Method, device, equipment and medium for determining mass ratio of components of alumina inclusions

Abstract

Embodiments of the present disclosure disclose a method, device, electronic and medium for determining the composition-mass ratio of alumina inclusions. A specific embodiment of the method comprises: in response to determining that alumina inclusions are detected in a smelting process of calcium-treated steel, obtaining smelting data; determining a shape factor of the alumina inclusions according to the smelting data; and determining a composition-mass ratio of the alumina inclusions according to the shape factor and a pre-determined composition-mass ratio prediction formula. The embodiment associates the shape factor with the composition-mass ratio of the alumina inclusions, effectively improves the efficiency and effect of the calcium treatment process, reduces the experimental detection cost, and facilitates the modification of the alumina inclusions into spherical inclusions, which will not be deformed or broken during the steel processing process, thereby improving the plasticity, toughness and fatigue resistance of the steel product.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of iron and steel metallurgy technology, and specifically to a method, apparatus, electronic device, and computer-readable medium for determining the composition-mass ratio of alumina inclusions. Background Technology

[0002] Non-metallic inclusions exist as independent phases in steel, and their composition, size, density, and shape significantly influence the mechanical properties and corrosion resistance of the steel. In aluminum-deoxidized steel, alumina inclusions tend to cause stress concentration and microcracks during processing, reducing the steel's plasticity, toughness, and fatigue resistance. However, spherical inclusions do not deform or break during steel processing.

[0003] Therefore, in the steelmaking process, calcium treatment is needed to modify irregular alumina inclusions into spherical liquid calcium aluminate inclusions. During this modification process, the composition and morphology of the alumina inclusions are related, making the study of these compositional and morphological changes significant. However, due to the large number of inclusions in steel and their tendency to collide, it is difficult to observe the changes in the composition and morphology of the alumina inclusions, as well as the progress of calcium treatment, posing many challenges to the modification of alumina inclusions. Summary of the Invention

[0004] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0005] Some embodiments of this disclosure provide methods, apparatus, electronic devices, and computer-readable media for determining the compositional mass ratio of alumina inclusions to address the technical problems mentioned in the background section above.

[0006] In a first aspect, some embodiments of this disclosure provide a method for determining the composition mass ratio of alumina inclusions. The method includes: acquiring smelting data in response to the detection of alumina inclusions during the smelting process of calcium-treated steel; determining the shape factor of the alumina inclusions based on the smelting data; and determining the composition mass ratio of the alumina inclusions based on the shape factor and a predetermined composition mass ratio prediction formula.

[0007] Secondly, some embodiments of this disclosure provide an apparatus for determining the composition-mass ratio of alumina inclusions. The apparatus includes: an acquisition unit configured to acquire smelting data in response to the detection of alumina inclusions during the smelting process of calcium-treated steel; a first determination unit configured to determine the shape factor of the alumina inclusions based on the smelting data; and a second determination unit configured to determine the composition-mass ratio of the alumina inclusions based on the shape factor and a predetermined composition-mass ratio prediction formula.

[0008] Thirdly, embodiments of this application provide an electronic device, the network 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, causing the one or more processors to implement the method described in any implementation of the first aspect.

[0009] Fourthly, embodiments of this application provide a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any of the implementations of the first aspect.

[0010] One embodiment of the above-described embodiments of this disclosure has the following beneficial effects: First, in response to the detection of alumina inclusions during the smelting process of calcium-treated steel, smelting data is acquired. Then, based on the obtained smelting data, the shape factor of the alumina inclusions is determined. Next, according to the shape factor and a predetermined composition-mass ratio prediction formula, the composition-mass ratio of the alumina inclusions is determined. This correlates the shape factor with the composition-mass ratio of the alumina inclusions, effectively improving the efficiency and effectiveness of the calcium treatment process, reducing the cost of experimental testing, and facilitating the modification of alumina inclusions into spherical inclusions. These inclusions do not deform or break during steel processing, thereby significantly improving the plasticity, toughness, and fatigue resistance of the steel products. Attached Figure Description

[0011] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0012] Figure 1 This is a schematic diagram of an application scenario of the method for determining the composition mass ratio of alumina inclusions according to this disclosure;

[0013] Figure 2 This is a flowchart of some embodiments of the method for determining the mass ratio of alumina inclusions according to this disclosure;

[0014] Figure 3This is a schematic diagram of the high-temperature confocal experimental setup and the placement of the alumina modification experiment in some embodiments of the method for determining the composition-mass ratio of alumina inclusions according to this disclosure;

[0015] Figure 4 This is a schematic flowchart of an alumina modification experiment in some embodiments of the method for determining the mass ratio of alumina inclusions according to the present disclosure;

[0016] Figure 5 This is a schematic diagram showing the shape changes of triangular alumina particles with different initial areas during calcium treatment in some embodiments of the method for determining the composition-mass ratio of alumina inclusions according to this disclosure.

[0017] Figure 6 This is a schematic diagram of the surface scan and average composition results of triangular alumina particles with an initial area of ​​7546.3 μm2 at different modification times in some embodiments of the method for determining the composition mass ratio of alumina inclusions according to the present disclosure.

[0018] Figure 7 This is a schematic diagram of the aspect ratio (AR), convexity (C), and sphericity (S) parameters of alumina inclusions in some embodiments of the method for determining the composition mass ratio of alumina inclusions according to the present disclosure;

[0019] Figure 8 This is a schematic diagram illustrating the overall regularity of alumina inclusion particles at different modification times in some embodiments of the method for determining the composition-mass ratio of alumina inclusions according to this disclosure;

[0020] Figure 9 This is a schematic diagram showing the fitting results between the composition and overall regularity of alumina inclusions at different times in some embodiments of the method for determining the composition-mass ratio of alumina inclusions according to this disclosure;

[0021] Figure 10 This is a schematic diagram comparing predicted results and experimental results in some embodiments of the method for determining the mass ratio of alumina inclusions according to this disclosure;

[0022] Figure 11 This is a schematic diagram of the liquid fraction of calcium aluminate inclusions with different CaO / Al2O3 mass ratios at 1873K in some embodiments of the method for determining the mass ratio of alumina inclusions according to this disclosure;

[0023] Figure 12 These are schematic diagrams of some embodiments of the apparatus for determining the mass ratio of alumina inclusions according to this disclosure;

[0024] Figure 13 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0025] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0026] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0027] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0028] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0029] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0030] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0031] Figure 1 This is a schematic diagram illustrating an application scenario of a method for determining the mass ratio of alumina inclusions according to some embodiments of this disclosure.

[0032] like Figure 1 As shown, in response to the detection of alumina inclusions during the smelting process of calcium-treated steel, server 101 can acquire smelting data 102, and then determine the shape factor 103 of the alumina inclusions based on the smelting data 102, and then determine the composition mass ratio 105 of the alumina inclusions based on the shape factor 103 and a predetermined composition mass ratio prediction formula 104.

[0033] It is understood that the method for determining the mass ratio of alumina inclusions can be executed by a terminal device or by server 101. The executing entity of the above method can also include a device formed by integrating the terminal device and server 101 through a network, or it can be executed by various software programs. The terminal device can be various electronic devices with information processing capabilities, including but not limited to smartphones, tablets, e-book readers, laptops, and desktop computers. The executing entity can also be server 101, software, etc. When the executing entity is software, it can be installed in the electronic devices listed above. It can be implemented as multiple software programs or software modules for example, to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0034] It should be understood that Figure 1 The number of servers shown is merely illustrative. Depending on implementation needs, any number of servers can be used.

[0035] Continue to refer to Figure 2 The flowchart 200 illustrates some embodiments of a method for determining the composition-mass ratio of alumina inclusions according to the present disclosure. This method for determining the composition-mass ratio of alumina inclusions includes the following steps:

[0036] Step 201: In response to the determination that alumina inclusions were detected during the smelting process of calcium-treated steel, smelting data is acquired.

[0037] In some embodiments, in response to the detection of alumina inclusions in the smelting process of calcium-treated steel (the smelting process generally refers to the calcium treatment process of molten steel), the subject performing the method for determining the composition mass ratio of alumina inclusions (e.g., Figure 1 The server shown can acquire smelting data via wired or wireless connection.

[0038] It should be noted that when the smelting process of the calcium-treated steel has not started or has started, the main component of the above-mentioned alumina inclusions is alumina (the proportion of alumina is 90% or more). When the smelting process of the calcium-treated steel starts, the alumina in the above-mentioned alumina inclusions reacts with the calcium in the calcium-treated steel to form calcium aluminate. The main component of the above-mentioned alumina inclusions will be transformed into calcium aluminate with a certain proportion of CaO / Al2O3 (i.e., it becomes calcium aluminate inclusions). For ease of understanding, in this application, alumina inclusions will still be used to characterize the above-mentioned inclusions (calcium aluminate inclusions).

[0039] It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future wireless connection methods.

[0040] In some optional implementations of some embodiments, the smelting data includes: shape change data of the alumina inclusions and size change data of the alumina inclusions.

[0041] Step 202: Determine the shape factor of the alumina inclusions based on the smelting data.

[0042] In some embodiments, based on the smelting data obtained in step 201, the aforementioned execution entity (e.g.) Figure 1 The server shown can determine the shape factor of the alumina inclusions. Specifically, the shape factor is used to characterize the regularity of the overall shape of the alumina inclusions.

[0043] In some optional implementations of certain embodiments, the execution entity may determine the shape factor based on the shape change data, size change data, and the following formula:

[0044] Wherein, OR represents the shape factor of the alumina inclusion, with a value range of 0 to 1, AR represents the aspect ratio of the alumina inclusion, C represents the convexity of the alumina inclusion, and S represents the sphericity of the alumina inclusion.

[0045] Step 203: Determine the composition mass ratio of alumina inclusions based on the shape factor and a predetermined composition mass ratio prediction formula.

[0046] In some embodiments, the executing entity can determine the compositional mass ratio of the alumina inclusions based on the shape factor and a predetermined compositional mass ratio prediction formula. The compositional mass ratio typically refers to the mass ratio of calcium oxide to alumina in the alumina inclusions. Here, the compositional mass ratio prediction formula is generally used to characterize the correspondence between the shape factor and the compositional mass ratio.

[0047] As an example, the above-mentioned formula for predicting the component mass ratio can be a correspondence table pre-made by researchers based on a large number of sample shape factors and sample component mass ratios. The above-mentioned execution entity can determine the sample component mass ratio that is the same as or similar to the above-mentioned shape factor in the above-mentioned correspondence table, and determine the sample component mass ratio corresponding to the sample shape factor as the component mass ratio of the above-mentioned alumina inclusions.

[0048] In some optional implementations of certain embodiments, the execution entity can determine the component mass ratio based on the following component mass ratio prediction formula and the aforementioned shape factor:

[0049] (CaO / Al2O3)=2.119·OR-1.667(R 2 =0.99) where CaO / Al2O3 represents the mass ratio of calcium oxide to aluminum oxide in the above alumina inclusions, R 2 This indicates the linear correlation between the mass ratio of calcium oxide and alumina in alumina inclusions and the shape factor. A value closer to 1 indicates a stronger predictive ability of the mass ratio prediction formula. As the CaO / Al₂O₃ ratio within the inclusion particles increases, the overall regularity (OR) of the particles continuously increases. Furthermore, when the OR is less than 0.787, the alumina inclusions can be considered to be Al₂O₃; therefore, the OR can be controlled within the range of 0.787 to 1.

[0050] In some optional implementations of certain embodiments, the above-described component mass ratio prediction formula is obtained according to the following steps:

[0051] Sample experimental data on calcium modification of alumina inclusions in molten steel are obtained. The sample experimental data includes sample shape factors and the corresponding sample component mass ratios. The correspondence between the sample shape factors and the corresponding sample component mass ratios is fitted to obtain a prediction formula to be trained. The component mass ratio is determined based on the prediction formula to be trained and the sample shape factors. The component mass ratio is compared with the sample component mass ratios, and the loss value of the prediction formula is determined based on the comparison result. In response to the loss value satisfying a preset condition, the prediction formula to be trained is determined as the prediction formula for the component mass ratio.

[0052] In some optional implementations of certain embodiments, in response to the loss value not meeting the preset conditions, the execution entity may adjust the corresponding parameters in the prediction formula to be trained.

[0053] Specifically, sample experimental data can be obtained through the following steps, and the formula for predicting the component mass ratio can be determined based on the sample experimental data:

[0054] Step 1: Preparation of experimental materials

[0055] The specific smelting steps for calcium-treated steel are as follows: 1) Place 780g of electrolytic iron into a high-purity MgO crucible with an outer diameter of 80mm, an inner diameter of 60mm, and a height of 130mm, and heat it in a silicon-molybdenum resistance furnace at a speed of 5L·min. -1 The temperature was raised to 1873 K at a heating rate of 5 L / min throughout the experiment. -12) After heating to 1873K, hold for 20 minutes to ensure complete melting of the electrolytic iron. Then, add silicon-calcium alloy wrapped in iron sheet to the molten steel every 5 minutes, with the added amounts being 1.3g, 1.3g, 2.6g, and 2.6g respectively. 3) 20 minutes after adding the silicon-calcium alloy, take samples using a quartz glass tube and water cool them. After the operation, water cool the crucible samples. The calcium-treated steel after water cooling was used as the steel for high-temperature confocal microscopy experiments. Its main components were determined by ICP-OES and an oxygen-nitrogen analyzer with an accuracy of ±0.5ppm. Before conducting the high-temperature confocal microscopy experiment, the experimental steel needs to be processed into a cylinder with a diameter of 5 mm and a height of 3 mm. In this experiment, sandpaper of grades 300#, 600#, 800#, 1200#, and 2400# was used for progressive polishing. After cleaning, the sample was polished with polishing liquids of 7 μm and 0.5 μm. Finally, the sample was placed in a beaker containing alcohol and then placed in an ultrasonic cleaner for 5 minutes.

[0056] The selection of alumina inclusions involved replacing the inherent alumina inclusions in the steel with alumina particles. Triangular Al2O3 particles with the desired shape characteristics were selected from commercially available high-purity (99.9%) α-Al2O3. The particle size ranged from 50 to 350 μm, and the thickness error of particles of different sizes should not exceed 5 μm. Finally, the selected particles were characterized by X-ray fluorescence (XRF) spectroscopy to ensure their composition.

[0057] Step 2: Modification Experiment

[0058] The method of this invention uses a high-temperature confocal microscope, model VL2000DX-SVF18SP, which can achieve rapid heating and cooling, with a maximum heating rate of up to 1000℃·min. -1 The maximum cooling rate can reach 1500℃·min -1 This allows for in-situ observation of experimental samples at high temperatures. Furthermore, temperature changes can be artificially controlled, which is beneficial for the melting of the steel sample surface.

[0059] Using a high-temperature confocal microscopy system, an experiment was designed to simulate the modification of calcium-modified alumina inclusions in molten steel. The changes in the shape and size of the target inclusions in the high-temperature molten steel during the modification process were observed in real time and continuously, and the start and end times of the modification could be accurately controlled.

[0060] Turn on all equipment of the high-temperature confocal microscope. After confirming that the high-temperature heating furnace is not in a vacuum state, open the furnace lid. Place the experimental steel and alumina particles into the alumina crucible according to the specific placement method, and place the alumina crucible horizontally on the platinum-rhodium thermocouple holder, as shown in the following manner. Figure 3 As shown, Figure 3This is a schematic diagram of the high-temperature confocal experimental setup and the placement of the alumina modification experiment in some embodiments of the method for determining the composition-mass ratio of alumina inclusions according to this disclosure.

[0061] Adjust the focus to achieve a clear image on the screen, then cover the furnace to ensure its airtightness. After evacuating the furnace chamber using a vacuum pump, continuously purge the chamber with ultrapure argon (99.999%) three times to prevent oxidation of the sample surface. During the argon purge, the valves must be adjusted slowly to prevent excessive airflow that could cause sample loss. In in-situ observation of inclusions at high temperatures, for confocal microscopy, only light emitted from points on the focal plane can pass through the exit pinhole. Light emitted from points outside the focal plane is defocused at the exit pinhole plane and most of it cannot pass through the central pinhole. Therefore, the observation target point on the focal plane appears bright, while non-observation points appear as a black background, increasing contrast and resulting in a clearer image. In the modification experiment, as the temperature rises to the experimental temperature, the steel sample surface melts. After the steel sample surface melts, due to surface tension, black shadows appear at the edges of the Al2O3 particles in the confocal image, affecting the judgment of particle shape. After the molten steel melts, the shape of inclusions cannot be clearly observed. This invention adds a CCD camera to the eyepiece to replace the confocal focusing device in existing equipment for recording, observing, and storing the modification process. Specific experimental parameters include: 5 megapixels; resolution of 2080*1552; and a frame rate of 50 FPS. All experimental processes are captured by a charge-coupled device (CCD) imaging system placed at the eyepiece, including the CCD camera and image analysis software. The digitized images are then read into the image analysis software. Changes in size and shape can be analyzed using the software. Only the particle projection on the horizontal plane needs to be captured; the contrast in each frame is used to distinguish alumina particles from the molten steel. In some cases, the image distinction between alumina particles and molten steel is not obvious; a clear boundary can be obtained by adjusting the image brightness and contrast.

[0062] Before starting the high-temperature confocal microscopy experiment, the Phase Diagram module in the FactSage thermodynamics software was used to calculate the melting point of the calcium-treated steel to be 1587℃. Therefore, for the first experiment, the temperature needed to be manually adjusted to the point where the surface of the molten steel melted, 100℃ below the melting point, to avoid heating too quickly and causing the steel sample to completely melt or the alumina inclusions to be lost from the surface of the molten steel. After completing the preliminary experimental steps, the temperature was set at 500 K·min. -1 The sample was heated to 1750 K at a rate of [missing value], and then at 50 K·min [missing value]. -1The temperature was increased to the experimental temperature of 1860 K at a certain rate. Upon reaching this temperature, the surface of the steel sample melted; this moment was recorded as the start time of Al2O3 particle modification. The sample was held at this temperature for a certain period to observe the modification behavior of the Al2O3 particles on the surface of the experimental steel sample. During the experiment, the argon gas flow rate needed to be reduced to 5-10 L / min. -1 To prevent excessive argon flow from causing alumina inclusions to drift away from the observation field, helium gas is introduced after the high-temperature confocal microscopy experiment to ensure rapid completion of the modification of the molten steel and inclusions. The cooling rate is 1200℃·min. -1 After modification, before sending the samples for electron microscopy analysis, they need to be stored in vials containing alcohol to prevent prolonged exposure to air, which could lead to oxidation of the experimental steel surface and the formation of numerous inclusions, affecting the normal observation of subsequent modification experiments. Figure 4 and Figure 5 As shown, Figure 4 This is a schematic flowchart of an alumina modification experiment in some embodiments of the method for determining the mass ratio of alumina inclusions according to the present disclosure. Figure 5 This is a schematic diagram illustrating the shape changes of triangular alumina particles with different initial areas during calcium treatment in some embodiments of the method for determining the composition-mass ratio of alumina inclusions according to this disclosure. Figure 5 During the calcium treatment process, the initial area was 7546.3 μm. 2 9895.9μm 2 16315.2μm 2 CCD images of the shape changes of triangular alumina particles were obtained, revealing that the irregular edges of the inclusions gradually smoothed out over time. Subsequent scanning electron microscopy (SEM) analysis of the inclusions at different time points was then performed. Figure 6 As shown, Figure 6 In some embodiments of the method for determining the composition-mass ratio of alumina inclusions according to the present disclosure, the initial area is 7546.3 μm. 2 A schematic diagram showing the surface scan and average composition results of triangular alumina particles at different modification times.

[0063] Step 3: Image Processing

[0064] Shape factors, aspect ratio (AR), convexity (C), and sphericity (S), which are commonly used to characterize the irregularity of particle shape, are expressed by the following formulas: Figure 7 As shown, Figure 7 This is a schematic diagram illustrating the aspect ratio (AR), convexity (C), and sphericity (S) of alumina inclusions in some embodiments of the method for determining the composition-mass ratio of alumina inclusions according to this disclosure. The aspect ratio (AR) is defined as the minimum Ferete diameter (D). minThe ratio between the maximum diameter and the maximum Ferrette diameter (D) max The Feret diameter of a particle is determined by the measured distance between parallel lines of the two boundaries of the particle profile. Convexity (C) represents the undulation and irregular complexity of the particle boundary curve, i.e., the particle area (S). A ) and the area of ​​the convex hull of the particle (S) A +S B The ratio of sphericity (S) to the circumference (P) of an equivalent circle. eq ) and its actual perimeter (P r The aspect ratio, convexity, and sphericity are defined by 2D images in the method of this invention. Their measured values ​​are between 0 and 1, and the closer the value of each parameter is to 1, the closer the shape of the particle is to a sphere. In order to improve accuracy, the method of this invention uses the overall regularity (OR) of the average values ​​of the aspect ratio (AR), convexity (C), and sphericity (S) parameters to characterize the shape of the particle, expressed by formula (1) as follows:

[0065]

[0066] The images recorded by the CCD camera were processed, binarized, and the overall regularity of alumina inclusion particles at different modification times was measured using ImageView software. The results were plotted on [the graph / plotting table]. Figure 8 middle, Figure 8 This is a schematic diagram illustrating the overall regularity of alumina inclusion particles at different modification times in some embodiments of the method for determining the composition-mass ratio of alumina inclusions according to this disclosure.

[0067] By introducing a shape factor to characterize the shape of inclusion particles, the relationship between the inclusion composition and the shape factor during the modification process is fitted, thereby enabling the prediction of the composition of alumina inclusions during the modification process using the shape factor.

[0068] Step 4: Building the Prediction Model

[0069] Linear fitting was performed using Origin to study the relationship between the composition and shape factor of alumina inclusions at different times, as follows: Figure 9 As shown, Figure 9 This is a schematic diagram showing the fitting results between the composition and overall regularity of alumina inclusions at different times in some embodiments of the method for determining the composition-mass ratio of alumina inclusions according to this disclosure. The fitting results show that the CaO / Al2O3 ratio of alumina inclusion particles and the overall regularity have a very good linear correlation (R0). 2 =0.99), as shown in Formula 2.

[0070] OR=0.787+0.472×(CaO / Al2O3) (R 2=0.99) (2)

[0071] As the ratio of calcium oxide to aluminum oxide (CaO / Al2O3) inside the inclusion particles increases, the overall regularity (OR) of the particles will continuously increase.

[0072] Step 5: Model Prediction

[0073] Formula 2 was used to predict the particle composition at different time points, and the results were compared with those obtained from experiments. Specific results are as follows: Figure 10 As shown, Figure 10 This is a schematic diagram comparing predicted results and experimental results in some embodiments of the method for determining the mass ratio of alumina inclusions according to this disclosure.

[0074] The results show that the predicted results obtained using this empirical formula are very close to the experimental measurements, proving that this empirical formula can be used to predict the compositional changes of inclusions during calcium treatment.

[0075] Simultaneously, using the Equilib database in the Factsage 7.1 thermodynamic software, the liquid fraction of calcium aluminate inclusions with different CaO / Al2O3 ratios at 1873 K was calculated, such as... Figure 11 As shown, Figure 11 This is a schematic diagram of the liquid fraction of calcium aluminate inclusions with different CaO / Al2O3 mass ratios at 1873 K in some embodiments of the method for determining the composition mass ratio of alumina inclusions according to the present disclosure.

[0076] The method predicts the composition of particles that begin to transform into fully spherical particles during the modification process. The prediction results are further validated using FactSage thermodynamic software, improving the accuracy of the method.

[0077] When the overall regularity of the inclusions is 1, that is, when the CaO / Al2O3 ratio of the inclusion particles is 0.451, the liquid fraction of the inclusions is 0.83. This further proves that the empirical formula between composition and overall regularity can be used to predict the composition of inclusions during the modification process through the overall regularity of the particles.

[0078] The purpose of calcium treatment is to transform irregular alumina into liquid spherical calcium aluminate, so it is crucial to determine the composition at which the transformation into liquid spherical inclusions begins. Using empirical formula (2), it can be seen that when the CaO / Al2O3 ratio of the inclusion particles is greater than 0.451, the calcium aluminate inclusions are ideal spherical inclusions.

[0079] This predictive method allows for the prediction of inclusion composition by calculating the shape factor of inclusions during the modification process, effectively improving the efficiency of calcium treatment and reducing experimental testing costs. Furthermore, it provides insights into the relationship between other shape-related properties and inclusion composition.

[0080] One embodiment of the above-described embodiments of this disclosure has the following beneficial effects: First, in response to the detection of alumina inclusions during the smelting process of calcium-treated steel, smelting data is acquired. Then, based on the obtained smelting data, the shape factor of the alumina inclusions is determined. Next, according to the shape factor and a predetermined composition-mass ratio prediction formula, the composition-mass ratio of the alumina inclusions is determined. This correlates the shape factor with the composition-mass ratio of the alumina inclusions, effectively improving the efficiency and effectiveness of the calcium treatment process, reducing the cost of experimental testing, and facilitating the modification of alumina inclusions into spherical inclusions. These inclusions do not deform or break during steel processing, thereby significantly improving the plasticity, toughness, and fatigue resistance of the steel products.

[0081] Further reference Figure 12 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a device for determining the composition-mass ratio of alumina inclusions. These device embodiments are similar to... Figure 2 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0082] like Figure 12 As shown, the alumina inclusion composition mass ratio determination apparatus 1200 in some embodiments includes: an acquisition unit 1201, a first determination unit 1202, and a second determination unit 1203. The acquisition unit 1201 is configured to acquire smelting data in response to the detection of alumina inclusions during the smelting process of calcium-treated steel; the first determination unit 1202 is configured to determine the shape factor of the alumina inclusions based on the smelting data; and the second determination unit 1203 is configured to determine the composition mass ratio of the alumina inclusions based on the shape factor and a predetermined composition mass ratio prediction formula.

[0083] In some optional implementations of some embodiments, the smelting data includes: shape change data of the alumina inclusions and size change data of the alumina inclusions.

[0084] In some optional implementations of some embodiments, the first determining unit is further configured to: determine the shape factor based on the shape change data, the size change data, and the following formula:

[0085] Wherein, OR represents the shape factor of the alumina inclusion, with a value range of 0 to 1, AR represents the aspect ratio of the alumina inclusion, C represents the convexity of the alumina inclusion, and S represents the sphericity of the alumina inclusion.

[0086] In some optional implementations of certain embodiments, the second determining unit is further configured to: determine the component mass ratio based on the following component mass ratio prediction formula and the above-mentioned shape factor:

[0087] (CaO / Al2O3)=2.119·OR-1.667(R 2 =0.99) where CaO / Al2O3 represents the mass ratio of calcium oxide to aluminum oxide in the above alumina inclusions, R 2 This indicates the degree of linear correlation between the mass ratio of calcium oxide and alumina in alumina inclusion particles and the shape factor.

[0088] In some optional implementations of certain embodiments, the above-mentioned composition mass ratio prediction formula is obtained according to the following steps: obtaining sample experimental data of calcium modification of alumina inclusions in molten steel, the sample experimental data including sample shape factors and sample composition mass ratios corresponding to the sample shape factors; fitting the correspondence between the sample composition mass ratios corresponding to the sample shape factors to obtain a prediction formula to be trained; determining the composition mass ratio based on the prediction formula to be trained and the sample shape factors; comparing the composition mass ratio with the sample composition mass ratio, and determining the loss value of the composition mass ratio prediction formula based on the comparison result; and determining the prediction formula to be trained as the composition mass ratio prediction formula in response to the loss value satisfying a preset condition.

[0089] In some optional implementations of some embodiments, the above-described apparatus further includes an adjustment unit configured to: adjust the corresponding parameters in the training prediction formula in response to the loss value not meeting a preset condition.

[0090] It is understandable that the units described in the device 1200 are related to the reference. Figure 2 The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 1200 and the units contained therein, and will not be repeated here.

[0091] One embodiment of the above-described embodiments of this disclosure has the following beneficial effects: First, in response to the detection of alumina inclusions during the smelting process of calcium-treated steel, smelting data is acquired. Then, based on the obtained smelting data, the shape factor of the alumina inclusions is determined. Next, according to the shape factor and a predetermined composition-mass ratio prediction formula, the composition-mass ratio of the alumina inclusions is determined. This correlates the shape factor with the composition-mass ratio of the alumina inclusions, effectively improving the efficiency and effectiveness of the calcium treatment process, reducing the cost of experimental testing, and facilitating the modification of alumina inclusions into spherical inclusions. These inclusions do not deform or break during steel processing, thereby significantly improving the plasticity, toughness, and fatigue resistance of the steel products.

[0092] The following is for reference. Figure 13 It illustrates electronic devices suitable for implementing some embodiments of this disclosure (e.g., Figure 1 The structural diagram of the server (1300) in the middle. Figure 13 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0093] like Figure 13 As shown, the electronic device 1300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 1301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1302 or a program loaded from a storage device 1308 into a random access memory (RAM) 1303. The RAM 1303 also stores various programs and data required for the operation of the electronic device 1300. The processing unit 1301, ROM 1302, and RAM 1303 are interconnected via a bus 1304. An input / output (I / O) interface 1305 is also connected to the bus 1304.

[0094] Typically, the following devices can be connected to I / O interface 1305: input devices 1306 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 1307 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; and communication devices 1309. Communication device 1309 allows electronic device 1300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 13 An electronic device 1300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 13 Each box shown can represent a device or multiple devices as needed.

[0095] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 1309, or installed from storage device 1308, or installed from ROM 1302. When the computer program is executed by processing device 1301, it performs the functions defined above in the methods of some embodiments of this disclosure.

[0096] It should be noted that, in some embodiments of this disclosure, the computer-readable medium described above may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0097] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0098] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquire smelting data in response to determining that alumina inclusions are detected during the smelting process of calcium-treated steel; determine the shape factor of the alumina inclusions based on the smelting data; and determine the composition-mass ratio of the alumina inclusions based on the shape factor and a predetermined composition-mass ratio prediction formula.

[0099] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0100] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0101] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including an acquisition unit, a first determining unit, and a second determining unit. The names of these units do not necessarily limit the specific unit itself; for example, a receiving unit may also be described as "a unit that acquires smelting data in response to the detection of alumina inclusions during the smelting process of calcium-treated steel."

[0102] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0103] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for determining the mass ratio of components in alumina inclusions, comprising: In response to the detection of alumina inclusions during the smelting process of calcium-treated steel, smelting data is acquired; The shape factor of the alumina inclusions is determined based on the smelting data. The composition mass ratio of the alumina inclusions is determined based on the shape factor and a predetermined composition mass ratio prediction formula. The smelting data includes: The shape change data and the size change data of the alumina inclusions; Determining the shape factor of the alumina inclusions based on the smelting data includes: The shape factor is determined based on the shape change data, the size change data, and the following formula: ， in, OR The shape factor of the alumina inclusions ranges from 0 to 1. AR The aspect ratio of the alumina inclusion is represented by C, the convexity of the alumina inclusion is represented by S, and the sphericity of the alumina inclusion is represented by S. The step of determining the composition mass ratio of the alumina inclusions based on the shape factor and a predetermined composition mass ratio prediction formula includes: The component mass ratio is determined according to the following component mass ratio prediction formula and the shape factor: ， Wherein, CaO / Al2O3 represents the mass ratio of calcium oxide to aluminum oxide in the alumina inclusions, and R 2 This indicates the degree of linear correlation between the mass ratio of calcium oxide and alumina in alumina inclusion particles and the shape factor; The formula for predicting the mass ratio of the components is obtained based on the following steps: Obtain sample experimental data on calcium modification of alumina inclusions in molten steel. The sample experimental data includes the sample shape factor and the mass ratio of the sample components corresponding to the sample shape factor. The corresponding relationship between the sample shape factor and the sample component mass ratio is fitted to obtain the prediction formula to be trained; The component quality ratio is determined based on the prediction formula to be trained and the sample shape factor. The component mass ratio is compared with the sample component mass ratio, and the loss value of the component mass ratio prediction formula is determined based on the comparison result; In response to the loss value satisfying a preset condition, the prediction formula to be trained is determined as the component mass ratio prediction formula; The method further includes: In response to the loss value not meeting the preset conditions, the corresponding parameters in the prediction formula to be trained are adjusted.

2. A device for determining the mass ratio of alumina inclusions, implementing the method as described in claim 1, comprising: The acquisition unit is configured to acquire smelting data in response to the detection of alumina inclusions during the smelting process of calcium-treated steel. The first determining unit is configured to determine the shape factor of the alumina inclusions based on the smelting data. The second determining unit is configured to determine the composition mass ratio of the alumina inclusions based on the shape factor and a predetermined composition mass ratio prediction formula.

3. The apparatus according to claim 2, wherein, The second determining unit is further configured as follows: The component mass ratio is determined according to the following component mass ratio prediction formula and the shape factor: ， Wherein, CaO / Al2O3 represents the mass ratio of calcium oxide to aluminum oxide in the alumina inclusions, and R 2 This indicates the degree of linear correlation between the mass ratio of calcium oxide and alumina in alumina inclusion particles and the shape factor.

4. An electronic device, comprising: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in claim 1.

5. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in claim 1.

Images