deviation types are classified into first-level deviation, second-level deviation, and third-level deviation, among which: When the deviation value is ≤5%, it is judged as a first-level deviation, and a fine-tuning strategy is executed: pressure fine-tuning is performed only through the frequency converter 52 to ensure parameter stability; When the deviation value is in the range of 5% to 15%, it is judged as a level two deviation, and the pressure-flow coordinated adjustment strategy is executed: start the variable frequency speed controller 52 and the electric control valve group 53, and adjust the oxygen supply pressure and the opening of the electric control valve group respectively, with an adjustment step of 5% / time. When the deviation value is greater than 15%, it is judged as a level three deviation and the emergency adjustment mechanism is triggered: the variable frequency speed controller 52 and the electronic control valve group adjust at the maximum response rate, while shortening the data sampling period to 5Hz to accelerate the closed-loop feedback speed.

[0063] Therefore, the preset operating condition standards are dynamically updated, enabling the operating condition control parameters to match the process requirements of the corresponding smelting stage in real time, greatly improving the control accuracy and adaptability of the smelting process. By implementing segmented and refined management of the entire smelting process, the problem that the traditional unified control mode cannot adapt to the process differences of different smelting stages is solved, and the control logic is more in line with the actual smelting rules. Compared with fixed operating condition standards, this dynamic adjustment method can effectively optimize smelting energy consumption, reduce raw material loss, improve the quality stability and yield of smelted products, reduce manual intervention, and improve the efficiency and automation level of smelting operations.

[0064] This embodiment uses the smelting of Q235B grade steel in a 100-ton electric arc furnace as an example to verify the actual smelting process: System parameter settings: The liquid oxygen storage tank 41 of the oxygen source module 4 has a capacity of 50m³, the vaporization capacity of the vaporizer 42 is 1000m³ / h, and the output pressure of the pressure stabilizing tank 43 is preset to 0.6MPa; the PLC controller of the intelligent adjustment module 5 has preset operating conditions: smelting temperature of 1600-1650℃, furnace pressure of 0.1-0.15MPa, carbon content of molten steel of 0.12%-0.20%, corresponding to an oxygen supply flow rate of 800-1200m³ / h; the pressure threshold of the safety protection module 6 is set to 0.8MPa; the nozzle tilt angle of the oxygen blowing execution module 3 is 35°. Start-up preparation: Turn on the liquid oxygen storage tank 41 and vaporizer 42 of oxygen source module 4 to vaporize the liquid oxygen into gaseous oxygen. Stabilize the pressure to 0.6MPa through the pressure stabilizing tank. Check the connection status of each module to ensure that there are no leaks in the pipeline and that the sensors are working properly. In the initial stage of smelting (furnace charge melting stage): the temperature sensor 21 of the operating condition sensing module 2 collects the furnace temperature as 1200℃ (below the preset lower limit), the pressure sensor 22 collects the pressure as 0.08MPa (below the preset lower limit), and the composition sensor 23 collects the carbon content as 0.8% (above the preset upper limit); after receiving the data, the central controller 51 determines that the oxygen supply flow and pressure need to be increased, sends a command to the frequency converter 52 to increase the output pressure of the pressure stabilizing tank 43 to 0.7MPa, and at the same time controls the electric control valve group 53 to be fully opened so that the oxygen supply flow reaches 1200m³ / h; the multi-nozzle oxygen blowing head 32 sprays oxygen into the furnace in a ring to accelerate the melting of the furnace charge and the oxidation of carbon elements; During the mid-smelting stage (steel heating stage): Temperature sensor 21 collects data showing that the temperature rises to 1620℃ (within the preset range), the pressure rises to 0.13MPa (within the preset range), and the carbon content drops to 0.25% (close to the preset upper limit); Central controller 51 adjusts frequency converter 52 to restore the pressure of pressure stabilizing tank 43 to 0.6MPa, electric control valve group 53 is adjusted to 60% opening, and oxygen supply flow rate is reduced to 1000m³ / h to maintain stable oxygen blowing; In the later stage of smelting (composition fine-tuning stage): the temperature rises to 1640℃, the carbon content drops to 0.15% (within the preset range); the central controller 51 controls the frequency converter 42 to adjust the pressure to 0.5MPa, the opening of the electric control valve group 53 is adjusted to 30%, and the oxygen supply flow rate is reduced to 800m³ / h, precisely controlling the oxidation reaction and avoiding excessive oxidation.

[0065] Throughout the smelting process, the pressure monitor 61 of the safety protection module 6 monitors the pressure of the oxygen supply pipeline in real time. When the pressure rises to 0.82MPa due to pipeline fluctuations, the central controller immediately controls the pressure relief valve 62 to open, releasing the pipeline pressure to 0.6MPa. At the same time, the audible and visual alarm 63 is triggered, and the staff promptly investigates the cause of the fluctuation to ensure the safe operation of the system. When the composition of the molten steel meets the standard, the central controller 51 controls the electrically controlled valve group 53 to close, stopping the oxygen supply and completing the oxygen blowing operation.

[0066] Implementation results: The smelting cycle was 52 minutes, which is 8 minutes shorter than the existing system; the oxygen consumption per unit of molten steel was 5.2 m³ / ton, which is 1.2 m³ / ton lower than the existing system; the carbon content uniformity error of molten steel was ±0.015%, and the pass rate reached 99.2%, all of which are better than the existing technical indicators.

[0067] Example 3: To more precisely control the oxygen distribution inside the furnace, the following settings are added based on Examples 1 and 2: Reference Figure 7 In step S7, when adjusting the oxygen supply parameters, the central controller 51 simultaneously calculates the diffusion coverage area of ​​oxygen in the furnace; thus, when the pressure in the furnace is too low, the oxygen supply pressure is appropriately increased to ensure that the oxygen injection covers the entire surface of the furnace charge; when the local temperature in the furnace is too high, the opening of the electronic control valve group is finely adjusted to reduce the oxygen flow in the local area and avoid excessive oxidation.

[0068] Example 4: To further ensure the reliability of intelligent oxygen supply regulation for the electric furnace oxygen blowing system, the following settings are added based on Examples 2 and 3: Reference Figure 8 Step S8 also includes a fault tolerance mechanism for adjustment failure. When the number of consecutive adjustments exceeds a preset value (in this embodiment, the preset value for consecutive adjustments is 3 times), and the deviation of the current operating data still exceeds the secondary adjustment threshold, the central controller 51 determines that the adjustment has failed and immediately starts the backup adjustment scheme. The backup adjustment scheme is as follows: if it is determined that the problem is caused by insufficient oxygen supply, the oxygen supply pressure is automatically increased to the safety threshold and the flow rate is increased to the emergency parameter; if it is determined that the problem is caused by excessive oxygen supply, the pressure and flow rate are automatically reduced to the minimum value, and an "adjustment abnormality" alarm is triggered to prompt the staff to check the equipment fault.

[0069] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. An oxygen blowing system for an electric furnace, characterized in that, include: Furnace body (1); The working condition sensing module (2) is used to collect furnace temperature, pressure and molten steel composition in real time; The intelligent adjustment module (5) is used to receive data collected by the working condition sensing module (2), calculate the optimal oxygen supply pressure, flow rate and injection angle, and output adjustment commands to the oxygen blowing execution module (3); Oxygen blowing execution module (3), the input end of the oxygen blowing execution module (3) is connected to the oxygen source module (4) through the oxygen delivery pipeline, and the oxygen blowing execution module (3) performs oxygen injection operation according to the output adjustment command; Safety protection module (6), which is integrated on the oxygen delivery pipeline, is used to trigger a rapid pressure relief action and send an abnormal alarm signal to the intelligent adjustment module (5) when the pressure of the oxygen delivery pipeline exceeds a preset threshold. Wherein: the output end of the oxygen blowing execution module (3) and the working condition sensing module (2) are located inside the furnace mouth of the furnace body (1). The output end of the working condition sensing module (2) is electrically connected to the intelligent adjustment module (5), and the output end of the intelligent adjustment module (5) is electrically connected to the oxygen blowing execution module (3).

2. The electric furnace oxygen blowing system according to claim 1, characterized in that: The working condition sensing module (2) includes a temperature sensor (21), a pressure sensor (22), and a composition sensor (23). The temperature sensor (21) is a thermocouple type sensor, the pressure sensor (22) is a high-temperature pressure transmitter, and the composition sensor (23) is an infrared spectral sensor. The data output terminals of the temperature sensor (21), pressure sensor (22), and composition sensor (23) are electrically connected to the multi-channel analog input interface of the intelligent adjustment module (5). The multi-channel analog input interface of the intelligent adjustment module (5) supports synchronous sampling and anti-interference filtering. The temperature sensor (21), pressure sensor (22), and composition sensor (23) are synchronously acquired through a unified clock signal. The working condition sensing module (2) sets up two or more copies of the same type of sensor. The shells of the temperature sensor (21), pressure sensor (22), and composition sensor (23) are all made of high-temperature resistant stainless steel and filled with heat-insulating ceramic cotton.

3. The electric furnace oxygen blowing system according to claim 2, characterized in that: The component sensor (23) includes a detection beam, a reference beam and an infrared detector. The detection beam is emitted by a collimating optical system, penetrates the gaseous reaction layer at the furnace opening and is received by the infrared detector. The reference beam is a wavelength beam that is not absorbed. After passing through the gaseous reaction layer, the reference beam enters the infrared detector synchronously with the detection beam. The infrared detector converts the received spectral signal into an electrical signal, and after high-speed analog-to-digital conversion, it is sent to the intelligent adjustment module (5) for component inversion calculation. The light source and detector of the component sensor (23) are equipped with a miniature cooling channel.

4. The electric furnace oxygen blowing system according to claim 1, characterized in that: The intelligent adjustment module (5) includes a central controller (51), a variable frequency speed controller (52), and an electric control valve group (53). The input terminal of the central controller (51) is electrically connected to the output terminals of each sensor of the working condition sensing module (2). The output terminal of the central controller (51) is electrically connected to the control signal input terminal of the variable frequency speed controller (52) and the control signal input terminal of the electric control valve group (53). The output terminal of the central controller (51) is electrically connected to an industrial touch screen human-machine interaction terminal.

5. The electric furnace oxygen blowing system according to claim 4, characterized in that: The central controller (51) includes a data processing unit, which uses a Kalman filter algorithm to denoise the digital signal; the data processing unit uses a moving average method to smooth the processed signal to obtain operating condition characteristic values.

6. The electric furnace oxygen blowing system according to claim 4, characterized in that: The central controller (51) also includes an adaptive model built based on the BP neural network algorithm and a collaborative decision-making unit that interacts with the adaptive model signals; the training samples of the adaptive model are constructed through multiple sets of historical datasets of different steel grades and different smelting stages, and the training samples of the adaptive model cover measured oxygen blowing response data under different smelting temperature ranges, different carbon content gradients, and different pressures; the input parameters of the adaptive model include temperature, pressure, carbon content, and oxygen content, and the output parameters of the adaptive model include target oxygen supply flow rate Q and target oxygen supply pressure P; The collaborative decision-making unit is used to dynamically generate adjustment weight coefficients for the adaptive model based on real-time operating condition feature values ​​and preset process objective functions.

7. The electric furnace oxygen blowing system according to claim 4, characterized in that: The oxygen blowing execution module (3) includes an oxygen blowing gun rod (31) and a multi-nozzle oxygen blowing head (32). The oxygen blowing gun rod (31) is made of high-temperature resistant stainless steel. The input end of the oxygen blowing gun rod (31) is connected to the output end of the variable frequency speed controller (52) via a pipeline. The electrically controlled valve group (53) is connected in series on the pipeline between the variable frequency speed controller (52) and the oxygen blowing gun rod (31). The input end of the multi-nozzle oxygen blowing head (32) is fixedly connected to the output end of the oxygen blowing gun rod (31). The multi-nozzle oxygen blowing head (32) has multiple nozzles evenly distributed around the axis of the oxygen blowing gun rod (31). The spray direction of the nozzles forms an angle of 30°-40° with the axis of the oxygen blowing gun rod (31).

8. The electric furnace oxygen blowing system according to claim 4, characterized in that: The oxygen source module (4) includes an industrial liquid oxygen storage tank (41), a vaporizer (42) and a pressure stabilizing tank (43). The output end of the industrial liquid oxygen storage tank (41) is connected to the vaporizer (42) and the pressure stabilizing tank (43) in sequence. The output end of the pressure stabilizing tank (43) is connected to the input end of the frequency converter (52) via a pipeline.

9. The electric furnace oxygen blowing system according to claim 4, characterized in that: The safety protection module (6) includes a pressure monitor (61), a pressure relief valve (62), and an alarm device (63). The pressure monitor (61) is installed on the oxygen supply pipeline near the oxygen source module (4). The pressure relief valve (62) is installed in parallel with the oxygen supply pipeline. The alarm device (63) is an audible and visual alarm. The pressure monitor (61), the pressure relief valve (62), and the alarm device (63) are all electrically connected to the central controller (51).

10. An intelligent oxygen supply regulation method for use in the electric furnace oxygen blowing system according to any one of claims 1-9, comprising the following steps: S1. System parameter setting: Before smelting begins, the system parameters are set. The system parameters include the steel grade, the liquid oxygen storage tank capacity of the oxygen source module (4), the vaporization capacity of the vaporizer (42) of the oxygen source module (4), the preset output pressure value of the pressure stabilizing tank (43) of the oxygen source module (4), and the preset operating condition standard of the PLC controller of the intelligent adjustment module (5). S2. Model calibration: Initialize the system and load the process parameter template for the corresponding steel grade, and simultaneously calibrate the zero point and range of each sensor; S3. Data Acquisition: At the start of smelting, the working condition sensing module (2) synchronously collects the temperature, pressure and carbon / oxygen content data of the electric furnace through a unified clock signal, and transmits the data to the data processing unit of the central controller (51) of the intelligent adjustment module (5); S4. Data preprocessing: The data processing unit performs multi-step processing on the raw signals collected by the sensors and outputs the current operating condition characteristic values ​​to the collaborative decision-making unit; S5. Deviation Analysis and Classification: Based on the parameter settings in step S1, the collaborative decision-making unit retrieves the preset standard interval of the corresponding stage of the adaptive model in step S2, calculates the deviation value between the current working condition characteristic value and the preset standard interval in real time, and classifies the deviation type. S6. Deviation Adjustment Decision: The collaborative decision-making unit executes a graded adjustment strategy based on the deviation type classification results; S7. Execution instruction generation: The collaborative decision-making unit outputs adjustment instructions to the variable frequency speed controller (52) and the electric control valve group of the intelligent adjustment module (5), so that the real-time operating condition data continuously approaches the preset standard range; S8. Adjustment feedback: After the adjustment command is executed by the variable frequency speed controller (52) and the electric control valve group (53) of the intelligent adjustment module (5), the central controller (51) of the intelligent adjustment module (5) waits for two sets of sampled data, compares the deviation between the adjusted working condition data and the target value, and makes another adjustment according to the deviation type until the working condition data is stable within the standard range. S9. Safety monitoring: After the adjustment command is executed by the variable frequency speed controller (52) and the electric control valve group (53) of the intelligent adjustment module (5), the central controller (51) of the intelligent adjustment module (5) monitors the pressure of the oxygen pipeline and the valve opening status of the pressure relief valve (62) of the safety protection module (6) in real time. If the pipeline pressure exceeds the safety threshold, the closed-loop adjustment is immediately suspended, and the pressure relief and alarm operations of the safety protection module (6) are executed first. After the pressure returns to normal, the adjustment process is restarted to avoid conflict between the adjustment action and the safety protection. S10. Model Training and Parameter Update: Based on the preset BP neural network structure, the working condition feature value output in step S4 is used as input, and the historical best oxygen blowing flow rate, nozzle angle and oxygen supply sequence are used as labels to carry out online incremental training.

11. The intelligent oxygen supply regulation method according to claim 10, characterized in that: The central controller (51) divides the smelting stage according to the smelting time, and the smelting stage includes the initial smelting stage, the middle smelting stage and the later smelting stage; the preset working condition standard in step S5 is dynamically updated according to the smelting progress.

12. The intelligent oxygen supply regulation method according to claim 10, characterized in that: The deviation type classification in step S5 includes first-level deviation, second-level deviation, and third-level deviation, wherein: When the deviation value is ≤5%, it is judged as a first-level deviation, and a fine-tuning strategy is executed: pressure fine-tuning is performed only through the frequency converter (52) to ensure parameter stability; When the deviation value is in the range of 5% to 15%, it is judged as a second-level deviation, and the pressure-flow coordinated adjustment strategy is executed: start the variable frequency speed controller (52) and the electric control valve group (53), and adjust the oxygen supply pressure and the opening range of the electric control valve group (53) respectively, with an adjustment step of 5% / time; When the deviation value is greater than 15%, it is judged as a level three deviation and the emergency adjustment mechanism is triggered: the variable frequency speed controller (52) and the electronic control valve group adjust at the maximum response rate, while shortening the data sampling period to 5Hz to accelerate the closed-loop feedback speed.

13. The intelligent oxygen supply regulation method according to claim 10, characterized in that: In step S7, when adjusting the oxygen supply parameters, the central controller (51) simultaneously calculates the diffusion coverage area of ​​oxygen in the furnace.

14. The intelligent oxygen supply regulation method according to claim 10, characterized in that: In step S8, when the number of consecutive adjustments exceeds the preset value and the deviation of the current working condition data still exceeds the secondary adjustment threshold, the central controller (51) determines that "adjustment failure" and immediately starts the backup adjustment scheme. The backup adjustment scheme is as follows: if it is determined that the oxygen supply is insufficient, the oxygen supply pressure is automatically increased to the safety threshold and the flow rate is increased to the emergency parameter; if it is determined that the oxygen supply is excessive, the pressure and flow rate are automatically reduced to the minimum value, and at the same time, the "adjustment abnormality" alarm is triggered to prompt the staff to check the equipment fault.

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