Dynamic control method and apparatus for electric arc furnace smelting state, device, and medium

Abstract

A dynamic control method and apparatus for an electric arc furnace smelting state, a device, and a medium. The method comprises: collecting smelting data generated by an electric arc furnace during a smelting process; and, calculating an arc length and a foamed slag height using the smelting data, further determining a submerged arc condition of the electric arc furnace on the basis of the arc length and the foamed slag height, and adjusting smelting parameters of the electric arc furnace on the basis of the condition; or, calculating differences between data sub-items and corresponding historical warning thresholds, and adjusting smelting parameters of the electric arc furnace on the basis of the calculation results. The method achieves real-time monitoring and intelligent analysis of the smelting state of the electric arc furnace, ensures the flexibility and efficiency of electric arc furnace operation, and improves energy utilization.

Description

An electric arc furnace smelting state dynamic control method, device, equipment and medium TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, in particular to an electric arc furnace smelting state dynamic control method, device, equipment and medium. BACKGROUND

[0002] Electric arc furnace steelmaking takes scrap steel as the main raw material and takes electric energy as the main energy source, widely adopts large-steel-amount flat bath smelting technology, and stable high voltage and long arc power supply operation is the premise of realizing high efficiency of electric arc furnace, and foamed slag submerged arc operation is the key to energy saving and efficiency improvement, which directly affects the operation efficiency and smelting technical index.

[0003] Carbon powder is a common foaming agent for foamed slag operation at present. In the smelting process, coke or carbon powder is added into the molten pool to realize foamed slag and molten pool stirring to meet the process technical requirements of steelmaking. In the traditional mode, the foamed slag state and operation of the electric arc furnace rely on manual judgment, which is greatly affected by the subjectivity of the relevant staff, and the intelligent degree is not high, which leads to the situation that the foamed slag or power supply system lags behind or adjusts excessively in the adjustment process, and further leads to long smelting period, low power utilization rate, high smelting power consumption, high electrode consumption and other problems. SUMMARY

[0004] In view of the above-mentioned shortcomings of the prior art, the present application provides an electric arc furnace smelting state dynamic control method, device, equipment and medium to solve the above technical problems.

[0005] The electric arc furnace smelting state dynamic control method provided by the present application comprises: obtaining smelting data of an electric arc furnace; obtaining an arc length and a foamed slag height based on the smelting data of the electric arc furnace, determining a submerged arc condition of the electric arc furnace according to the calculated arc length and foamed slag height, and adjusting smelting parameters of the electric arc furnace based on the submerged arc condition; or, calculating the difference between each sub-item data and its corresponding historical early warning threshold value respectively, and adjusting the smelting parameters of the electric arc furnace based on the calculation result.

[0006] In an embodiment of the present application, the submerged arc state of the electric arc furnace is determined based on the arc length and the foamy slag height, and the smelting parameters of the electric arc furnace are adjusted based on the submerged arc state, including: training an initial neural network model based on historical arc lengths and historical foamy slag heights to obtain an electric arc furnace submerged arc state determination model, the historical arc lengths and the historical foamy slag heights being obtained based on historical smelting data in the electric arc furnace smelting data; inputting a current arc length and a current foamy slag height into the electric arc furnace submerged arc state determination model to obtain a current submerged arc state of the electric arc furnace, the current arc length and the current foamy slag height being obtained based on current smelting data in the electric arc furnace smelting data; and adjusting the smelting parameters of the electric arc furnace based on the current submerged arc states corresponding to different current arc lengths and different current foamy slag heights in the current smelting data to achieve dynamic control of the smelting state of the electric arc furnace.

[0007] In an embodiment of the present application, the foamy slag height is obtained based on the electric arc furnace smelting data, including: obtaining electric arc furnace images, electric arc furnace structure parameters, and furnace charge information of the electric arc furnace based on the electric arc furnace smelting data; performing image recognition on the electric arc furnace images to determine a comprehensive liquid level height of the electric arc furnace; obtaining a molten steel level height of the electric arc furnace according to the electric arc furnace structure parameters and the furnace charge information, including obtaining a molten steel upper surface area based on the electric arc furnace structure parameters, and obtaining a total amount of charged materials, a total amount of slag, and a molten steel density based on the furnace charge information; obtaining a total mass of molten steel in the electric arc furnace based on the total amount of charged materials and the total amount of slag; and calculating the total mass of molten steel, the molten steel density, and the molten steel upper surface area to obtain the molten steel level height; and calculating the comprehensive liquid level height and the molten steel level height to obtain the foamy slag height.

[0008] In an embodiment of the present application, the initial neural network model is trained based on historical arc lengths and historical foamy slag heights, including: regarding any one historical arc length and any historical foamy slag height as a historical data pair, and labeling the historical data pair based on a preset submerged arc state label, the submerged arc state label including excellent, normal, and poor; dividing the labeled historical data into a training data set and a verification data set; training the initial neural network model based on the training data set to learn an initial mapping relationship between input historical data pairs and electric arc furnace submerged arc states; verifying the initial mapping relationship based on the verification data set, and optimizing the initial mapping relationship based on the verification result to obtain an optimized neural network model, and determining the optimized neural network model as the electric arc furnace submerged arc state determination model.

[0009] In an embodiment of the present application, the smelting parameters of the electric arc furnace are adjusted, including: if the current arc-embedding state is excellent, the smelting parameters of the electric arc furnace are processed for speed-up, the speed-up processing at least includes increasing the power supply intensity or reducing the carbon powder injection speed; if the current arc-embedding state is normal, the smelting parameters of the electric arc furnace are not processed; if the current arc-embedding state is poor, the smelting parameters of the electric arc furnace are processed for speed-down, the speed-down processing at least includes reducing the power supply intensity or increasing the carbon powder injection speed.

[0010] In an embodiment of the present application, the difference between each sub-item data and its corresponding historical early warning threshold value is calculated respectively, and the smelting parameters of the electric arc furnace are adjusted based on the calculation results, including: the historical early warning value is obtained based on the historical data in the electric arc furnace smelting data, and the current measurement value is obtained based on the current data in the electric arc furnace smelting data; the comparison result is obtained by comparing the current measurement value and the historical early warning value, and the difference between the current measurement value and the historical early warning value is calculated; when the difference is greater than a preset difference threshold value, the smelting parameters of the electric arc furnace are dynamically processed based on the comparison result, the dynamic processing includes speed-up processing or speed-down processing.

[0011] In an embodiment of the present application, the sub-item data includes the smelting temperature and the comprehensive liquid level height of the electric arc furnace, and the smelting parameters of the electric arc furnace are adjusted based on the calculation results, including: when the difference between the current smelting temperature of the electric arc furnace and the historical temperature early warning value is greater than a preset temperature difference threshold value, if the current smelting temperature is higher than the historical temperature early warning value, the smelting parameters of the electric arc furnace are processed for speed-down; if the current smelting temperature is lower than the historical temperature early warning value, the smelting parameters of the electric arc furnace are processed for speed-up; when the difference between the current comprehensive liquid level height of the electric arc furnace and the historical height early warning value is greater than a preset height threshold value, if the current comprehensive liquid level height is higher than the historical height early warning value, the smelting parameters of the electric arc furnace are processed for speed-down; if the current comprehensive liquid level height is lower than the historical height early warning value, the smelting parameters of the electric arc furnace are processed for speed-up.

[0012] The present application provides a kind of dynamic control device of electric arc furnace smelting state, the device includes: data acquisition module, for obtaining the smelting data of electric arc furnace;First electric arc furnace smelting state control module, for obtaining arc length and foam slag height based on the electric arc furnace smelting data, determine the arc-embedding condition of electric arc furnace according to calculated arc length and foam slag height, and adjust the smelting parameters of electric arc furnace based on the arc-embedding state;Second electric arc furnace smelting state control module is used to calculate the difference between each sub-item data and its corresponding historical early warning threshold value respectively, and the smelting parameters of electric arc furnace are adjusted based on the calculation result.

[0013] The application provides an electronic device, comprising a processor, a memory and a communication bus; the communication bus is used for connecting the processor and the memory; the processor is used for executing a computer program stored in the memory to realize the dynamic control method of the smelting state of the electric arc furnace.

[0014] The application provides a computer readable storage medium, which stores a computer program used for making a computer execute the dynamic control method of the smelting state of the electric arc furnace.

[0015] The application has the following beneficial effects: the dynamic control method of the smelting state of the electric arc furnace provided in the application realizes real-time monitoring and intelligent analysis of the smelting state of the electric arc furnace by collecting data generated in the smelting process of the electric arc furnace and further judging whether the collected data can calculate the electric arc length and the foamy slag height; if the electric arc length and the foamy slag height can be calculated, the buried arc condition of the electric arc furnace is determined by the calculated electric arc length and foamy slag height, and the smelting parameters of the electric arc furnace are adjusted accordingly; if the electric arc length and the foamy slag height cannot be calculated, the difference between each sub-item data and the corresponding historical early warning threshold value is calculated, and the smelting parameters are adjusted according to the calculation result. The method realizes real-time monitoring and intelligent analysis of the smelting state of the electric arc furnace, not only ensures the flexibility and efficiency of the operation of the electric arc furnace, but also improves the utilization rate of energy.

[0016] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the application. BRIEF DESCRIPTION OF DRAWINGS

[0017] The drawings incorporated into the specification and constituting a part of the specification, show embodiments consistent with the application, and together with the specification, serve to explain the principles of the application. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained from these drawings without creative labor for those skilled in the art. In the drawings:

[0018] Fig. 1 is a schematic diagram of an implementation environment of the dynamic control method of the smelting state of the electric arc furnace according to an example embodiment of the application;

[0019] Fig. 2 is a flowchart of the dynamic control method of the smelting state of the electric arc furnace according to an example embodiment of the application;

[0020] Fig. 3 is a schematic diagram of the steps of the method for adjusting the smelting parameters of the electric arc furnace according to the buried arc state according to an example embodiment of the application;

[0021] Fig. 4 is a schematic diagram of the steps of adjusting the smelting parameters of the electric arc furnace according to an example embodiment of the application;

[0022] Figure 5 is a block diagram of a dynamic control device for arc furnace smelting state, according to an example embodiment of the present application;

[0023] Figure 6 is a block diagram of a first arc furnace smelting state control module, according to an example embodiment of the present application;

[0024] Figure 7 shows a structural schematic diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application. DETAILED DESCRIPTION

[0025] The present application will be described with reference to the attached drawings and preferred embodiments, and other advantages and effects of the present application will be easily understood by those skilled in the art from the contents of the present specification. The present application can be implemented or applied in other different specific embodiments, and the details in the present specification can be modified or changed based on different views and applications, without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application, and are not intended to limit the scope of protection of the present application.

[0026] It should be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concept of the present application, and only the components related to the present application are shown in the diagrams, rather than being drawn according to the number, shape and size of the components in actual implementation. The shapes, number and proportions of the components in actual implementation can be arbitrarily changed, and the layout pattern of the components can be more complex.

[0027] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application, however, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details, and in other embodiments, the well-known structures and devices are shown in the form of block diagrams rather than in the form of details, to avoid making the embodiments of the present application difficult to understand.

[0028] Figure 1 is a schematic diagram of an implementation environment of a dynamic control method for arc furnace smelting state, according to an example embodiment of the present application.

[0029] As shown in FIG. 1, the implementation environment of the dynamic control method of the arc furnace smelting state includes a data acquisition module 101 and a computer device 102. Among them, the data acquisition module 101 is responsible for collecting the original data of the arc furnace in the smelting process, which includes but is not limited to the structural parameters, operating parameters, power consumption, and state information of the smelting material of the arc furnace. Specifically, the structural parameters of the arc furnace can specifically include the cross-sectional area of the arc furnace, and the operating parameters can be subdivided into main circuit current, voltage level, etc.; the state information of the smelting material may involve raw material quality, slag amount, etc. Its data acquisition equipment can be various sensors (such as temperature sensors, current transformers, image capture devices, etc.) installed on the arc furnace, or external monitoring equipment connected with the arc furnace through wireless or wired mode. The specific implementation form of the data acquisition module is not limited in the present application.

[0030] The computer device 102 is used to process the original data collected by the data acquisition module 101, and the specific working process is as follows: an initial neural network model is constructed, and the historical records in the original data are used to train it to obtain a model capable of judging the arc furnace buried arc state. Then the real-time information in the original data is input into the model to obtain the current arc furnace operation parameter adjustment suggestion, so as to dynamically control the operation of the arc furnace according to the suggestions, and ensure that the arc furnace can operate efficiently and stably. The computer device 102 can be a high-performance processor, a professional industrial computing platform, or an embedded system integrated in the arc furnace control system. The specific type of the computer device is also not limited in the present application.

[0031] FIG. 2 is a flowchart of the dynamic control method of the arc furnace smelting state according to an example embodiment of the present application.

[0032] As shown in FIG. 2, in an example embodiment, the dynamic control method of the arc furnace smelting state at least includes steps S210 to S230, which are described in detail as follows:

[0033] Step S210, obtaining smelting data of the arc furnace.

[0034] In an embodiment of the present application, the smelting data of the arc furnace is collected, and the parameters include arc furnace structural parameters, furnace material related parameters, and smelting state related parameters. The arc furnace structural parameters include the depth and cross-sectional area of the arc furnace, the furnace material related parameters include the total amount of the furnace material and the total slag amount, and the smelting state parameters include the total liquid level height and the smelting temperature of the arc furnace. It should be noted that the data sources of the above smelting data include but are not limited to image acquisition devices such as cameras, sensors such as thermometers, and material information in smelting logs, etc. The data acquisition equipment and method are not limited in the present application.

[0035] Step S220, based on the arc furnace smelting data, the arc length and the foamy slag height are obtained, the submerged arc condition of the arc furnace is determined according to the calculated arc length and the foamy slag height, and the smelting parameters of the arc furnace are adjusted based on the submerged arc condition.

[0036] Figure 3 is a schematic diagram of the steps of the method for adjusting the smelting parameters of the arc furnace based on the submerged arc condition according to an example embodiment of the present application. As shown in Figure 3, adjusting the smelting parameters of the arc furnace based on the submerged arc condition at least includes steps S221 to S224, which are as follows:

[0037] Step S221, collecting arc furnace smelting data, and obtaining arc length and foamy slag height based on the arc furnace smelting data.

[0038] In an embodiment of the present application, the arc length is obtained based on the arc furnace smelting data, which includes: obtaining initial voltage information and initial current information based on the arc furnace smelting data; performing data processing on the initial voltage information and the initial current information respectively to obtain voltage effective value and current effective value; and calculating the arc length based on a preset arc length calculation formula using the voltage effective value and the current effective value.

[0039] In a specific embodiment of the present application, the power supply information of the power supply system to the arc furnace is measured by a multimeter to obtain a plurality of initial current information and a plurality of initial voltage information; considering that the measurement values are affected by the measurement personnel and the measuring instruments, there may be certain deviations, so the initial current information and the initial voltage information are further processed, the data processing process of which includes but is not limited to data cleaning, removing outliers and missing values, and standardizing or normalizing the data, and then the arc length is calculated based on the processed current effective value and voltage effective value, and the calculation formula is as follows:

[0040] wherein I is the current effective value, V a is the voltage effective value, p a is the resistivity, L is the arc length, a, b, c, d, e, f, m are coefficients related to the current.

[0041] It should be noted that the data preprocessing method and the calculation formula of the arc length mentioned in the above embodiments are only exemplary. The data preprocessing method can be adjusted according to the actual situation of the collected data and the application needs of the data. The calculation of the arc length is based on different application scenarios, different current and voltage collection methods, and different materials. The arc length can be calculated based on any other available arc length calculation formula according to the current value and the voltage value. The present application does not make any specific limitation on the specific steps and contents of the data preprocessing and the arc length calculation formula. Similarly, the coefficient related to the current in the above formula (1) is also obtained based on different actual scenarios. It can be a preset information or a data determined by the real-time scenario. The present application does not make any limitation on the determination method and value of the coefficient.

[0042] In an embodiment of the present application, the foamy slag height is obtained based on the arc furnace smelting data, including: obtaining the arc furnace image, the arc furnace structure parameter, and the furnace charge information of the arc furnace based on the arc furnace smelting data; performing image recognition on the arc furnace image to determine the comprehensive liquid level height of the arc furnace; obtaining the steel liquid level height of the arc furnace according to the arc furnace structure parameter and the furnace charge information; and calculating the foamy slag height based on the comprehensive liquid level height and the steel liquid level height. Wherein, the steel liquid level height of the arc furnace is obtained according to the arc furnace structure parameter and the furnace charge information, including: obtaining the steel liquid upper surface area based on the arc furnace structure parameter, and obtaining the total amount of furnace charge, the total slag amount, and the steel liquid density based on the furnace charge information; obtaining the total mass of the steel liquid in the arc furnace based on the total amount of furnace charge and the total slag amount; and calculating the total mass of the steel liquid, the steel liquid density, and the steel liquid upper surface area to obtain the steel liquid level height.

[0043] In a specific embodiment of the present application, the image of the arc furnace is collected by the image collection device, and the image is recognized based on the recognition result combined with the depth value of the arc furnace to obtain the liquid level height. Specifically: the image inside the arc furnace is photographed by the image collection device (for example, an industrial camera installed above the arc furnace), and the collected image is preprocessed, such as grayscale, filtering and denoising, etc. to facilitate subsequent image recognition processing. Then, a pre-trained deep learning model, such as a convolutional neural network (CNN), is used to identify specific features in the image, such as arcs, liquid level boundaries, etc. During the recognition process, key points or regions representing the liquid level position in the image are marked, so that by analyzing the position information of these key points or regions, the actual height of the liquid level relative to the furnace mouth can be calculated combined with the known geometric size and depth value (i.e. the distance from the furnace mouth to the furnace bottom) of the arc furnace, that is, the above-mentioned comprehensive liquid level height.

[0044] In another specific embodiment of the present application, the molten steel surface height is calculated based on the structural parameters of the electric arc furnace and the charge information. Specifically, the cross-sectional area of the electric arc furnace is obtained based on the structural parameters of the electric arc furnace, and the cross-sectional area is determined as the upper surface area of the molten steel; the total amount of the charge and the amount of slag are obtained based on the relevant information of the charge, and the total instruction of the molten steel is determined based on the total amount of the charge and the amount of slag; and the depth of the molten steel, i.e., the molten steel surface height, is calculated based on the total mass of the molten steel, the density of the molten steel, and the upper surface area of the molten steel. The specific calculation formula is as follows:

[0045] wherein h2 is the molten steel surface height, p is the density of the molten steel, A(h) is the upper surface area of the molten steel, G Ss is the total amount of the charge, and G sl is the total amount of slag.

[0046] In addition, the formula for calculating the foamed slag height based on the comprehensive liquid surface height and the molten steel surface height is as follows: h3 = h1 - h2 Equation (3)

[0047] wherein h3 is the foamed slag height, h1 is the comprehensive liquid surface height, and h2 is the molten steel surface height.

[0048] It should be noted that in the above embodiments, the image acquisition frequency can be set according to actual needs to ensure timely capture of changes in the furnace conditions, and the present application does not make any limitation on the image acquisition device and the image acquisition frequency. In addition, the image recognition can be realized based on a deep learning model, or based on edge detection, threshold segmentation, contour tracking, or any other feasible way, and the present application does not make any specific limitation on the specific way of image recognition.

[0049] Step S222, training the initial neural network model based on the historical arc length and the historical foamed slag height to obtain an electric arc furnace arc-embedding state determination model, wherein the historical arc length and the historical foamed slag height are obtained based on the historical smelting data in the electric arc furnace smelting data.

[0050] In an embodiment of the present application, the initial neural network model is trained based on historical arc length and historical foamy slag height, including: regarding any one historical arc length and any historical foamy slag height as a historical data pair, and labeling the historical data pair based on a preset submerged arc state label, the submerged arc state label including excellent, normal, and poor; dividing the labeled historical data into a training data set and a validation data set; training the initial neural network model based on the training data set to learn an initial mapping relationship between the input historical data pair and the arc furnace submerged arc state; verifying the initial mapping relationship based on the validation data set, and optimizing the initial mapping relationship based on the verification result to obtain an optimized neural network model, and determining the optimized neural network model as the arc furnace submerged arc state determination model.

[0051] In a specific embodiment of the present application, first, each historical arc length is combined with the historical foamy slag height at the corresponding time to form a historical data pair. Then, according to the expert's experience knowledge or a preset standard, the label of the submerged arc state of each historical data pair is labeled, and the label can be divided into three categories: "excellent" indicates that the submerged arc state is very ideal, and the arc is completely covered by the foamy slag; "normal" indicates that the submerged arc state is general, and part of the arc is covered; "poor" indicates that the submerged arc state is poor, and most of the arc is not covered by the foamy slag. Next, all the labeled historical data pairs are randomly divided into two parts: one part is used as a training data set for training the initial neural network model; the other part is used as a validation data set for verifying the generalization ability of the model during the training process. Then, in the training stage, the training data set is input into the initial neural network model, and the model learns the mapping relationship between the input historical data pair and the arc furnace submerged arc state through forward propagation, and during the training process, the model parameters are adjusted through the back propagation algorithm to minimize the difference between the predicted value and the actual label. Then, after each training iteration, the performance of the model is evaluated using the validation data set, and if the model performs poorly on the validation data set, the model parameters are adjusted for further training until a satisfactory performance indicator is achieved. Finally, the model optimized through multiple training and verification cycles is determined as the arc furnace submerged arc state determination model.

[0052] In step S223, the current arc length and the current foamy slag height are input into the arc furnace submerged arc state determination model to obtain the current submerged arc state of the arc furnace, wherein the current arc length and the current foamy slag height are obtained based on the current smelting data in the arc furnace smelting data.

[0053] In an embodiment of the present application, the arc length and the foamy slag height in the electric arc furnace are monitored in real time by sensors (such as non-contact distance measuring sensors or image recognition devices, etc.) installed on the electric arc furnace. In addition, considering that the collected raw data may contain noise, it is necessary to perform preprocessing such as filtering and denoising, data smoothing, etc. to ensure that the data input into the model is accurate and reliable. Then, the processed data is input into the arc-buried state determination model of the electric arc furnace which has been trained, so that the model automatically calculates and outputs the arc-buried state of the current electric arc furnace according to the learned mapping relationship after receiving the input data. The state may be "excellent", "normal" or "poor". In order to achieve the best smelting effect, the relevant parameters of the electric arc furnace, such as current intensity and power distribution, are automatically adjusted according to the arc-buried state output by the model.

[0054] In step S224, the smelting parameters of the electric arc furnace are adjusted based on the current arc-buried state corresponding to different current arc lengths and different current foamy slag heights in the current smelting data, so as to realize dynamic control of the smelting state of the electric arc furnace.

[0055] In an embodiment of the present application, adjusting the smelting parameters of the electric arc furnace includes: if the current arc-buried state is excellent, speeding up the smelting parameters of the electric arc furnace, which at least includes increasing the power supply intensity or reducing the carbon powder injection speed; if the current arc-buried state is normal, no processing is performed on the smelting parameters of the electric arc furnace; and if the current arc-buried state is poor, slowing down the smelting parameters of the electric arc furnace, which at least includes reducing the power supply intensity or increasing the carbon powder injection speed.

[0056] In an embodiment of the present application, the arc-buried state is determined according to the relative relationship between the foamy slag height and the arc length, specifically as follows: h3 < k3 * L + k4 i

[0057] When h3 ≥ k1 * L + k2, it is determined that the arc-buried state is excellent.

[0058] When k3 * L + k4 ≥ h3 < k1 * L + k2, it is determined that the arc-buried state is normal.

[0059] When h3 < k3 * L + k4, it is determined that the arc-buried state is poor.

[0060] Wherein, h3 is the foamy slag height, L is the arc length, k i is the correlation coefficient.

[0061] It should be noted that k i is the correlation coefficient for determining the relative relationship between the foamy slag height and the arc length, which depends on different electric arc furnace smelting scenarios and is different. The present application does not limit the determination method and specific value of the coefficient.

[0062] Therefore, when h3≥k1*L+k2, i.e. the result of the judgment of the foamed slag arc buried state is that the foamed slag arc buried state is good, the smelting parameters of the electric arc furnace are processed to speed up, specifically, the power supply intensity can be increased or the carbon powder injection speed can be reduced;

[0063] When k3*L+k4≥h3<k1*L+k2, i.e. the result of the judgment of the foamed slag arc buried state is that the foamed slag arc buried state is normal, no adjustment is made to the smelting parameters of the electric arc furnace;

[0064] When h3<k3*L+k4, i.e. the result of the judgment of the foamed slag arc buried state is that the foamed slag arc buried state is poor, the smelting parameters of the electric arc furnace are processed to slow down, specifically, the power supply intensity can be reduced or the carbon powder injection speed can be increased.

[0065] In a specific implementation of the present application, k1 is set to 0.85, k2 is set to 10, k3 is set to 0.8, and k4 is set to 5, and then based on the current buried state corresponding to different current arc lengths and different current foamed slag heights in the current smelting data, the smelting parameters of the electric arc furnace are adjusted as follows:

[0066] When there is a relationship of h3≥0.85*L+10 between the real-time foamed slag height h3 and the arc length L, i.e. the result of the judgment of the foamed slag arc buried state is that the foamed slag arc buried state is good, the system can increase the power supply intensity or reduce the carbon powder injection speed.

[0067] When there is a relationship of 0.8*L+5<h3<0.85*L+10 between the real-time foamed slag height h3 and the arc length L, i.e. the result of the judgment of the foamed slag arc buried state is that the foamed slag arc buried state is normal, the system does not make adjustment.

[0068] When there is a relationship of h3≤0.8*L+5 between the real-time foamed slag height h3 and the arc length L, i.e. the result of the judgment of the foamed slag arc buried state is that the foamed slag arc buried state is poor, the power supply intensity can be reduced or the carbon powder injection speed can be increased.

[0069] FIG. 4 is a schematic diagram of the steps of adjusting the smelting parameters of the electric arc furnace according to an example embodiment of the present application.

[0070] As shown in FIG. 4, when the arc length and the foamed slag height can be obtained based on the smelting data, the model is trained based on the historical arc length and the historical foamed slag height to obtain an electric arc furnace arc buried state determination model, and the current arc length and the current foamed slag height are taken as input values of the electric arc furnace arc buried state determination model to obtain the current arc buried state of the electric arc furnace. If the current arc buried state is poor, the power supply intensity is reduced or the carbon powder speed is increased; if the current arc buried state is normal, no adjustment is made to the smelting parameters of the electric arc furnace; if the current arc buried state is excellent, the power supply intensity is increased or the carbon powder speed is reduced.

[0071] Step S230, respectively calculate the difference between each sub-item data and its corresponding historical early warning threshold, and adjust the smelting parameters of the electric arc furnace based on the calculation results.

[0072] In an embodiment of the present application, the difference between each sub-item data and its corresponding historical early warning threshold is calculated respectively, and the smelting parameters of the electric arc furnace are adjusted based on the calculation results, including: obtaining a historical early warning value based on the historical data in the electric arc furnace smelting data, and obtaining a current measurement value based on the current data in the electric arc furnace smelting data; comparing the current measurement value and the historical early warning value to obtain a comparison result, and calculating the difference between the current measurement value and the historical early warning value; when the difference is greater than a preset difference threshold, dynamically processing the smelting parameters of the electric arc furnace based on the comparison result, and the dynamic processing includes speed-up processing or speed-down processing.

[0073] Specifically, when the sub-item data in the smelting parameters includes the smelting temperature and the comprehensive liquid level of the electric arc furnace, adjusting the smelting parameters of the electric arc furnace based on the calculation results includes: when the difference between the current smelting temperature of the electric arc furnace and the historical temperature early warning value is greater than a preset temperature difference threshold, if the current smelting temperature is higher than the historical temperature early warning value, the smelting parameters of the electric arc furnace are processed by speed-down; if the current smelting temperature is lower than the historical temperature early warning value, the smelting parameters of the electric arc furnace are processed by speed-up; when the difference between the current comprehensive liquid level of the electric arc furnace and the historical height early warning value is greater than a preset height threshold, if the current comprehensive liquid level is higher than the historical height early warning value, the smelting parameters of the electric arc furnace are processed by speed-down; if the current comprehensive liquid level is lower than the historical height early warning value, the smelting parameters of the electric arc furnace are processed by speed-up.

[0074] In one embodiment of the present application, temperature data in the electric arc furnace is collected regularly by temperature sensors (such as thermocouples, etc.) installed at different parts of the electric arc furnace, and warning information is recorded each time the temperature is abnormal. By analyzing the historical temperature data and the warning information, the temperature threshold that caused the warning in the past is counted, which is the "historical warning temperature". For example, the historical data shows that when the temperature of the electric arc furnace reaches 1600℃, the device overheating alarm has occurred, and 1600℃ is the historical warning temperature. Then, the current smelting temperature of the electric arc furnace is monitored in real time, and the current smelting temperature data is also obtained by sensors installed on the electric arc furnace. If the monitored current smelting temperature exceeds the historical warning temperature, and the value that exceeds the preset temperature threshold, that is, the difference between the current smelting temperature and the historical temperature warning value is greater than the preset temperature difference threshold, it is determined that the current smelting state of the electric arc furnace is poor. Since the smelting state is poor, the electric arc furnace will have an overheating risk, so when it is determined that the current smelting state of the electric arc furnace is poor, the speed reduction processing program is started immediately, and the speed reduction processing measures at least include reducing the power supply intensity (such as reducing the current or voltage input to the electric arc furnace) to reduce heat generation and prevent the temperature from continuing to rise. In addition, the cooling speed can also be increased by increasing the carbon powder injection speed to help the electric arc furnace cool down, thereby protecting the equipment from damage.

[0075] In another embodiment of the present application, the liquid level data in the electric arc furnace is collected regularly by liquid level sensors (such as ultrasonic sensors or laser range finders, etc.) installed at different parts of the electric arc furnace, and warning information is recorded each time the liquid level is abnormal. By analyzing the historical liquid level data and the warning information, the liquid level threshold that caused the warning in the past is counted, which is the "historical warning liquid level". For example, the historical data shows that when the liquid level of the electric arc furnace reaches 2 meters, there is a risk of overflow, and 2 meters is the historical warning liquid level. Then, the current comprehensive liquid level of the electric arc furnace is monitored in real time, and the current liquid level data is also obtained by sensors installed on the electric arc furnace. If the monitored current comprehensive liquid level exceeds the historical warning liquid level, and the value that exceeds the preset liquid level threshold, that is, the difference between the current comprehensive liquid level and the historical warning liquid level is greater than the preset liquid level difference threshold, it is determined that the current smelting state of the electric arc furnace is poor. Since the smelting state is poor, there is a risk of overflow, so when it is determined that the current smelting state of the electric arc furnace is poor, the speed reduction processing program is started immediately, and the speed reduction processing measures at least include reducing the power supply intensity (such as reducing the current or voltage input to the electric arc furnace) to slow down the smelting process and prevent the liquid level from continuing to rise. In addition, the liquid level can also be controlled by adjusting the raw material input rate or changing other smelting parameters to help the electric arc furnace maintain within a safe liquid level range, thereby protecting the equipment from damage.

[0076] It should be noted that the historical early warning temperature and the historical early warning liquid level mentioned in the above embodiments are obtained based on the historical data thereof, and can be determined in a self-learning manner or in a calibration manner. The specific determination manner is determined based on the actual electric arc furnace smelting scene, and the application does not make any limitation on the determination manner of the historical early warning value and the specific value thereof. Similarly, the corresponding preset temperature difference threshold and the preset liquid level difference threshold are generated based on the actual smelting scene, and the application also does not make any limitation on the determination manner and the specific value thereof.

[0077] It should be noted that the specific processing content of the speed-up processing and the speed-down processing mentioned in the above embodiments is only exemplary, and in actual application scenarios, different ways can be used to achieve the purpose of speed-up / speed-down processing based on the current application scenario. The application does not make any limitation on the specific processing manner.

[0078] Fig. 5 is a block diagram of an electric arc furnace smelting state dynamic control device according to an exemplary embodiment of the application. The device can be applied to the implementation environment shown in Fig. 1. The device can also be applied to other exemplary implementation environments and be specifically configured in other devices. The embodiment does not limit the implementation environment to which the device is applied.

[0079] As shown in Fig. 5, the exemplary electric arc furnace smelting state dynamic control device includes:

[0080] Among them, the data acquisition module 510 is configured to acquire smelting data of the electric arc furnace; the first electric arc furnace smelting state control module 520 is configured to obtain an arc length and a foamy slag height based on the smelting data of the electric arc furnace, determine a buried arc condition of the electric arc furnace according to the calculated arc length and foamy slag height, and adjust smelting parameters of the electric arc furnace based on the buried arc condition; and the second electric arc furnace smelting state control module 530 is configured to calculate a difference between each sub-item data and a corresponding historical early warning threshold value respectively, and adjust the smelting parameters of the electric arc furnace based on the calculation result.

[0081] In addition, the first electric arc furnace smelting state control module 520 includes a plurality of sub-modules, as shown in Fig. 6, a model training sub-module 521, a buried arc state determination sub-module 522, and an electric arc furnace smelting state control sub-module 523. Specifically as follows:

[0082] The model training submodule 521 is configured to train an initial neural network model based on historical electric arc lengths and historical foamy slag heights to obtain an electric arc furnace submerged arc state determination model, the historical electric arc lengths and the historical foamy slag heights being obtained based on historical smelting data in electric arc furnace smelting data; the submerged arc state determination submodule 522 is configured to input a current electric arc length and a current foamy slag height into the electric arc furnace submerged arc state determination model to obtain a current submerged arc state of the electric arc furnace, the current electric arc length and the current foamy slag height being obtained based on current smelting data in the electric arc furnace smelting data; and the electric arc furnace smelting state control submodule 523 is configured to adjust smelting parameters of the electric arc furnace based on current submerged arc states corresponding to different current electric arc lengths and different current foamy slag heights in the current smelting data, so as to realize dynamic control of the smelting state of the electric arc furnace.

[0083] It should be noted that the electric arc furnace smelting state dynamic control device provided in the above embodiments and the electric arc furnace smelting state dynamic control method provided in the above embodiments belong to the same concept, and the specific operation manner of each module and unit has been described in detail in the method embodiments, which will not be described here. In actual application, the functions of the device can be completed by different functional modules according to the needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above, and this will not be limited here.

[0084] Embodiments of the present application also provide an electronic device, including: one or more processors; a storage device configured to store one or more programs, when the one or more programs are executed by the one or more processors, the electronic device implements the electric arc furnace smelting state dynamic control method provided in each of the above embodiments.

[0085] FIG. 7 shows a structural schematic diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application. It should be noted that the computer system 700 of the electronic device shown in FIG. 7 is only an example, and should not limit the functions and use range of the embodiments of the present application.

[0086] As shown in FIG. 7, the computer system 700 includes a central processing unit (CPU) 701 which can perform various appropriate actions and processes in accordance with a program stored in a read only memory (ROM) 702 or a program loaded from a storage section 708 into a random access memory (RAM) 703, such as performing the methods described in the above embodiments. In the RAM 703, various programs and data required for the operation of the system are also stored. The CPU 501, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0087] Connected to the I / O interface 705 are an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as necessary. A removable recording medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 710 as necessary, so that a computer program read therefrom is installed into the storage section 708 as necessary.

[0088] In particular, according to embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing a computer program for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 709, and / or installed from the removable recording medium 711. When the computer program is executed by the central processing unit (CPU) 701, various functions defined in the system of the present application are performed.

[0089] It should be noted that the computer-readable medium in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may, for example, be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, the computer-readable signal medium can include a data signal propagated in a baseband or as a carrier wave in a propagated data signal, in which the computer-readable computer program is carried. Such a propagated data signal can take on many forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit the program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained in the computer-readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, or the like, or any suitable combination thereof.

[0090] The flowcharts and block diagrams in the drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Each block in the flowcharts or block diagrams can represent a module, a program segment, or a portion of code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in a different order than that shown in the figures. For example, two blocks noted in succession can actually be executed substantially concurrently, or they can sometimes be executed in reverse order, depending on the functionality involved. It should also be noted that each block in the flowcharts or block diagrams, and combinations of blocks in the flowcharts or block diagrams, can be implemented by special-purpose hardware-based systems, which perform the specified functions or operations, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0091] The units described in the embodiments of the present application can be implemented in the form of software, or can be implemented in the form of hardware, and the described units can also be arranged in a processor. In some cases, the names of the units do not constitute a limitation on the units themselves.

[0092] Another aspect of the present application also provides a computer readable storage medium having stored thereon a computer program, which, when executed by a processor of a computer, causes the computer to perform the dynamic control method of the electric arc furnace smelting state as described above. The computer readable storage medium can be included in the electronic device described in the above embodiments, or can exist separately and not be assembled into the electronic device.

[0093] Another aspect of the present application also provides a computer program product or computer program, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device performs the dynamic control method of the electric arc furnace smelting state provided in each of the above embodiments.

[0094] The above embodiments only exemplarily illustrate the principles and effects of the present application, and are not used to limit the present application. Any person skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by those skilled in the art without departing from the spirit and technical thought of the present application should be covered by the claims of the present application.

Claims

1. A method for dynamic control of the state of an electric arc furnace, characterized in that, The method comprises: obtaining smelting data of an electric arc furnace; based on the electric arc furnace smelting data, obtaining an arc length and a foamy slag height, determining a submerged arc condition of the electric arc furnace according to the calculated arc length and foamy slag height, and adjusting smelting parameters of the electric arc furnace based on the submerged arc condition; or, respectively calculating the difference between each sub-item data and its corresponding historical early warning threshold, and adjusting the smelting parameters of the electric arc furnace based on the calculated results.

2. The method of dynamic control of the state of smelting in an electric arc furnace as claimed in claim 1, characterised in that, Adjusting the smelting parameters of the electric arc furnace based on the submerged arc condition comprises: training an initial neural network model based on historical arc lengths and historical foamy slag heights to obtain an electric arc furnace submerged arc condition determination model, wherein the historical arc lengths and the historical foamy slag heights are obtained based on historical smelting data in the electric arc furnace smelting data; inputting a current arc length and a current foamy slag height into the electric arc furnace submerged arc condition determination model to obtain a current submerged arc condition of the electric arc furnace, wherein the current arc length and the current foamy slag height are obtained based on current smelting data in the electric arc furnace smelting data; based on the current submerged arc conditions corresponding to different current arc lengths and different current foamy slag heights in the current smelting data, adjusting the smelting parameters of the electric arc furnace to achieve dynamic control of the electric arc furnace smelting state.

3. The method of dynamic control of the state of smelting in an electric arc furnace as claimed in claim 2, characterised in that, Obtaining the foamy slag height based on the electric arc furnace smelting data comprises: obtaining an electric arc furnace image, an electric arc furnace structure parameter, and a furnace charge information of the electric arc furnace based on the electric arc furnace smelting data; performing image recognition on the electric arc furnace image to determine a comprehensive liquid level height of the electric arc furnace; obtaining a molten steel level height of the electric arc furnace according to the electric arc furnace structure parameter and the furnace charge information, comprising: obtaining a molten steel upper surface area based on the electric arc furnace structure parameter, and obtaining a total amount of charged material, a total amount of slag, and a molten steel density based on the furnace charge information; obtaining a total mass of molten steel in the electric arc furnace based on the total amount of charged material and the total amount of slag; and calculating the total mass of molten steel, the molten steel density, and the molten steel upper surface area to obtain the molten steel level height; calculating the foamy slag height based on the comprehensive liquid level height and the molten steel level height.

4. The dynamic control method of the arc furnace smelting state according to any one of claims 1 to 3, characterized in that, Training an initial neural network model based on historical arc lengths and historical foamy slag heights comprises: regarding any one historical arc length and any historical foamy slag height as a historical data pair, and labeling the historical data pair based on a preset submerged arc condition label, wherein the submerged arc condition label includes excellent, normal, and poor; dividing the labeled historical data into a training data set and a verification data set; training the initial neural network model based on the training data set to learn an initial mapping relationship between the input historical data pair and the electric arc furnace submerged arc condition; verifying the initial mapping relationship based on the verification data set, optimizing the initial mapping relationship based on the verification result to obtain an optimized neural network model, and determining the optimized neural network model as the electric arc furnace submerged arc condition determination model.

5. The method of dynamic control of the state of smelting in an electric arc furnace as claimed in claim 4, characterised in that, Adjusting the smelting parameters of the electric arc furnace comprises: If the current arc-buried state is excellent, a speed-up process is performed on the smelting parameters of the electric arc furnace, the speed-up process at least including increasing the power supply intensity or reducing the carbon powder injection speed; If the current arc-buried state is normal, no process is performed on the smelting parameters of the electric arc furnace; If the current arc-buried state is poor, a speed-down process is performed on the smelting parameters of the electric arc furnace, the speed-down process at least including reducing the power supply intensity or increasing the carbon powder injection speed.

6. The method of dynamic control of the arc furnace smelting state according to claim 1, characterised by the fact that, The difference between each sub-item data and its corresponding historical early warning threshold value is calculated respectively, and the smelting parameters of the electric arc furnace are adjusted based on the calculation results, including: The historical early warning value is obtained based on the historical data in the electric arc furnace smelting data, and the current measurement value is obtained based on the current data in the electric arc furnace smelting data; The comparison result is obtained by comparing the current measurement value and the historical early warning value, and the difference between the current measurement value and the historical early warning value is calculated; When the difference is greater than a preset difference threshold value, the smelting parameters of the electric arc furnace are dynamically processed based on the comparison result, the dynamic processing including speed-up processing or speed-down processing.

7. The method of dynamic control of the state of smelting in an electric arc furnace as claimed in claim 6, characterised in that, The sub-item data includes the smelting temperature and the comprehensive liquid level height of the electric arc furnace, The smelting parameters of the electric arc furnace are adjusted based on the calculation results, including: When the difference between the current smelting temperature of the electric arc furnace and the historical temperature early warning value is greater than a preset temperature difference threshold value, if the current smelting temperature is higher than the historical temperature early warning value, the smelting parameters of the electric arc furnace are speeded down; if the current smelting temperature is lower than the historical temperature early warning value, the smelting parameters of the electric arc furnace are speeded up; When the difference between the current comprehensive liquid level height of the electric arc furnace and the historical height early warning value is greater than a preset height threshold value, if the current comprehensive liquid level height is higher than the historical height early warning value, the smelting parameters of the electric arc furnace are speeded down; if the current comprehensive liquid level height is lower than the historical height early warning value, the smelting parameters of the electric arc furnace are speeded up.

8. An apparatus for dynamic control of the state of an electric arc furnace, characterized in that The device includes: A data acquisition module for acquiring smelting data of the electric arc furnace; A first electric arc furnace smelting state control module for obtaining the arc length and the foamy slag height based on the electric arc furnace smelting data, determining the arc-buried state of the electric arc furnace according to the calculated arc length and foamy slag height, and adjusting the smelting parameters of the electric arc furnace based on the arc-buried state; A second electric arc furnace smelting state control module for calculating the difference between each sub-item data and its corresponding historical early warning threshold value respectively, and adjusting the smelting parameters of the electric arc furnace based on the calculation results.

9. An electronic device, comprising: A processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute the computer program stored in the memory to realize the dynamic control method of the electric arc furnace smelting state as claimed in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer program is stored thereon, the computer program is used to make the computer execute the dynamic control method of the electric arc furnace smelting state as claimed in any one of claims 1-7.

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