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

Abstract

The invention provides a dynamic control method, device and equipment for the smelting state of an electric arc furnace and a medium. According to the method, smelting data generated in the smelting process of the electric arc furnace are collected, the electric arc length and the foam slag height are calculated through the smelting data, and the submerged arc condition of the electric arc furnace is further determined according to the electric arc length and the foam slag height; according to the condition, the smelting parameters of the electric arc furnace are adjusted, or the difference value between each subitem data and the corresponding historical early warning threshold value is calculated, and the smelting parameters of the electric arc furnace are adjusted according to the calculation result; according to the method, real-time monitoring and intelligent analysis of the smelting state of the electric arc furnace are achieved, the operation flexibility and efficiency of the electric arc furnace are guaranteed, and meanwhile the energy utilization rate is increased.

Description

Technical Field

[0001] The present application relates to the field of intelligent control technology, and in particular to a method, device, equipment and medium for dynamically controlling the smelting state of an electric arc furnace. Background Art

[0002] Electric arc furnace steelmaking uses scrap steel as the main raw material and electricity as the main energy source. It widely adopts flat-pool smelting technology with large steel retention. Stable high-voltage and long-arc power supply operation is a prerequisite for achieving efficient operation of the electric arc furnace, and foamy slag submerged arc operation is the key to energy saving and efficiency improvement, which directly affects operating efficiency and smelting technical indicators.

[0003] Carbon powder is a commonly used foaming agent in the current foam slag operation. During the smelting process, coke is often added to the molten pool or carbon powder is sprayed to achieve the foam slag and molten pool stirring work to meet the process and technical requirements of steelmaking. In the traditional mode, the foam slag state and operation of the electric arc furnace rely on manual judgment, which is greatly influenced by the subjective influence of relevant staff and has a low degree of intelligence, resulting in delayed timing or excessive adjustment of the foam slag or power supply system during the adjustment process, which in turn leads to problems such as long smelting cycle, low power utilization, high smelting power consumption and electrode consumption. Summary of the invention

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

[0005] The present application provides a method for dynamically controlling the smelting state of an electric arc furnace, the method comprising: acquiring smelting data of the electric arc furnace; obtaining arc length and foamy slag height based on the electric arc furnace smelting data, determining the submerged arc 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 submerged arc state; or, respectively calculating the difference between each sub-item data and its corresponding historical warning threshold, and adjusting the smelting parameters of the electric arc furnace based on the calculated results.

[0006] In one 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 a historical arc length and a historical foamy slag height to obtain an arc furnace submerged arc state determination model, wherein the historical arc length and the historical foamy slag height are obtained based on historical smelting data in the arc furnace smelting data; inputting a current arc length and a current foamy slag height into the arc furnace submerged arc state determination model to obtain a current submerged arc state 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 arc furnace smelting data; adjusting the smelting parameters of the electric arc furnace based on the current submerged arc state 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 arc furnace.

[0007] In one embodiment of the present application, the height of foamed slag is obtained based on the electric arc furnace smelting data, including: obtaining an electric arc furnace image, electric arc furnace structural parameters, and 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 the comprehensive liquid level of the electric arc furnace; obtaining the molten steel level of the electric arc furnace according to the electric arc furnace structural parameters and the charge information, including obtaining the upper surface area of ​​the molten steel based on the electric arc furnace structural parameters, and obtaining the total amount of charge input, the total amount of slag, and the density of the molten steel based on the charge information; obtaining the total mass of the molten steel in the electric arc furnace based on the total amount of charge input and the total amount of slag; calculating the total mass of the molten steel, the density of the molten steel, and the upper surface area of ​​the molten steel to obtain the molten steel level; calculating the comprehensive liquid level and the molten steel level to obtain the foamed slag height.

[0008] In one embodiment of the present application, an initial neural network model is trained based on historical arc lengths and historical foam slag heights, including: treating any historical arc length and any historical foam slag height as a historical data pair, and labeling the historical data pair based on a preset submerged arc state label, wherein the submerged arc state 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 the initial mapping relationship between the input historical data pair and the submerged arc state of the arc furnace; 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 submerged arc state determination model for the arc furnace.

[0009] In one embodiment of the present application, the smelting parameters of the electric arc furnace are adjusted, including: if the current submerged arc state is excellent, the smelting parameters of the electric arc furnace are speeded up, and the speed-up process at least includes increasing the power supply intensity or reducing the carbon powder blowing speed; if the current submerged arc state is normal, the smelting parameters of the electric arc furnace are not processed; if the current submerged arc state is poor, the smelting parameters of the electric arc furnace are slowed down, and the speed-down process at least includes reducing the power supply intensity or increasing the carbon powder blowing speed.

[0010] In one embodiment of the present application, the difference between each sub-item data and its corresponding historical warning threshold is calculated respectively, and the smelting parameters of the electric arc furnace are adjusted based on the calculated results, including: obtaining a historical 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 warning value to obtain a comparison result, and calculating the difference between the current measurement value and the historical 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.

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

[0012] The present application provides a dynamic control device for the smelting state of an electric arc furnace, the device comprising: a data acquisition module, used to obtain the smelting data of the electric arc furnace; a first electric arc furnace smelting state control module, used to obtain the arc length and the foam slag height based on the electric arc furnace smelting data, determine the submerged arc state of the electric arc furnace according to the calculated arc length and the foam slag height, and adjust the smelting parameters of the electric arc furnace based on the submerged arc state; a second electric arc furnace smelting state control module, used to respectively calculate the difference between each sub-item data and its corresponding historical warning threshold, and adjust the smelting parameters of the electric arc furnace based on the calculated result.

[0013] The present application provides 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 a computer program stored in the memory to implement the dynamic control method of the arc furnace smelting state as described above.

[0014] The present application provides a computer-readable storage medium on which a computer program is stored, wherein the computer program is used to enable a computer to execute the dynamic control method of the arc furnace smelting state as described above.

[0015] Beneficial effects of the present application: The dynamic control method of the arc furnace smelting state proposed in the present application collects the data generated by the arc furnace during the smelting process, and further determines whether the collected data can calculate the arc length and the foam slag height; if the arc length and the foam slag height can be calculated, the arc submerged state of the arc furnace is determined by the calculated arc length and the foam slag height, and the smelting parameters of the arc furnace are adjusted accordingly; if the arc length and the foam slag height cannot be calculated, the difference between each sub-item data and its corresponding historical warning threshold is calculated, and the smelting parameters are adjusted according to the calculation results. This method realizes real-time monitoring and intelligent analysis of the arc furnace smelting state, which not only ensures the flexibility and efficiency of the arc furnace operation, but also improves the utilization rate of energy.

[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0018] Figure 1 It is a schematic diagram of an implementation environment of a method for dynamically controlling the smelting state of an arc furnace shown in an exemplary embodiment of the present application;

[0019] Figure 2 is a flow chart of a method for dynamically controlling the smelting state of an electric arc furnace shown in an exemplary embodiment of the present application;

[0020] Figure 3 It is a schematic diagram of the steps of a method for adjusting smelting parameters of an electric arc furnace in a submerged arc state, shown in an exemplary embodiment of the present application;

[0021] Figure 4is a schematic diagram of the steps of adjusting the smelting parameters of an electric arc furnace shown in an exemplary embodiment of the present application;

[0022] Figure 5 is a block diagram of a dynamic control device for an arc furnace smelting state shown in an exemplary embodiment of the present application;

[0023] Figure 6 is a block diagram of a first electric arc furnace smelting state control module shown in an exemplary embodiment of the application;

[0024] Figure 7 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] The following will describe the implementation methods of the present application with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints 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, not for limiting the scope of protection of the present application.

[0026] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[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. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0028] Figure 1 It is a schematic diagram of the implementation environment of the dynamic control method of the arc furnace smelting state shown in an exemplary embodiment of the present application.

[0029] like Figure 1As shown, the implementation environment of the dynamic control method of the smelting state of the electric arc furnace 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 electric arc furnace during the smelting process, which data include but are not limited to the structural parameters, operating parameters, power consumption, and status information of the smelting materials of the electric arc furnace. Specifically, the structural parameters of the electric arc furnace may specifically include the cross-sectional area of ​​the electric arc furnace, and the operating parameters may be subdivided into the main circuit current, voltage level, etc.; the status information of the smelting material may involve the quality of the raw materials, the amount of slag, etc. The data acquisition equipment can be a variety of sensors installed on the electric arc furnace (such as temperature sensors, current transformers, image capture devices, etc.), or it can be an external monitoring device connected to the electric arc furnace by wireless or wired means. This application does not impose any restrictions on the specific implementation form of the data acquisition module.

[0030] The computer device 102 is used to process the raw data collected by the data acquisition module 101. The specific workflow is as follows: construct an initial neural network model, and train it using the historical records in the raw data to obtain a model that can determine the arc state of the arc furnace. Then the real-time information in the raw data is input into the model to obtain the current arc furnace operating parameter adjustment suggestions, so as to dynamically control the operation of the arc furnace according to these suggestions to 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. This application also does not impose any restrictions on the specific type of computer devices.

[0031] Figure 2 It is a flow chart of a method for dynamically controlling the smelting state of an electric arc furnace shown in an exemplary embodiment of the present application.

[0032] like Figure 2 As shown, in an exemplary embodiment, the method for dynamically controlling the smelting state of an electric arc furnace includes at least steps S210 to S230, which are described in detail as follows:

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

[0034] In one embodiment of the present application, the smelting data of the electric arc furnace is collected, and its parameters include the structural parameters of the electric arc furnace, the parameters related to the charge, and the parameters related to the smelting state, wherein the structural parameters of the electric arc furnace include the depth and cross-sectional area of ​​the electric arc furnace, the parameters related to the charge include the total amount of charge and the total amount of slag, and the smelting state parameters include the total liquid level height and smelting temperature of the electric 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 the smelting record log, etc., and the present application does not impose any specific restrictions on its data acquisition equipment and methods.

[0035] Step S220, obtaining arc length and foam slag height based on the arc furnace smelting data, determining the submerged arc state of the arc furnace according to the calculated arc length and foam slag height, and adjusting the smelting parameters of the arc furnace based on the submerged arc state.

[0036] Figure 3 FIG. 1 is a schematic diagram of a method for adjusting the smelting parameters of an electric arc furnace in a submerged arc state according to an exemplary embodiment of the present application. Figure 3 As shown, adjusting the smelting parameters of the electric arc furnace based on the submerged arc state at least includes steps S221 to S224, which are as follows:

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

[0038] In one embodiment of the present application, the arc length is obtained based on the arc furnace smelting data, including: 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 the effective value of voltage and the effective value of current; based on a preset arc length calculation formula, calculating the effective value of voltage and the effective value of current to obtain the arc length.

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

[0040]

[0041] Where I is the effective value of current, V a is the effective value of voltage, ρ a is the resistivity, L is the arc length, and a, b, c, d, e, f, and m are coefficients related to the current.

[0042] It should be noted that the data preprocessing method and arc length calculation formula mentioned in the above embodiments are only for illustrative purposes. The data preprocessing method can make corresponding preprocessing adjustments based on the actual situation of the collected data and the application of the data; and the calculation of the arc length is based on different application scenarios, different current and voltage collection methods, and different materials for adaptive adjustment. The arc length can be calculated based on the current value and voltage value based on any other achievable arc length calculation formula. The present application does not make any specific restrictions on the specific steps and contents of its data preprocessing, and the arc length calculation formula. Similarly, the coefficients related to the current in the above formula (1) are also obtained based on different actual scenarios. They can be preset information or data determined by the current real-time scenario. The present application does not impose any restrictions on the determination method and value of the coefficients.

[0043] In one embodiment of the present application, the height of foamed slag is obtained based on the smelting data of the electric arc furnace, including: obtaining the electric arc furnace image, the structural parameters of the electric arc furnace, and the charge information of the electric arc furnace based on the smelting data of the electric arc furnace; performing image recognition on the electric arc furnace image to determine the comprehensive liquid level of the electric arc furnace; obtaining the steel liquid level of the electric arc furnace according to the structural parameters of the electric arc furnace and the charge information; and calculating the comprehensive liquid level and the steel liquid level to obtain the height of foamed slag. Among them, the steel liquid level of the electric arc furnace is obtained based on the structural parameters of the electric arc furnace and the charge information, including: obtaining the upper surface area of ​​the steel liquid based on the structural parameters of the electric arc furnace, and obtaining the total amount of charge, the total amount of slag, and the density of the steel liquid based on the charge information; obtaining the total mass of the steel liquid in the electric arc furnace based on the total amount of charge and the total amount of slag; calculating the total mass of the steel liquid, the density of the steel liquid, and obtaining the upper surface area of ​​the steel liquid to obtain the steel liquid level.

[0044] In a specific embodiment of the present application, an image of an arc furnace is acquired by an image acquisition device, and image recognition is performed on the image, and the liquid level is obtained based on the recognition result combined with the depth value of the arc furnace in the present application. Specifically: an image acquisition device (for example, an industrial camera installed above the arc furnace) is used to capture an image of the inside of the arc furnace, and the acquired image is preprocessed, such as grayscale, filtering and denoising, for 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 surface boundaries, etc., and in the recognition process, key points or areas representing the liquid surface position in the image are marked, so that by analyzing the position information of these key points or areas, the actual height of the liquid surface relative to the furnace mouth, i.e., the above-mentioned comprehensive liquid level, can be calculated in combination with the known geometric dimensions and depth values ​​of the arc furnace (i.e., the distance from the furnace mouth to the furnace bottom).

[0045] In another specific embodiment of the present application, the height of the molten steel level is calculated by the structural parameters of the electric arc furnace of the present application and the charge information. Specifically, the cross-sectional area of ​​the electric arc furnace is obtained according to the structural parameters of the electric arc furnace of the present application, and its cross-sectional area is determined as the upper surface area of ​​the molten steel; the total amount of charge and slag are obtained based on the relevant information of the charge, and the total instruction of the molten steel is determined according to the total amount of charge and slag, and the depth of the molten steel, i.e., the height of the molten steel level, is further calculated according to 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:

[0046]

[0047] Where, h2 is the height of the molten steel surface, ρ 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 material fed into the furnace, G sl is the total slag amount.

[0048] In addition, the formula for calculating the foam slag height by the comprehensive liquid level height and the steel liquid level height is as follows:

[0049] h3=h1-h2 Formula (3)

[0050] Among them, h3 is the foam slag height, h1 is the comprehensive liquid level height, and h2 is the steel liquid level height.

[0051] It should be noted that in the above embodiment, the image acquisition frequency can be set according to actual needs to ensure that changes in the conditions in the furnace are captured in a timely manner. This application does not impose any restrictions on the image acquisition device and its image cooking machine frequency. In addition, its image recognition can be implemented based on a deep learning model, or based on any other feasible method such as edge detection, threshold segmentation, contour tracking, etc. This application does not impose any specific restrictions on the specific method of its image recognition.

[0052] Step S222, training the initial neural network model based on the historical arc length and the historical foam slag height to obtain an arc furnace submerged arc state determination model, wherein the historical arc length and the historical foam slag height are obtained based on the historical smelting data in the arc furnace smelting data.

[0053] In one embodiment of the present application, an initial neural network model is trained based on historical arc lengths and historical foamy slag heights, including: treating any historical arc length and any historical foamy slag height as a historical data pair, and labeling the historical data pair based on preset submerged arc state labels, the submerged arc state labels 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 the initial mapping relationship between the input historical data pair and the submerged arc state of the arc furnace; 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 submerged arc state determination model for the arc furnace.

[0054] In a specific embodiment of the present application, first, each historical arc length and the historical foam slag height at the corresponding moment are combined into a historical data pair. Then, according to the expert's experience or preset standards, each historical data pair is labeled with the arc state, and its labels can be divided into three categories: "excellent" means that the arc state is very ideal and the arc is completely covered by foam slag; "normal" means that the arc state is general and part of the arc is covered; "poor" means that the arc state is not good and most of the arc is not covered by foam 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 verification data set to test 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, so that the model learns the mapping relationship between the input historical data pairs and the arc state of the arc furnace 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 validation data set is used to evaluate the performance of the model. If the model performs poorly on the validation data set, the model parameters are adjusted to continue training until satisfactory performance indicators are achieved. Finally, the model is optimized after multiple training and validation cycles and is determined as the arc furnace submerged arc state determination model.

[0055] Step S223, inputting the current arc length and the current foam slag height 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 foam slag height are obtained based on the current smelting data in the arc furnace smelting data.

[0056] In one embodiment of the present application, the arc length and foam slag height in the arc furnace are monitored in real time by sensors installed on the arc furnace (such as non-contact ranging sensors or image recognition equipment, etc.). In addition, considering that the collected raw data may contain noise, pre-processing is required, such as filtering denoising, data smoothing and other processing measures to ensure that the data input to the model is accurate and reliable. Then, the processed data is passed as input to the trained arc furnace submerged arc state determination model, so that after receiving the input data, the model automatically calculates according to the learned mapping relationship and outputs the current arc state of the arc furnace, which may be "excellent", "normal" or "poor". So that the relevant parameters of the arc furnace, such as current intensity, power distribution, etc., can be automatically adjusted according to the submerged arc state output by the model in the future to achieve the best smelting effect.

[0057] Step S224, based on the current submerged arc state corresponding to different current arc lengths and different current foamed slag heights in the current smelting data, the smelting parameters of the arc furnace are adjusted to achieve dynamic control of the smelting state of the arc furnace.

[0058] In one embodiment of the present application, the smelting parameters of the electric arc furnace are adjusted, including: if the current submerged arc state is excellent, the smelting parameters of the electric arc furnace are speeded up, and the speed-up process at least includes increasing the power supply intensity or reducing the carbon powder blowing speed; if the current submerged arc state is normal, the smelting parameters of the electric arc furnace are not processed; if the current submerged arc state is poor, the smelting parameters of the electric arc furnace are slowed down, and the speed-down process at least includes reducing the power supply intensity or increasing the carbon powder blowing speed.

[0059] In one embodiment of the present application, the submerged arc state is determined according to the relative relationship between the foam slag height and the arc length, as follows: h3<k3*L+k i

[0060] When h3≥k1*L+k2, the submerged arc state is judged to be excellent;

[0061] When k3*L+k4≥h3<k1*L+k2, the submerged arc state is determined to be normal;

[0062] When h3<k3*L+k4, the submerged arc state is judged to be poor.

[0063] Among them, h3 is the height of foam slag, L is the arc length, k i is the correlation coefficient.

[0064] It should be noted that k i To determine the correlation coefficient of the relative relationship between the slag foam height and the arc length, the coefficient varies depending on different arc furnace smelting scenarios, and this application does not impose any restrictions on the determination method and specific value of the coefficient.

[0065] Therefore, when h3≥k1*L+k2, that is, the foam slag buried arc state is judged to be good, the smelting parameters of the electric arc furnace are accelerated. Specifically, the power supply intensity can be increased or the carbon powder spraying speed can be reduced.

[0066] When k3*L+k4≥h3<k1*L+k2, that is, the foam slag buried arc state is judged to be normal, the smelting parameters of the arc furnace are not adjusted;

[0067] When h3<k3*L+k4, that is, the foamy slag buried arc state is judged to be poor, the smelting parameters of the electric arc furnace are slowed down, specifically, the power supply intensity is reduced or the carbon powder blowing speed is increased.

[0068] 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. Based on the current submerged arc state corresponding to different current arc lengths and different current foam slag heights in the current smelting data, the smelting parameters of the electric arc furnace are adjusted as follows:

[0069] When the relationship between the real-time foam slag height h3 and the arc length L is h3≥0.85*L+10, that is, the foam slag buried arc state judgment result is that the foam slag buried arc state is good, the system can increase the power supply intensity or reduce the carbon powder blowing speed.

[0070] When the relationship between the real-time foam slag height h3 and the arc length L is 0.8*L+5<h3<0.85*L+10, that is, the foam slag buried arc state judgment result is that the foam slag buried arc state is normal, the system does not make any adjustments.

[0071] When the relationship between the real-time foam slag height h3 and the arc length L is h3≤0.8*L+5, that is, the foam slag buried arc state judgment result is that the foam slag buried arc state is poor, the power supply intensity can be reduced or the carbon powder blowing speed can be increased.

[0072] Figure 4 It is a schematic diagram of the steps for adjusting the smelting parameters of an electric arc furnace shown in an exemplary embodiment of the present application.

[0073] like Figure 4As shown, when the arc length and foam slag height can be obtained based on the smelting data, the model training is performed based on the historical arc length and the historical foam slag height to obtain the arc furnace submerged arc state determination model, and the current arc length and the current foam slag height are used as the input values ​​of the arc furnace submerged arc state determination model to obtain the current arc furnace submerged arc state. If the current submerged arc state is poor, the power supply intensity is reduced or the carbon powder speed is increased; if the current submerged arc state is normal, the smelting parameters of the arc furnace are not adjusted; if the current submerged arc state is excellent, the power supply intensity is increased or the carbon powder speed is reduced.

[0074] Step S230, respectively calculating the difference between each sub-item data and its corresponding historical warning threshold, and adjusting the smelting parameters of the electric arc furnace based on the calculation result.

[0075] In one embodiment of the present application, the difference between each sub-item data and its corresponding historical warning threshold is calculated respectively, and the smelting parameters of the electric arc furnace are adjusted based on the calculated results, including: obtaining a historical 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 warning value to obtain a comparison result, and calculating the difference between the current measurement value and the historical 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.

[0076] Specifically, when the sub-item data in the smelting parameters include the smelting temperature and comprehensive liquid level 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 warning value is greater than the preset temperature difference threshold, if the current smelting temperature is higher than the historical temperature warning value, the smelting parameters of the electric arc furnace are slowed down; if the current smelting temperature is lower than the historical temperature warning value, the smelting parameters of the electric arc furnace are accelerated; when the difference between the current comprehensive liquid level of the electric arc furnace and the historical height warning value is greater than the preset height threshold, if the current comprehensive liquid level is higher than the historical height warning value, the smelting parameters of the electric arc furnace are slowed down; if the current comprehensive liquid level is lower than the historical height warning value, the smelting parameters of the electric arc furnace are accelerated.

[0077] In a specific embodiment of the present application, temperature data in the arc furnace is collected regularly by installing temperature sensors (such as thermocouples, etc.) at different parts of the arc furnace, and the warning information of each temperature abnormality is recorded. The historical temperature data and warning information are analyzed, and the temperature threshold that caused the warning in history is statistically calculated. This threshold is the "historical warning temperature". For example, historical data shows that when the temperature of the arc furnace reaches 1600°C, there has been an equipment overheating alarm, so 1600°C is the historical warning temperature. Then, the current smelting temperature of the arc furnace is monitored in real time, and its current smelting temperature data is also obtained by the sensor installed on the arc furnace. If the current smelting temperature monitored exceeds the historical warning temperature, and the value exceeded is greater than 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, then it is determined that the current smelting state of the arc furnace is poor. Given that the arc furnace will be at risk of overheating when the smelting state is poor, when it is determined that the current smelting state of the arc furnace is poor, the speed reduction process is immediately started, and the speed reduction process measures at least include reducing the power supply intensity (such as reducing the current or voltage input to the arc furnace) to reduce heat generation and prevent the temperature from continuing to rise. In addition, the cooling can be accelerated by increasing the carbon powder injection speed to help the arc furnace cool down, thereby protecting the equipment from damage.

[0078] In another specific embodiment of the present application, liquid level data in the electric arc furnace is collected regularly by installing liquid level sensors (such as ultrasonic sensors or laser rangefinders, etc.) at different parts of the electric arc furnace, and the warning information is recorded every time the liquid level is abnormal. The historical liquid level data and warning information are analyzed, and the threshold of the liquid level that caused the warning in history is calculated, and this threshold is the "historical warning liquid level". For example, historical data show that when the liquid level of the electric arc furnace reaches 2 meters, there was a risk of overflow, so 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 its current liquid level data is also obtained by the sensor installed on the electric arc furnace. If the monitored current comprehensive liquid level exceeds the historical warning liquid level, and the value exceeded is greater than 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, then it is determined that the current smelting state of the electric arc furnace is poor. Since poor smelting conditions mean the risk of overflow, when the current smelting condition of the electric arc furnace is determined to be poor, the speed reduction procedure is immediately started, and the speed reduction 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 a safe liquid level range, thereby protecting the equipment from damage.

[0079] It should be noted that the historical warning temperature and historical warning liquid level mentioned in the above embodiments are obtained based on their historical data, which can be determined by self-learning or by calibration. The specific determination method is based on the actual arc furnace smelting scene. This application does not impose any restrictions on the determination method of its historical warning value and its specific value. Similarly, the corresponding preset temperature difference threshold and preset liquid level height difference threshold are also generated based on the actual smelting scene, and this application does not impose any restrictions on its determination method and specific value.

[0080] It should be noted that the specific processing contents of the speed-up and speed-down processing mentioned in the above embodiments are only for illustrative purposes. In actual application scenarios, different methods can be used to achieve the purpose of speed-up / speed-down processing based on the current application scenarios. This application does not impose any restrictions on the specific processing methods.

[0081] Figure 5 is a block diagram of a dynamic control device for arc furnace smelting state shown in an exemplary embodiment of the present application. The device can be applied to Figure 1 The device may also be applicable to other exemplary implementation environments and specifically configured in other devices, and this embodiment does not limit the implementation environment to which the device is applicable.

[0082] like Figure 5 As shown, the exemplary dynamic control device of the electric arc furnace smelting state includes:

[0083] Among them, the data acquisition module 510 is used to collect the smelting data of the arc furnace; the first arc furnace smelting state control module 520 is used to obtain the arc length and the foam slag height based on the arc furnace smelting data, determine the submerged arc condition of the arc furnace according to the calculated arc length and the foam slag height, and adjust the smelting parameters of the arc furnace based on the submerged arc condition; the second arc furnace smelting state control module 530 is used to respectively calculate the difference between each sub-item data and its corresponding historical warning threshold, and adjust the smelting parameters of the arc furnace based on the calculated results.

[0084] In addition, the first arc furnace smelting state control module 520 includes multiple submodules, such as Figure 6 As shown, the model training submodule 521, the submerged arc state determination submodule 521, and the arc furnace smelting state control submodule 523 are as follows:

[0085] The model training submodule 521 is used to train the initial neural network model based on the historical arc length and the historical foam slag height to obtain the arc furnace submerged arc state determination model, and the historical arc length and the historical foam slag height are obtained based on the historical smelting data in the arc furnace smelting data; the submerged arc state determination submodule 522 is used to input the current arc length and the current foam slag height into the arc furnace submerged arc state determination model to obtain the current submerged arc state of the arc furnace, and the current arc length and the current foam slag height are obtained based on the current smelting data in the arc furnace smelting data; the arc furnace smelting state control submodule 523 is used to adjust the smelting parameters of the arc furnace based on the current submerged arc state corresponding to different current arc lengths and different current foam slag heights in the current smelting data, so as to realize dynamic control of the arc furnace smelting state.

[0086] It should be noted that the dynamic control device for the smelting state of an arc furnace provided in the above embodiment and the dynamic control method for the smelting state of an arc furnace provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment and will not be repeated here. In practical applications, the dynamic control device for the smelting state of an arc furnace provided in the above embodiment can allocate the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0087] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the dynamic control method of the arc furnace smelting state provided in the above-mentioned embodiments.

[0088] Figure 7 The structure diagram of the computer system suitable for implementing the electronic device of the embodiment of the present application is shown. It should be noted that: Figure 7 The computer system 700 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0089] like Figure 7As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 702 or the program loaded from the storage part 708 to the random access memory (RAM) 703, such as executing the method described in the above embodiment. In the RAM 703, various programs and data required for system operation are also stored. The CPU 501, ROM 702 and 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.

[0090] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including 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 needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed so that a computer program read therefrom is installed into the storage section 708 as needed.

[0091] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 709, and / or installed from a removable medium 711. When the computer program is executed by a central processing unit (CPU) 701, various functions defined in the system of the present application are executed.

[0092] It should be noted that the computer-readable medium shown in the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal can take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0093] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0094] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0095] Another aspect of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor of a computer, the computer executes the method for dynamically controlling the smelting state of an electric arc furnace as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0096] Another aspect of the present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the dynamic control method of the arc furnace smelting state provided in each of the above embodiments.

[0097] The above embodiments are merely illustrative of the principles and effects of the present application, and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for dynamically controlling the smelting state of an electric arc furnace, characterized in that: The method comprises: Obtain smelting data of electric arc furnace; Based on the arc furnace smelting data, the arc length and the foam slag height are obtained, the arc submerged state of the arc furnace is determined according to the calculated arc length and the foam slag height, and the smelting parameters of the arc furnace are adjusted based on the arc submerged state; or, The difference between each sub-item data and its corresponding historical warning threshold is calculated respectively, and the smelting parameters of the electric arc furnace are adjusted based on the calculation results.

2. The method for dynamically controlling the smelting state of an electric arc furnace according to claim 1, characterized in that: The smelting parameters of the electric arc furnace are adjusted based on the submerged arc state, including: The initial neural network model is trained based on the historical arc length and the historical foam slag height to obtain an arc furnace submerged arc state determination model, wherein the historical arc length and the historical foam slag height are obtained based on the historical smelting data in the arc furnace smelting data; Inputting the current arc length and the current foam slag height 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 foam slag height are obtained based on the current smelting data in the arc furnace smelting data; Based on the current submerged arc state corresponding to different current arc lengths and different current foamy slag heights in the current smelting data, the smelting parameters of the electric arc furnace are adjusted to achieve dynamic control of the smelting state of the electric arc furnace.

3. The method for dynamically controlling the smelting state of an electric arc furnace according to claim 2, characterized in that: The foamy slag height is obtained based on the electric arc furnace smelting data, including: Based on the electric arc furnace smelting data, an electric arc furnace image, electric arc furnace structural parameters, and charge information of the electric arc furnace are obtained; Performing image recognition on the electric arc furnace image to determine the comprehensive liquid level height of the electric arc furnace; The height of the molten steel level of the electric arc furnace is obtained according to the electric arc furnace structural parameters and the charge information, including: obtaining the upper surface area of ​​the molten steel based on the electric arc furnace structural parameters, and obtaining the total amount of charge, the total amount of slag, and the density of the molten steel based on the charge information; obtaining the total mass of the molten steel in the electric arc furnace based on the total amount of charge and the total amount of slag; calculating the total mass of the molten steel, the density of the molten steel, and the upper surface area of ​​the molten steel to obtain the height of the molten steel level; The height of the foamed slag is obtained by calculating the comprehensive liquid level height and the steel liquid level height.

4. The method for dynamically controlling the smelting state of an electric arc furnace according to any one of claims 1 to 3, characterized in that: The initial neural network model is trained based on the historical arc length and historical foam slag height, including: Considering any historical arc length and any historical foam slag height as a historical data pair, and marking the historical data pair based on a preset submerged arc state label, wherein the submerged arc state label includes excellent, normal, and poor; Divide the annotated 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 the initial mapping relationship between the input historical data pair and the arc-buried state of the arc furnace; The initial mapping relationship is verified based on the verification data set, and the initial mapping relationship is optimized based on the verification result to obtain an optimized neural network model, and the optimized neural network model is determined as an arc furnace submerged arc state determination model.

5. The method for dynamically controlling the smelting state of an electric arc furnace according to claim 4, characterized in that: Adjust the smelting parameters of the electric arc furnace, including: If the current submerged arc state is excellent, the smelting parameters of the electric arc furnace are speeded up, and the speed-up process at least includes increasing the power supply intensity or reducing the carbon powder spraying speed; If the current submerged arc state is normal, the smelting parameters of the electric arc furnace will not be processed; If the current submerged arc state is poor, the smelting parameters of the electric arc furnace are slowed down, and the speed reduction process at least includes reducing the power supply intensity or increasing the carbon powder spraying speed.

6. The method for dynamically controlling the smelting state of an electric arc furnace according to claim 1, characterized in that: Calculate the difference between each sub-item data and its corresponding historical warning threshold, and adjust the smelting parameters of the electric arc furnace based on the calculation results, including: Obtaining a historical warning value based on historical data in the electric arc furnace smelting data, and obtaining a current measurement value based on current data in the electric arc furnace smelting data; Comparing the current measurement value with the historical warning value to obtain a comparison result, and calculating the difference between the current measurement value and the historical warning value; When the difference is greater than a preset difference threshold, the smelting parameters of the electric arc furnace are dynamically processed based on the comparison result, and the dynamic processing includes speed-up processing or speed-down processing.

7. The method for dynamically controlling the smelting state of an electric arc furnace according to claim 6, characterized in that: The sub-item data include the smelting temperature and comprehensive liquid level of the electric arc furnace, Adjust the arc furnace smelting parameters based on the calculation results, including: When the difference between the current smelting temperature of the electric arc furnace and the historical temperature warning value is greater than the preset temperature difference threshold, if the current smelting temperature is higher than the historical temperature warning value, the smelting parameters of the electric arc furnace are slowed down; if the current smelting temperature is lower than the historical temperature warning value, the smelting parameters of the electric arc furnace are accelerated; When the difference between the current comprehensive liquid level height of the electric arc furnace and the historical height warning value is greater than the preset height threshold, if the current comprehensive liquid level height is higher than the historical height warning value, the smelting parameters of the electric arc furnace are slowed down; if the current comprehensive liquid level height is lower than the historical height warning value, the smelting parameters of the electric arc furnace are accelerated.

8. A dynamic control device for the smelting state of an electric arc furnace, characterized in that: The device comprises: Data acquisition module, used to obtain smelting data of electric arc furnace; a first electric arc furnace smelting state control module, configured to obtain an arc length and a foam slag height based on the electric arc furnace smelting data, determine a submerged arc state of the electric arc furnace according to the calculated arc length and foam slag height, and adjust smelting parameters of the electric arc furnace based on the submerged arc state; The second electric arc furnace smelting state control module is used to calculate the difference between each sub-item data and its corresponding historical warning threshold, and adjust the smelting parameters of the electric arc furnace based on the calculation results.

9. An electronic device, characterized in that: It comprises 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 a computer program stored in the memory to implement the dynamic control method of the arc furnace smelting state as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program is used to enable a computer to execute the method for dynamically controlling the smelting state of an electric arc furnace as described in any one of claims 1-7.

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