Intelligent control system and method for high-purity molten steel smelting converter

Abstract

The present application relates to the technical field of steelmaking automation, in particular to a high-purity molten steel smelting converter intelligent control system and method, comprising: a data acquisition module for acquiring the operating parameters related to the converter during molten steel smelting, the operating parameters including molten steel temperature, gas composition, oxygen quantity and blowing time; a curve generation module for determining the change curve of the corresponding operating parameters during molten steel smelting according to the acquired operating parameters, the change curve including the change curve corresponding to the molten steel temperature, gas composition, oxygen quantity and blowing time; a composition display module for analyzing and processing the change curve according to the identified change curve of the operating parameters, determining the occurrence probability of the operating parameters and the target value corresponding to the occurrence probability when the purity of the molten steel reaches the requirement; the production efficiency and stability of the molten steel smelting process are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steelmaking automation, in particular to a high-purity molten steel smelting converter intelligent control system and method. BACKGROUND

[0002] With the continuous development of the steel industry technology, the single technology of metallurgical process is becoming mature, further improving the production level, reducing the cost and enhancing the competitiveness, which puts forward more stringent requirements on the molten steel temperature control level in the steelmaking-continuous casting production process. The molten steel temperature must be controlled in a relatively narrow range to meet the requirements of efficient continuous casting production. Therefore, in modern continuous casting production, stable molten steel temperature conditions are the guarantee of stable continuous casting production, and appropriate molten steel superheat is an important condition for obtaining high-quality casting blanks, and effective control of the whole process of molten steel temperature is the key to ensure the smooth and orderly production rhythm. Therefore, improving the operation level of the whole process of molten steel temperature control in the ladle from the converter tapping to the ladle furnace refining and the continuous casting rotary table, and preparing the molten steel temperature conditions before continuous casting are important aspects of embodying the above technical ideas.

[0003] For example, Chinese patent publication No. CN109857067A discloses a steelmaking multi-process temperature coordination control system and method in a big data environment, which belongs to the field of steelmaking automatic control. It solves the problem of reducing the converter tapping temperature while meeting the continuous casting superheat condition. The steelmaking multi-process temperature coordination control method proposed in the present application uses the lowest possible converter tapping temperature to meet the superheat requirement of the continuous casting process. Compared with the existing method of increasing the converter tapping temperature to ensure the continuous casting superheat, the converter tapping temperature can be reduced, the oxygen consumption, the cooling material consumption, the molten steel oxygen content and the molten steel phosphorus content can be reduced, thereby reducing the cost of the converter process.

[0004] However, the prior art does not consider the related parameters of the molten steel and the probability and target value of the related parameters when running when controlling and processing the molten steel, which leads to the fact that the automatic control of steelmaking cannot be adjusted in time according to the current situation. SUMMARY

[0005] In order to solve the above technical problems, the technical scheme adopted by the present application is: a high-purity molten steel smelting converter intelligent control system, comprising: a data acquisition module, used for acquiring the running parameters of the converter related to the molten steel smelting, the running parameters including the molten steel temperature, the gas composition, the oxygen quantity and the blowing time.

[0006] A curve generation module is used for determining the change curve of the corresponding running parameters during the molten steel smelting according to the acquired running parameters, and the change curve includes the change curve corresponding to the molten steel temperature, the gas composition, the oxygen quantity and the blowing time.

[0007] The component display module is used for analyzing the change curve according to the identified change curve of the operating parameter, determining the occurrence probability of the operating parameter when the purity of the molten steel reaches the requirement, and determining the target value corresponding to the occurrence probability.

[0008] The data storage module is used for storing the occurrence probability of the operating parameter and the target value, and generating a control strategy according to the stored data.

[0009] A high-purity molten steel smelting converter intelligent control method comprises the following steps: S1, collecting operating parameters of a converter during molten steel smelting, including molten steel temperature, gas composition, oxygen quantity and blowing time.

[0010] S2, drawing change curves of the molten steel temperature, the gas composition, the oxygen quantity and the blowing time according to the collected operating parameters.

[0011] S3, identifying key information in the change curve of the operating parameter, analyzing the change amount of the molten steel temperature, recording extreme point data, and performing corresponding processing according to a preset temperature range; determining the change trend of the gas composition, calculating the change proportion value of the key component, and drawing a change curve of the component coefficient with time; determining the change degree of the oxygen quantity according to the oxygen flow and the contact area, and drawing a change curve of the oxygen quantity.

[0012] S4, extracting feature values based on the change curve of the operating parameter, determining a feature value set corresponding to the preset molten steel purity when the feature values reach the preset molten steel purity; analyzing the probability distribution of the feature values in the smelting process, identifying the data of peak state and skew as a target feature set; performing iterative processing on the target feature set, and outputting a final target value.

[0013] S5, storing the occurrence probability of the operating parameter and the target value; generating a control strategy according to the stored data, and selecting a strategy with the highest similarity to the target value as the control strategy for current processing.

[0014] The beneficial effects of the present application are as follows: the present application collects operating parameters in the molten steel smelting process in real time, including molten steel temperature, gas composition, oxygen quantity and blowing time, draws change curves according to these parameters, analyzes the curves, determines the occurrence probability of the operating parameter when the purity of the molten steel reaches the requirement, and determines the corresponding target value; the system also has a data storage function, can generate an optimized control strategy according to the stored data, can grasp the state of the smelting process in real time, provides a basis for optimized control, makes the purity control of the molten steel more accurate, improves product quality, selects a strategy with the highest similarity to the target value as the control strategy for current processing, improves smelting efficiency and production efficiency, realizes automatic and intelligent monitoring and control of the molten steel smelting process, reduces manual intervention, and improves production efficiency and stability. BRIEF DESCRIPTION OF DRAWINGS

[0015] The application will be further described below with reference to the drawings and examples.

[0016] Figure 1 Fig. 1 is a system schematic diagram of a high-purity molten steel smelting converter intelligent control system.

[0017] Figure 2 Fig. 2 is a logic schematic diagram of a high-purity molten steel smelting converter intelligent control system.

[0018] Figure 3 Fig. 3 is a flow schematic diagram of a high-purity molten steel smelting converter intelligent control method. DETAILED DESCRIPTION

[0019] Embodiments of the application will be described in detail below. The embodiments described below are exemplary and are only used to explain the application and cannot be understood as a limitation of the application. If the specific technology or conditions are not specified in the embodiments, the technology or conditions described in the literature in the art or according to the product manual are used.

[0020] Reference Figure 1 , Figure 2 A high-purity molten steel smelting converter intelligent control system comprises: a data acquisition module that acquires real-time converter operating parameters through various sensors, such as temperature sensors, component analyzers, flow meters, etc.; the sensors are connected to the data acquisition module through data lines or wireless networks to ensure the real-time and accuracy of the data; the data acquisition module transmits real-time data to a curve generation module; the curve generation module draws curves of each operating parameter changing with time according to the received data; the curve generation module transmits the generated change curve data to a component display module; the component display module identifies and analyzes the curve data to determine the occurrence probability and target value of each operating parameter when the purity of the molten steel meets the standard; the component display module transmits the occurrence probability and target value obtained by analysis to a data storage module; the data storage module stores these data in a database for subsequent query, analysis and use.

[0021] The data acquisition module is used to acquire the operating parameters related to the converter during molten steel smelting, and the operating parameters include molten steel temperature, gas composition, oxygen quantity and blowing time.

[0022] The curve generation module is used to determine the change curves of the corresponding operating parameters during molten steel smelting according to the acquired operating parameters, and the change curves include the change curves corresponding to the molten steel temperature, gas composition, oxygen quantity and blowing time.

[0023] The component display module is used to analyze and process the change curves according to the identified change curves of the operating parameters, and determine the occurrence probability of the operating parameters and the target value corresponding to the occurrence probability when the purity of the molten steel meets the requirements.

[0024] A data storage module is configured to store the occurrence probability and target value of the operating parameter, and generate a control strategy based on the stored data.

[0025] A molten steel temperature measuring instrument is used to measure the temperature of the molten steel. For example, a computerized molten steel temperature measuring instrument can quickly and accurately measure the temperature of the molten steel. These temperature measuring instruments are usually made of high-temperature resistant materials to ensure normal operation under extreme temperatures.

[0026] A gas composition detector is used to measure the composition of the gas, including harmful gases and flammable gases. In the steel production process, the detection of gas composition is crucial for safety and environmental control. For example, a portable gas composition detector can be used for real-time detection on site.

[0027] An oxygen detector is used to measure the amount of oxygen. The oxygen detector is specifically designed to measure the oxygen content. In the steel smelting process, the control of oxygen has an important influence on the reaction efficiency and product quality. The oxygen detector can monitor the oxygen concentration in real time to ensure that it is within the appropriate range.

[0028] A timer is used to measure the blowing time. The corresponding time during blowing is calculated and recorded as the blowing time to determine the corresponding time point in the molten steel treatment process.

[0029] The change curve of the molten steel temperature in the curve generation module is represented as follows: the change of the molten steel temperature corresponding to the blowing time is obtained, and the change curve of the molten steel temperature is obtained to determine the intensity of the chemical reaction in the current converter.

[0030] When focusing on the change curve of the molten steel temperature, the main concern is the temperature change rate per unit time. The temperature sensor installed on the furnace wall or in the molten steel monitors the change of the molten steel temperature with time. Combined with the specific heat capacity and mass information, the heat absorbed by the molten steel in a certain period of time can be calculated. The heat absorbed by the molten steel at the corresponding time is obtained by calculating the product of the specific heat capacity, mass, and corresponding temperature change amount.

[0031] The heat absorbed by the molten steel, the specific heat capacity, and the mass are obtained to obtain the change amount of the molten steel temperature. The change amount of the molten steel temperature is taken as the ordinate, and the time corresponding to the blowing time is taken as the abscissa to obtain the change curve of the molten steel temperature.

[0032] The calculation method of the change amount of the molten steel temperature is represented as follows: where ΔΤ(ί) represents the change in temperature of the molten steel as a function of time t, which is the change in temperature of the molten steel from the initial time t = 0 to the current time t; Q1(t') represents the heat absorbed by the molten steel at time t', where t' is the integration variable, m represents the mass of the molten steel, c p represents the specific heat capacity of the molten steel, i.e., the energy required to raise 1 °C per unit mass of the molten steel; dt' represents the integration infinitesimal, which is a small increment of time interval, and is used to accumulate the contributions of heat input at different time points to the temperature change during integration; represents the integral sign, indicating integration over time t' from the initial time 0 to the current time t; then the calculated change in temperature of the molten steel is the temperature change curve of the molten steel.

[0033] After obtaining the temperature change curve of the molten steel, further processing is needed for the currently identified temperature of the molten steel, so the temperature change curve of the molten steel also includes the following processing methods.

[0034] Record the data corresponding to each extreme point in the temperature change curve of the molten steel, determine whether the temperature of each extreme point is within the preset temperature range, if it is within the preset temperature range, output the average temperature of the extreme point and the adjacent point; if it is greater than the upper limit of the preset temperature range, judge the rate of change of the slope value of the temperature change curve of the molten steel at the position close to the extreme point, and output the maximum value of the rate of change of the slope value; if it is less than the lower limit of the preset temperature range, obtain the median of the adjacent points of the extreme point, and output the corresponding median.

[0035] The purpose of this processing step is to analyze the temperature change curve of the molten steel in detail in order to understand its temperature change characteristics and make appropriate processing according to these characteristics. Specifically, the purpose of this step includes the following.

[0036] Determine whether the temperature of the extreme point is within the preset range: this is to determine whether the temperature of the molten steel is within a safe or ideal range. If the temperature is too high or too low, it may affect the production process and product quality.

[0037] Calculate the average temperature of the extreme point and the adjacent point: when the temperature of the extreme point is within the preset range, outputting the average temperature of the extreme point and the adjacent point can better reflect the temperature condition of the region, which helps to more accurately control the temperature.

[0038] Judge the rate of change of the slope value of the temperature change curve of the molten steel at the position close to the extreme point: when the temperature of the extreme point exceeds the upper limit of the preset range, analyzing the rate of change of the slope value can help understand the rate and stability of temperature change, which is very important for predicting temperature trends and taking appropriate control measures.

[0039] Median of adjacent points of the extreme point: When the temperature of the extreme point is lower than the lower limit of the preset range, outputting the median of the adjacent points can provide a more robust temperature indicator, avoiding deviation caused by excessively low extreme point temperature.

[0040] Suppose the preset molten steel temperature range is 1500°C to 1600°C. There is an extreme point in the recorded molten steel temperature change curve, with a temperature of 1550°C, which is within the preset range; therefore, the average temperature of this extreme point and its adjacent points will be calculated, assuming the average is 1540°C, which can be used to represent the temperature conditions in this area.

[0041] If another extreme point has a temperature of 1650°C, exceeding the upper limit of the preset range; in this case, the rate of change of the slope value of the temperature change curve near this extreme point will be analyzed; suppose the maximum value of the rate of change of the slope value is found to be 0.5°C / s, which means the temperature is rising very quickly in this area, and measures may need to be taken to control the temperature rise.

[0042] If there is another extreme point with a temperature of 1450°C, which is lower than the lower limit of the preset range; in this case, the median temperature of the adjacent points of this extreme point will be obtained. Assuming the median is 1480°C, this value can be used to represent the temperature conditions in this area, avoiding deviation caused by excessively low extreme point temperature.

[0043] Through these processing steps, the variation characteristics of molten steel temperature can be more comprehensively understood, and the production process and product quality can be optimized based on these characteristics.

[0044] When processing gas components, the identified gas components can include carbon monoxide, carbon dioxide, oxygen, nitrogen, and other gas components such as sulfide SO2, nitrogen oxide NOx, etc. At this time, under different current temperature change rates, the total amount of gas produced will also be different, and the content of each gas in the component will also be different. At this time, it is necessary to identify whether the proportion of the gas component produced under the current temperature change rate is the same as the preset proportion. If it is different, identify the corresponding temperature change rate and related gas components to predict the impurity components that can exist after the current molten steel is smelted, and whether the purity of the molten steel can meet the preset requirements.

[0045] The change curve of the gas component is used to evaluate the efficiency and quality of the smelting process by analyzing the change of gas elements during smelting. The content to be detected is to identify the gas elements in the molten steel, especially oxygen and nitrogen. The content of these gas elements in the molten steel has an important influence on the quality and performance of the steel. For example, excessive oxygen content can increase the brittleness of the steel, and excessive nitrogen content can affect the strength and toughness of the steel.

[0046] The identification method of the change curve of the gas composition is to determine the key components of the gas discharged during the smelting of the molten steel, and to obtain the change curve of the gas composition according to the change trend of the key components of the gas.

[0047] The key components can be the contents of oxygen, nitrogen, sulfur, and carbon to determine the main components of the current discharged gas.

[0048] The change trend of the key components is represented as calculating the change ratio value of the key components between different time points for each key component in the gas composition; the change ratio value is the ratio of the difference between the component content at the current time point and the component content at the initial time point.

[0049] It is compared whether each identified key component is within the preset range, and the component coefficient of the gas composition is obtained according to the change ratio value of the currently identified key component, and the change of the component coefficient with the time point is output as the change curve of the gas composition.

[0050] The collected gas samples are sent to the laboratory for analysis; gas analyzers such as gas chromatographs and infrared spectrometers are used; the contents of each key gas component in the sample are measured; the contents of each key component at each sampling time point are recorded; the change curve of each key component with time is plotted with time as the horizontal axis and gas content as the vertical axis; each point on the curve represents the content of the key component at a specific time point.

[0051] The change ratio value of the key component is set as follows: for each key component, the ratio of the current key component content to the preset component range is calculated; if the key component content is within the preset component range, the change ratio value is 1; if the key component content is lower than the minimum value of the preset component range, the change ratio value is the ratio of the current key component content to the minimum value of the preset component range; if the key component content is higher than the maximum value of the preset component range, the change ratio value is the ratio of the current key component content to the maximum value of the preset component range.

[0052] The weighted average of the change ratio values of all key components is calculated to obtain the component coefficient, and the weight of the weighted calculation is determined according to the importance of each key component and the degree of overall influence on the molten steel.

[0053] The change of the calculated component coefficient with the time point is plotted into a curve; the horizontal axis represents time and the vertical axis represents the component coefficient; each point on the curve represents the component coefficient of the gas composition at a specific time point.

[0054] The purpose of the current step is to monitor and evaluate the change of the key components of the gas: by calculating the change ratio value of the key components between different time points, the change trend of the gas composition in the production process can be understood; this is crucial for controlling product quality and production efficiency.

[0055] Determine if the gas composition is within the preset range: By comparing each key component identified whether it is within the preset range, it can be determined whether the production process is in normal state; if the gas composition exceeds the preset range, measures may need to be taken to adjust.

[0056] Evaluate the degree of overall impact of gas composition on molten steel: By calculating the weighted average of the change ratio value of all key components, the composition coefficient can be obtained, which can comprehensively evaluate the degree of overall impact of gas composition on molten steel; this is of great significance to optimize the production process and product quality.

[0057] Provide real-time data support and visual display: By plotting the change of composition coefficient over time into a curve, real-time data support and visual display can be provided for production personnel, helping them better understand the changes of gas composition in the production process, so as to make more intelligent decisions.

[0058] For example, suppose in the steel production process, the changes of oxygen, carbon dioxide and carbon monoxide, three key gas components, need to be monitored; by collecting gas samples and analyzing, the content of these three components at different time points can be obtained; then, according to the preset composition range, the change ratio value of each component can be calculated; finally, the composition coefficient is obtained by weighted calculation, and its change over time is plotted into a curve; if the curve shows that the composition coefficient suddenly rises at a certain time point, it may mean that the gas composition has changed abnormally, and the production personnel can take appropriate measures to adjust accordingly.

[0059] The change curve of oxygen amount, by obtaining the current input oxygen flow, the contact area of oxygen and molten steel, determines the change degree of current oxygen amount, takes the change degree of oxygen amount as the output change curve of oxygen amount, to determine whether the current input oxygen amount can make the chemical reaction proceed normally.

[0060] The change degree of oxygen amount is represented as, according to the oxygen flow, the contact area of oxygen and molten steel, the oxygen flux is calculated, according to the oxygen flux, the reaction rate constant and the carbon concentration in molten steel, the reaction rate is obtained, the oxygen flux and the reaction rate are taken as the output change degree of oxygen amount.

[0061] Oxygen flow FO2: real-time monitoring of oxygen flow using flow meter.

[0062] Contact area of oxygen and molten steel A: according to the design parameters of the lance and the position of the lance in the molten steel, estimate the actual contact area of oxygen and molten steel.

[0063] The oxygen flux, the amount of oxygen passing through a unit area per unit time, can be calculated using the following formula: JO2 = FO2 / A; where JO2 represents the oxygen flux, FO2 is the oxygen flow rate, and A is the contact area between the oxygen and the molten steel.

[0064] To construct the oxygen amount variation curve, data on oxygen flow rate and contact area at different time points can be recorded to plot a curve of oxygen flux versus time. This curve can help analyze the trend of oxygen amount variation and adjust the oxygen flow rate accordingly.

[0065] Based on the oxygen flux and the carbon content in the molten steel, the chemical reaction rate can be estimated. Generally, the reaction rate can be represented as: r = k × JO2 × C; where r is the reaction rate, k is the reaction rate constant, and C is the carbon concentration in the molten steel.

[0066] By comparing the actual reaction rate with the expected reaction rate, if the reaction rate is found to be lower than expected, the oxygen flow rate needs to be increased or the lance position needs to be adjusted to increase the contact area; conversely, if the reaction rate is too high, the oxygen flow rate should be appropriately reduced.

[0067] A PID controller or other types of controllers are used to automatically adjust the oxygen flow rate to maintain a stable reaction rate.

[0068] The data from temperature sensors, oxygen flow meters, and pressure sensors are integrated into the control system to form a closed-loop control system.

[0069] Temperature changes are used as feedback signals to further optimize the adjustment strategy for oxygen flow rate, ensuring the normal progress of the chemical reaction.

[0070] If r < target r, increase FO2 or adjust the lance position to increase A; if r > target r, decrease FO2 or adjust the lance position to decrease A.

[0071] Constantly adjust until the optimal reaction rate is reached and maintained within the target range, and use historical data to continuously optimize control parameters to improve control accuracy.

[0072] The implementation of the occurrence probability of the operating parameter and the target value corresponding to the occurrence probability includes: based on the variation curve of the operating parameter, extracting characteristic values of the molten steel temperature, gas composition, and oxygen amount, determining corresponding multiple sets of characteristic values of the molten steel temperature, gas composition, and oxygen amount when the molten steel reaches the preset molten steel purity, denoted as an initial characteristic set, and determining the probability distribution of the initial characteristic set in the smelting process.

[0073] At this time, the way to mark the initial characteristic set is to convert the molten steel temperature, gas composition, and oxygen amount into dimensionless characteristic values and mark them respectively to determine the probability distribution of the corresponding initial characteristic set under the corresponding molten steel purity.

[0074] The probability distribution calculation method is to calculate the current data by using the kernel density estimation of the current acquired initial feature set, as shown in the following formula. Wherein, f h (x) represents the kernel density estimation function, x represents the element in the initial feature set to be estimated, x i represents the element of the i-th initial feature set, n represents the number of elements in the initial feature set, K() represents the kernel function, which is usually a symmetric function, used to weight the contribution near each element in the initial feature set, h represents the bandwidth function, which controls the width of the kernel function and affects the smoothness of the estimation, i = 1, 2, …, n; for the bandwidth function, h = 1.07 x σ x x n -1 / 5 ; wherein, σ x represents the standard deviation of the initial feature set.

[0075] According to the calculation value of the kernel density estimation function, the probability distribution of the molten steel temperature, gas composition and oxygen content under different molten steel purity can be known, which can mark the current molten steel smelting state, and the obvious peak and skew data in the output data of the kernel density estimation function can be extracted. These data are identified to determine whether these peak and skew data meet the current molten steel smelting expectation, and whether the current operating parameters will be abnormal and other conditions when these values occur, so as to find the target value that needs to be mainly monitored and identified.

[0076] According to the probability distribution of the initial feature set, the data calculated from the initial feature set will have peak and skew data as the target feature set, and the target feature set is iterated, and the data output after iteration is the target value.

[0077] The calculation method of iterating the target feature set is to first initialize the prediction value corresponding to all elements in the target feature set. The value of the prediction value initialization is the mean value of the elements in the target feature set. The residual between all elements in the target feature set and the corresponding prediction value is calculated, and the decision tree corresponding to the residual is trained. The output result of the decision tree is the residual related prediction value, so that the decision tree can fit the residual as much as possible, and the prediction value can accurately match the value of the elements in the target feature set under the condition of minimizing the residual. The decision tree and the prediction value are iterated, and the data output after iteration is the target value. The iteration number is m, and m = 1, 2, …, M.

[0078] The residual between all elements in the target feature set and the corresponding prediction value is shown in the following formula.

[0079] Wherein, r j represents the residual of the j-th element in the target feature set, yj denotes the value of the jth element in the target feature set, denotes the predicted value of the jth element in the target feature set after the mth iteration, denotes the predicted value of the jth element in the target feature set after the (m-1)th iteration, j represents the number of elements in the target feature set, j = 1, 2, …, J; at this time, in order to prevent the denominator from being zero, the predicted value is set to be not 1.

[0080] When the residual corresponding to the target feature set is less than the preset residual, the predicted value is updated, and the updated predicted value is the output target value.

[0081] where λ represents the learning rate, h j,m denotes the estimated value of the jth element in the target feature set corresponding to the decision tree after the mth iteration.

[0082] By continuously training new decision trees to fit the residual and gradually updating the predicted value, the estimated value of the decision tree is an estimate of the difference between the current predicted value and the true value, and the predicted value is updated by weighting, so that the model can better fit the training data after each iteration; the learning rate controls the updating amplitude in each iteration, which helps to avoid overfitting; the target value obtained by such processing can reflect the trend and related situation of the current running parameters, which is convenient for judging whether the current molten steel smelting process is normal and whether related processing is needed.

[0083] The generation mode of the control strategy includes: according to the obtained occurrence probability of the running parameter and the target value corresponding to the occurrence probability, selecting a strategy set with a similarity greater than a preset similarity threshold from the preset strategy, and selecting the strategy set with the highest similarity with the running parameter as the control strategy for the current processing, to judge the current processing process.

[0084] At this time, the similarity is calculated by calculating the target value and the corresponding value in the preset strategy to obtain the Pearson correlation coefficient, and the preset similarity threshold is set to 0.7 at this time. All strategies greater than the threshold after calculation form a strategy set, and the occurrence probability of the related running parameter in the strategy set is compared with the occurrence probability of the current running parameter. The part with the maximum Pearson coefficient after calculation is output to determine the current control strategy.

[0085] As Figure 3 shown, the present application also provides a high-purity molten steel smelting converter intelligent control method, which comprises: S1, collecting the running parameters of the converter during molten steel smelting, including molten steel temperature, gas composition, oxygen quantity and blowing time.

[0086] S2, according to the collected operating parameters, draw the change curve of the molten steel temperature, gas composition, oxygen quantity and blowing time.

[0087] S3, identify the key information in the operating parameter change curve, analyze the change amount of the molten steel temperature, record the extreme point data, and perform corresponding processing according to the preset temperature range; determine the change trend of the gas composition, calculate the change proportion value of the key component, and draw the change curve of the component coefficient with time; according to the oxygen flow and the contact area, determine the change degree of the oxygen quantity, and draw the change curve of the oxygen quantity.

[0088] S4, based on the change curve of the operating parameters, extract the characteristic value, determine the corresponding characteristic value set when the preset molten steel purity is reached; analyze the probability distribution of the characteristic value in the smelting process, and identify the peak state and skewed data as the target characteristic set; perform iterative processing on the target characteristic set, and output the final target value.

[0089] S5, store the occurrence probability and the target value of the operating parameters; according to the stored data, generate a control strategy, and select the strategy with the highest similarity to the target value as the current processing control strategy.

[0090] Although the embodiments of the present application have been shown and described above, it can be understood that the above-mentioned embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and modifications to the above-mentioned embodiments within the scope of the present application, which are still covered by the protection scope of the present application.

Claims

1. An intelligent control system for a high-purity steelmaking converter, characterized in that, include: The data acquisition module is used to collect relevant operating parameters of the converter during steelmaking, including steel temperature, gas composition, oxygen quantity, and blowing time. The curve generation module is used to determine the change curves of the corresponding operating parameters during steel smelting based on the acquired operating parameters. The change curves include the change curves of steel temperature, gas composition, oxygen quantity and blowing time. The composition display module is used to analyze and process the change curves of the identified operating parameters to determine the probability of occurrence of the operating parameters and the target value corresponding to the probability of occurrence when the purity of molten steel meets the requirements. The data storage module is used to store the occurrence probability and target value of the operating parameters, and to generate control strategies based on the stored data; The temperature change curve of molten steel is represented by obtaining the heat absorbed by the molten steel, its specific heat capacity, and its mass, and then using the temperature change of the molten steel as the vertical axis and the time corresponding to the blowing time as the horizontal axis to obtain the temperature change curve of the molten steel. The calculation method for the change in molten steel temperature is expressed as follows: ;in, The change in molten steel temperature is expressed as a function of time t; Indicates the time of molten steel The heat absorbed, It is an integral variable. Indicates the quality of molten steel. This indicates the specific heat capacity of molten steel. Let represent the integral element, and let represent the minute increment of the time interval. Indicates the integral symbol; The steel temperature change curve also includes the following processing methods: record the data corresponding to each extreme point in the steel temperature change curve, determine whether the steel temperature at each extreme point is within the preset temperature range, if it is within the preset temperature range, output the average temperature of the extreme point and its adjacent points; if it is greater than the upper limit of the preset temperature range, determine the rate of change of the slope value of the steel temperature change curve near the extreme point, and output the maximum value of the rate of change of the slope value; if it is less than the lower limit of the preset temperature range, obtain the median of the adjacent points of the extreme point, and output the corresponding median.

2. The intelligent control system for a high-purity steelmaking converter according to claim 1, characterized in that, The method for identifying the gas composition change curve is to determine the key components of the gas discharged during steel smelting, and obtain the gas composition change curve based on the changing trend of the key components. The trend of change of key components is represented by calculating the percentage change of each key component at different time points for each key component present in the gas composition. The system compares whether each identified key component is within a preset range, and calculates the component coefficient of the gas component according to the change ratio of the currently identified key component. The calculated component coefficient is then plotted as a curve over time; the horizontal axis represents time, and the vertical axis represents the component coefficient; each point on the curve represents the component coefficient of the gas component at a specific time point.

3. The intelligent control system for a high-purity steelmaking converter according to claim 1, characterized in that, The oxygen quantity change curve is obtained by acquiring the current input oxygen flow rate and the actual contact area between oxygen and molten steel to determine the degree of change in the current oxygen quantity, and the degree of change in the oxygen quantity is used as the output oxygen quantity change curve.

4. The intelligent control system for a high-purity steelmaking converter according to claim 3, characterized in that, The degree of change in oxygen quantity is expressed as follows: the oxygen flux is calculated based on the oxygen flow rate and the contact area between oxygen and molten steel; the reaction rate is obtained based on the oxygen flux, the reaction rate constant, and the carbon concentration in the molten steel; and the oxygen flux and reaction rate are used as the degree of change in the output oxygen quantity.

5. The intelligent control system for a high-purity steelmaking converter according to claim 1, characterized in that, The methods for achieving the occurrence probability of operating parameters and the target value corresponding to the occurrence probability include: based on the change curve of operating parameters, extracting the feature values ​​of molten steel temperature, gas composition and oxygen content, determining the corresponding multiple sets of feature values ​​of molten steel temperature, gas composition and oxygen content when molten steel reaches the preset molten steel purity, recording them as the initial feature set, and determining the probability distribution of the initial feature set in the smelting process; Based on the probability distribution of the initial feature set, the data with kurtosis and skewness are selected from the data after the initial feature set is calculated and used as the target feature set. The target feature set is iterated and the data after the iteration is completed is output as the target value.

6. The intelligent control system for a high-purity steelmaking converter according to claim 1, characterized in that, The method for generating control strategies includes: based on the occurrence probability of the obtained operating parameters and the target value corresponding to the occurrence probability, selecting a set of strategies from the preset strategies that have a similarity greater than a preset similarity threshold with the target value, and taking the strategy with the highest similarity between the strategy set and the operating parameters as the current control strategy.

7. A method for intelligent control of a high-purity steel smelting converter, using the intelligent control system for a high-purity steel smelting converter as described in claim 1, characterized in that... include: S1 collects the operating parameters of the converter during steelmaking, including steel temperature, gas composition, oxygen content and blowing time. S2, based on the collected operating parameters, plot the change curves of molten steel temperature, gas composition, oxygen content and blowing time; S3 identifies key information in the operating parameter change curves, analyzes the change in molten steel temperature, records extreme point data, and performs corresponding processing according to the preset temperature range; determines the change trend of gas composition, calculates the change ratio of key components, and plots the change curve of composition coefficient over time; and determines the degree of change of oxygen quantity based on oxygen flow rate and contact area, and plots the change curve of oxygen quantity. S4. Based on the change curve of the operating parameters, extract feature values ​​and determine the set of feature values ​​corresponding to the preset purity of molten steel; analyze the probability distribution of feature values ​​in the smelting process and identify peaked and skewed data as target feature sets; The target feature set is iteratively processed to output the final target value; S5 stores the probability of occurrence and target value of the running parameters; based on the stored data, it generates a control strategy and selects the strategy with the highest similarity to the target value as the current control strategy.

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