Method for predicting the composition of molten iron applied to an automated steelmaking process

Abstract

The present invention belongs to the technical field of automated steel production and discloses a method for predicting the composition of molten iron applied to an automated steel production process, wherein the method is: performing a sampling and an examination of molten iron in a process in which the molten iron is tapped from a torpedo tank into a ladle, and calculating a loss of composition of this molten iron, wherein the composition of the molten iron is calibrated before the molten iron is tapped into the ladle;Performing a sampling and analysis of the molten iron in a process where the molten iron is transferred from the ladle to a converter, and calculating any loss in the composition of this molten iron, wherein the composition of the molten iron is calibrated before the molten iron is transferred to the converter. The present invention is easy to use and implement, and can acquire more accurate and reliable real-time parameters for computational models at all levels; in its specific application, the present invention does not require the modernization of existing automated steelmaking equipment; and it can be applied to a wide range of automated steelmaking scenarios.

Description

TECHNICAL AREA The present invention relates to the technical field of automated steel production and in particular to a method for predicting the composition of molten iron. STATE OF THE ART An automated steelmaking process relies on various models, such as a static calculation model, a process control model, and a dynamic calculation model, to accurately and continuously monitor and control the melting process. This ensures the safety and stability of the automated steelmaking process and the production of steel that meets expectations. When using a model to control and monitor the automated steelmaking process, the numerical values ​​of the specific parameters input into the model directly determine the model output. Specifically for an automated converter steelmaking process, the prior art often involves creating a model and calculating a theory for each corresponding device and parameter. This involves calculating the specific composition, temperature, required oxygen supply, etc., of molten iron in specific torpedo tanks, ladles, converters, or other steelmaking devices as raw data. Based on the computational model and the raw data, the consumption of raw materials and auxiliary materials, the control of thermal equilibrium, and the control of oxygen equilibrium, etc., are precisely controlled to ultimately achieve automated steelmaking. It is noteworthy thatIn the automated converter steelmaking process, the ore, after being melted into molten iron, must be transferred several times from an upper-level steelmaking unit to a lower-level steelmaking unit, with the molten iron remaining in a molten state during the transfer. Influenced by external factors such as temperature, solubility, reactivity, etc., a discrepancy often arises between the actual content of a particular element in the actual composition of the molten iron and the raw data. If this discrepancy is not corrected and the original theoretical calculated value of the molten iron composition is directly input into the next-level computational model, discrepancies inevitably occur in the output results of the next-level computational model as well.which directly affect the automated steel production process; if a sampling / testing device is added to the next-level steel production equipment to acquire the real-time parameters of the corresponding steel production equipment, not only are high demands placed on the pressure and high-temperature resistance of the sampling / testing device itself, but the sampling / testing is also very difficult, while innovations in the device have negative effects, such as increased costs of the steel production lines, which affects the overall efficiency of the steel plant. CONTENT OF THE PRESENT INVENTION Based on this, it is necessary to provide a method for predicting the composition of molten iron. This method is applied to an automated steelmaking process, taking into account changes in the composition of the molten iron caused by various influencing factors during the transfer process. These changes are then analyzed to correct errors, and the composition of the molten iron is predicted before it enters a next-level steelmaking unit. This provides more accurate, reliable, and real-time model calculation parameters for the automated steelmaking process, ensuring its stable progress. The technical solutions of the present invention are as follows: The present invention provides a method for predicting the composition of molten iron applied to an automated steelmaking process, wherein the method is: performing a sampling and examination of molten iron in a process in which the molten iron is tapped from a torpedo tank into a ladle, and calculating a loss in the composition of this molten iron, wherein the composition of the molten iron is calibrated before the molten iron is tapped into the ladle; performing a sampling and examination of the molten iron in a process in which the molten iron is transferred from the ladle into a converter, and calculating a loss in the composition of this molten iron, wherein the composition of the molten iron,Before the molten iron is transferred to the converter, it is calibrated. Before the molten iron is tapped into the ladle, samples are taken and the composition of the molten iron is analyzed. The results of these analyses are calibrated based on the influence of external factors such as temperature, solubility, and reactivity on the content of a specific element in the molten iron. This allows the composition of the molten iron tapped from the torpedo tank to be predicted and entered into the corresponding computational model of the next-level steelmaking equipment. Such sampling, analysis, calibration, and prediction methods are easy to use, and the operator does not need to upgrade the existing automated steelmaking equipment but simply rely on them.Samples of molten iron are taken during the transfer of the molten iron between the torpedo tank and the ladle and sent for analysis, which improves operability. Since calibration is required after receiving the analysis results, and the calibrated data is used to predict the specific composition of the molten iron after tapping into the ladle, the interference of the model data by other factors can be further eliminated. This allows for a more accurate adaptation to the actual situation and ensures precise and reliable control of the automated steelmaking process. The same applies to the transfer of the molten iron from the ladle to the converter. Optionally, it is provided that a portion of the molten iron present in the ladle before tapping from the torpedo tank is taken as the first process sample, and the remaining molten iron is tapped into the ladle; the first process sample is sent for analysis, and an analysis of the composition of the first process sample is obtained in order to calculate a calibration result for the first process sample. Optionally, a first sampling duration threshold and a tapping duration threshold are set; the sampling duration of the first process sample does not exceed the first sampling duration threshold, and the tapping duration, during which the molten iron remaining after the first process sample is tapped into the ladle, does not exceed the tapping duration threshold. Setting appropriate first sampling duration and tapping duration thresholds minimizes the undue influence of the transfer process on the composition of the molten iron. Furthermore, the testing and tapping processes are carried out simultaneously after sampling, which can also promote the proper and smooth operation of the automated steelmaking process. Optionally, after the initial process sample is sent for analysis, a mass percent analysis of at least one element—carbon, silicon, manganese, phosphorus, sulfur, and titanium—is performed. The molten iron remains in a molten state during transfer from an upper-level steelmaking unit to a lower-level steelmaking unit. The carbon, sulfur, phosphorus, and other element content in molten iron is strongly influenced by factors such as temperature, oxygen supply, and reactivity. Therefore, it is necessary to analyze the specific composition of each element to provide more sufficient, reliable, and dimensionally accurate computational data for relevant lower-level steelmaking unit models.Optionally, for a specific element E in the first process sample, a loss percentage of that element's composition is calculated according to the following formula: where in the first formula, represents a mass percent of element E in the molten iron in the torpedo tank, represents a yield coefficient of element E in a tapping process and is an empirical value, and represents a loss coefficient of element E in the tapping process and is an empirical value. Extensive on-site testing and cumulative data analysis will yield the aforementioned empirical values, with different yield coefficients and loss coefficients specifically selected for different elements E to precisely correct for the loss of each specific element in the specific process. Optionally, it is provided that for the specific element E in the first process sample, a product of the mass percent of element E, which is determined after sending the first process sample for analysis, and the loss percent of the composition of this specific element E is calculated as a calibration result of the first process sample, whereby the calibration result of the first process sample is entered into a calculation model as the actual mass percent of element E in the ladle in order to control the automated steelmaking process. Optionally, a portion of the molten iron, which is present before being transferred from the ladle to the converter, is taken as a second process sample, and the remaining molten iron is transferred to the converter; the second process sample is sent for analysis, and an analysis of the composition of the second process sample is obtained in order to calculate a calibration result for the second process sample. Optionally, a second sampling duration threshold and a transfer duration threshold are set; wherein the sampling duration of the second process sample does not exceed the second sampling duration threshold, and wherein the transfer duration during which the molten iron remaining after the second process sample is transferred to the converter does not exceed the transfer duration threshold; and wherein, after the second process sample has been sent for analysis, a quantity of one mass percent of at least one element of carbon, silicon, manganese, phosphorus, sulfur, and titanium in the second process sample is analyzed. Optionally, it is provided that for a specific element E in the second process sample, a loss percent of the composition of this element is calculated according to the following formula: where in the second formula stands for a mass percent of element E in the molten iron in the ladle, where stands for a yield coefficient of element E during the transfer of the molten iron into the converter and is an empirical value, and where stands for a loss coefficient of element E during the transfer of the molten iron into the converter and is an empirical value. Optionally, it is provided that for the specific element E in the second process sample, a product of the mass percent of element E, which is determined after sending the second process sample for analysis, and the loss percent of the composition of this specific element E is calculated as a calibration result of the second process sample, whereby the calibration result of the second process sample is entered into the calculation model as the actual mass percent of element E in the ladle in order to control the automated steel production process. Advantageous effects of the technical solutions of the present invention include: The present invention is easy to use and implement, and can acquire more accurate and reliable real-time parameters for computational models of all levels; in its specific application, the present invention does not require the modernization of existing automated steelmaking equipment; and it can be applied to a wide range of automated steelmaking scenarios. BRIEF DESCRIPTION OF THE DRAWINGS To more clearly illustrate the technical solutions in the embodiments of the present invention and in the prior art, the drawings required to describe the embodiments or the prior art are briefly presented below. Obviously, the drawings in the following description represent only some embodiments of the present invention. The person skilled in the art can also derive further drawings from the structures shown in these drawings without inventive effort. Fig. 1 shows a flowchart of a method for predicting the composition of molten iron applied to an automated steelmaking process, implemented by a specific embodiment. The realization of the objectives, the functional features and advantages of the present invention are further described with reference to the accompanying drawings in conjunction with the exemplary embodiments. DETAILED DESCRIPTION The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention and do not include all embodiments. All other embodiments that can be achieved by a person skilled in the art without inventive effort based on the embodiments in the present invention should fall within the scope of protection of the present invention. It should be noted that all directional terms (such as above, below, left, right, front, back ...) in the embodiments of the present invention are used only to describe a relative positional relationship, movement and the like between different components in a specific position (as shown in the accompanying figure), whereby the directional term is changed accordingly if the specific position is changed. Expressions concerning the terms "first," "second," etc., are used solely for descriptive purposes and should not be interpreted as indicating or implying relative importance or the number of specified technical characteristics. Thus, the characteristics defined as "first" or "second" can explicitly or implicitly include at least one of the specified characteristics. Furthermore, the use of "and / or" defines three solutions. For example, "A and / or B" includes a technical solution A, a technical solution B, and a technical solution that fulfills both A and B simultaneously. Furthermore, the technical solutions of different embodiments can be combined, but this must be based on implementation by a person skilled in the art. A combination of technical solutions is to be considered non-existent and therefore not within the scope of protection of the present invention if this combination contains contradictions or cannot be realized. Reference is made to Fig. 1. In this specific embodiment, a method for predicting the composition of molten iron applied to an automated steelmaking process is provided, wherein the method is: performing a sampling and investigation of molten iron in a process in which the molten iron is tapped from a torpedo tank into a ladle, and calculating a loss of composition of this molten iron, wherein the composition of the molten iron is calibrated before the molten iron is tapped into the ladle;Performing a sampling and examination of the molten iron in a process in which the molten iron is transferred from the ladle to a converter, and calculating a loss of composition of this molten iron, wherein the composition of the molten iron is calibrated before the molten iron is transferred to the converter. In this specific embodiment, it is provided that a portion of the molten iron present in the ladle before tapping from the torpedo tank is taken as the first process sample, and the remaining molten iron is tapped into the ladle; the first process sample is sent for examination, and an examination result of the composition of the first process sample is obtained in order to calculate a calibration result of the first process sample. In this specific embodiment, it is provided that a first sampling duration threshold is set to 30 seconds and a tapping duration threshold is set to 5 minutes; wherein a sampling duration of the first process sample does not exceed the first sampling duration threshold, and wherein a tapping duration, in which the molten iron remaining after the first process sample has been taken is tapped into the ladle, does not exceed the tapping duration threshold. In this specific embodiment, it is provided that after sending the first process sample for examination, a quantity of one mass percent of at least one element of carbon, silicon, manganese, phosphorus, sulfur and titanium in the first process sample is examined. In this specific embodiment, it is provided that for a specific element E in the first process sample, a loss percent of the composition of this element is calculated according to the following formula: where in the first formula represents a mass percent of element E in the molten iron in the torpedo tank, where represents a yield coefficient of element E in a tapping process and is an empirical value, and where Pls represents a loss coefficient of element E in the tapping process and is an empirical value. In the present specific embodiment, it is provided that for the specific element E in the first process sample, a product of the mass percent of element E, which is determined after sending the first process sample for examination, and the loss percent of the composition of this specific element E is calculated as the calibration result of the first process sample, wherein the calibration result of the first process sample is entered into a calculation model as the actual mass percent of element E in the ladle in order to control the automated steelmaking process. In this specific embodiment, it is provided that a portion of the molten iron, which is present before being transferred from the ladle to the converter, is taken as a second process sample and the remaining molten iron is transferred to the converter; the second process sample is sent for examination and an examination result of the composition of the second process sample is obtained in order to calculate a calibration result of the second process sample. In this specific embodiment, a second sampling duration threshold is set to 30 seconds and a transfer duration threshold to 5 minutes; wherein the sampling duration of the second process sample does not exceed the second sampling duration threshold, and wherein the transfer duration during which the molten iron remaining after the second process sample is transferred to the converter does not exceed the transfer duration threshold; and wherein, after the second process sample has been sent for analysis, a quantity of one percent by mass of at least one element of carbon, silicon, manganese, phosphorus, sulfur, and titanium in the second process sample is analyzed. In this specific embodiment, it is provided that for a specific element E in the second process sample, a loss percent of the composition of this element is calculated according to the following formula: where in the second formula stands for a mass percent of element E in the molten iron in the ladle, where stands for a yield coefficient of element E during the transfer of the molten iron into the converter and is an empirical value, and where stands for a loss coefficient of element E during the transfer of the molten iron into the converter and is an empirical value. In the present specific embodiment, it is provided that for the specific element E in the second process sample, a product of the mass percent of element E, which is determined after sending the second process sample for examination, and the loss percent of the composition of this specific element E is calculated as the calibration result of the second process sample, wherein the calibration result of the second process sample is entered into the calculation model as the actual mass percent of element E in the ladle in order to control the automated steelmaking process. Within the framework of the automated converter steelmaking process provided in the present application, eight verification examples were obtained: First verification example: A sample of the molten iron composition in the torpedo tank is taken over a sampling period of 10 seconds and analyzed in a laboratory. The results are as follows: C: 4.37%, Si: 0.42%, Mn: 0.36%, P: 0.130%, S: 0.045%, and Ti: 0.025%. After calculation according to Formula 1 for prediction, the relevant composition is corrected to: C: 4.27%, Si: 0.41%, Mn: 0.36%, P: 0.130%, S: 0.045%, and Ti: 0.0238%. The molten iron is transferred from the ladle to the converter over a period of 4.5 minutes. After calculation according to formula 2 for prediction, the relevant composition is corrected to: C: 4.17%, Si: 0.403%, Mn: 0.36%, P: 0.130%, S: 0.045%, Ti: 0.0235%. To verify the accuracy of the prediction, samples are taken simultaneously from the pan and the converter for analysis. The laboratory results are compared as follows: The composition of the molten iron in the pan is: C: 4.268%, Si: 0.409%, Mn: 0.36%, P: 0.130%, S: 0.045%, Ti: 0.0228%. The composition of the molten iron in the converter is: C: 4.16%, Si: 0.398%, Mn: 0.36%, P: 0.130%, S: 0.045%, Ti: 0.0236%. The above comparison results show that the predicted composition is closer to the actual composition and provides a data basis for a dynamic and static calculation model of steel production. Second verification example: During smelting at a specific temperature in a steel plant, a sample of the molten iron composition is taken from the torpedo tank. The sampling time is 18 seconds, and the sample is analyzed in a laboratory. The results are: C: 4.23%, Si: 0.45%, Mn: 0.39%, P: 0.135%, S: 0.055%, and Ti: 0.028%. After calculation according to Formula 1 for prediction, the relevant composition is corrected to: C: 4.13%, Si: 0.437%, Mn: 0.39%, P: 0.135%, S: 0.055%, Ti: 0.0266%. The molten iron is transferred from the ladle to the converter, with a transfer time of 4.2 minutes. After calculating the prediction according to Formula 2, the relevant composition is corrected to: C: 4.03%, Si: 0.432%, Mn: 0.36%, P: 0.130%, S: 0.055%, Ti: 0.0263%. To verify the accuracy of the prediction, samples are taken simultaneously from the pan and the converter for analysis.The results of the laboratory examination are compared as follows: The composition of the molten iron in the pan is: C: 4.128%, Si: 0.435%, Mn: 0.36%, P: 0.130%, S: 0.055%, Ti: 0.0265%. The composition of the molten iron in the converter is: C: 4.02%, Si: 0.43%, Mn: 0.36%, P: 0.130%, S: 0.055%, Ti: 0.0261%. The above comparison results show that the predicted composition is closer to the actual composition and provides a data basis for a dynamic and static calculation model of steel production. Third verification example: During smelting at a specific temperature in a steel plant, a sample of the molten iron composition is taken from the torpedo tank. The sampling time is 15 seconds, and the sample is analyzed in a laboratory. The results are: C: 4.59%, Si: 0.22%, Mn: 0.55%, P: 0.110%, S: 0.038%, and Ti: 0.033%. After calculation according to Formula 1 for prediction, the relevant composition is corrected to: C: 4.49%, Si: 0.213%, Mn: 0.55%, P: 0.110%, S: 0.038%, Ti: 0.0314%. The molten iron is transferred from the ladle to the converter, with a transfer time of 4.8 minutes. After calculation according to formula 2 for prediction, the relevant composition is corrected to: C: 4.39%, Si: 0.211%, Mn: 0.55%, P: 0.110%, S: 0.038%, Ti: 0.031%. To verify the accuracy of the prediction, samples are taken simultaneously from the pan and the converter for analysis. The laboratory results are compared as follows: The composition of the molten iron in the pan is: C: 4.48%, Si: 0.211%, Mn: 0.55%, P: 0.110%, S: 0.038%, Ti: 0.0312%. The composition of the molten iron in the converter is: C: 4.37%, Si: 0.21%, Mn: 0.55%, P: 0.110%, S: 0.038%, Ti: 0.0309%. The above comparison results show that the predicted composition is closer to the actual composition and provides a data basis for a dynamic and static calculation model of steel production. Fourth verification example: During smelting at a specific temperature in a steel plant, a sample of the molten iron composition is taken from the torpedo tank. The sampling time is 20 seconds, and the sample is analyzed in a laboratory. The results are: C: 3.98%, Si: 0.62%, Mn: 0.46%, P: 0.098%, S: 0.033%, and Ti: 0.021%. After calculation according to Formula 1 for prediction, the relevant composition is corrected to: C: 3.88%, Si: 0.601%, Mn: 0.46%, P: 0.098%, S: 0.033%, Ti: 0.020%. The molten iron is transferred from the ladle to the converter, with a transfer time of 4.1 minutes. After calculating the prediction according to Formula 2, the relevant composition is corrected to: C: 3.78%, Si: 0.595%, Mn: 0.46%, P: 0.098%, S: 0.033%, Ti: 0.0198%. To verify the accuracy of the prediction, samples are taken simultaneously from the pan and the converter for analysis.The results of the laboratory examination are compared as follows: The composition of the molten iron in the pan is: C: 387%, Si: 0.609%, Mn: 0.46%, P: 0.098%, S: 0.033%, Ti: 0.0218%. The composition of the molten iron in the converter is: C: 3.76%, Si: 0.598%, Mn: 0.46%, P: 0.098%, S: 0.033%, Ti: 0.0216%. The above comparison results show that the predicted composition is closer to the actual composition and provides a data basis for a dynamic and static calculation model of steel production. Fifth verification example: During smelting at a specific temperature in a steel plant, no sampling is performed in the torpedo tank. Instead, a sample is taken after tapping the molten iron into the ladle. The sampling time is 25 seconds, and the sample is analyzed in a laboratory. The results are: C: 4.98%, Si: 0.60%, Mn: 0.36%, P: 0.090%, S: 0.038%, and Ti: 0.025%. The molten iron is then transferred from the ladle to the converter in 4.9 minutes. After calculation according to Formula 2 for prediction, the relevant composition is corrected to: C: 4.88%, Si: 0.594%, Mn: 0.36%, P: 0.090%, S: 0.038%, Ti: 0.0248%. To verify the accuracy of the prediction, a sample is taken from the converter for analysis. The results of the laboratory analysis are compared as follows: The composition of the molten iron in the converter is: C: 4.85%, Si: 0.598%, Mn: 0.36%, P: 0.090%, S: 0.038%, Ti: 0.0246%. The above comparison results show that the predicted composition is closer to the actual composition and provides a data basis for a dynamic and static calculation model of steel production. Sixth verification example: During smelting at a specific temperature in a steel plant, no sampling is performed in the torpedo tank. Instead, a sample is taken after tapping the molten iron into the ladle. The sampling time is 30 seconds, and the sample is analyzed in a laboratory. The results are: C: 3.58%, Si: 0.69%, Mn: 0.45%, P: 0.088%, S: 0.023%, and Ti: 0.019%. The molten iron is then transferred from the ladle to the converter over a period of 5.0 minutes. After calculation according to Formula 2 for prediction, the relevant composition is corrected to: C: 3.48%, Si: 0.683%, Mn: 0.45%, P: 0.088%, S: 0.023%, Ti: 0.0188%. To verify the accuracy of the prediction, a sample is taken from the converter for analysis. The results of the laboratory analysis are compared as follows: The composition of the molten iron in the converter is: C: 3.45%, Si: 0.680%, Mn: 0.45%, P: 0.088%, S: 0.023%, Ti: 0.0186%. The above comparison results show that the predicted composition is closer to the actual composition and provides a data basis for a dynamic and static calculation model of steel production. Seventh verification example: During smelting at a specific temperature in a steel plant, no sampling is performed in the torpedo tank. Instead, a sample is taken after tapping the molten iron into the ladle. The sampling time is 26 seconds, and the sample is analyzed in a laboratory. The results are: C: 4.33%, Si: 0.55%, Mn: 0.66%, P: 0.078%, S: 0.038%, and Ti: 0.025%. The molten iron is then transferred from the ladle to the converter in 4.7 minutes. After calculation according to Formula 2 for prediction, the relevant composition is corrected to: C: 4.23%, Si: 0.544%, Mn: 0.66%, P: 0.078%, S: 0.038%, Ti: 0.0248%. To verify the accuracy of the prediction, a sample is taken from the converter for analysis. The results of the laboratory analysis are compared as follows: The composition of the molten iron in the converter is: C: 4.22%, Si: 0.548%, Mn: 0.66%, P: 0.078%, S: 0.038%, Ti: 0.0246%. The above comparison results show that the predicted composition is closer to the actual composition and provides a data basis for a dynamic and static calculation model of steel production. Eighth verification example: During smelting at a specific temperature in a steel plant, no sampling is performed in the torpedo tank. Instead, a sample is taken after tapping the molten iron into the ladle. The sampling time is 28 seconds, and the sample is analyzed in a laboratory. The results are: C: 4.28%, Si: 0.42%, Mn: 0.35%, P: 0.095%, S: 0.032%, and Ti: 0.027%. The molten iron is then transferred from the ladle to the converter in 3.8 minutes. After calculation according to Formula 2 for prediction, the relevant composition is corrected to: C: 4.18%, Si: 0.416%, Mn: 0.35%, P: 0.095%, S: 0.032%, Ti: 0.0267%. To verify the accuracy of the prediction, a sample is taken from the converter for analysis. The results of the laboratory analysis are compared as follows: The composition of the molten iron in the converter is: C: 4.14%, Si: 0.412%, Mn: 0.35%, P: 0.095%, S: 0.032%, Ti: 0.0262%. From the eight verification examples mentioned above, it is clearly evident that the data obtained by the method for predicting the composition of molten iron applied to an automated steelmaking process, provided in this specific embodiment, correspond to the actual data to a high degree, with the predicted composition being closer to the actual composition and providing a stable, reliable and accurate data basis for dynamic and static calculation models of steelmaking. The foregoing shows only preferred embodiments of the present invention, which, however, do not limit the scope of protection of the present invention. Any equivalent structural transformation carried out within the scope of the inventive concept of the present invention using the contents of the description and the figure of the present invention, or the direct / indirect application in other related technical fields, also falls within the scope of protection of the present invention.

Claims

Method for predicting the composition of molten iron applied to an automated steelmaking process, characterized in that the method is: performing a sampling of molten iron in a first process in which the molten iron is tapped from a torpedo tank into a ladle to obtain a first process sample, examining the first process sample to obtain a first examination result of the first process sample, and calculating a loss of composition of the molten iron in the first process based on the first examination result, calibrating the composition of the molten iron in the ladle;and carrying out a sampling of the molten iron in a second process in which the molten iron is transferred from the ladle to a converter, to obtain a second process sample, examining the second process sample to obtain a second examination result of the second process sample, and calculating a loss of composition of the molten iron in the second process based on the second examination result, calibrating the composition of the molten iron in the converter, whereby a portion of the molten iron standing before its tapping from the torpedo tank into the ladle is taken as the first process sample and the remaining molten iron is tapped into the ladle;wherein the first process sample is sent for examination and the first examination result of the composition of the first process sample is obtained in order to calculate a calibration result of the first process sample, and an initial sampling duration threshold and a tapping duration threshold are set; wherein a sampling duration of the first process sample does not exceed the first sampling duration threshold, wherein a tapping duration in which the molten iron remaining after taking the first process sample is tapped into the ladle does not exceed the tapping duration threshold, and after sending the first process sample for examination, a mass percent quantity of at least one element of carbon, silicon, manganese, phosphorus, sulfur, and titanium in the first process sample is examined, and for a specific element E in the first process sample, a loss percent quantity of the composition ΔP tap E ,hm; This element is calculated according to the following formula: ΔP tap E , hm = ( l − Rls tap E ) × P torp E , hm − Pls tap E where in the first formula P torp E , hm Rls tap E represents one percent by mass of the element E in the molten iron in the torpedo tank. represents a yield coefficient of element E in a tapping process and is an empirical value, and where Pls tap Please tap E represents a loss coefficient of element E in the tapping process and is an empirical value, and for the specific element E in the first process sample, a product of the mass percent of element E determined after sending the first process sample for analysis and the loss percent of the composition ΔP tap E ,hm this specific element E is calculated as the calibration result of the first process sample, the calibration result of the first process sample, as the actual mass percent of element E in the ladle, is entered into a calculation model to control the automated steelmaking process. The method according to claim 1, characterized in that a portion of the molten iron, which is present before its transfer from the ladle to the converter, is taken as the second process sample and the remaining molten iron is transferred to the converter; wherein the second process sample is sent for examination and the examination result of the composition of the second process sample is obtained in order to calculate a calibration result of the second process sample. The method according to claim 2, characterized in that a second sampling duration threshold and a transfer duration threshold are set; wherein a sampling duration of the second process sample does not exceed the second sampling duration threshold, wherein a transfer duration in which the molten iron remaining after the second process sample is transferred to the converter does not exceed the transfer duration threshold; wherein, after sending the second process sample for examination, a quantity of one mass percent of at least one element of carbon, silicon, manganese, phosphorus, sulfur and titanium in the second process sample is examined. The method according to claim 3, characterized in that for a specific element E in the second process sample, a loss percent of the composition ΔP chrg E , hm This element is calculated according to the following formula: ΔP chrg E , hm = ( l − Rls chrg E ) × P ladle E , hm − Pls chrg E where in the second formula P platele E , hm Rls represents one percent by mass of the element E in the molten iron in the pan, where Rls chrg E for a yield coefficient of element E during the transfer of the molten iron into the converter and is an empirical value, and where Pls chrg E represents a loss coefficient of element E during the transfer of molten iron into the converter and is an empirical value. Method according to claim 4, characterized in that for the specific element E in the second process sample, a product of the mass percent of element E, which is determined after sending the second process sample for examination, and the loss percent of the composition ΔP chrg E , hm The calibration result of the second process sample is calculated as the calibration result of this specific element E, with the calibration result of the second process sample being entered into the calculation model as the actual mass percent of element E in the converter in order to control the automated steel production process.

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