Automatic control system and method for steelmaking alloy feeding amount

Abstract

The invention discloses an automatic control system for the addition amount of steelmaking alloys. The system collects production data, secondary data and inspection data, uses the collected data to determine the optimization mode of elements, calculates the optimization type of elements, pre-processes the data to obtain standard data, performs compensation calculation on the element set values, optimizes the addition amount of scrap steel and alloys to obtain optimization calculation results, executes, displays and stores the optimization calculation results, realizes automatic control of scrap steel and alloys in the steelmaking process, reduces the waste of steelmaking costs due to human judgment, reduces the fluctuation of molten steel composition after steelmaking, and improves the hit rate of molten steel and the level of automatic control in the steelmaking process.

Description

Technical Field

[0001] The present invention relates to the technical field of steelmaking control, and in particular to an automatic control system and method for steelmaking alloy feeding amount. Background Art

[0002] Over the past decade, steel companies have significantly increased their use of scrap steel in the steelmaking process, which not only reduces carbon emissions but also significantly lowers steel production costs. At the same time, intensified competition in the steel industry has led steel companies to increasingly prioritize reducing process costs, with controlling alloy costs in the steelmaking process being particularly critical. Therefore, maintaining high-precision composition accuracy in the steelmaking process while further reducing alloy costs, even with high scrap steel usage, has become a key challenge for steel companies.

[0003] Currently, the addition of scrap steel and alloys to steelmaking relies primarily on empirical estimates and manual adjustments by operators based on compositional testing results. This empirically based alloy addition is subject to significant systematic deviations, leading to compositional fluctuations and increased alloy costs. Currently, the steel industry is focusing on methods and equipment for adding scrap steel and alloys.

[0004] Therefore, it is necessary to further develop automatic control models for scrap steel and alloys in the steelmaking process to improve the hit rate of components and further reduce steelmaking alloys. Summary of the Invention

[0005] The object of the present invention is to provide an automatic control system for the addition amount of steelmaking alloys, which realizes high-precision composition hit and low-cost control of steelmaking alloys under the condition of high scrap steel addition. The control model collects scrap steel and alloy addition data, molten steel weighing data, inspection and testing composition data, steel grade composition setting value data, steel grade composition fixed compensation data, scrap steel and alloy composition setting value data, scrap steel and alloy price data respectively through a data acquisition module; the collected data are corrected for incoming material composition, calculated element type, data preprocessing, self-learning compensation, linkage compensation, comprehensive compensation, and optimization calculation are performed through an optimization model module, thereby obtaining optimization result data of scrap steel and alloys; the optimization result data are operated and executed through a sending execution module; the optimization result data are presented to operators and technicians in real time through a terminal display module; the optimization result data are stored in a database, analyzed, and self-learned by a model through a data storage module.

[0006] The technical problem solved by the present invention is: an automatic optimization control model for scrap steel and alloy in the steelmaking process.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An automatic control system for steelmaking alloy feeding amount comprises the following steps:

[0009] Step 1: Based on the current smelting process and smelting heat, collect data from the previous steelmaking process and the current process;

[0010] Step 2: Calculate the data collected in step 1 through the pattern determination program to obtain the incoming material composition correction data for the current smelting heat;

[0011] Step 3: Based on the incoming material composition correction data in step 2, calculate the composition elements of the current heat through the element type calculation program to obtain the element control type;

[0012] Step 4: preprocessing the collected data through a data preprocessing program according to the control type of the mode and element to obtain preprocessed data;

[0013] Step 5: Calculate the comprehensive compensation value of each element based on the pre-processed data, the control type of the element, the self-learning compensation program, the linkage compensation program, and the comprehensive compensation program, and then calculate the compensated data;

[0014] Step 6: Based on the pre-processed data, the element control type, and the compensated data, the optimization calculation program is used to perform the optimization calculation of the alloy addition for the current heat to obtain the optimization result data of the alloy addition;

[0015] Step 7: Based on the optimization result data of the alloy addition, the data is sent for execution, displayed and stored through the secondary system.

[0016] As a further solution of the present invention: in step one, the collected data includes scrap steel and alloy addition data, molten steel weighing data, inspection and testing composition data, steel grade composition setting value data, steel grade composition fixed compensation data, scrap steel and alloy composition setting value data, and scrap steel and alloy price data.

[0017] As a further solution of the present invention: in step 2, the mode determination program is used to calculate the incoming material composition correction data used in the current heat, and the modes include a test mode and a theoretical calculation mode;

[0018] In most scenarios, the inspection mode uses the incoming material inspection and composition test data of the current process to achieve high reliability, and the optimization calculation of alloy addition is directly performed based on the inspection and composition data.

[0019] The theoretical calculation model is that in a few scenarios, the reliability of the incoming material inspection and testing composition of the current process is low and cannot be directly used for the optimization calculation of alloy addition.

[0020] As a further solution of the present invention: the calculation of the correction data of the incoming material composition is carried out in a test mode or a theoretical calculation mode;

[0021] In the inspection mode, the incoming material composition correction data is the incoming material inspection composition data of the current process;

[0022] In the theoretical calculation mode, the incoming material composition correction data is the incoming material calculated composition of the current process, which is calculated based on the weight of the incoming molten steel of the previous process, the incoming material inspection and testing composition of the previous process, the weight of the alloy and scrap steel added in the previous process, and the set composition of the alloy and scrap steel added in the previous process.

[0023] As a further solution of the present invention: in step three, the control type of the element includes non-calculated element, upper limit element, target value element and non-warning element.

[0024] As a further solution of the present invention: in step 4, data preprocessing is to extract features, integrate data and clean data from the collected data to obtain standard data.

[0025] As a further solution of the present invention: in step 5, the comprehensive compensation value includes a fixed compensation value, a linkage compensation value, and a self-learning compensation value;

[0026] The fixed compensation value is the fixed compensation data of the steel composition of the collected data;

[0027] The linkage compensation value is calculated by the linkage compensation program based on the pre-processed data and the control type of the element;

[0028] The self-learning compensation value is calculated by the self-learning compensation program based on the pre-processed data and the control type of the element.

[0029] As a further solution of the present invention: the compensated data is used to compensate for different set value types of each element according to the comprehensive compensation value, and the calculation formula is as follows:

[0030]

[0031] Among them, i is the set value type, including maximum value, minimum value, and target value; j is the name of the element; Set the initial value of type i for element j; is the comprehensive compensation value of the set value type i for element j; Sets the value of element j to the compensated value of type i.

[0032] As a further solution of the present invention: in step 6, the boundary conditions of the optimization calculation program include the following 4 types;

[0033] Furthermore, the target value of element j corresponds to the boundary condition:

[0034] XO 0j +X1A 1j +…+X m Amj =(X0+X1+…+X m )Y j ;

[0035] Furthermore, the boundary condition corresponding to the minimum value of element j is:

[0036] XO 0j +X1A 1j +…+X m A mj ≥(X0+X1+…+X m )Y min,j ;

[0037] Furthermore, the boundary condition corresponding to the maximum value of element j is:

[0038] XO 0j +X1A 1j +…+X m A mj ≤(X0+X1+…+X m )Y max，j ;

[0039] Furthermore, the boundary conditions corresponding to the addition of scrap steel and alloy are:

[0040] X i ≥0;

[0041] Furthermore, the boundary conditions corresponding to the total amount of scrap steel and alloy added are:

[0042] X1+…+X m ≤w total ;

[0043] Where j is the name of the element; X0 is the weight of the incoming molten steel from the previous process; a 0j is the test content of element j in the incoming material of the previous process; m is the total number of types of alloy or scrap steel added in the previous process; i is the number of alloy and scrap steel added in the previous process; X i A is the added weight of the i-th alloy or scrap steel; i,j Y is the target value content of element j in the i-th alloy or scrap steel; min,j is the minimum value of steel element j; Y max,j is the maximum value of steel element j; w total The upper limit of the weight of scrap steel and alloys allowed to be added to molten steel.

[0044] As a further solution of the present invention: an automatic control system for steelmaking alloy feeding amount, comprising:

[0045] The data acquisition module is used to obtain data of the previous steelmaking process and the current process according to the current smelting process and smelting heat, and send the collected data to the optimization model module through electrical means;

[0046] Optimization model module: The optimization model module is used to calculate the collected data through the pattern determination program to obtain the correction data of the incoming material composition of the current smelting furnace;

[0047] According to the incoming material composition correction data, the optimization model module calculates the composition elements of the current heat through the element type calculation program to obtain the control type of the element;

[0048] According to the control type of the mode and element, the optimization model module preprocesses the collected data through a data preprocessing program to obtain preprocessed data;

[0049] According to the pre-processed data, the control type of the element, the self-learning compensation program, the linkage compensation program, and the comprehensive compensation program, the comprehensive compensation value of each element is calculated, and then the compensated data is calculated;

[0050] According to the pre-processed data, element control type and compensated data, the optimization calculation program is used to perform the optimization calculation of alloy addition for the current heat to obtain the optimization result data of alloy addition;

[0051] The execution module is used to execute the optimization result data of alloy addition through the secondary system;

[0052] Terminal display module, which is used to present the optimization result data of alloy addition to operators and technicians in real time;

[0053] The data storage module is used to store and analyze the optimization result data of alloy addition in the database.

[0054] The beneficial effects of the present invention are as follows: the present invention collects scrap steel and alloy addition data, molten steel weighing data, inspection and testing composition data, steel grade composition setting value data, steel grade composition fixed compensation data, scrap steel and alloy composition setting value data, and scrap steel and alloy price data through a data acquisition module; performs incoming material composition correction, element type calculation, data preprocessing, self-learning compensation, linkage compensation, comprehensive compensation, and optimization calculation on the collected data through an optimization model module, thereby obtaining optimization result data of scrap steel and alloy; operates and executes the optimization result data through a sending execution module; presents the optimization result data to operators and technicians in real time through a terminal display module; and stores and analyzes the optimization result data in a database through a data storage module, thereby realizing automatic control of scrap steel and alloys during the steelmaking process, reducing the waste of steelmaking costs due to human judgment, reducing the fluctuation of molten steel composition after steelmaking, and improving the molten steel hit rate and automation control level during the steelmaking process. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The present invention will be further described below with reference to the accompanying drawings.

[0056] Figure 1 It is a process flow chart of the present invention;

[0057] Figure 2 It is a flowchart of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0059] Example 1

[0060] See also Figure 1 As shown, the present invention is a method for automatically controlling the amount of alloy feed in steelmaking, comprising the following steps:

[0061] Step 1: Based on the current smelting process and smelting heat, collect data from the previous steelmaking process and the current process;

[0062] The data collected in step 1 include scrap steel and alloy addition data, molten steel weighing data, test composition data, steel grade composition setting value data, steel grade composition fixed compensation data, scrap steel and alloy composition setting value data, and scrap steel and alloy price data;

[0063] Furthermore, the added data of scrap steel and alloys include but are not limited to the previous process smelting heat, current smelting heat, process, steel grade, type of added scrap steel, weight of each type of scrap steel, type of added alloy, and weight of each type of alloy added;

[0064] Furthermore, weighing data includes but is not limited to smelting heat, molten steel weight, and process;

[0065] Furthermore, the test composition data includes the smelting furnace, process, test batch, and the test value of each element content;

[0066] Furthermore, the steel grade component setting value data includes but is not limited to the steel grade, process, and setting values ​​of each element, wherein the setting values ​​of the elements include minimum value, target value, and maximum value;

[0067] Furthermore, the fixed compensation data of the steel grade composition is the compensation value of the elements contained in the steel grade, which is used to compensate and correct the set value of each element;

[0068] Furthermore, methods for obtaining composition setting value data for scrap steel and alloys include but are not limited to the following two methods: 1. Setting fixed values ​​based on experience; 2. Calculating the composition setting data based on the inspection composition data of this type of scrap steel or alloy over a certain period. The certain period is generally one of the batch, week, half month, or month; the calculation method includes but is not limited to the average, median, and weighted average.

[0069] Furthermore, methods for obtaining price data of scrap steel and alloys include but are not limited to the following:

[0070] 1. Set fixed values ​​based on experience;

[0071] 2. Calculating the composition setting data based on the purchasing data of the type of scrap steel or alloy for a certain period;

[0072] The certain period generally includes the current batch, week, half month or month; the calculation method includes but is not limited to the average, median, and weighted average;

[0073] Step 2: Calculate the data collected in step 1 through the pattern determination program to obtain the incoming material composition correction data for the current smelting heat;

[0074] The mode determination program is used to calculate the incoming material composition correction data used in the current heat, and the modes include but are not limited to the following: 1. Inspection mode, 2. Theoretical calculation mode;

[0075] Furthermore, the inspection mode is that in most scenarios, the incoming material inspection and composition testing data of the current process has high reliability, and the optimization calculation of alloy addition is directly used based on the inspection and composition data;

[0076] Among them, most scenarios include but are not limited to: Argon station process, LF process after RH, RH process after LF, second addition of LF process, second addition after RH, LF process and RH process after adding a small amount of alloy at the Argon station;

[0077] In the inspection mode, the incoming material composition correction data is the incoming material inspection composition data of the current process;

[0078] Furthermore, the theoretical calculation model is that in a few scenarios, the reliability of the incoming material inspection and testing composition of the current process is low and cannot be directly used for the optimization calculation of alloy addition;

[0079] Among them, a few scenarios include but are not limited to: LF process or RH process after adding a lot of scrap steel and alloy to the argon station;

[0080] In the theoretical calculation mode, the incoming material composition correction data is the incoming material calculation composition Y of the current process j It is calculated based on the weight of the incoming molten steel from the previous process, the composition of the incoming material tested in the previous process, the weight of the alloy and scrap steel added in the previous process, and the set composition of the alloy and scrap steel added in the previous process. The calculation formula is as follows:

[0081]

[0082] Where j is the name of the element; X0 is the weight of the incoming molten steel from the previous process; Y 0j is the test content of element j in the incoming material of the previous process; m is the total number of types of alloy or scrap steel added in the previous process; i is the number of alloy and scrap steel added in the previous process; X i is the added weight of the i-th alloy or scrap steel; Y i,j is the content of element j in the i-th alloy or scrap steel;

[0083] Step 3: Based on the incoming material composition correction data in step 2, calculate the composition elements of the current heat through the element type calculation program to obtain the element control type;

[0084] Among them, the control types of elements include non-calculated elements, upper limit elements, target value elements and non-warning elements;

[0085] Uncalculated elements do not require optimization calculations;

[0086] The composition of the upper limit element needs to be between the set minimum value and the set maximum value;

[0087] The composition of the target value element needs to be equal to the set target value;

[0088] Non-warning elements are elements that exceed or approach the maximum value of the element and do not require warnings;

[0089] Step 4: Preprocessing the collected data using a data preprocessing program according to the control type of the mode and element to obtain preprocessed data;

[0090] Among them, the data preprocessing program is used to extract features, integrate data, and clean data to obtain standard data;

[0091] Step 5: Calculate the comprehensive compensation value of each element based on the pre-processed data, the control type of the element, the self-learning compensation program, the linkage compensation program, and the comprehensive compensation program, and then calculate the compensated data;

[0092] Among them, the comprehensive compensation value includes fixed compensation value, linkage compensation value and self-learning compensation value;

[0093] Furthermore, the fixed compensation value is fixed compensation data of the steel composition of the collected data;

[0094] Furthermore, the linkage compensation value is calculated by the linkage compensation program based on the preprocessed data and the control type of the element;

[0095] Furthermore, the self-learning compensation value is calculated by the self-learning compensation program based on the pre-processed data and the control type of the element;

[0096] Furthermore, the comprehensive compensation program calculates the final compensation value of each element based on the fixed compensation value, linkage compensation value, and self-learning compensation value of each element, thereby obtaining the compensated data;

[0097] Among them, the compensated data compensates the different set value types of each element according to the comprehensive compensation value. The calculation formula is as follows:

[0098]

[0099] Among them, i is the set value type, including maximum value, minimum value, and target value; j is the name of the element; Set the initial value of type i for element j; is the comprehensive compensation value of the set value type i for element j; Sets the value of element j to the compensated value of type i.

[0100] Step 6: Based on the pre-processed data, the element control type, and the compensated data, the optimization calculation program is used to perform the optimization calculation of the alloy addition for the current heat to obtain the optimization result data of the alloy addition;

[0101] The pre-processed data includes alloy price, alloy and scrap steel composition, incoming material composition correction data, incoming molten steel weight and steel grade composition setting data;

[0102] Among them, the boundary conditions of the optimization calculation program include the following four types;

[0103] Furthermore, the target value of element j corresponds to the boundary condition:

[0104] XOt 0j +X1A 1j +…+X m A mj =(X0+X1+…+X m )Y j ;

[0105] Furthermore, the boundary condition corresponding to the minimum value of element j is:

[0106] XOt 0j +X1A 1j +…+X m A mj ≥(X0+X1+…+X m )Y min,j ;

[0107] Furthermore, the boundary condition corresponding to the maximum value of element j is:

[0108] XOt 0j +X1A 1j +…+X m A mj ≤(X0+X1+…+X m )Y max,j ;

[0109] Furthermore, the boundary conditions corresponding to the addition of scrap steel and alloy are:

[0110] X i ≥0;

[0111] Furthermore, the boundary conditions corresponding to the total amount of scrap steel and alloy added are:

[0112] X1+…+X m ≤w totol ;

[0113] Wherein, j is the name of the element; X0 is the weight of the incoming molten steel from the previous process; A 0j is the test content of element j in the incoming material of the previous process; m is the total number of types of alloy or scrap steel added in the previous process; i is the number of alloy and scrap steel added in the previous process; X i A is the added weight of the i-th alloy or scrap steel; i,j Y is the target value content of element j in the i-th alloy or scrap steel; min,j is the minimum value of steel element j; Y max,j is the maximum value of steel element j; w totalThe upper limit of the weight of scrap steel and alloys allowed to be added to molten steel;

[0114] Step 7: Based on the optimization result data of the alloy addition, the data is sent for execution, displayed and stored through the secondary system.

[0115] Example 2

[0116] See Figure 2 As shown, the present invention is an automatic control system for steelmaking alloy feeding amount, including a data acquisition module, an optimization model module, a sending execution module, a terminal display module and a data storage module;

[0117] The data acquisition module is used to obtain data of the previous steelmaking process and the current process according to the current smelting process and smelting heat, and send the collected data to the optimization model module through electrical means;

[0118] Optimization model module: The optimization model module is used to calculate the collected data through the pattern determination program to obtain the correction data of the incoming material composition of the current smelting furnace;

[0119] According to the incoming material composition correction data, the optimization model module calculates the composition elements of the current heat through the element type calculation program to obtain the control type of the element;

[0120] According to the control type of the mode and element, the optimization model module performs data preprocessing on the collected data through a data preprocessing program to obtain preprocessed data;

[0121] According to the pre-processed data, the control type of the element, the self-learning compensation program, the linkage compensation program, and the comprehensive compensation program, the comprehensive compensation value of each element is calculated, and then the compensated data is calculated;

[0122] According to the pre-processed data, element control type and compensated data, the optimization calculation program is used to perform the optimization calculation of alloy addition for the current heat to obtain the optimization result data of alloy addition;

[0123] The execution module is used to execute the optimization result data of alloy addition through the secondary system;

[0124] Terminal display module, which is used to present the optimization result data of alloy addition to operators and technicians in real time;

[0125] The data storage module is used to store and analyze the optimization result data of alloy addition in the database.

[0126] The data acquired by the data acquisition module includes scrap steel and alloy addition data, molten steel weighing data, inspection and testing composition data, steel grade composition setting value data, steel grade composition fixed compensation data, scrap steel and alloy composition setting value data, and scrap steel and alloy price data;

[0127] The added data of scrap steel and alloys include but are not limited to the previous process smelting heat, current smelting heat, process, steel grade, type of added scrap steel, added weight of each type of scrap steel, type of added alloy, and added weight of each type of alloy;

[0128] The weighing data includes but is not limited to smelting heat, molten steel weight, and process;

[0129] The test composition data include smelting furnace, process, test batch, and test value of each element content;

[0130] The steel grade component setting value data includes but is not limited to steel grade, process, and setting values ​​of each element. The setting values ​​of the elements include minimum value, target value, and maximum value;

[0131] The fixed compensation data of the steel grade composition is the compensation value of the elements contained in the steel grade, which is used to compensate and correct the set values ​​of the elements;

[0132] The methods for obtaining the composition setting value data of the scrap steel and alloy include but are not limited to the following two methods: 1. Setting fixed values ​​based on experience; 2. Calculating the composition setting data based on the inspection composition data of a certain period of time for this type of scrap steel or alloy. The certain period is generally one of the batch, week, half month, or month; the calculation methods include but are not limited to the average, median, and weighted average;

[0133] Methods for obtaining the price data for scrap steel and alloys include, but are not limited to, the following: 1. Setting fixed values ​​based on experience; 2. Calculating the composition data based on procurement data for that type of scrap steel or alloy over a specific period. The specific period generally includes a batch, week, biweekly, or monthly period; and the calculation methods include, but are not limited to, average, median, and weighted average.

[0134] The mode determination program is used to calculate the incoming material composition correction data used in the current heat, wherein the modes include but are not limited to the following: 1. inspection mode, 2. theoretical calculation mode;

[0135] In the inspection mode, in most scenarios, the incoming material inspection and composition data of the current process is highly reliable, and the inspection and composition data can be directly used for the optimization calculation of alloy addition; the majority of scenarios include but are not limited to: the argon station process, the LF process after RH, the RH process after LF, the second addition of the LF process, the second addition after RH, the LF process and the RH process after a small amount of alloy addition at the argon station. In this inspection mode, the incoming material composition correction data is the incoming material inspection and composition data of the current process;

[0136] Among them, the theoretical calculation mode is used in a few scenarios where the reliability of the incoming material inspection and testing composition of the current process is low and cannot be directly used for the optimization calculation of alloy addition; the few scenarios include but are not limited to: the LF process or RH process after the argon station adds a lot of scrap steel and alloy. In the theoretical calculation mode, the incoming material composition correction data is the incoming material calculated composition Y j It is calculated based on the weight of the incoming molten steel from the previous process, the composition of the incoming material tested in the previous process, the weight of the alloy and scrap steel added in the previous process, and the set composition of the alloy and scrap steel added in the previous process. The calculation formula is as follows:

[0137]

[0138] Where j is the name of the element; X0 is the weight of the incoming molten steel from the previous process; Y 0j is the test content of element j in the incoming material of the previous process; m is the total number of types of alloy or scrap steel added in the previous process; i is the number of alloy and scrap steel added in the previous process; X i is the added weight of the i-th alloy or scrap steel; Y i,j is the content of element j in the i-th alloy or scrap steel.

[0139] The control types of each element include non-calculated element, upper limit element, target value element, and no warning element;

[0140] Wherein, in the optimization model, the non-calculated elements do not need to be optimized;

[0141] In the optimization model, the composition of the upper limit element needs to be between the set minimum value and the set maximum value;

[0142] Wherein, in the optimization model, the component of the target value element needs to be equal to the set target value;

[0143] Wherein, in the optimization model, the non-warning element is an element that exceeds or approaches the element maximum value and does not require a warning;

[0144] Comprehensive compensation value includes fixed compensation value, linkage compensation value and self-learning compensation value;

[0145] Wherein, the fixed compensation value is the fixed compensation data of the steel composition of the collected data;

[0146] Wherein, the linkage compensation value is calculated by a linkage compensation program based on the pre-processed data and the control type of the element;

[0147] Wherein, the self-learning compensation value is calculated by a self-learning compensation program based on the pre-processed data and the control type of the element;

[0148] The comprehensive compensation program calculates the final compensation value of each element according to the fixed compensation value, linkage compensation value and self-learning compensation value of each element, thereby obtaining the compensated data;

[0149] The compensated data compensates different set value types of each element according to the comprehensive compensation value, and the calculation formula is as follows:

[0150]

[0151] Among them, i is the set value type, including maximum value, minimum value, and target value; j is the name of the element; Set the initial value of type i for element j; is the comprehensive compensation value of the set value type i for element j; The compensated value of type i for element j;

[0152] The pre-processed data includes: alloy price, alloy and scrap steel composition, incoming material composition correction data, incoming molten steel weight, and steel grade composition setting data;

[0153] The boundary conditions of the optimization calculation program include the following four types:

[0154] Among them, the target value of element j corresponds to the boundary condition:

[0155] XO 0j +X1A 1j +…+X m A mj =(X0+X1+…+X m )Y j

[0156] Among them, the boundary condition corresponding to the minimum value of element j is:

[0157] XO 0j +X1A 1j +…+X m A mj ≥(X0+X1+…+X m )Y min,j

[0158] Among them, the boundary condition corresponding to the maximum value of element j is:

[0159] XO 0j +X1A 1j +…+X m A mj ≤(X0+X1+…+X m )Y max,j

[0160] Among them, the boundary conditions corresponding to the addition amount of scrap steel and alloy are:

[0161] X i ≥0

[0162] Among them, the boundary conditions corresponding to the total amount of scrap steel and alloy added are:

[0163] X1+…+X m ≤w total

[0164] Wherein, j is the name of the element; X0 is the weight of the incoming molten steel from the previous process; A 0j is the test content of element j in the incoming material of the previous process; m is the total number of types of alloy or scrap steel added in the previous process; i is the number of alloy and scrap steel added in the previous process; X i A is the added weight of the i-th alloy or scrap steel; i,j Y is the target value content of element j in the i-th alloy or scrap steel; min,j is the minimum value of steel element j; Y max,j is the maximum value of steel element j; w totol The upper limit of the weight of scrap steel and alloys allowed to be added to molten steel.

[0165] The execution module is used to execute the optimization result data of alloy addition through the secondary system;

[0166] Terminal display module, which is used to present the optimization result data of alloy addition to operators and technicians in real time;

[0167] The data storage module is used to store and analyze the optimization result data of alloy addition in the database.

[0168] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for automatically controlling the amount of steelmaking alloy feed, characterized in that: The following steps are involved: Step 1: Based on the current smelting process and smelting heat, collect data from the previous steelmaking process and the current process; Step 2: Calculate the data collected in step 1 through the pattern determination program to obtain the incoming material composition correction data for the current smelting heat; Step 3: Based on the incoming material composition correction data in step 2, calculate the composition elements of the current heat through the element type calculation program to obtain the element control type; Step 4: Preprocessing the collected data using a data preprocessing program according to the control type of the mode and element to obtain preprocessed data; Step 5: Calculate the comprehensive compensation value of each element based on the pre-processed data, the control type of the element, the self-learning compensation program, the linkage compensation program, and the comprehensive compensation program, and then calculate the compensated data; Step 6: Based on the pre-processed data, the element control type, and the compensated data, the optimization calculation program is used to perform the optimization calculation of the alloy addition for the current heat to obtain the optimization result data of the alloy addition; Step 7: Based on the optimization result data of the alloy addition, the data is sent for execution, displayed and stored through the secondary system.

2. The method for automatically controlling the amount of steelmaking alloy feed according to claim 1, characterized in that: In step 1, the collected data include scrap steel and alloy addition data, molten steel weighing data, inspection and testing composition data, steel grade composition setting value data, steel grade composition fixed compensation data, scrap steel and alloy composition setting value data, and scrap steel and alloy price data.

3. The automatic control method of the amount of steel-making alloy feed according to claim 1, characterized in that: In step 2, the mode determination program is used to calculate the incoming material composition correction data used in the current heat, and the modes include the inspection mode and the theoretical calculation mode; In most scenarios, the inspection mode uses the incoming material inspection and composition test data of the current process to achieve high reliability, and the optimization calculation of alloy addition is directly performed based on the inspection and composition data. The theoretical calculation model is that in a few scenarios, the reliability of the incoming material inspection and testing composition of the current process is low and cannot be directly used for the optimization calculation of alloy addition.

4. The method for automatically controlling the amount of steelmaking alloy feed according to claim 3, characterized in that: The calculation of the incoming material composition correction data is carried out through the inspection mode or the theoretical calculation mode; In the inspection mode, the incoming material composition correction data is the incoming material inspection composition data of the current process; In the theoretical calculation mode, the incoming material composition correction data is the incoming material calculated composition of the current process, which is calculated based on the weight of the incoming molten steel of the previous process, the incoming material inspection and testing composition of the previous process, the weight of the alloy and scrap steel added in the previous process, and the set composition of the alloy and scrap steel added in the previous process.

5. The method for automatically controlling the amount of steelmaking alloy feed according to claim 1, characterized in that: In step 3, the control types of elements include non-calculated elements, upper limit elements, target value elements, and non-warning elements.

6. The method for automatically controlling the amount of steelmaking alloy feed according to claim 1, characterized in that: In step four, data preprocessing involves feature extraction, data integration, and data cleaning of the collected data to obtain standard data.

7. The method for automatically controlling the amount of steelmaking alloy feed according to claim 1, characterized in that: In step 5, the comprehensive compensation value includes fixed compensation value, linkage compensation value, and self-learning compensation value; The fixed compensation value is the fixed compensation data of the steel composition of the collected data; The linkage compensation value is calculated by the linkage compensation program based on the pre-processed data and the control type of the element; The self-learning compensation value is calculated by the self-learning compensation program based on the pre-processed data and the control type of the element.

8. The method for automatically controlling the amount of steelmaking alloy feed according to claim 7, characterized in that: The compensated data compensates for the different set value types of each element according to the comprehensive compensation value. The calculation formula is as follows: Among them, i is the set value type, including maximum value, minimum value, and target value; j is the name of the element; Set the initial value of type i for element j; is the comprehensive compensation value of the set value type i for element j; Sets the value of element j to the compensated value of type i.

9. The method for automatically controlling the amount of alloy feed for steelmaking according to claim 8, characterized in that: In step six, the boundary conditions of the optimization calculation program include: The target value of element j corresponds to the boundary condition: X0A 0j +X1A 1j +…+X m FLUENT mj =(X0+X1+…+X m )Y j 4 The boundary conditions corresponding to the minimum value of element j are: X0A 0j +X1A 1j +…+X m FLUENT mj ≥(X0+X1+…+X m )Y min,j 4 The boundary conditions corresponding to the maximum value of element j are: X0A 0j +X1A 1j +…+X m FLUENT mj ≤(X0+X1+…+X m )Y max,j 4 The boundary conditions corresponding to the addition amount of scrap steel and alloy are: X i ≥0； The total amount of scrap steel and alloy added corresponds to the boundary conditions: X1+…+X m ≤w total ; Wherein, j is the name of the element; X0 is the weight of the incoming molten steel from the previous process; A 0j is the test content of element j in the incoming material of the previous process; m is the total number of types of alloy or scrap steel added in the previous process; i is the number of alloy and scrap steel added in the previous process; X i A is the added weight of the i-th alloy or scrap steel; i,j Y is the target value content of element j in the i-th alloy or scrap steel; min,j is the minimum value of steel element j; Y max,j is the maximum value of steel element j; w total The upper limit of the weight of scrap steel and alloys allowed to be added to molten steel.

10. A control system for the automatic control method of steelmaking alloy feeding amount as claimed in claim 1, characterized in that: include: The data acquisition module is used to obtain data of the previous steelmaking process and the current process according to the current smelting process and smelting heat, and send the collected data to the optimization model module through electrical means; Optimization model module: The optimization model module is used to calculate the collected data through the pattern determination program to obtain the correction data of the incoming material composition of the current smelting furnace; According to the incoming material composition correction data, the optimization model module calculates the composition elements of the current heat through the element type calculation program to obtain the control type of the element; According to the control type of the mode and element, the optimization model module performs data preprocessing on the collected data through a data preprocessing program to obtain preprocessed data; According to the pre-processed data, the control type of the element, the self-learning compensation program, the linkage compensation program, and the comprehensive compensation program, the comprehensive compensation value of each element is calculated, and then the compensated data is calculated; According to the pre-processed data, element control type and compensated data, the optimization calculation program is used to perform the optimization calculation of alloy addition for the current heat to obtain the optimization result data of alloy addition; The execution module is used to execute the optimization result data of alloy addition through the secondary system; Terminal display module, which is used to present the optimization result data of alloy addition to operators and technicians in real time; The data storage module is used to store and analyze the optimization result data of alloy addition in the database.

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