CN113362903A - Method for intelligently adding lime in TSC (thyristor switched capacitor) stage of large converter - Google Patents

Abstract

The invention relates to a method for intelligently adding lime in TSC (thyristor switched capacitor) stage of a large converter, belonging to the technical field of ferrous metallurgy. The technical scheme of the invention is as follows: predicting according to a metallurgical mechanism by adopting a mechanism model, training data, deeply learning artificial intelligence based on a BP neural network, carrying out standardization processing on input data based on the mechanism model, constructing internal relation between the input data and output data, and obtaining the output data through artificial intelligence calculation. The invention has the beneficial effects that: by adopting a mode of 'mechanism model + artificial intelligence prediction', on the basis of fully analyzing influence factors of the lime addition amount of the converter, the internal relation among the influence factors of the converter is constructed, the method for intelligently adding lime in TSC (thyristor switched capacitor) stages of the large-scale converter under different material conditions is realized, the problem of accurate addition of the lime of the converter is solved, quality accidents and production accidents are reduced, and the smelting cost is reduced.

Description

Method for intelligently adding lime in TSC (thyristor switched capacitor) stage of large converter

Technical Field

The invention relates to a method for intelligently adding lime in TSC (thyristor switched capacitor) stage of a large converter, belonging to the technical field of ferrous metallurgy.

Background

Lime is a main auxiliary material for converter smelting, and has the main function of forming a reasonable slag system in the smelting process and removing harmful elements such as steel slag P, S and the like through converter slag. Aiming at the converter controlled by the sublance method, lime is added mainly before the TSC of the sublance is measured, namely the TSC smelting stage of the converter. The general algorithm for the lime addition is to calculate through binary alkalinity, the method obtains the CaO content of lime through the detection and test of lime raw materials, and then calculates the lime addition according to the silicon content of molten iron.

Although the method can provide a certain guidance for the lime addition amount in the converter operation process, the method is not accurate, the dynamic change of lime components is not fully considered, the influence of other Ca-containing auxiliary materials such as dolomite, steel slag and other raw materials is not fully considered, the influence of lime melting at the TSC stage on the dephosphorization effect is not fully considered, the influence of lime on the converter smelting heat balance is not fully considered, and the difference between different devices and between shifts is not fully considered. Particularly, under the background of reducing iron consumption of the converter in the whole country at present, a large amount of self-produced cold materials such as steel slag and the like are added into each steel company, and the self-produced cold materials have poor component stability and cause great production fluctuation. All the factors can cause inaccuracy of lime addition, if the lime addition is too small, the converter smelting slag amount is small, the converter dephosphorization effect is influenced, the product components are not compatible, and the long service life of the converter lining is not facilitated; if the lime is added in too large amount, not only can the cost be wasted, but also serious production accidents such as large smelting slag amount and abnormal splashing in the smelting process can be caused, and steel can not be tapped at the end point of the converter.

Patent application No. 201710074989.4 discloses a converter alkalinity dynamic control method, which corrects the alkalinity based on the setting and adjustment of the end point temperature, the end point carbon, the end point phosphorus, the dephosphorization efficiency and the converter slag amount.

Patent application number 201310449548.X discloses a method for determining lime using amount by calculating converter remaining slag amount, the method determines the remaining slag amount through molten iron silicon content, obtains the actual converter remaining slag weight through observing the furnace slag volume and calculating the slag density, and adds lime into a converter according to the proportion of the remaining slag amount to the lime 1: 1-0.5.

In a document [ application of a YaoHu, Yangzhou Chengzhou, Xushenglin, gray algorithm in calculation of lime addition amount in converter steelmaking, university of Hangzhou electronic technology, 2015], the deviation between the current heat of a 50t converter and the lime addition amount is forecasted through the gray algorithm, and a residual sequence is established for correction.

Although the above method involves prediction of lime addition, all factors affecting lime addition are not fully considered, and the inherent relationship between the factors is not considered. Such as: the influence of the stability of the team, the converter equipment and the materials is not considered, and the influence of the lime serving as an auxiliary raw material with a certain cooling effect on the heat balance of the converter is not considered. Due to the complex internal relation of the multiple factors, the current conventional mathematical statistical method cannot accurately predict and guide production. Therefore, a reliable method for guiding the addition of lime into the converter is needed.

Disclosure of Invention

The invention aims to provide a method for intelligently adding lime in TSC (thyristor switched capacitor) stage of a large converter. Mainly aiming at a 260t large-scale top-bottom combined blown converter, the slagging method is a single slag method, the charging system is quantitative charging, and sublance control is adopted. The method adopts a mode of 'mechanism model + artificial intelligence prediction', constructs the internal relation among all the influence factors of the converter on the basis of fully analyzing the influence factors of the lime addition amount of the converter, and realizes the intelligent addition of the lime at the TSC stage of the large converter under different material conditions.

The technical scheme of the invention is as follows: a method for intelligently adding lime into a large converter comprises the following steps:

(1) predicting according to a metallurgical mechanism by adopting a mechanism model, wherein the formula of the mechanism model is as follows:

wherein W_shRepresenting the lime adding amount, and the data range is 22kg/t-49 kg/t;

a_shcrepresenting the mass percent of CaO of lime, and the data range is 88-93 percent;

a_shsrepresents lime SiO₂Mass percent, data range 2% -5%;

W_qsrepresenting the addition amount of light-burned dolomite, and the data range is 7.5kg/t-30.1 kg/t;

a_qscrepresenting the CaO mass percent of the light-burned dolomite, and the data range is 40-48 percent;

a_qssstands for light-burned dolomite SiO₂Mass percent, data range 0.1% -3.3%;

W_gzrepresenting the addition of steel slag and other cold materials, and the data range is less than 113 kg/t;

a_gzcrepresenting the CaO mass percent of the steel slag, and the data range is 20-45 percent;

a_gzsSiO for steel slag₂Mass percent, data range 8% -23%;

W_tsrepresenting the adding amount of molten iron, and the data range is 900kg/t-1085 kg/t;

a_strepresenting the mass percent of Si in the molten iron, and the data range is 0.15-0.60%;

2.14 conversion of Si to SiO₂The conversion coefficient of (a);

r represents alkalinity which is determined by an alkalinity selection model, and the alkalinity is divided into four gradients, wherein the values are respectively 3, 3.5, 4 and 4.5;

(2) carrying out artificial intelligence prediction, adopting a BP neural network architecture as a basis for deep learning artificial intelligence, wherein the input of the artificial intelligence prediction is 16-dimensional data, the output of the artificial intelligence prediction is 1-dimensional data, the 16-dimensional input data are respectively the lime amount of a mechanism model, a smelting team, a smelting furnace base, steel grade requirements, scrap steel, molten iron temperature, molten iron weight, molten iron manganese, molten iron silicon, molten iron phosphorus, steel slag, dolomite addition amount, TSC phosphorus, TSC manganese, TSC carbon and TSC temperature, and the 1-dimensional output data are the lime amount; the input data is standardized based on a mechanism model, and the processing method is as follows:

y is a parameter value to be standardized, y_stdIs the standard deviation of this set of parameters, y_aveIs the average of this set of parameters;

constructing an internal relation between input data and output data, and obtaining the output data through artificial intelligence calculation;

(3) the correction error required for the calculation is reduced by means of the denormalization, which is carried out by means of the following formula

y＝y*×y_ave+y_std

(4) The excitation function is of the form:

in the step (1), the principle of selecting a mechanism model is as follows:

the phosphorus content of the steel grade requires to establish 4 gradient gears, wherein the phosphorus mass percentage requirement is less than or equal to 0.015 percent, the phosphorus mass percentage requirement is more than 0.015 percent and less than or equal to 0.020 percent, the phosphorus mass percentage requirement is more than 0.020 percent and less than or equal to 0.025 percent, and the phosphorus mass percentage requirement is more than 0.025 percent;

for steel grades with the end point phosphorus element mass percentage less than 0.02% and the end point temperature more than 1650 ℃, the converter end point needs high-temperature steel tapping, the dephosphorization efficiency is lower than that of common steel grades, so the alkalinity is selected to be higher, the grade of the steel grade such as phosphorus mass percentage requires the first gear, and the alkalinity is selected to be 4.5; the requirement of phosphorus mass percent is two, and the alkalinity is selected to be 4;

for steel grades with the end point phosphorus element mass percentage more than 0.02% and the end point temperature more than 1650 ℃, as the steel grades require to keep partial phosphorus content and the subsequent ferrophosphorus adjustment is needed, the process alkalinity of the steel grade is low, which is beneficial to the decarbonization and phosphorus conservation of a converter, the sensitivity of the steel grade to the molten iron phosphorus mass percentage is low, and the alkalinity is 3;

for steel grades with the end point temperature of less than 1650 ℃, the phosphorus element requirement of the steel grade and the phosphorus element requirement of molten iron need to be comprehensively considered, so that the dephosphorization rate in the smelting process and the phosphorus element of tapping meet the control requirement, the phosphorus mass percentage of the molten iron is less than or equal to 0.15 percent, the phosphorus mass percentage requirement of the steel grade is that firstly, the alkalinity is selected to be 4; when the mass percent of phosphorus in molten iron is less than or equal to 0.15 percent and the mass percent of phosphorus in steel grade is required to be 2, the alkalinity is selected to be 3.5; the phosphorus mass percent of the molten iron is less than or equal to 0.15 percent, and the phosphorus mass percent of the steel grade is required to be 3.0 when the phosphorus mass percent of the steel grade is three and four; the mass percent of phosphorus in molten iron is more than 0.15 percent, the mass percent of phosphorus in steel grade is that firstly, the alkalinity is selected to be 4.5; when the mass percent of phosphorus in molten iron is more than 0.15 percent and the mass percent of phosphorus in steel grade is required to be two, the alkalinity is selected to be 4; when the mass percent of phosphorus in molten iron is more than 0.15 percent and the mass percent of phosphorus in steel grade is ③, the alkalinity is 3.5; when the mass percent of phosphorus in molten iron is more than 0.15 percent and the mass percent of phosphorus in steel grade is required to be four, the alkalinity is selected to be 3.

In the step (2), the neural network model structure needs to adopt a structure with 3 hidden layers, the number of nodes of the first hidden layer is 3, the number of nodes of the second hidden layer is 50, and the number of nodes of the third hidden layer is 5, the model has strong nonlinear mapping capability, and is suitable for solving engineering problems such as converter smelting process with complex internal influence factors. Therefore, the optimal solution can be well found out by matching the intelligent model with the theoretical model, and the problem of inaccurate lime addition is solved.

Training data of artificial intelligent prediction are screened by a data preference rule model, data acquisition errors or no typicality data are screened out, the model calculation precision is improved, the interference of error data on model calculation is reduced, and a specific preference rule is as follows:

the weight of molten iron and the weight of scrap steel are selected preferably from 280t to 320 t. For a 260t large converter, the loading capacity is about 300t generally, and if the loading capacity does not meet the requirement, the problem of inaccurate data acquisition exists;

the silicon content of the molten iron is less than 0.6 percent, the selection is preferred, the silicon content is too high, the problem of laboratory inspection deviation exists, meanwhile, the too high silicon content is a special event, special operation of a converter is needed, and the universality and the representativeness are avoided;

thirdly, the lime addition amount is more than 6t, the selection is preferred, and for a large converter, the problem of data acquisition error due to too small lime addition amount is solved;

the addition amount of the light-burned dolomite is more than 2t, the light-burned dolomite is selected preferentially, the light-burned dolomite is mainly added, the requirements of slag melting of a large-sized converter and protection of a furnace lining are mainly met, the addition amount is too small, and the problem of data acquisition errors exists;

the adding amount of the ore is mainly related to the heat balance in the furnace, therefore, when the temperature of the molten iron is more than 1320 ℃, the amount of the scrap steel is less than 30t, and the mass percent of silicon in the molten iron is more than 0.3 percent, the ore is added and selected preferentially, and the rest of the ore has the problem of ore mining omission.

The invention has the beneficial effects that: by adopting a mode of 'mechanism model + artificial intelligence prediction', on the basis of fully analyzing influence factors of the lime addition amount of the converter, the internal relation among the influence factors of the converter is constructed, the method for intelligently adding lime in TSC (thyristor switched capacitor) stages of the large-scale converter under different material conditions is realized, the problem of accurate addition of the lime of the converter is solved, quality accidents and production accidents are reduced, and the smelting cost is reduced.

Drawings

FIG. 1 is an artificial intelligence algorithm neural network architecture diagram of the present invention;

FIG. 2 is a flow chart of the present invention;

FIG. 3 is a scatter plot of the effectiveness of use of an embodiment of the present invention;

fig. 4 is a histogram of the effects of use of an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions of the embodiments of the present invention with reference to the drawings of the embodiments, and it is obvious that the described embodiments are a small part of the embodiments of the present invention, rather than all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without creative work based on the embodiments of the present invention belong to the protection scope of the present invention.

The implementation steps are as follows:

(1) predicting according to a metallurgical mechanism by adopting a mechanism model, wherein the formula of the mechanism model is as follows:

wherein W_shRepresenting the lime adding amount, and the data range is 22kg/t-49 kg/t;

a_shcrepresents lime CaO mass percent, data range 88% -93%;

a_shsrepresents lime SiO₂Mass percent, data range 2% -5%;

W_qsrepresenting the addition amount of light-burned dolomite, and the data range is 7.5kg/t-30.1 kg/t;

a_qscrepresenting the CaO mass percent of the light-burned dolomite, and the data range is 40-48 percent;

a_qssstands for light-burned dolomite SiO₂Mass percent, data range 0.1% -3.3%;

W_gzrepresenting the addition of steel slag and other cold materials, and the data range is less than 113 kg/t;

a_gzcrepresenting the CaO mass percent of the steel slag, and the data range is 20-45 percent;

a_gzsSiO for steel slag₂Mass percent, data range 8% -23%;

W_tsrepresenting the adding amount of molten iron, and the data range is 900kg/t-1085 kg/t;

a_strepresenting the mass percent of Si in the molten iron, and the data range is 0.15-0.60%;

2.14 conversion of Si to SiO₂The conversion coefficient of (a);

r represents alkalinity which is determined by an alkalinity selection model, and the alkalinity is divided into four gradients, wherein the values are respectively 3, 3.5, 4 and 4.5;

the principle of mechanism model selection is as follows:

the phosphorus content of the steel grade requires to establish 4 gradient gears, wherein the phosphorus mass percentage requirement is less than or equal to 0.015 percent, the phosphorus mass percentage requirement is more than 0.015 percent and less than or equal to 0.020 percent, the phosphorus mass percentage requirement is more than 0.020 percent and less than or equal to 0.025 percent, and the phosphorus mass percentage requirement is more than 0.025 percent;

if the final temperature of the steel grade is more than 1650 ℃, the grade required by phosphorus mass percentage is first, and the alkalinity is selected to be 4.5; the requirement of phosphorus mass percent is two, and the alkalinity is selected to be 4; the phosphorus mass percentage requirement is that the phosphorus mass percentage is;

for steel grades with the end point temperature of less than 1650 ℃, the mass percentage of phosphorus in molten iron is less than or equal to 0.15 percent, the requirement of the mass percentage of phosphorus in the steel grades is that firstly, the alkalinity is selected to be 4; when the mass percent of phosphorus in molten iron is less than or equal to 0.15 percent and the mass percent of phosphorus in steel grade is required to be 2, the alkalinity is selected to be 3.5; the phosphorus mass percent of the molten iron is less than or equal to 0.15 percent, and the phosphorus mass percent of the steel grade is required to be 3.0 when the phosphorus mass percent of the steel grade is three and four; the mass percent of phosphorus in molten iron is more than 0.15 percent, the mass percent of phosphorus in steel grade is that firstly, the alkalinity is selected to be 4.5; when the mass percent of phosphorus in molten iron is more than 0.15 percent and the mass percent of phosphorus in steel grade is required to be two, the alkalinity is selected to be 4; when the mass percent of phosphorus in molten iron is more than 0.15 percent and the mass percent of phosphorus in steel grade is ③, the alkalinity is 3.5; when the mass percent of phosphorus in molten iron is more than 0.15 percent and the mass percent of phosphorus in steel grade is required to be four, the alkalinity is selected to be 3.

(2) Carrying out artificial intelligence prediction, adopting a BP neural network architecture as a basis for deep learning artificial intelligence, wherein the input of the artificial intelligence prediction is 16-dimensional data, the output of the artificial intelligence prediction is 1-dimensional data, the 16-dimensional input data are respectively the lime amount of a mechanism model, a smelting team, a smelting furnace base, steel grade requirements, scrap steel, molten iron temperature, molten iron weight, molten iron manganese, molten iron silicon, molten iron phosphorus, steel slag, dolomite addition amount, TSC phosphorus, TSC manganese, TSC carbon and TSC temperature, and the 1-dimensional output data are the lime amount; the input data is standardized based on a mechanism model, and the processing method is as follows:

y is a parameter value to be standardized, y_stdIs the standard deviation of this set of parameters, y_aveIs the average of this set of parameters;

constructing an internal relation between input data and output data, and obtaining the output data through artificial intelligence calculation;

the correction error required for the calculation is reduced by means of the denormalization, which is carried out by means of the following formula

y＝y*×y_ave+y_std

The excitation function is of the form:

the BP neural network structure is a structure with 3 hidden layers, the number of nodes of the first hidden layer is 3, the number of nodes of the second hidden layer is 50, and the number of nodes of the third hidden layer is 5.

Training data of artificial intelligent prediction are screened by a data preference rule model, data acquisition errors or no typicality data are screened out, the model calculation precision is improved, the interference of error data on model calculation is reduced, and a specific preference rule is as follows:

selecting the weight of molten iron and the weight of scrap steel in a range of 280t to 320 t;

the silicon content percentage of the molten iron is less than 0.6 percent, and the selection is preferred;

thirdly, selecting lime with the addition amount of more than 6t preferentially;

fourthly, the addition amount of the light-burned dolomite is more than 2t, and the selection is preferred;

fifthly, when the temperature of molten iron is higher than 1320 ℃, the amount of scrap steel is lower than 30t, and the mass percent of silicon in the molten iron is higher than 0.3%, adding ore, and selecting preferentially.

Example (b):

the implementation unit has three 260t top-bottom combined blown converters, and the converters are controlled by sublance guns and are respectively a converter No. 1, a converter No. 2 and a converter No. 3, and the staff work in four-shift three-operation mode and are respectively four shifts, groups A, B, C and D.

Under the raw material conditions that the content of CaO in the sintered ore is 7 percent and the content of SiO in the sintered ore is 7 percent₂The content of the lime is 3 percent, the content of the lime CaO is 88 percent, and the lime SiO is₂2 percent of light-burned dolomite CaO, 40 percent of light-burned SiO₂0.1 percent, 20 percent of CaO content in the steel slag and SiO₂The content is 8%. By the method, the lime addition is predicted, the actual production on site is guided, and no component accident or production accident occurs in the heat of the embodiment.

The raw material conditions are that the CaO content of the sintered ore is 10 percent and the SiO content is₂6 percent of lime CaO, 93 percent of lime SiO₂5 percent of light-burned dolomite CaO and 48 percent of light-burned SiO₂3.3 percent, the CaO content of the steel slag is 45 percent, and SiO₂The content was 23%. By the method, the lime addition is predicted, the actual production on site is guided, and no component accident or production accident occurs in the heat of the embodiment.

The raw material conditions are that the CaO content of the sintered ore is 8 percent and the SiO content is₂The content of the lime is 4 percent, the content of the lime CaO is 90 percent, and the lime SiO is₂4 percent of light-burned dolomite CaO, 44 percent of light-burned SiO ₂2 percent, 35 percent of CaO content in the steel slag and SiO₂The content is 15%. By the method, the lime addition is predicted, the actual production on site is guided, and no component accident or production accident occurs in the heat of the embodiment.

As can be seen from the above examples, the predicted lime content value follows the actual addition amount well, and the average deviation amount is-0.154 ton.

Claims (4)

1. A method for intelligently adding lime in TSC (thyristor switched capacitor) stage of large converter is characterized by comprising the following steps:

(1) predicting according to a metallurgical mechanism by adopting a mechanism model, wherein the formula of the mechanism model is as follows:

wherein W_shRepresenting the lime adding amount, and the data range is 22kg/t-49 kg/t;

a_shcrepresenting the mass percent of CaO of lime, and the data range is 88-93 percent;

a_shsrepresents lime SiO₂Mass percent, data range 2% -5%;

W_qsrepresenting the addition amount of light-burned dolomite, and the data range is 7.5kg/t-30.1 kg/t;

a_qscrepresenting the CaO mass percent of the light-burned dolomite, and the data range is 40-48 percent;

a_qssstands for light-burned dolomite SiO₂Mass percent, data range 0.1% -3.3%;

W_gzrepresenting the addition of steel slag and other cold materials, and the data range is less than 113 kg/t;

a_gzcrepresenting the CaO mass percent of the steel slag, and the data range is 20-45 percent;

a_gzsSiO for steel slag₂Mass percent, data range 8% -23%;

W_tsrepresenting the adding amount of molten iron, and the data range is 900kg/t-1085 kg/t;

a_strepresenting the mass percent of Si in the molten iron, and the data range is 0.15-0.60%;

2.14 conversion of Si to SiO₂The conversion coefficient of (a);

r represents alkalinity which is determined by an alkalinity selection model, and the alkalinity is divided into four gradients, wherein the values are respectively 3, 3.5, 4 and 4.5;

(2) carrying out artificial intelligence prediction, adopting a BP neural network architecture as a basis for deep learning artificial intelligence, wherein the input of the artificial intelligence prediction is 16-dimensional data, the output of the artificial intelligence prediction is 1-dimensional data, the 16-dimensional input data are respectively the lime amount of a mechanism model, a smelting team, a smelting furnace base, steel grade requirements, scrap steel, molten iron temperature, molten iron weight, molten iron manganese, molten iron silicon, molten iron phosphorus, steel slag, dolomite addition amount, TSC phosphorus, TSC manganese, TSC carbon and TSC temperature, and the 1-dimensional output data are the lime amount; the input data is standardized based on a mechanism model, and the processing method is as follows:

y is a parameter value to be standardized, y_stdIs the standard deviation of this set of parameters, y_aveIs the average of this set of parameters;

constructing an internal relation between input data and output data, and obtaining the output data through artificial intelligence calculation;

(3) the correction error required for the calculation is reduced by means of the denormalization, which is carried out by means of the following formula

y＝y*×y_ave+y_std

(4) The excitation function is of the form:

2. the method for intelligently adding lime at TSC (TSC-period) stages of large-scale converters according to claim 1, which is characterized in that: in the step (1), the principle of selecting a mechanism model is as follows:

the phosphorus content of the steel grade requires to establish 4 gradient gears, wherein the phosphorus mass percentage requirement is less than or equal to 0.015 percent, the phosphorus mass percentage requirement is more than 0.015 percent and less than or equal to 0.020 percent, the phosphorus mass percentage requirement is more than 0.020 percent and less than or equal to 0.025 percent, and the phosphorus mass percentage requirement is more than 0.025 percent;

if the final temperature of the steel grade is more than 1650 ℃, the grade required by phosphorus mass percentage is first, and the alkalinity is selected to be 4.5; the requirement of phosphorus mass percent is two, and the alkalinity is selected to be 4; the phosphorus mass percentage requirement is that the phosphorus mass percentage is that the phosphorus mass percentage is not less than that the phosphorus mass percentage;

for steel grades with the end point temperature of less than 1650 ℃, the mass percentage of phosphorus in molten iron is less than or equal to 0.15 percent, the requirement of the mass percentage of phosphorus in the steel grades is that firstly, the alkalinity is selected to be 4; when the mass percent of phosphorus in molten iron is less than or equal to 0.15 percent and the mass percent of phosphorus in steel grade is required to be 2, the alkalinity is selected to be 3.5; the phosphorus mass percent of the molten iron is less than or equal to 0.15 percent, and the phosphorus mass percent of the steel grade is required to be 3.0 when the phosphorus mass percent of the steel grade is three and four; the mass percent of phosphorus in molten iron is more than 0.15 percent, the mass percent of phosphorus in steel grade is that firstly, the alkalinity is selected to be 4.5; when the mass percent of phosphorus in molten iron is more than 0.15 percent and the mass percent of phosphorus in steel grade is required to be two, the alkalinity is selected to be 4; when the mass percent of phosphorus in molten iron is more than 0.15 percent and the mass percent of phosphorus in steel grade is ③, the alkalinity is 3.5; when the mass percent of phosphorus in molten iron is more than 0.15 percent and the mass percent of phosphorus in steel grade is required to be four, the alkalinity is selected to be 3.

3. The method for intelligently adding lime into a large converter according to claim 1, wherein the method comprises the following steps: in the step (2), the BP neural network structure is a structure of 3 hidden layers, the number of nodes of a first hidden layer is 3, the number of nodes of a second hidden layer is 50, and the number of nodes of a third hidden layer is 5.

4. The method for intelligently adding lime into a large converter according to claim 1, wherein the method comprises the following steps: in the step (2), training data of artificial intelligent prediction are screened by a data preference rule model, data acquisition errors or no typicality data are screened out, the model calculation precision is improved, the interference of error data on model calculation is reduced, and the specific preference rule is as follows:

selecting the weight of molten iron and the weight of scrap steel in a range of 280t to 320 t;

the silicon content percentage of the molten iron is less than 0.6 percent, and the selection is preferred;

thirdly, selecting lime with the addition amount of more than 6t preferentially;

fourthly, the addition amount of the light-burned dolomite is more than 2t, and the selection is preferred;

fifthly, when the temperature of molten iron is higher than 1320 ℃, the amount of scrap steel is lower than 30t, and the mass percent of silicon in the molten iron is higher than 0.3%, adding ore, and selecting preferentially.

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