Prediction method and device for scrap steel melting process based on VIS-NIR spectroscopy

Abstract

The present invention provides a method and device for predicting the melting process of scrap steel based on VIS-NIR spectroscopy, and relates to the technical field of steelmaking. The method comprises: obtaining historical VIS-NIR spectral data in the molten pool during the converter steelmaking process of historical furnaces to construct a calculation model; obtaining first VIS-NIR spectral data, scrap steel ratio, and molten iron temperature of the furnace to be predicted; obtaining second VIS-NIR spectral data after a preset time interval, obtaining the proportion coefficient of the liquid phase component in the molten pool based on the calculation model, scrap steel ratio, molten iron temperature, and second VIS-NIR spectral data, and obtaining a prediction result of the scrap steel melting process in the molten pool based on the proportion coefficient of the liquid phase component in the molten pool. The present invention can realize the analysis and prediction of the scrap steel melting process in the converter steelmaking process, and provides a new method for real-time online monitoring of scrap steel melting for steelmaking production.

Description

Technical Field

[0001] The present invention relates to the technical field of steelmaking, and in particular to a method and device for predicting the melting process of scrap steel based on VIS-NIR spectroscopy. Background Art

[0002] Scrap steel is a key raw material for the modern steel industry and a vital ferrous resource for energy conservation and environmental protection. Vigorously promoting the efficient utilization of scrap steel not only meets the strategic needs of my country's steel industry's transformation and upgrading and low-carbon, green development, but also represents an effective way to reduce my country's dependence on foreign iron ore and safeguard its ferrous resources. Currently, converter smelting with a high scrap ratio is a key method for consuming large quantities of scrap steel. However, increasing the scrap ratio fed into the furnace can lead to problems such as difficulty controlling the composition of the tapped steel and excessively low tapping temperatures, seriously impacting the efficient and stable operation of the converter. Therefore, rapid scrap melting and the prediction and monitoring of the melting process have become application bottlenecks that urgently need to be overcome in current converter high scrap ratio smelting technology.

[0003] Currently, researchers have studied the melting behavior of scrap steel and established scrap steel melting models under different conditions based on moving boundary layer theory and experimental data. However, existing reports mainly use rotating scrap steel rods to generate forced convection, and the conclusions drawn are difficult to accurately guide actual production. Summary of the Invention

[0004] In order to solve the technical problem of how to predict and monitor the rapid melting and melting process of scrap steel, the present invention provides a method and device for predicting the melting process of scrap steel based on VIS-NIR spectroscopy. The technical solution is as follows:

[0005] In one aspect, a method for predicting the melting progress of scrap steel based on VIS-NIR spectroscopy is provided. The method is implemented by a device for predicting the melting progress of scrap steel based on VIS-NIR spectroscopy. The method comprises:

[0006] S1. Obtain historical VIS-NIR spectral data of the molten pool during the converter steelmaking process of historical heats, and construct a calculation model based on the historical VIS-NIR spectral data.

[0007] S2. Acquire first VIS-NIR spectrum data, scrap ratio, and molten iron temperature in the molten pool during the converter steelmaking process of the heat to be predicted.

[0008] S3. Acquire second VIS-NIR spectrum data after a preset time interval, and determine whether the second VIS-NIR spectrum data is smaller than the first VIS-NIR spectrum data.

[0009] If yes, proceed to step S4.

[0010] If not, the prediction ends.

[0011] S4. Obtain a proportion coefficient of the liquid phase component in the molten pool according to the calculation model, scrap steel ratio, molten iron temperature, and the second VIS-NIR spectrum data, and obtain a prediction result of the scrap steel melting process in the molten pool according to the proportion coefficient of the liquid phase component in the molten pool.

[0012] Optionally, S1 acquires historical VIS-NIR spectral data in the molten pool during converter steelmaking of historical heats, and constructs a calculation model based on the historical VIS-NIR spectral data, including:

[0013] The historical VIS-NIR spectral data in the molten pool during the converter steelmaking process of historical heats are continuously collected by a spectrum acquisition probe installed inside the oxygen nozzle.

[0014] A calculation model is constructed based on historical VIS-NIR spectral data, as well as the scrap steel ratio and molten iron temperature of the furnace corresponding to the historical VIS-NIR spectral data.

[0015] Optionally, S4 obtains a proportion coefficient of a liquid phase component in the molten pool according to the calculation model, the scrap steel ratio, the molten iron temperature, and the second VIS-NIR spectrum data, and obtains a prediction result of the scrap steel melting process in the molten pool according to the proportion coefficient of the liquid phase component in the molten pool, including:

[0016] S41. According to the calculation model, scrap steel ratio and molten iron temperature, obtain the predicted spectrometer measurement signal at the end moment of solid-to-liquid phase transformation, and calculate the spectrometer measurement signal at the start moment of solid-to-liquid phase transformation based on the predicted spectrometer measurement signal at the end moment of solid-to-liquid phase transformation.

[0017] S42. Calculate the proportion coefficient of the liquid phase component in the molten pool based on the spectrometer measurement signal at the predicted end point of the solid-to-liquid phase transformation, the spectrometer measurement signal at the start point of the solid-to-liquid phase transformation, the second VIS-NIR spectrum data, and the relationship function.

[0018] S43. Obtain a prediction result of the scrap steel melting process in the molten pool according to the proportion coefficient of the liquid phase components in the molten pool.

[0019] Optionally, calculating the spectrometer measurement signal at the start time of the solid-to-liquid phase transformation based on the predicted spectrometer measurement signal at the end time of the solid-to-liquid phase transformation in S41 includes:

[0020] The spectrometer measurement signal at the end point of the historical solid-to-liquid phase transformation and the spectrometer measurement signal at the start point of the historical solid-to-liquid phase transformation are obtained, and the corresponding relationship between the spectrometer measurement signal at the end point of the solid-to-liquid phase transformation and the spectrometer measurement signal at the start point of the solid-to-liquid phase transformation is established.

[0021] According to the predicted spectrometer measurement signal at the end point of the solid-to-liquid phase transformation and the corresponding relationship, the spectrometer measurement signal at the start point of the solid-to-liquid phase transformation is calculated.

[0022] Optionally, the correspondence between the spectrometer measurement signal at the end point of the solid-to-liquid phase transformation and the spectrometer measurement signal at the start point of the solid-to-liquid phase transformation includes:

[0023] For the melting process on the molten pool surface, the ratio of the spectrometer measurement signal at the start of the solid-to-liquid phase transformation to the spectrometer measurement signal at the end of the solid-to-liquid phase transformation is between 1.05 and 1.15.

[0024] For the melting process inside the molten pool, the ratio of the spectrometer measurement signal at the start of the solid-to-liquid phase transformation to the spectrometer measurement signal at the end of the solid-to-liquid phase transformation is between 1.25 and 1.5.

[0025] Optionally, the relationship function in S42 is as shown in the following formula (1):

[0026] (1)

[0027] Where, It represents the proportion of liquid phase in the molten pool at any time. It represents the proportion of liquid phase inside the molten pool at the initial moment, The spectrometer measurement signal indicates the moment when the solid phase inside the molten pool begins to transform into the liquid phase. represents the spectrometer measurement signal at any time, The spectrometer measurement signal indicates the end point of the transformation from solid phase to liquid phase inside the molten pool.

[0028] Optionally, the method further comprises:

[0029] Acquire third VIS-NIR spectrum data after a preset time interval, and determine whether the third VIS-NIR spectrum data is smaller than the second VIS-NIR spectrum data.

[0030] If so, the proportion coefficient of the liquid phase component in the molten pool is obtained according to the calculation model, scrap steel ratio and molten iron temperature, and the prediction result of the scrap steel melting process in the molten pool is obtained according to the proportion coefficient of the liquid phase component in the molten pool.

[0031] If not, the calculation model is optimized according to the third VIS-NIR spectrum data, scrap steel ratio and molten iron temperature.

[0032] On the other hand, a device for predicting the melting process of scrap steel based on VIS-NIR spectroscopy is provided. The device is applied to a method for predicting the melting process of scrap steel based on VIS-NIR spectroscopy. The device includes: a top-blown oxygen gun with an embedded spectral acquisition probe, an optical fiber, a spectrometer, a computer, and a database.

[0033] The top-blown oxygen lance with an embedded spectrum acquisition probe is used to collect historical VIS-NIR spectrum data in the molten pool during the converter steelmaking process of historical heats, the first VIS-NIR spectrum data in the molten pool during the converter steelmaking process of the heat to be predicted, and the second VIS-NIR spectrum data after a preset time interval.

[0034] Optical fiber is used to transmit the spectral data collected by the spectrum acquisition probe to the spectrometer.

[0035] Spectrometer, used to convert optical signals into electrical signals and output them to a computer.

[0036] The computer is used to build a calculation model according to the historical VIS-NIR spectrum data and determine whether the second VIS-NIR spectrum data is smaller than the first VIS-NIR spectrum data.

[0037] If so, the proportion coefficient of the liquid phase component in the molten pool is obtained based on the calculation model, scrap steel ratio, molten iron temperature and the second VIS-NIR spectrum data, and the prediction result of the scrap steel melting process in the molten pool is obtained based on the proportion coefficient of the liquid phase component in the molten pool.

[0038] If not, the prediction ends.

[0039] Database, used for data storage.

[0040] Optionally, historical VIS-NIR spectral data of the molten pool during converter steelmaking processes of historical heats are obtained, and a calculation model is constructed based on the historical VIS-NIR spectral data, including:

[0041] The historical VIS-NIR spectral data in the molten pool during the converter steelmaking process of historical heats are continuously collected by a spectrum acquisition probe installed inside the oxygen nozzle.

[0042] A calculation model is constructed based on historical VIS-NIR spectral data, as well as the scrap steel ratio and molten iron temperature of the furnace corresponding to the historical VIS-NIR spectral data.

[0043] Optionally, a ratio coefficient of a liquid phase component in the molten pool is obtained based on the calculation model, the scrap steel ratio, the molten iron temperature, and the second VIS-NIR spectrum data, and a prediction result of the scrap steel melting process in the molten pool is obtained based on the ratio coefficient of the liquid phase component in the molten pool, including:

[0044] S41. According to the calculation model, scrap steel ratio and molten iron temperature, obtain the predicted spectrometer measurement signal at the end moment of solid-to-liquid phase transformation, and calculate the spectrometer measurement signal at the start moment of solid-to-liquid phase transformation based on the predicted spectrometer measurement signal at the end moment of solid-to-liquid phase transformation.

[0045] S42. Calculate the proportion coefficient of the liquid phase component in the molten pool based on the spectrometer measurement signal at the predicted end point of the solid-to-liquid phase transformation, the spectrometer measurement signal at the start point of the solid-to-liquid phase transformation, the second VIS-NIR spectrum data, and the relationship function.

[0046] S43. Obtain a prediction result of the scrap steel melting process in the molten pool according to the proportion coefficient of the liquid phase components in the molten pool.

[0047] Optionally, calculating the spectrometer measurement signal at the start time of the solid-to-liquid phase transformation based on the predicted spectrometer measurement signal at the end time of the solid-to-liquid phase transformation includes:

[0048] The spectrometer measurement signal at the end point of the historical solid-to-liquid phase transformation and the spectrometer measurement signal at the start point of the historical solid-to-liquid phase transformation are obtained, and the corresponding relationship between the spectrometer measurement signal at the end point of the solid-to-liquid phase transformation and the spectrometer measurement signal at the start point of the solid-to-liquid phase transformation is established.

[0049] According to the predicted spectrometer measurement signal at the end point of the solid-to-liquid phase transformation and the corresponding relationship, the spectrometer measurement signal at the start point of the solid-to-liquid phase transformation is calculated.

[0050] Optionally, the correspondence between the spectrometer measurement signal at the end point of the solid-to-liquid phase transformation and the spectrometer measurement signal at the start point of the solid-to-liquid phase transformation includes:

[0051] For the melting process on the molten pool surface, the ratio of the spectrometer measurement signal at the start of the solid-to-liquid phase transformation to the spectrometer measurement signal at the end of the solid-to-liquid phase transformation is between 1.05 and 1.15.

[0052] For the melting process inside the molten pool, the ratio of the spectrometer measurement signal at the start of the solid-to-liquid phase transformation to the spectrometer measurement signal at the end of the solid-to-liquid phase transformation is between 1.25 and 1.5.

[0053] Optionally, the relationship function is as shown in the following formula (1):

[0054] (1)

[0055] Where, It represents the proportion of liquid phase in the molten pool at any time. It represents the proportion of liquid phase inside the molten pool at the initial moment, The spectrometer measurement signal indicates the moment when the solid phase inside the molten pool begins to transform into the liquid phase. represents the spectrometer measurement signal at any time, The spectrometer measurement signal indicates the end point of the transformation from solid phase to liquid phase inside the molten pool.

[0056] Optionally, the computer is further configured to:

[0057] Acquire third VIS-NIR spectrum data after a preset time interval, and determine whether the third VIS-NIR spectrum data is smaller than the second VIS-NIR spectrum data.

[0058] If so, the proportion coefficient of the liquid phase component in the molten pool is obtained according to the calculation model, scrap steel ratio and molten iron temperature, and the prediction result of the scrap steel melting process in the molten pool is obtained according to the proportion coefficient of the liquid phase component in the molten pool.

[0059] If not, the calculation model is optimized according to the third VIS-NIR spectrum data, scrap steel ratio and molten iron temperature.

[0060] On the other hand, a device for predicting the melting process of scrap steel based on VIS-NIR spectroscopy is provided. The device for predicting the melting process of scrap steel based on VIS-NIR spectroscopy comprises: a processor; and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, any one of the above-mentioned methods for predicting the melting process of scrap steel based on VIS-NIR spectroscopy is implemented.

[0061] On the other hand, a computer-readable storage medium is provided, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned methods for predicting the scrap steel melting process based on VIS-NIR spectroscopy.

[0062] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0063] The present invention provides a new prediction method and device for online monitoring of scrap steel melting process based on VIS-NIR spectroscopy, which can be applied to all converters using top-blown oxygen supply and has a capacity range of 30t to 400t.

[0064] Based on spectral analysis of the fire zone, this invention enables full-process monitoring and prediction of the converter steelmaking scrap melting process, thereby optimizing blowing operations, improving endpoint accuracy, and reducing smelting costs. Application of this invention can increase the prediction accuracy of scrap melting during converter steelmaking by 4-8%, improve the endpoint accuracy by 3-5%, reduce endpoint oxygen by 10-30 ppm, and save 2-4 kg / t of steel material, resulting in an overall economic benefit exceeding 2 yuan / t. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0066] Figure 1 This is a flow chart of a method for predicting scrap steel melting progress based on VIS-NIR spectroscopy provided by an embodiment of the present invention;

[0067] Figure 2 This is a flow chart of a prediction method for online monitoring of scrap steel melting progress based on VIS-NIR spectroscopy provided by an embodiment of the present invention;

[0068] Figure 3 This is a block diagram of a device for predicting scrap steel melting progress based on VIS-NIR spectroscopy provided by an embodiment of the present invention;

[0069] Figure 4 1 is a schematic structural diagram of a device for predicting scrap steel melting progress based on VIS-NIR spectroscopy provided by an embodiment of the present invention;

[0070] In the figure, 1. Top-blown oxygen lance with embedded spectrum acquisition probe; 2. Optical fiber; 3. Spectrometer; 4. Computer; 5. Database. DETAILED DESCRIPTION

[0071] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0072] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0073] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0074] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0075] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0076] The embodiment of the present invention provides a method for predicting the melting process of scrap steel based on VIS-NIR spectroscopy. The method can be implemented by a prediction device for the melting process of scrap steel based on VIS-NIR spectroscopy. The prediction device for the melting process of scrap steel based on VIS-NIR spectroscopy can be a terminal or a server. Figure 1 、 2 The flowchart of the method for predicting scrap steel melting progress based on VIS-NIR spectroscopy is shown. The processing flow of the method may include the following steps:

[0077] S1. Obtain historical VIS-NIR spectral data of the molten pool during the converter steelmaking process of historical heats, and construct a calculation model based on the historical VIS-NIR spectral data.

[0078] Optionally, the above step S1 may include the following steps S11-S12:

[0079] S11. Continuously collect historical VIS-NIR spectral data in the molten pool during the converter steelmaking process of historical heats using a spectrum acquisition probe installed inside the oxygen nozzle.

[0080] S12. Construct a calculation model based on historical VIS-NIR spectral data, and the scrap steel ratio and molten iron temperature of the furnace corresponding to the historical VIS-NIR spectral data.

[0081] In one feasible implementation, a spectrum acquisition probe is installed inside the oxygen nozzle, and the VIS-NIR (Visible-Near Infrared) spectrum in the molten pool of the converter steelmaking process is continuously collected through an optical path formed by the oxygen lance nozzle and the oxygen jet.

[0082] Among them, the signal measured by the spectrometer is composed of the luminescence spectrum signal of the sample itself, the environmental radiation signal and the radiation signal of the spectrometer itself, and the final spectral data is obtained through the spectrometer response function.

[0083] Specifically, the spectrometer is the core component of thermal emission spectroscopy measurement. When using it for thermal emission measurement, the contribution ratio of the sample and the instrument environment radiation in the acquired signal varies significantly due to differences in experimental conditions, so the corresponding measurement and calibration methods also vary.

[0084] Emissivity is an inherent property of matter and is defined as the amount of energy emitted by a surface as heat ( ) is equal to the energy emitted by the black body surface at the same temperature ( ), that is:

[0085] (1)

[0086] Where, is the emissivity, is the wavelength, is the radiant energy (radiance), Theoretically, we only need to obtain the DN (Digital Number) values ​​corresponding to the sample and blackbody radiation energy at the same temperature to obtain the thermal emissivity (same as emissivity) of the sample, that is:

[0087] (2)

[0088] Where, and The DN value of the sample and blackbody is the true value, excluding instrument and environmental radiation. However, in actual measurements, the contribution of radiation from the spectrometer itself and the environment inside the sample cavity cannot be ignored.

[0089] When performing thermal emission measurements, the spectrometer's measurement signal It consists of two parts: one is the radiation energy of the sample , and the other part is the energy radiated from the spectrometer itself Of the energy radiated by the spectrometer, only the part modulated by the interferometer will affect the measurement result. The signal that has not been modulated by the interferometer appears as a constant in the measured interference pattern and will not affect the final result after Fourier transform. The energy radiated by the spectrometer in the measurement signal mainly comes from the radiation of the detector itself, and the signal measured by the spectrometer is actually the difference between the sample and detector radiation signals, that is:

[0090] (3)

[0091] Where, is the signal measured by the instrument, dimensionless; and are the energy and radiance of the sample and detector radiation, respectively; is the instrument response function. The energy from the sample It is usually composed of two parts. In addition to the radiation energy of the sample itself, the light radiated from the environment inside the sample cavity will also enter the detector after being reflected by the sample surface. There are:

[0092] (4)

[0093] Where, is the emissivity, is the sample emissivity, is the energy (radiance) of blackbody radiation at a given temperature, is the reflectivity, is the sample reflectance, is the temperature, is the temperature of the sample, is the energy radiated from the internal environment of the sample cavity to the sample surface, is the ambient temperature inside the sample chamber. Equations (3) and (4) are widely used in the processing and analysis of thermal emission measurement data.

[0094] During the phase change process, the luminescence spectrum signal of the sample itself in the molten pool mainly comes from two parts. One is the thermal radiation spectrum signal on the surface of the molten pool, and the other is the thermal radiation signal inside the molten pool after being absorbed by the molten pool surface.

[0095] The calculation of the luminescence spectrum signal of the sample itself in the molten pool can be simplified to the following formula (5):

[0096] (5)

[0097] Where, 、 are the energies of the solid and liquid phase radiation on the sample surface, respectively; 、 are the energies of radiation from the solid phase and liquid phase inside the sample, respectively; is the transmittance of internal radiation energy through the molten pool surface, which can be approximated as:

[0098] (6)

[0099] Where, 、 are the transmittances of the energy radiated from the inside of the molten pool through the solid phase and liquid phase on the surface of the molten pool, respectively; 、 and are the areas occupied by the solid and liquid phases on the molten pool surface and the total area of ​​the molten pool surface, respectively.

[0100] Since the spectral signal measured by the spectrometer mainly comes from the normal direction of the sample, and the radiation signals in other directions of the sample are difficult to be measured by the spectrometer, the proportion of the solid phase to the liquid phase in the molten pool can be simplified as the ratio of the cross-sectional areas of each system of the sample in that direction.

[0101] The transmittance of the radiation energy inside the sample through the sample surface can be estimated by the ratio of the solid and liquid phases on the molten pool surface and the transmittance of the radiation energy inside the molten pool through the solid and liquid phases on the molten pool surface (as shown in Equation 6). It can be approximated to zero. Substituting the simplified formula (6) into formula (5) yields the following general formula:

[0102] (7)

[0103] The ambient temperature of the spectrometer during the measurement process is generally constant, so the radiation energy of the spectrometer can be eliminated by the difference method. The impact of

[0104] (8)

[0105] If the surface and interior of the sample are each considered as a system, the radiation energy of the liquid or solid phase can be simplified as a function of the area occupied by the solid and liquid phases. Combining formula (1), we can obtain:

[0106] (9)

[0107] and,

[0108] (10)

[0109] Where, 、 and are the areas occupied by the solid and liquid phases inside the molten pool and the total area inside the molten pool, respectively. 、 are the emissivities of the molten pool surface and the solid phase inside, respectively.

[0110] To eliminate the spectrometer response function The influence of , let:

[0111] (11)

[0112] Where, The spectrometer measurement signal at any moment of the solid-to-liquid phase transition, The spectrometer measurement signal is the moment when the solid phase transforms to the liquid phase during the melting process (on the surface of the molten pool or inside the molten pool). It is the spectrometer measurement signal at the end point of the transformation from solid phase to liquid phase during the melting process (on the surface of the molten pool or inside the molten pool).

[0113] Crystals have a definite spatial lattice structure. When heat is provided externally, the energy is first used to overcome the interaction forces between atoms or molecules, causing them to transform from an ordered solid state to a disordered liquid state. During this transformation process, the system is in a "latent heat" state, that is, it absorbs heat but does not increase the average kinetic energy of the molecules, so the temperature does not rise. Until all the lattice structures are destroyed, that is, all the solids are converted into liquids, continued heating will cause the kinetic energy of the liquid particles to increase, thereby causing the temperature to rise, and vice versa. Therefore, in the process of crystal melting or solidification, despite the coexistence of solid and liquid, the temperature of the crystal will remain at a fixed melting point until it is completely melted. Therefore, during the solid-liquid phase transition process, the effect of temperature changes on the radiation energy of the sample can be ignored. Then:

[0114] (12)

[0115] For the melting process, if the measurement signal at the beginning of the transformation from solid phase to liquid phase is used as the reference value, , , then the signal measured by the instrument is:

[0116] (13)

[0117] For the melting process, the end point of the transformation from solid phase to liquid phase is , , , then the signal measured by the instrument is:

[0118] (14)

[0119] During the melting process, due to the influence of the phase change "latent heat", the change of the molten pool temperature during the scrap melting process can be further ignored. Under these conditions, the ambient radiation at any two moments during the melting process can be approximately equal. Because solid iron is denser than liquid iron, the liquid iron produced during the solid-liquid phase transition will reach the surface of the melt pool. This means that liquid iron will only form within the melt pool after the surface has completely transformed into liquid. Therefore, in the early stages of the melting process, it can be assumed that no phase transition has occurred within the melt pool, meaning that the radiation energy within the melt pool is constant.

[0120] Then formula (11) can be simplified as:

[0121] (15)

[0122] Where, is the spectrometer measurement signal at the initial moment of transformation from solid phase to liquid phase on the surface of the molten pool, is the spectrometer measurement signal at the end point of the transformation from solid phase to liquid phase on the molten pool surface, The spectrometer measurement signal at any moment when the solid phase on the molten pool surface transforms into the liquid phase is is the proportion of solid phase on the surface of the molten pool at any time, is the proportion of liquid phase on the surface of the molten pool at any time.

[0123] make , , and ignoring the influence of radiation inside the molten pool, we have:

[0124] (16)

[0125] As the melting of the metallic iron continues to absorb heat, the temperature inside the molten pool will drop. Therefore, after the surface of the molten pool has finished melting, the temperature inside the molten pool will briefly rise. During this process, the radiation energy from the surface of the molten pool can be considered constant, while the rising temperature inside the molten pool will cause the radiation energy from the inside of the molten pool to continue to increase until the inside of the molten pool begins to melt.

[0126] For the melting process inside the molten pool, the radiation energy on the surface of the molten pool can be considered constant, and the radiation energy in the furnace after reflection from the surface of the molten pool is also constant. If the start of melting inside the molten pool is taken as the initial state, and the complete conversion of metallic iron into liquid phase is taken as the end state, then Equation (11) can be simplified to:

[0127] (17)

[0128] Where, is the spectrometer measurement signal at the initial moment of transformation from solid phase to liquid phase inside the molten pool. The spectrometer measurement signal at the end point of the transformation from solid phase to liquid phase inside the molten pool, The spectrometer measurement signal at any moment when the solid phase inside the molten pool transforms into the liquid phase, is the proportion of the solid phase inside the molten pool at any time, is the proportion of liquid phase inside the molten pool at any time.

[0129] make , , and ignoring the influence of radiation inside the molten pool, we have:

[0130] (18)

[0131] In summary, the online measurement of the VIS-NIR spectrum in the molten pool at different times can be used to calculate the proportion coefficient of the liquid phase system in the molten pool, that is, the scrap steel melting process.

[0132] At present, in the traditional process of domestic converter smelting, scrap steel accounts for about 10% to 25%. Therefore, in the converter smelting process, the melting of scrap steel will only go through the internal melting stage. Due to the difference in the amount of scrap steel added, the VIS-NIR spectrum obtained by the initial measurement is not the initial state of the internal melting process, but the spectral characteristics corresponding to the amount of scrap steel added and the molten iron temperature. Therefore, for this process, and The value of is unknown and needs to be determined by and The relationship between the molten iron temperature and the scrap ratio is further obtained. In addition, in the actual production process, due to the difference in the amount of scrap added and the molten iron temperature, the ratio of each heat and There are some differences in the value of and The value of is corrected. That is:

[0133] (19)

[0134] Where, Indicates the temperature of molten iron, Indicates the scrap steel ratio.

[0135] The establishment of the calculation model needs to be based on actual production conditions. In the early stage of VIS-NIR spectrum measurement, the calculation model is trained by continuously collecting VIS-NIR spectra in the molten pool. The specific operation process is as follows:

[0136] a) The experimental measurement results show that the luminescence intensity of the VIS-NIR spectrum of solid-phase metallic iron is significantly higher than that of liquid-phase metallic iron at the same temperature, and the luminescence intensity of the VIS-NIR spectrum will increase significantly as the temperature rises. Therefore, as the scrap steel in the molten pool melts, the luminescence intensity of the VIS-NIR spectrum will gradually decrease until the scrap steel melts completely. When the scrap steel melts completely, the molten pool enters a rapid temperature rise stage, during which the luminescence intensity of the VIS-NIR spectrum will gradually increase. The minimum value of the luminescence intensity of the VIS-NIR spectrum measured during this process is approximately equal to .

[0137] b) Through the collection The calculation model is trained by comparing the scrap steel ratio and molten iron temperature of the corresponding furnace, and the trained calculation model is used to predict the and Value. Among them, and The ratio is between 1.25 and 1.5.

[0138] The present invention proposes a prediction method for online monitoring of scrap steel melting progress based on VIS-NIR spectroscopy. By measuring and analyzing the VIS-NIR spectrum in the molten pool in actual production, the melting progress of scrap steel in the furnace can be predicted in real time.

[0139] In order to predict the melting process of scrap steel in the molten pool, the collected VIS-NIR spectral data needs to be processed. The specific operation logic is as follows: Steps S2-S4:

[0140] S2. Acquire first VIS-NIR spectrum data, scrap ratio, and molten iron temperature in the molten pool during the converter steelmaking process of the heat to be predicted.

[0141] In a feasible implementation method, after the converter body is shaken straight, the VIS-NIR spectrum in the furnace is collected (3 consecutive times, each time with an interval of 1ms, and the average value of the three collected data is taken as the effective measurement value), and the molten iron temperature and scrap steel ratio of this furnace are loaded.

[0142] S3. Acquire second VIS-NIR spectrum data after a preset time interval, and determine whether the second VIS-NIR spectrum data is smaller than the first VIS-NIR spectrum data.

[0143] If yes, proceed to step S4.

[0144] If not, the prediction ends.

[0145] In one feasible implementation, after a 1-second interval, the VIS-NIR spectrum of the furnace is collected again. The intensities of the two VIS-NIR spectra are compared. If the intensity of the newly collected spectrum is less than the intensity of the previously collected spectrum, the calculation proceeds to the next step. If the intensity of the newly collected spectrum is greater than the intensity of the previously collected spectrum, the calculation ends.

[0146] S4. Obtain a proportion coefficient of the liquid phase component in the molten pool according to the calculation model, scrap steel ratio, molten iron temperature, and the second VIS-NIR spectrum data, and obtain a prediction result of the scrap steel melting process in the molten pool according to the proportion coefficient of the liquid phase component in the molten pool.

[0147] Optionally, the above step S4 may include the following steps S41-S43:

[0148] S41. Obtain the spectrometer measurement signal at the predicted end point of solid-to-liquid phase transformation based on the calculation model, scrap ratio, and molten iron temperature. , based on the spectrometer measurement signal at the predicted end point of solid-to-liquid transformation Calculate the spectrometer measurement signal at the start of the solid-to-liquid phase transition .

[0149] Optionally, the spectrometer measurement signal at the predicted end point of the solid-to-liquid phase transformation in S41 is Calculate the spectrometer measurement signal at the start of the solid-to-liquid phase transition ,include:

[0150] S411, obtaining the spectrometer measurement signal at the end point of the historical solid-to-liquid phase transformation And the spectrometer measurement signal at the beginning of the historical solid-to-liquid phase transformation , construct the spectrometer measurement signal at the end point of solid-to-liquid phase transformation The spectrometer measurement signal at the beginning of the solid-liquid phase transition The corresponding relationship.

[0151] S412, based on the spectrometer measurement signal at the predicted end point of the solid phase to liquid phase transformation And the corresponding relationship, the spectrometer measurement signal at the beginning of the solid phase to liquid phase transformation is calculated .

[0152] S42, based on the spectrometer measurement signal at the predicted end point of the solid-to-liquid phase transformation , the spectrometer measurement signal at the beginning of the solid-to-liquid phase transformation , the second VIS-NIR spectrum data and the relationship function are used to calculate the proportion coefficient of the liquid phase component in the molten pool.

[0153] Optionally, the relationship function in S42 is as shown in the following formula (20):

[0154] (20)

[0155] Where, It represents the proportion of liquid phase in the molten pool at any time. It represents the proportion of liquid phase inside the molten pool at the initial moment, The spectrometer measurement signal indicates the moment when the solid phase inside the molten pool begins to transform into the liquid phase. represents the spectrometer measurement signal at any time, The spectrometer measurement signal indicates the end point of the transformation from solid phase to liquid phase inside the molten pool.

[0156] In a feasible implementation, the above calculation model can be used to calculate and The value, combined with the spectral intensity value collected this time, can be used to calculate the proportion R of the liquid phase system in the molten pool, and further calculate the melting process of the scrap steel.

[0157] S43. Obtain a prediction result of the scrap steel melting process in the molten pool according to the proportion coefficient of the liquid phase components in the molten pool.

[0158] Optionally, the method further comprises:

[0159] Acquire third VIS-NIR spectrum data after a preset time interval, and determine whether the third VIS-NIR spectrum data is smaller than the second VIS-NIR spectrum data.

[0160] If so, the proportion coefficient of the liquid phase component in the molten pool is obtained according to the calculation model, scrap steel ratio and molten iron temperature, and the prediction result of the scrap steel melting process in the molten pool is obtained according to the proportion coefficient of the liquid phase component in the molten pool.

[0161] If not, the calculation model is optimized according to the third VIS-NIR spectrum data, scrap steel ratio and molten iron temperature.

[0162] In a feasible implementation, after a time interval of 1s, the VIS-NIR spectrum in the furnace is collected again, and the magnitude relationship of the two VIS-NIR spectrum intensities is compared. If the spectrum intensity value collected this time is smaller than the spectrum intensity value collected previously, the melting process of the scrap steel is calculated. If the spectrum intensity value collected this time is larger than the spectrum intensity value collected previously, the loop is exited and the spectrum intensity value collected this time is used as the The value output is combined with the initial input molten iron temperature and scrap ratio of this furnace to re-optimize the calculation model.

[0163] Example 1, 100t steelmaking converter:

[0164] like Figure 2 As shown, first, the VIS-NIR spectrum in the molten pool during converter smelting is collected in situ to build a calculation model and establish The relationship function between molten iron temperature and scrap ratio is used for subsequent calculations.

[0165] After the calculation model is built, the scrap ratio and molten iron temperature of this heat are loaded to predict the heat value, and calculated based on experimental data Value (based on the actual situation on site, select ).

[0166] The VIS-NIR spectral data in the molten pool were continuously collected (at a time interval of 1 s) and combined with formula (20) to calculate the proportion coefficient of the liquid phase components in the molten pool. Furthermore, the melting process of the scrap steel in the molten pool was calculated by calculating the proportion coefficient of the liquid phase system in the molten pool.

[0167] Furthermore, when the intensity of the VIS-NIR spectrum collected is less than the intensity of the VIS-NIR spectrum collected previously, it means that the scrap steel in the molten pool has been melted and has entered the rapid heating stage. Therefore, the intensity of the spectrum collected in this stage is approximately equal to the intensity of the spectrum collected in this heat. value.

[0168] Furthermore, the The value, molten iron temperature and scrap ratio are taken into the model as new inputs for calculation to further optimize the model calculation accuracy.

[0169] The application of this method can improve the accuracy of scrap melting prediction during converter smelting by 4-6%, and this accuracy further increases with the number of smelting furnaces. The application of this method can also increase the converter endpoint hit rate by 3-4%, reduce endpoint oxygen by 10-25 ppm, and save 2-3.5 kg / t of steel material consumption, resulting in an overall economic benefit of more than 1.5 yuan / t.

[0170] Example 2, 300t dephosphorization converter:

[0171] The VIS-NIR spectrum in the converter is collected by a top-blown oxygen gun (1) with an embedded spectrum acquisition probe, and the spectrum signal is transmitted to the spectrometer (3) through an optical fiber (2) for spectroscopic processing. The spectrometer (3) converts the optical signal into an electrical signal and outputs it to the computer (4). The computer (4) calculates the spectrum according to the Figure 2 The data processing method shown in the figure calculates and analyzes the spectral signal and calculates the molten iron temperature, scrap ratio and The values ​​and other data are stored in the database (5) for training the computational model.

[0172] After the calculation model is built, the scrap ratio and molten iron temperature of this heat are loaded to predict the heat value, and calculated based on experimental data Value (based on the actual situation on site, select ).

[0173] The VIS-NIR spectral data in the molten pool were continuously collected (at a time interval of 1 s) and combined with formula (20) to calculate the proportion coefficient of the liquid phase components in the molten pool. Furthermore, the melting process of the scrap steel in the molten pool was calculated by calculating the proportion coefficient of the liquid phase system in the molten pool.

[0174] Furthermore, when the intensity of the VIS-NIR spectrum collected is less than the intensity of the VIS-NIR spectrum collected previously, it means that the scrap steel in the molten pool has been melted and has entered the rapid heating stage. Therefore, the intensity of the spectrum collected in this stage is approximately equal to the intensity of the spectrum collected in this heat. value.

[0175] Furthermore, the The value, molten iron temperature and scrap ratio are taken into the model as new inputs for calculation to further optimize the model calculation accuracy.

[0176] The application of this method can improve the accuracy of scrap melting prediction during converter smelting by 5-8%, and this accuracy further increases with the number of smelting furnaces. The application of this method can also increase the converter endpoint hit rate by 4-5%, reduce endpoint oxygen by 15-30 ppm, and save 3-4 kg / t of steel material consumption, resulting in an overall economic benefit of more than 2 yuan / t.

[0177] In the embodiment of the present invention, a new prediction method and device for online monitoring of scrap steel melting process based on VIS-NIR spectroscopy are provided, which can be applied to all converters using top-blown oxygen supply and have a suitable capacity range of 30t to 400t.

[0178] Based on spectral analysis of the fire zone, this invention enables full-process monitoring and prediction of the converter steelmaking scrap melting process, thereby optimizing blowing operations, improving endpoint accuracy, and reducing smelting costs. Application of this invention can increase the prediction accuracy of scrap melting during converter steelmaking by 4-8%, improve the endpoint accuracy by 3-5%, reduce endpoint oxygen by 10-30 ppm, and save 2-4 kg / t of steel material, resulting in an overall economic benefit exceeding 2 yuan / t.

[0179] Figure 3 This is a block diagram of a device for predicting the melting process of scrap steel based on VIS-NIR spectroscopy according to an exemplary embodiment. The device is used in a method for predicting the melting process of scrap steel based on VIS-NIR spectroscopy. Figure 3 The device includes a top-blown oxygen gun with an embedded spectrum acquisition probe, an optical fiber, a spectrometer, a computer and a database.

[0180] The spectral acquisition probe is installed inside the oxygen nozzle and is used to collect historical VIS-NIR spectral data in the molten pool during the converter steelmaking process of the previous heat, the first VIS-NIR spectral data in the molten pool during the converter steelmaking process of the heat to be predicted, and the second VIS-NIR spectral data after a preset time interval.

[0181] Optical fiber is used to transmit the spectral data collected by the spectrum acquisition probe to the spectrometer.

[0182] Spectrometer, used to convert optical signals into electrical signals and output them to a computer.

[0183] The computer is used to build a calculation model according to the historical VIS-NIR spectrum data and determine whether the second VIS-NIR spectrum data is smaller than the first VIS-NIR spectrum data.

[0184] If so, the proportion coefficient of the liquid phase component in the molten pool is obtained based on the calculation model, scrap steel ratio, molten iron temperature and the second VIS-NIR spectrum data, and the prediction result of the scrap steel melting process in the molten pool is obtained based on the proportion coefficient of the liquid phase component in the molten pool.

[0185] If not, the prediction ends.

[0186] Database, used for data storage.

[0187] In the embodiment of the present invention, a new prediction method and device for online monitoring of scrap steel melting process based on VIS-NIR spectroscopy are provided, which can be applied to all converters using top-blown oxygen supply and have a suitable capacity range of 30t to 400t.

[0188] Based on spectral analysis of the fire zone, this invention enables full-process monitoring and prediction of the converter steelmaking scrap melting process, thereby optimizing blowing operations, improving endpoint accuracy, and reducing smelting costs. Application of this invention can increase the prediction accuracy of scrap melting during converter steelmaking by 4-8%, improve the endpoint accuracy by 3-5%, reduce endpoint oxygen by 10-30 ppm, and save 2-4 kg / t of steel material, resulting in an overall economic benefit exceeding 2 yuan / t.

[0189] Figure 4 Schematic diagram of a device for predicting scrap steel melting process based on VIS-NIR spectroscopy provided by an embodiment of the present invention. Figure 4 As shown, the prediction device for scrap steel melting process based on VIS-NIR spectrum may include the above Figure 3 The device for predicting the melting progress of scrap steel based on VIS-NIR spectroscopy is shown. Optionally, the device for predicting the melting progress of scrap steel based on VIS-NIR spectroscopy 410 may include a first processor 2001 .

[0190] Optionally, the scrap steel melting progress prediction device 410 based on VIS-NIR spectroscopy may further include a memory 2002 and a transceiver 2003 .

[0191] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0192] The following combination Figure 4 The components of the scrap steel melting process prediction device 410 based on VIS-NIR spectroscopy are described in detail:

[0193] The first processor 2001 is the control center of the scrap steel melting process prediction device 410 based on VIS-NIR spectroscopy, and can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs) or one or more field programmable gate arrays (FPGAs).

[0194] Optionally, the first processor 2001 may execute various functions of the scrap steel melting progress prediction device 410 based on VIS-NIR spectroscopy by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0195] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 4 CPU0 and CPU1 are shown in FIG.

[0196] In a specific implementation, as an embodiment, the prediction device 410 for scrap steel melting process based on VIS-NIR spectroscopy may also include multiple processors, such as Figure 4 1 and 2. The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0197] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0198] Alternatively, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and capable of being accessed by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or exist independently and accessed through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0199] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0200] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 4 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0201] Optionally, the transceiver 2003 may be integrated with the first processor 2001 or may exist independently and communicate with the first processor 2001 through the interface circuit of the scrap steel melting process prediction device 410 based on VIS-NIR spectroscopy ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0202] It should be noted that Figure 4 The structure of the scrap steel melting process prediction device 410 based on VIS-NIR spectroscopy shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0203] In addition, the technical effects of the device 410 for predicting the scrap steel melting process based on VIS-NIR spectroscopy can refer to the technical effects of the method for predicting the scrap steel melting process based on VIS-NIR spectroscopy described in the above method embodiment, and will not be repeated here.

[0204] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0205] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0206] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0207] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0208] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0209] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0210] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0211] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0212] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0213] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0214] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0215] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical disks.

[0216] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for predicting scrap steel melting progress based on VIS-NIR spectroscopy, characterized in that: The method comprises: S1. Obtain historical VIS-NIR spectral data of the molten pool during converter steelmaking for historical heats, and construct a calculation model based on the historical VIS-NIR spectral data and the scrap steel ratio and molten iron temperature of the heats corresponding to the historical VIS-NIR spectral data; S2. Acquire first VIS-NIR spectrum data, scrap ratio, and molten iron temperature in the molten pool during the converter steelmaking process of the heat to be predicted; S3, obtaining second VIS-NIR spectrum data after a preset time interval, and determining whether the second VIS-NIR spectrum data is smaller than the first VIS-NIR spectrum data; If yes, proceed to step S4; If not, the prediction ends; S4. Determining a proportion coefficient of a liquid phase component in the molten pool according to the calculation model, the scrap steel ratio, the molten iron temperature, and the second VIS-NIR spectrum data, and obtaining a prediction result of the scrap steel melting progress in the molten pool according to the proportion coefficient of the liquid phase component in the molten pool; The step S4 of obtaining a proportion coefficient of a liquid phase component in the molten pool according to the calculation model, the scrap steel ratio, the molten iron temperature, and the second VIS-NIR spectrum data, and obtaining a prediction result of the scrap steel melting process in the molten pool according to the proportion coefficient of the liquid phase component in the molten pool, includes: S41. Obtaining a predicted spectrometer measurement signal at the end point of solid-to-liquid phase transformation based on the calculation model, the scrap steel ratio, and the molten iron temperature, and calculating a spectrometer measurement signal at the start point of solid-to-liquid phase transformation based on the predicted spectrometer measurement signal at the end point of solid-to-liquid phase transformation; S42, calculating a proportion coefficient of the liquid phase component in the molten pool based on the spectrometer measurement signal at the predicted end point of the solid-to-liquid phase transformation, the spectrometer measurement signal at the start point of the solid-to-liquid phase transformation, the second VIS-NIR spectrum data, and the relationship function; S43, obtaining a prediction result of the melting progress of the scrap steel in the molten pool according to the proportion coefficient of the liquid phase component in the molten pool; The step S41 of calculating the spectrometer measurement signal at the start time of the solid-to-liquid phase transformation based on the predicted spectrometer measurement signal at the end time of the solid-to-liquid phase transformation comprises: Acquire the spectrometer measurement signal at the end point of the historical solid-to-liquid phase transformation and the spectrometer measurement signal at the start point of the historical solid-to-liquid phase transformation, and establish a corresponding relationship between the spectrometer measurement signal at the end point of the solid-to-liquid phase transformation and the spectrometer measurement signal at the start point of the solid-to-liquid phase transformation; Calculating the spectrometer measurement signal at the start time of the solid-to-liquid phase transformation based on the predicted spectrometer measurement signal at the end time of the solid-to-liquid phase transformation and the corresponding relationship; The correspondence between the spectrometer measurement signal at the end point of the solid-to-liquid phase transformation and the spectrometer measurement signal at the start point of the solid-to-liquid phase transformation includes: For the melting process of the molten pool surface, the ratio of the spectrometer measurement signal at the start of the solid-to-liquid phase transformation to the spectrometer measurement signal at the end of the solid-to-liquid phase transformation is between 1.05 and 1.15; For the melting process inside the molten pool, the ratio of the spectrometer measurement signal at the start of the solid-to-liquid phase transformation to the spectrometer measurement signal at the end of the solid-to-liquid phase transformation is between 1.25 and 1.5; The relationship function in S42 is shown in the following formula (1): （1） Where, It represents the proportion of liquid phase in the molten pool at any time. It represents the proportion of liquid phase inside the molten pool at the initial moment, The spectrometer measurement signal indicates the moment when the solid phase inside the molten pool begins to transform into the liquid phase. represents the spectrometer measurement signal at any time, The spectrometer measurement signal indicates the end point of the transformation from solid phase to liquid phase inside the molten pool.

2. The method for predicting scrap steel melting progress based on VIS-NIR spectroscopy according to claim 1, characterized in that: The step S1 of obtaining historical VIS-NIR spectral data in the molten pool during the converter steelmaking process of historical heats includes: The historical VIS-NIR spectral data in the molten pool during the converter steelmaking process of historical heats are continuously collected by a spectrum acquisition probe installed inside the oxygen nozzle.

3. The method for predicting scrap steel melting progress based on VIS-NIR spectroscopy according to claim 1, characterized in that: The method further comprises: Acquire third VIS-NIR spectrum data after a preset time interval, and determine whether the third VIS-NIR spectrum data is less than the second VIS-NIR spectrum data; If yes, then the proportion coefficient of the liquid phase component in the molten pool is obtained according to the calculation model, the scrap steel ratio and the molten iron temperature, and the melting progress prediction result of the scrap steel in the molten pool is obtained according to the proportion coefficient of the liquid phase component in the molten pool; If not, the calculation model is optimized according to the third VIS-NIR spectrum data, the scrap steel ratio and the molten iron temperature.

4. A device for predicting the melting progress of scrap steel based on VIS-NIR spectroscopy, wherein the device is used to implement the method for predicting the melting progress of scrap steel based on VIS-NIR spectroscopy according to any one of claims 1 to 3, characterized in that: The device comprises: a top-blown oxygen gun with an embedded spectrum acquisition probe, an optical fiber, a spectrometer, a computer and a database; The top-blown oxygen lance with an embedded spectrum acquisition probe is used to collect historical VIS-NIR spectrum data in the molten pool during the converter steelmaking process of the historical heats, first VIS-NIR spectrum data in the molten pool during the converter steelmaking process of the heat to be predicted, and second VIS-NIR spectrum data after a preset time interval; The optical fiber is used to transmit the spectral data collected by the spectrum collection probe to the spectrometer; The spectrometer is used to convert the optical signal into an electrical signal and output it to a computer; The computer is configured to construct a calculation model based on the historical VIS-NIR spectral data, and the scrap steel ratio and molten iron temperature of the heat corresponding to the historical VIS-NIR spectral data, to determine whether the second VIS-NIR spectral data is less than the first VIS-NIR spectral data; If so, the proportion coefficient of the liquid phase component in the molten pool is obtained according to the calculation model, the scrap steel ratio, the molten iron temperature, and the second VIS-NIR spectrum data, and the melting progress prediction result of the scrap steel in the molten pool is obtained according to the proportion coefficient of the liquid phase component in the molten pool; If not, the prediction ends; The database is used for data storage.

5. A device for predicting scrap steel melting progress based on VIS-NIR spectroscopy, characterized in that: The prediction device for scrap steel melting process based on VIS-NIR spectroscopy includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the method according to any one of claims 1 to 3.

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