CN115662537A - Converter tapping amount prediction method and system - Google Patents
Converter tapping amount prediction method and system Download PDFInfo
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Abstract
The invention belongs to the technical field of ferrous metallurgy, and particularly relates to a converter tapping amount prediction method and system based on a composite prediction model.
Description
Technical Field
The invention relates to the technical field of ferrous metallurgy, in particular to a converter tapping quantity prediction method and system based on a composite prediction model.
Background
At present, the domestic crude steel production is mainly carried out by a long process represented by a blast furnace-converter, the metal raw materials produced by the converter mainly comprise molten iron and scrap steel, and the auxiliary materials mainly comprise lime, dolomite, some iron-containing cold materials and the like. The molten steel obtained finally mainly comes from the conversion of metal materials, but in the smelting process, a part of iron elements are oxidized to become smoke dust and enter a dust removal flue, a part of iron elements enter steel slag, and a part of iron elements are lost due to ways such as splashing and the like, the iron loss of the ways is difficult to quantify accurately, and large change exists between furnaces. With the continuous improvement of the detection level, before smelting begins, information such as the weight of molten iron, the components of the molten iron and the like can be accurately obtained, but the sources of scrap steel are very refuted, and the classification means of the scrap steel is limited at present, so that the information of the components of the scrap steel entering a furnace cannot be accurately obtained, and the information brings great difficulty for accurately predicting the steel output.
Some steel mills are equipped with weighing devices on ladle cars behind furnaces, but the specific numerical value of the steel tapping amount can be accurately obtained after the steel tapping is finished. However, in the actual production process, alloying is often completed in the tapping process, and if the alloy is weighed again after tapping is completed to obtain accurate molten steel amount, the time rhythm is slowed down, and the efficient smelting is influenced. Therefore, many enterprises add alloy according to the previous experience steel tapping amount correction term and the estimated steel tapping amount before steel tapping, which is not favorable for precise control of the alloy and is easy to cause two extreme situations, wherein one extreme situation is that the alloy addition amount is too much, so that the components exceed the target component range and the molten steel quality is influenced. Secondly, the addition amount of the alloy is insufficient, and a large amount of alloy is still supplemented during LF refining, so that the high-efficiency and stable smelting is not facilitated.
Disclosure of Invention
In order to solve the problems in the prior art, the invention mainly aims to provide a converter tapping quantity prediction method and system based on a composite prediction model, based on the mass conservation law, the molten iron converted into molten steel can be calculated through a mechanism model, the yield and loss of other iron elements are comprehensively considered, a data statistical model is established, and the composite prediction model formed by the mechanism model and the statistical model is synthesized, so that the tapping quantity can be accurately predicted before smelting is finished; the steel quantity can be accurately predicted before the smelting of the converter is finished, and important reference is provided for timely and accurate alloy addition.
To solve the above technical problem, according to an aspect of the present invention, the present invention provides the following technical solutions:
a method for predicting the tapping quantity of a converter comprises the following steps:
s1, establishing a model database, and collecting historical production data to form the model database;
s2, establishing a mechanism model, and calculating the molten steel amount W converted from molten iron 1 And calculating the actual steel output and W 1 Difference value W of 2 ；
S3, establishing a statistical model by W 2 As a dependent variable of the statistical model, carrying out stepwise regression by using the collected smelting process indexes including molten iron components, molten iron temperature, lime consumption, light burned dolomite consumption, particle steel consumption, oxygen consumption, end point components and end point temperature as independent variables to obtain the statistical model;
s4, field application, collecting field production data, and substituting into a mechanism model to calculate W 1 Then the steel amount is brought into a statistical model to obtain a steel amount correction term Y, and the steel amount correction term Y are added to obtain a predicted steel amount W 1 +Y。
As a preferable aspect of the method for predicting the tapping amount of the converter according to the present invention, wherein: further comprising:
s5, periodically correcting the model parameters, and performing alloy batching by referring to the tapping quantity; after tapping, the actual production data of the furnace is recorded in a model database and is used for updating the parameters of the periodic statistical model.
As a preferable aspect of the method for predicting the tapping amount of the converter according to the present invention, wherein: in the step S1, when the model database is established, in the production record of the last month, the duplicated data is deleted, the incomplete data is removed, and N furnaces are randomly selected as sample data.
As a preferable aspect of the method for predicting the tapped quantity of the converter according to the present invention, the method comprises: in the step S2, the mechanism model is established without considering other loss paths, and only considering the loss of impurity elements, the mechanism model is calculated by the weight of molten steel converted from molten iron as follows:
W 1 ＝W iron ×(α i -β i )
wherein, W iron Represents the charging amount of molten iron, i represents impurity elements in the molten iron including C, si, mn, P and S, α represents the mass percentage of the impurity elements in the molten iron, and β represents the mass percentage of the impurity elements in the molten steel at the end of the smelting.
W 2 The calculation formula of (a) is as follows:
W 2 ＝W steel -W 1
wherein, W steel Representing the actual tap-off amount of the sample data.
As a preferable aspect of the method for predicting the tapped quantity of the converter according to the present invention, the method comprises: in the step S3, based on the sample data, the W of each furnace is calculated according to the mechanism model 1 Then the actual steel output W is synthesized steel Calculating the difference W between the two 2 In W with 2 As dependent variables, the collected smelting process index items including molten iron components, molten iron temperature, lime consumption, light-burned dolomite consumption, particle steel consumption, oxygen consumption, end point components and end point temperature are used as independent variables to perform stepwise linear regression, and a statistical model of a tapping quantity correction term Y is obtained as follows:
Y＝λ 1 X 1 +λ 2 X 2 +λ 3 X 3 +…+λ m X m +L
where λ represents a model coefficient, X represents a selected index term, m represents the number of index terms, and L represents a constant term.
As a self-service hairThe preferable scheme of the converter tapping quantity prediction method is disclosed, wherein: in the step S4, when the model is actually applied, a plurality of index items exist in the composite prediction model formed based on the mechanism model and the statistical model, the index item data required by the model calculation is collected in real time in the application process, and the index item data is brought into the mechanism model to calculate W 1 Then the steel quantity correction term Y is obtained by introducing the statistical model, and the predicted steel quantity W can be obtained before the smelting is finished 1 + Y, provide important reference for timely and accurate alloy batching.
As a preferable aspect of the method for predicting the tapped quantity of the converter according to the present invention, the method comprises: in the step S5, the model is periodically corrected, after the model is actually applied, the model-guided heat data is recorded in the model database, and the parameter correction of the statistical model is periodically performed according to the step S3.
In order to solve the above technical problem, according to another aspect of the present invention, the present invention provides the following technical solutions:
another object of the present invention is to provide a converter tap-quantity prediction system for implementing the converter tap-quantity prediction method.
Another object of the present invention is to provide an information data processing terminal for implementing the method for predicting a tapped steel amount of a converter.
Another object of the present invention is to provide a computer-readable storage medium, comprising instructions, which when executed on a computer, cause the computer to execute the converter tap-quantity prediction method described above.
The invention has the following beneficial effects:
the invention provides a converter tapping amount prediction method and a converter tapping amount prediction system, based on the mass conservation law, the conversion of molten iron into molten steel amount can be calculated through a mechanism model, the acquisition and loss of other iron elements are comprehensively considered, a data statistical model is established, and a composite prediction model formed by the mechanism model and the statistical model is integrated, so that the converter tapping amount can be accurately predicted, important references are provided for alloy ingredients, the accurate control of the alloy is facilitated, the component hit rate and the steel product stability in the converter steelmaking process are improved, and the converter tapping amount prediction method and the converter tapping amount prediction system have good cost reduction and efficiency improvement effects.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the embodiments or technical solutions of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a schematic view of the steel tapping amount prediction method of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The following will clearly and completely describe the technical solutions in the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a converter tapping quantity prediction method and system based on a composite prediction model, wherein the composite prediction model formed by a comprehensive mechanism model and a statistical model can realize accurate prediction of the converter tapping quantity, provides important reference for alloy batching, is beneficial to accurate control of alloy, improves component hit rate and steel product stability in the converter steelmaking process, and has good cost reduction and efficiency improvement effects.
According to one aspect of the invention, the invention provides the following technical scheme:
a converter tapping amount prediction method comprises the following steps:
s1, establishing a model database, and collecting historical production data to form the model database; when the model database is established, in the production record of the last month, deleting repeated data, eliminating incomplete data, and randomly selecting N furnaces as sample data.
S2Establishing a mechanism model to calculate the molten steel amount W converted from molten iron 1 And calculating the actual steel output and W 1 Difference value W of 2 (ii) a The method is characterized in that other loss ways are not considered in the establishment of a mechanism model, and only from the point of impurity element loss, the mechanism model is calculated by molten steel weight converted by molten iron as follows:
W 1 ＝W iron ×(α i -β i )
wherein, W iron Represents the charging amount of molten iron, i represents impurity elements in the molten iron, including C, si, mn, P, and S, α represents the mass percentage of the impurity elements in the molten iron, and β represents the mass percentage of the impurity elements in the molten steel at the end of the smelting.
W 2 The calculation formula of (a) is as follows:
W 2 ＝W steel -W 1
wherein, W steel Representing the actual tap-off amount of the sample data.
S3, establishing a statistical model by W 2 As a dependent variable of the statistical model, carrying out stepwise regression by using the collected smelting process indexes including molten iron components, molten iron temperature, lime consumption, light burned dolomite consumption, particle steel consumption, oxygen consumption, end point components and end point temperature as independent variables to obtain the statistical model; based on sample data, calculating W of each furnace according to a mechanism model 1 Then the actual steel tapping amount W is synthesized steel Calculating the difference W between the two 2 In W with 2 Taking collected smelting process index items including molten iron components, molten iron temperature, lime consumption, light burned dolomite consumption, particle steel consumption, oxygen consumption, end point components and end point temperature as independent variables, and performing stepwise linear regression to obtain a statistical model of a tapping quantity correction term Y as follows:
Y＝λ 1 X 1 +λ 2 X 2 +λ 3 X 3 +…+λ m X m +L
where λ represents a model coefficient, X represents a selected index term, m represents the number of index terms, and L represents a constant term.
S4, field application and collectionOn-site production data, carry-over to mechanistic model calculation W 1 Then, the steel amount is introduced into a statistical model to obtain a steel amount correction term Y, and the steel amount correction term Y are added to obtain a predicted steel amount W 1 + Y. When the model is actually applied, a plurality of index items exist in a composite prediction model formed on the basis of a mechanism model and a statistical model, index item data required by model calculation are collected in real time in the application process, and are brought into the mechanism model to calculate W 1 Then the steel amount is brought into a statistical model to obtain a steel amount correction term Y, and the predicted steel amount W can be obtained before smelting is finished 1 + Y, provide important reference for timely and accurate alloy batching.
S5, periodically correcting the model parameters, and performing alloy batching by referring to the tapping amount; after tapping, the actual production data of the furnace is recorded in a model database and is used for periodically updating the parameters of the statistical model; and (3) periodically correcting the model, namely after the model is actually applied, recording the model guide heat data in a model database, and periodically correcting the parameters of the statistical model according to the step S3.
Another object of the present invention is to provide a converter tap-quantity prediction system for implementing the converter tap-quantity prediction method.
Another object of the present invention is to provide an information data processing terminal for implementing the method for predicting a tapped steel amount of a converter.
Another object of the present invention is to provide a computer-readable storage medium, comprising instructions which, when run on a computer, cause the computer to execute the converter tap-quantity prediction method described above.
The technical solution of the present invention is further illustrated by the following specific examples.
Example 1
A method for predicting the tapping quantity of a converter comprises the following steps:
s1, establishing a model database, deleting repeated data and defective data in production records of a month, and randomly selecting 180-furnace production data as sample data;
s2, calculating W by adopting the established mechanism model 1 And W 2 Extracting molten iron loading amount and molten iron composition of 180-furnace sample dataThe information on the composition of the end point of smelting and the actual amount of tapped steel is substituted into the following formula to calculate W of each furnace 1 And W 2 ；
W 1 ＝W iron ×(α i -β i )
Wherein, W iron Represents the charging amount of molten iron, i represents impurity elements in the molten iron including C, si, mn, P and S, α represents the mass percentage of the impurity elements in the molten iron, and β represents the mass percentage of the impurity elements in the molten steel at the end of the smelting.
W 2 The calculation formula of (a) is as follows:
W 2 ＝W steel -W 1
wherein, W steel Representing the actual tap-off amount of the sample data.
S3, establishing a statistical model by W 2 As a dependent variable of the statistical model, the step-by-step regression is carried out by taking the collected smelting process indexes such as molten iron composition, molten iron temperature, lime consumption, light burned dolomite consumption, particle steel consumption, oxygen consumption, end point composition, end point temperature and the like as independent variables to obtain the statistical model of the steel tapping quantity correction term Y as follows:
Y＝0.908X 1 +0.832X 2 +1.380X 3 -4.874
in the formula, X 1 Indicates the amount of scrap charged, X 2 Denotes the C content, X, of the molten iron 3 Represents the Si content of the molten iron, model R 2 =0.904, which shows that the composite predictive model fits the raw data well.
S4, field application, namely acquiring index item data required by model calculation in real time based on a mechanism model and a statistical model, and substituting the index item data into the mechanism model to calculate W 1 Then the steel is brought into a statistical model to obtain a steel-tapping quantity correction term Y, and the final steel-tapping quantity is W 1 + Y, as shown in Table 1:
table 1 example 1 effect of application of composite prediction model
As can be seen from the table 1, the error between the actual steel tapping amount and the steel tapping amount of 10 continuous production furnaces predicted based on the composite prediction model is +/-2%, which indicates that the model prediction precision is high, the steel tapping amount can be well predicted, and guidance and reference are provided for alloying.
S5, correcting model parameters, namely performing alloy batching according to the calculated steel quantity, after tapping is finished, recording 10 furnaces of data in a model database, taking 190 furnaces of sample data, and periodically updating and correcting the model parameters, please refer to example 2.
Example 2
A method for predicting the tapping quantity of a converter comprises the following steps:
s1, selecting sample data from a database, and totaling 190 furnaces;
s2, extracting the information of the molten iron loading amount, the molten iron components, the smelting end point components, the actual steel tapping amount and the like of 190 furnace sample data, and substituting the information into the following formula to calculate the W of each furnace 1 And W 2 ；
W 1 ＝W iron ×(α i -β i )
Wherein, W iron Represents the charging amount of molten iron, i represents impurity elements in the molten iron including C, si, mn, P and S, α represents the mass percentage of the impurity elements in the molten iron, and β represents the mass percentage of the impurity elements in the molten steel at the end of the smelting.
W 2 The calculation formula of (a) is as follows:
W 2 ＝W steel -W 1
wherein, W steel Representing the actual tap-off amount of the sample data.
S3, establishing a statistical model by W 2 As a dependent variable of the statistical model, the step-by-step regression is carried out by taking the collected smelting process indexes such as molten iron composition, molten iron temperature, lime consumption, light burned dolomite consumption, particle steel consumption, oxygen consumption, end point composition, end point temperature and the like as independent variables to obtain the tappingStatistical model of the quantitative correction term Y:
Y＝0.915X 1 +0.824X 2 +1.219X 3 -4.937
in the formula, X 1 Indicates the amount of scrap charged, X 2 Denotes the C content, X, of the molten iron 3 Indicating the Si content of the molten iron. Model R 2 =0.905, which shows that the composite predictive model can fit the raw data well.
S4, field application, namely acquiring index item data required by model calculation in real time based on a mechanism model and a statistical model, and substituting the index item data into the mechanism model to calculate W 1 Then the steel is brought into a statistical model to obtain a steel-tapping quantity correction term Y, and the final steel-tapping quantity is W 1 +Y；
Table 2 example 2 effect of application of composite prediction model
As can be seen from Table 2, the error between the 30-heat tapping amount of continuous production calculated based on the composite prediction model and the actual tapping amount is +/-2%, which indicates that the model has high prediction precision, can well predict the tapping amount and provides guidance and reference for alloying.
S5, correcting model parameters, namely performing alloy batching according to the calculated steel quantity, after tapping is finished, recording 30 furnaces of data in a model database, sampling data of 220 furnaces, and periodically updating and correcting the model parameters, please refer to example 3.
Example 3
A method for predicting the tapping quantity of a converter comprises the following steps:
s1, selecting sample data from a database, and totaling 220 furnaces;
s2, extracting the information of the molten iron loading amount, the molten iron components, the smelting end point components, the actual steel tapping amount and the like of 220 furnace sample data, and substituting the information into the following formula to calculate the W of each furnace 1 And W 2 ；
W 1 ＝W iron ×(α i -β i )
Wherein, W iron Represents the charging amount of molten iron, i represents impurity elements in the molten iron including C, si, mn, P and S, α represents the mass percentage of the impurity elements in the molten iron, and β represents the mass percentage of the impurity elements in the molten steel at the end of the smelting.
W 2 The calculation formula of (a) is as follows:
W 2 ＝W steel -W 1
wherein, W steel Representing the actual tap-off amount of the sample data.
S3, establishing a statistical model by W 2 As a dependent variable of the statistical model, the step-by-step regression is carried out by taking the collected smelting process indexes such as molten iron components, molten iron temperature, lime consumption, light burned dolomite consumption, particle steel consumption, oxygen consumption, end point components, end point temperature and the like as independent variables to obtain the statistical model of the steel tapping quantity correction term Y:
Y＝0.914X 1 +0.9X 2 +1.695X 3 +10.201X 4 -5.683
in the formula, X 1 Represents the scrap charging amount, X 2 Denotes the C content, X, of the molten iron 3 Denotes the Si content, X, of the molten iron 4 Indicating the S content of the molten iron. Model R 2 =0.908, which shows that the composite predictive model fits the raw data well.
S4, field application, namely acquiring index item data required by model calculation in real time based on a mechanism model and a statistical model, and substituting the index item data into the mechanism model to calculate W 1 Then, the steel is brought into a statistical model to obtain a steel tapping quantity correction term Y, and the final steel tapping quantity is W 1 +Y；
S5, correcting model parameters, carrying out alloy batching according to the calculated steel quantity, after steel discharge is finished, recording the model-guided heat production data in a model database, periodically updating and correcting the model parameters according to the step 3, and adjusting the correction period according to actual needs.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the present specification and directly/indirectly applied to other related technical fields within the spirit of the present invention are included in the scope of the present invention.
Claims (10)
1. A method and a system for predicting the tapping quantity of a converter are characterized by comprising the following steps:
s1, establishing a model database, and collecting historical production data to form the model database;
s2, establishing a mechanism model, and calculating the molten steel amount W converted from molten iron 1 And calculating the actual steel tapping amount and W 1 Difference value W of 2 ；
S3, establishing a statistical model by W 2 As a dependent variable of the statistical model, carrying out stepwise regression by using the collected smelting process indexes comprising molten iron components, molten iron temperature, lime consumption, light-burned dolomite consumption, particle steel consumption, oxygen consumption, end point components and end point temperature as independent variables to obtain a statistical model;
s4, field application is carried out, field production data are collected, and a mechanism model is substituted to calculate W 1 Then, the steel amount is introduced into a statistical model to obtain a steel amount correction term Y, and the steel amount correction term Y are added to obtain a predicted steel amount W 1 +Y。
2. The method for predicting the tapping amount of the converter according to claim 1, further comprising:
s5, periodically correcting the model parameters, and performing alloy batching by referring to the tapping quantity; after tapping, the actual production data of the furnace is recorded in a model database and is used for updating the parameters of the periodic statistical model.
3. The method for predicting the tapping amount of the converter according to claim 1 or 2, wherein in the step S1, when the model database is established, in the production record of the last month, repeated data is deleted, incomplete data is removed, and N furnaces are randomly selected as sample data.
4. The method for predicting the tapping amount of the converter according to claim 1 or 2, wherein in the step S2, the mechanism model is established without considering other loss paths, and only from the viewpoint of the loss of impurity elements, the mechanism model for calculating the weight of the molten steel transformed from the molten iron is as follows:
W 1 ＝W iron ×(α i -β i )
wherein, W iron Represents the charging amount of molten iron, i represents impurity elements in the molten iron including C, si, mn, P and S, α represents the mass percentage of the impurity elements in the molten iron, and β represents the mass percentage of the impurity elements in the molten steel at the end of the smelting.
W 2 The calculation formula of (a) is as follows:
W 2 ＝W steel -W 1
wherein, W steel Representing the actual tap-off amount of the sample data.
5. The method for predicting tapping amount of a converter according to claim 1 or 2, wherein in the step S3, W for each furnace is calculated based on the sample data and the mechanism model 1 Then the actual steel output W is synthesized steel Calculating the difference W between the two 2 In W with 2 Taking collected smelting process index items including molten iron components, molten iron temperature, lime consumption, light burned dolomite consumption, particle steel consumption, oxygen consumption, end point components and end point temperature as independent variables, and performing stepwise linear regression to obtain a statistical model of a tapping quantity correction term Y as follows:
Y＝λ 1 X 1 +λ 2 X 2 +λ 3 X 3 +…+λ m X m +L
where λ represents a model coefficient, X represents a selected index term, m represents the number of index terms, and L represents a constant term.
6. The method for predicting tapping quantity of a converter according to claim 1 or 2, wherein in step S4, when the model is actually applied, a plurality of index items exist in a composite prediction model formed based on the mechanism model and the statistical model, data of the index items required by model calculation are collected in real time in the application process, and the data are substituted into the mechanism model to calculate W 1 Then the steel quantity correction term Y is obtained by introducing the statistical model, and the predicted steel quantity W can be obtained before the smelting is finished 1 + Y, provide important reference for timely and accurate alloy batching.
7. The method for predicting tapping amount of a converter according to claim 1 or 2, wherein the model is periodically modified in step S5, and after the model is actually applied, the model-guided heat data is recorded in the model database, and the parameter modification of the statistical model is periodically performed according to step S3.
8. A converter tap-amount prediction system that implements the converter tap-amount prediction method of any one of claims 1 to 7.
9. An information data processing terminal for implementing the method for predicting the tapped quantity of the converter according to any one of claims 1 to 7.
10. A computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the converter tap-quantity prediction method of any one of claims 1 to 7.
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