CN111933223B - Automatic batching method in steelmaking alloying process - Google Patents
Automatic batching method in steelmaking alloying process Download PDFInfo
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- 238000009628 steelmaking Methods 0.000 title claims abstract description 68
- 238000000034 method Methods 0.000 title claims abstract description 63
- 230000008569 process Effects 0.000 title claims abstract description 35
- 238000005275 alloying Methods 0.000 title claims abstract description 29
- 229910045601 alloy Inorganic materials 0.000 claims abstract description 175
- 239000000956 alloy Substances 0.000 claims abstract description 175
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims abstract description 32
- 238000005094 computer simulation Methods 0.000 claims abstract description 23
- 229910000831 Steel Inorganic materials 0.000 claims abstract description 22
- 238000004519 manufacturing process Methods 0.000 claims abstract description 22
- 239000010959 steel Substances 0.000 claims abstract description 22
- 238000013178 mathematical model Methods 0.000 claims abstract description 21
- 229910052742 iron Inorganic materials 0.000 claims abstract description 16
- 239000004615 ingredient Substances 0.000 claims abstract description 15
- 229910000599 Cr alloy Inorganic materials 0.000 claims description 44
- 239000000463 material Substances 0.000 claims description 13
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 8
- 229910052799 carbon Inorganic materials 0.000 claims description 8
- 230000003993 interaction Effects 0.000 claims description 6
- 239000007769 metal material Substances 0.000 claims description 6
- 239000000203 mixture Substances 0.000 claims description 4
- 238000007670 refining Methods 0.000 claims description 3
- 239000011651 chromium Substances 0.000 description 105
- 238000004364 calculation method Methods 0.000 description 12
- 229910000604 Ferrochrome Inorganic materials 0.000 description 7
- 239000010936 titanium Substances 0.000 description 7
- 229910000519 Ferrosilicon Inorganic materials 0.000 description 6
- RTAQQCXQSZGOHL-UHFFFAOYSA-N Titanium Chemical compound [Ti] RTAQQCXQSZGOHL-UHFFFAOYSA-N 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 229910000914 Mn alloy Inorganic materials 0.000 description 5
- 239000000788 chromium alloy Substances 0.000 description 5
- PYLLWONICXJARP-UHFFFAOYSA-N manganese silicon Chemical compound [Si].[Mn] PYLLWONICXJARP-UHFFFAOYSA-N 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 229910052719 titanium Inorganic materials 0.000 description 4
- 238000003723 Smelting Methods 0.000 description 3
- 229910052804 chromium Inorganic materials 0.000 description 3
- 239000011572 manganese Substances 0.000 description 3
- 229910000616 Ferromanganese Inorganic materials 0.000 description 2
- 229910001200 Ferrotitanium Inorganic materials 0.000 description 2
- 229910052782 aluminium Inorganic materials 0.000 description 2
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- DALUDRGQOYMVLD-UHFFFAOYSA-N iron manganese Chemical compound [Mn].[Fe] DALUDRGQOYMVLD-UHFFFAOYSA-N 0.000 description 2
- 229910052748 manganese Inorganic materials 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 239000002994 raw material Substances 0.000 description 2
- VYZAMTAEIAYCRO-UHFFFAOYSA-N Chromium Chemical compound [Cr] VYZAMTAEIAYCRO-UHFFFAOYSA-N 0.000 description 1
- 229910000640 Fe alloy Inorganic materials 0.000 description 1
- PWHULOQIROXLJO-UHFFFAOYSA-N Manganese Chemical compound [Mn] PWHULOQIROXLJO-UHFFFAOYSA-N 0.000 description 1
- 229910000805 Pig iron Inorganic materials 0.000 description 1
- 229910000676 Si alloy Inorganic materials 0.000 description 1
- 229910001069 Ti alloy Inorganic materials 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000009749 continuous casting Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000004512 die casting Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- XWHPIFXRKKHEKR-UHFFFAOYSA-N iron silicon Chemical compound [Si].[Fe] XWHPIFXRKKHEKR-UHFFFAOYSA-N 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 229910052710 silicon Inorganic materials 0.000 description 1
- 239000002436 steel type Substances 0.000 description 1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention provides an automatic batching method in a steelmaking alloying process, which comprises the following steps: 1) Finishing input information and output information, wherein the input information is basic data of steelmaking production, and the output information comprises the ingredient content of molten iron, scrap steel and alloy; 2) The operation rules are arranged, and output information can be obtained from the input information through the operation rules; 3) Converting the operation rule into a mathematical model, and converting the mathematical model into a computer model, wherein the computer model comprises a plurality of operation programs; 4) And (3) automatically calculating actual output information of the steelmaking production by using a computer model, wherein the actual output information of the steelmaking production comprises the ingredient content of molten iron, scrap steel and alloy. The technical scheme of the invention can realize automatic batching in the steelmaking alloy process and complete the basic data of automatic batching of the whole rated cost.
Description
Technical Field
The invention relates to the technical field of smelting steel, in particular to an automatic batching method in a steelmaking alloying process.
Background
The quota is the standard or the level to be observed in the aspects of manpower, material resources, financial resources, utilization and consumption, obtained achievements and the like in the production and operation activities of the steelmaking enterprises. The rated cost refers to the cost of manufacturing the finished product under the condition that each working procedure of the company operates normally, and does not refer to the cost actually occurring. The method provides data support for company product structure adjustment, contract review order and lead decision. The existing rated cost accounting requires personnel to fill the rated cost system after completing basic data by means of manual calculation and electronic calculation form. The existing defects mainly include long time consumption, certain related work inspection, large calculation workload, easy error and difficult dynamic adjustment when single item changes.
Disclosure of Invention
The invention aims to provide an automatic batching method in a steelmaking alloying process, which can realize automatic batching, can finish the basic data of automatic batching of the whole rated cost, can show batching consistency and accuracy, and can reduce the operation difficulty of staff.
In order to achieve the above purpose, the invention provides an automatic batching method for steelmaking alloying process suitable for converter, electric furnace and continuous casting and die casting, which comprises the following specific technical scheme:
an automatic batching method for a steelmaking alloying process comprises the following steps:
1) Finishing input information and output information, wherein the input information is basic data of steelmaking production, and the output information comprises the ingredient content of molten iron, scrap steel and alloy;
2) The operation rules are arranged, and output information can be obtained from the input information through the operation rules;
3) Converting the operation rule into a mathematical model, and converting the mathematical model into a computer model, wherein the computer model comprises a plurality of operation programs;
4) And (3) automatically calculating actual output information of the steelmaking production by using a computer model, wherein the actual output information of the steelmaking production comprises the ingredient content of molten iron, scrap steel and alloy.
Further, in the above automatic batching method in the steelmaking alloying process, in step 1), the basic data of steelmaking production includes steelmaking production line, steelmaking brand, steelmaking standard, special batching rules, and target components of elements.
Further, in the automatic batching method in the steelmaking alloying process, in step 2), the operation rule includes generating an alloy batching amount formula and an element component growth formula according to the constant data, and determining the alloy priority.
Further, in the automatic batching method in the steelmaking alloying process, in step 2), the constant data is at least one piece of steelmaking line data, and each piece of steelmaking line data includes a correspondence between steel material consumption and metal material consumption, a carbon control allowance, an element base table, an alloy batching coefficient, an alloy grade base table, an alloy yield base table, and an alloy initial value of residual useful elements before refining.
Further, in the above automatic batching method in the steelmaking alloying process, in step 2), the formula of the alloy batching amount is as follows, M alloy batching amount=m element target component×alloy batching coefficient ≡m alloy grade ≡alloy yield, and formula (1), M means a certain element.
Further, in the above automatic batching method in the steelmaking alloying process, in step 2), the element component growth formula is as follows, M element component growth = M alloy batching amount +.alloy batching coefficient +.100×m alloy grade×alloy yield, and formula (2), M refers to a certain element.
Further, in the above automatic batching method in the steelmaking alloying process, in step 2), the basic principle of determining the alloy priority includes:
the first alloy has a priority greater than the second alloy and the third alloy,
wherein: the first alloy refers to an alloy that affects only the main element and the main element is not affected by the addition of other alloys;
the second alloy refers to an alloy that affects a plurality of elements, and an alloy that affects a plurality of elements refers to an alloy that affects other elements in addition to the main element;
the third alloy refers to an alloy in which a plurality of alloys affect the same element;
the same class of alloys is considered to be priced in a priority that is higher for low prices than for high prices.
Further, in the above automatic batching method in the steelmaking alloying process, in step 3), the mathematical model is used for describing the selection relationship between the elements and the alloys corresponding to the elements, and the contents of the mathematical model include: an M element component growth formula, an M alloy batching formula, a basic balance mathematical formula of elements, interaction of alloys and alloys, and interaction of alloys and elements.
Further, in the above automatic batching method in the steelmaking alloying process, in step 3), the basic equilibrium mathematical formula of the element means setting element critical value constants, selecting critical conditions according to the element, and then listing the piecewise equations of the element selection, and obtaining the alloy dosage from the piecewise equations.
Further, in the automatic batching method in the steelmaking alloying process, the basic equilibrium mathematical formulas of the high Cr alloy, the low Cr alloy and the micro Cr alloy are as follows:
high Cr alloy is called high Cr, low Cr alloy is called low Cr, and micro Cr alloy is called micro Cr;
the ratio of the C content to the Cr content in the target component of the element is expressed as C/Cr, and x1, x2 and x3 are selected from the values of C/Cr as critical value constants, wherein x1 > x2 > x3;
when C/Cr is more than or equal to x1, all high Cr is used;
when C/Cr is between x1-x2, the equation needs to be solved:
high Cr-expanded cr+low Cr-expanded cr=cr content; high Cr expansion c+low Cr expansion c=c content;
high Cr expansion cr= (Cr target value x2-C target value)/(x 2-x 1); low Cr expansion Cr= (Cr target value x1-C target value)/(x 1-x 2)
When C/Cr is equal to x2, all low Cr is used;
when C/Cr is between x2-x3, the equation needs to be solved:
micro Cr expansion cr+low Cr expansion cr=cr content; micro Cr expansion c+low Cr expansion c=c content;
low Cr expansion cr= (Cr target value x3-C target value)/(x 3-x 2); micro Cr expansion cr= (Cr target value x2-C target value)/(x 2-x 3);
when C/Cr is less than x3, all micro Cr is used.
The technical scheme of the invention can realize automatic batching in the steelmaking alloy process and complete the basic data of automatic batching of the whole rated cost. And the consistency and accuracy of ingredients can be embodied, and the difficulty of related staff is reduced. The computer modeling adopts a white box model, and the model of the alloy composition or the steelmaking internal process/program is obtained from the standpoint of system realization.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. Wherein:
FIG. 1 is a schematic diagram of the operational (white box model) framework of the automatic batching method in the steelmaking alloying process of the present invention.
FIG. 2 is a schematic diagram of the operational rules of automatic batching in the steelmaking alloy process of the present invention.
Fig. 3 is a schematic diagram of basic operation construction of input element target value formation.
FIG. 4 is a schematic diagram of the computational architecture reconstructed after being influenced by the specific alloy selection.
Detailed Description
The invention will be described in detail below with reference to the drawings in connection with embodiments. The examples are provided by way of explanation of the invention and not limitation of the invention. Indeed, it will be apparent to those skilled in the art that modifications and variations can be made in the present invention without departing from the scope or spirit of the invention. For example, features illustrated or described as part of one embodiment can be used on another embodiment to yield still a further embodiment. Accordingly, it is intended that the present invention encompass such modifications and variations as fall within the scope of the appended claims and their equivalents.
As shown in fig. 1 to 4, according to an embodiment of the present invention, there is provided an automatic batching method for a steelmaking alloying process, comprising the steps of:
1) The input information and the output information are arranged, the input information is basic data of steelmaking production,
the basic data of the steelmaking production comprises steelmaking production lines, steelmaking brands, steelmaking standards, special batching rules and target components of elements.
Such as low titanium ferrochrome and low carbon ferrochrome, which have the same chromium content as the carbon content, but when the titanium content in the steel is not too high, the low titanium ferrochrome is selected for the batching, and the low titanium ferrochrome belongs to the special batching rules.
The special batching rules are as follows: titanium sponge and ferrotitanium are both added elements of Ti, but when a special smelting mode is encountered, the original alloy ferrotitanium is converted into titanium sponge for addition.
The output information is the ingredient content of molten iron, scrap steel, alloy and the like.
The batching system outputs batching materials, and the iron and steel raw materials can also comprise soft iron, pig iron and the like besides molten iron and scrap steel.
The alloys are of a wide variety and are referred to herein generally. Alloy raw materials include: ferrosilicon, ferrochromium, ferromanganese, ferrosilicon, aluminum wire, aluminum particles, etc. Of course, there are special calls depending on the composition: silicon manganese alloy FeMn68Si18, high carbon ferrochrome FeCr55C10, low carbon ferrochrome FeCr55C0.5, etc.
2) And sorting the operation rule, and obtaining output information from the input information through the operation rule.
The operation rule is to generate an alloy batching formula and an element component growth formula according to the constant data, and determine the alloy priority. The operation rule comprises constant data, a calculation formula and alloy priority. Alloy priority refers to the order (priority) selected for operation.
The operation rule is divided into a plurality of sub operation rules, each sub operation rule is a process of forming alloy n ingredients by adopting the operation rule and comprises at least one sub operation rule.
The sub-operation rule is to generate an alloy batching formula and an element component growth formula according to the appointed constant data, and determine the basic operation priority level of the alloy.
The constant data is at least one piece of steelmaking production line data, and each piece of steelmaking production line data comprises a corresponding relation between steel and iron material consumption and metal material consumption, a carbon control allowance, an element base table, an alloy batching coefficient, an alloy grade base table, an alloy yield base table, an alloy initial value of residual useful elements (Cr, ni and the like) before refining and other basic data required to be acquired.
The alloy grade base table is a two-dimensional matrix table of alloy grade and component element grade; the alloy yield base table is a two-dimensional matrix table composed of alloy and element yield, and the two table formats of the alloy grade base table and the alloy yield base table are the same as each other, as shown in table 1.
TABLE 1 alloy grade base Table or alloy yield base Table
| Sequence number | Element(s) | Alloy 1 | Alloy 2 | Alloy 3 | …… |
| 1 | C | ||||
| 2 | Mn | ||||
| 3 | Si | ||||
| 4 | P | ||||
| 5 | S | ||||
| 6 | …… |
The calculation formula comprises a calculation formula of a certain alloy ingredient amount, a certain element ingredient growth formula and a basic balance mathematical formula of elements:
the formula of the alloy proportioning amount of a certain class is as follows,
m alloy charge = target component of M element x alloy charge coefficient +.m alloy grade +.m alloy yield, equation (1), M refers to an element.
The formula for the growth of a certain element component is as follows,
m element component increase = M alloy batch ≡alloy batch coefficient x 100×m alloy grade x alloy yield, formula (2), M refers to an element; regarding the relation between the iron and steel material consumption and the metal material consumption, the relation may be expressed as iron and steel material consumption=f (metal material consumption), and as a function f (), a linear function may be used, or a multi-element equation may be used, such as iron and steel material consumption=1050 kg-metal material consumption.
The basic principles for determining alloy priority include:
the first alloy has a priority greater than the second alloy and the third alloy,
wherein: the first alloy refers to an alloy that affects only the main element and the main element is not affected by the addition of other alloys;
the second alloy refers to an alloy that affects a plurality of elements, and an alloy that affects a plurality of elements refers to an alloy that affects other elements in addition to the main element;
the third alloy refers to an alloy in which a plurality of alloys affect the same element;
for example, the silicon-manganese alloy contains two elements of Si and Mn, the priority of the silicon-manganese alloy is higher than that of the silicon-iron alloy, and the priority of the silicon-manganese alloy is also higher than that of the ferromanganese alloy.
The same class of alloy considers the principle of priority according to the price, and the priority of low price is higher than that of high price;
it is necessary to confirm the initial priority of alloy calculation after comprehensive evaluation.
The base operational priorities of the alloys are shown in table 2.
TABLE 1 alloy priority
In the above table, the elements 1 and 2 refer to the elements corresponding to the alloy with the priority 1, and the priority 1 is higher than the priority 5, that is, when in selection, the selection order is priority 1, priority 2, priority 3, priority 4 and priority 5 in sequence. For example: ni plate can be selected, and the Ni plate has almost no impurity and accompanying element, so the priority of the Ni plate is 1 grade.
3) Converting the operation rule into a mathematical model, and converting the mathematical model into a computer model, wherein the computer model comprises a plurality of operation programs; each element corresponds to an algorithm of one or more alloys.
The operation rule is converted into a mathematical model which is used for describing the selection relation of the elements and the alloys corresponding to the elements. The contents of the mathematical model include: an M element component growth formula, an M alloy batching formula, a basic balance mathematical formula of elements, interaction of alloys and alloys, and interaction of alloys and elements. The mathematical model logic is converted into a computer model using a computer language.
The basic equilibrium mathematical formula of the element refers to setting element critical value constants, selecting critical conditions according to the element, listing the sectional equations selected by the element, and obtaining the alloy consumption by the sectional equations. The mathematical model involves an element Mn, cr, si, S, al, ti, B.
Examples: the mathematical model and the computer model are described by taking the calculation of the association of C with Cr as an example.
Three chromium alloys are commonly used: the high Cr alloy (abbreviated as high Cr), the low Cr alloy (abbreviated as low Cr) and the micro Cr alloy (abbreviated as micro Cr) describe basic equilibrium mathematical formulas: because chromium alloys have different carbon contents and different purchase prices, in some cases, the use of high or low Cr alone for the C element cannot obtain the optimal solution, while the direct use of micro Cr is obviously uneconomical and has too high cost. Only if critical conditions are confirmed, the computer model can be converted for automatic selection by a computer.
Setting element critical value constant:
the ratio of the C content to the Cr content in the target component of the element is expressed as C/Cr, and x1, x2 and x3 are selected from the values of C/Cr as critical value constants, wherein x1 > x2 > x3.
A critical value x1 selected by high Cr, a critical value x2 selected by low Cr and a critical value x3 selected by micro Cr;
the target component of the element is as follows: when C/Cr is more than or equal to x1, single high Cr alloy is selected to meet the requirement during the material mixing;
the target component of the element is as follows: when x2 is less than C/Cr is less than x1, the high Cr alloy and the low Cr alloy are selected for material mixing, so that the requirements can be met;
the target component of the element is as follows: when C/Cr=x2, the single low Cr alloy is selected to meet the requirement during the material mixing;
the target component of the element is as follows: when x3 is less than C/Cr is less than x2, the low Cr alloy and the micro Cr alloy are selected to meet the requirement during the material mixing;
the target component of the element is as follows: when C/Cr is less than or equal to x3, single micro Cr alloy is selected to meet the requirement during the material mixing;
the above selection scheme for three chromium alloys can reduce the cost to the minimum on the premise of meeting the requirements of element target components. The relationship between the C/Cr ratio and the selection of three chromium alloys is shown in Table 3.
TABLE 2 selection relationship of C/Cr ratio and chromium alloy
| C/Cr | >＝x1 | x2-x1 | ＝x2 | x3-x2 | <＝x3 |
| High Cr | √ | √ | |||
| Low Cr | √ | √ | √ | ||
| Micro Cr | √ | √ |
The mathematical model of the selection of high Cr alloys, low Cr alloys and micro Cr alloys, i.e. Cr alloys, can be listed according to the segmentation equation in table 3. Wherein, the high Cr expansion Cr means the value of Cr element provided by the high Cr alloy, the high Cr expansion C means the value of C element provided by the high Cr alloy, and other shorthand meanings are analogized.
When C/Cr is more than or equal to x1, all high Cr is used;
when C/Cr is between x1-x2, the equation needs to be solved: high Cr-expanded cr+low Cr-expanded cr=cr content; high Cr expansion c+low Cr expansion c=c content;
when C/Cr is equal to x2, all low Cr is used;
when C/Cr is between x2-x3, the equation needs to be solved: micro Cr expansion cr+low Cr expansion cr=cr content; micro Cr expansion c+low Cr expansion c=c content;
when C/Cr is less than x3, all micro Cr is used.
When C/Cr is between x1-x2, both high Cr alloys and low Cr alloys are required, and the formula for C/Cr is between x1-x2 demonstrates the alloy distribution relationship as follows:
high Cr expansion cr+low Cr expansion cr=cr target value; target value of high Cr expansion c+low Cr expansion c=c
Deducing:
high Cr expansion Cr x1 = high Cr expansion C; low Cr expansion Cr x2 = low Cr expansion C
Deducing:
high Cr expansion cr+low Cr expansion cr=cr target value; target value of high Cr expansion Cr x1+ low Cr expansion Cr x2 = C
Deducing:
high Cr expansion cr= (Cr target value x2-C target value)/(x 2-x 1); low Cr expansion Cr= (Cr target value x1-C target value)/(x 1-x 2)
Analogize: when C/Cr is between x2-x3, both low Cr alloys and micro Cr alloys are required, and the formula when C/Cr is between x2-x3 demonstrates the alloy distribution relationship as follows:
low Cr expansion cr= (Cr target value x3-C target value)/(x 3-x 2); micro Cr expansion cr= (Cr target value x2-C target value)/(x 2-x 3).
Other elements also have critical values, and a plurality of elements are like a multi-element equation, y1, y2 and y3 … … yn=f (x 1, x2 and x3 … … xn), x is an element target value, y is the quantity of the selected alloy, and the selected alloy requires an optimal solution.
The above formula (i.e. mathematical model) is converted into a computer model to obtain the operation program of high Cr alloy, low Cr alloy and micro Cr alloy. The computer model carries out automatic operation to judge which alloy is added, and the amount of each alloy is added. As shown in fig. 1 and 2, fig. 1 is a schematic diagram of an operation (white box model) framework of an automatic batching method in a steelmaking alloying process according to the present invention, and fig. 2 is a schematic diagram of an operation rule of automatic batching in a steelmaking alloying process according to the present invention. Fig. 2 is a concept and fig. 1 is the result of the concept. Fig. 2 illustrates how manual computation becomes automatic computation. In the prior art, information is input- > manually calculated- > output information. In the application, manual calculation is changed into automatic calculation, modeling is needed, and the specific process is as follows: the input information- > the arrangement manual calculation rule is converted into a mathematical model- > the mathematical model is logically converted into a computer model, and the computer model realizes automatic calculation- > the output information. Fig. 1 shows an architecture of automatic calculation. The mathematical model logic may be converted to a computer model using a computer language. Such as: "when C/Cr is equal to or greater than x1, all high Cr is used; "pseudocode converted to computer language is: "IF C/Cr > =x1, high cr=cr element target component×alloy batch coefficient ≡high Cr alloy grade ≡high Cr alloy yield", i.e., the addition amount of the high Cr alloy can be obtained.
The special alloy is selected and specified, and the special alloy is not suitable for the basic equilibrium mathematical formula of the elements. The special alloy is selected and designated because some steel grades cannot be selected according to the principle of conventional ingredients, and the special alloy is designated so that the ingredients are more reasonable and meet the actual application, but the basic balance mathematical formula of the alloy priority and elements is changed. Such as certain types of steel only allowing for the addition of manganese metal, or certain types of steel only allowing for the addition of low titanium alloys, etc., which change the basic equilibrium mathematical formulas for the alloy's priority and elements.
For example, some steel types are normally mixed with normal ferrosilicon, and when Ti is less than or equal to a certain critical value, the alloy is mixed with high-purity ferrosilicon (the normal ferrosilicon cannot be used). The special alloy is selected from the specification that when Ti is less than or equal to a certain critical value, the alloy is matched with high-purity ferrosilicon. The particular alloy chosen is one that is typically more expensive. If Cr, si, mn, etc. are used, the alloy is special for Cr alloy, si alloy, mn alloy. The more expensive alloy cannot be selected for ordinary smelting, which increases the cost.
4) And automatically calculating to obtain the actual output information of steelmaking production by using a computer model.
The actual output information of the steelmaking process includes the burden content of the molten iron, scrap steel and alloy.
As shown in fig. 3 and 4, according to the input information (element 1, element 2, element 3, element 4, element 5, element 6, … …), the actual output information (alloy 1, alloy 2, alloy 3, alloy 4, alloy 5, alloy 6, alloy 7, … …) of the steelmaking process is automatically calculated by using a computer model (an operation program of a plurality of alloys), and the actual output information of the steelmaking process further comprises the ingredient content of molten iron, scrap steel and the like.
The actual output information of steelmaking production is obtained, and automatic batching of steelmaking alloying is realized.
From the above description, it can be seen that the above embodiments of the present invention achieve the following technical effects:
the computer modeling adopts a white box model, and the model of the alloy composition or the steelmaking internal process/program is obtained from the standpoint of system realization. The automatic batching of the steelmaking alloying process can be realized, and the basic data of the automatic batching of the whole rated cost is completed. And the consistency and accuracy of ingredients can be embodied, and the difficulty of related staff is reduced.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. An automatic batching method in a steelmaking alloying process is characterized by comprising the following steps:
1) Finishing input information and output information, wherein the input information is basic data of steelmaking production, and the output information comprises the ingredient content of molten iron, scrap steel and alloy;
2) The operation rules are arranged, and output information can be obtained from the input information through the operation rules;
3) Converting the operation rule into a mathematical model, and converting the mathematical model into a computer model, wherein the computer model comprises a plurality of operation programs;
4) Automatically calculating actual output information of the steelmaking process by using a computer model, wherein the actual output information of the steelmaking process comprises the ingredient contents of molten iron, scrap steel and alloy,
in step 3), the mathematical model is used for describing the selected relation between the elements and the alloys corresponding to the elements, and the content of the mathematical model comprises:
an M element component growth formula, an M alloy batching formula, a basic balance mathematical formula of elements, interaction of alloys and alloys, interaction of alloys and elements,
in step 3), the basic equilibrium mathematical formula of the element refers to setting element critical value constants, selecting critical conditions according to the element, then listing the piecewise equations selected by the element, obtaining the alloy dosage from the piecewise equations,
the basic equilibrium mathematical formulas for high Cr alloys, low Cr alloys and micro Cr alloys are as follows:
high Cr alloy is called high Cr, low Cr alloy is called low Cr, and micro Cr alloy is called micro Cr;
the ratio of the C content to the Cr content in the target component of the element is expressed as C/Cr, and x1, x2 and x3 are selected from the values of C/Cr as critical value constants, wherein x1 > x2 > x3;
when C/Cr is more than or equal to x1, all high Cr is used;
when C/Cr is between x1-x2, the equation needs to be solved:
high Cr-expanded cr+low Cr-expanded cr=cr content; high Cr expansion c+low Cr expansion c=c content;
high Cr expansion cr= (Cr target value x2-C target value)/(x 2-x 1); low Cr expansion Cr= (Cr target value x1-C target value)/(x 1-x 2)
When C/Cr is equal to x2, all low Cr is used;
when C/Cr is between x2-x3, the equation needs to be solved:
micro Cr expansion cr+low Cr expansion cr=cr content; micro Cr expansion c+low Cr expansion c=c content;
low Cr expansion cr= (Cr target value x3-C target value)/(x 3-x 2); micro Cr expansion cr= (Cr target value x2-C target value)/(x 2-x 3);
when C/Cr is less than or equal to x3, all micro Cr is used.
2. The automatic batching method for steelmaking alloying processes according to claim 1, wherein,
in step 1), the basic data of the steelmaking production comprises steelmaking production lines, steelmaking brands, steelmaking standards, special batching rules and target components of elements.
3. The automatic batching method for steelmaking alloying processes according to claim 1, wherein,
in step 2), the operation rule includes generating an alloy batching formula and an element component growth formula according to the constant data, and determining the alloy priority.
4. The automatic batching method for steelmaking alloying processes according to claim 3, characterized in that,
in the step 2), the constant data is at least one piece of steelmaking production line data, and each piece of steelmaking production line data comprises a corresponding relation between steel material consumption and metal material consumption, a carbon control allowance, an element base table, an alloy batching coefficient, an alloy grade base table, an alloy yield base table and an alloy initial value of residual useful elements before refining.
5. The automatic batching method for steel-making alloying processes according to claim 3, wherein in step 2), the alloy batching amount formula is as follows,
m alloy charge = M element target component x alloy charge coefficient +.m alloy grade +.alloy yield, equation (1), M refers to an element.
6. The automatic batching method for steelmaking alloying processes according to claim 3, wherein in step 2), the element composition growth formula is as follows,
m element component increase = M alloy batch ≡alloy batch coefficient x 100×m alloy grade x alloy yield, formula (2), M refers to a certain element.
7. A method for automatically batching a steelmaking alloying process according to claim 3 wherein in step 2), the basic principle of determining alloy priority comprises:
the first alloy has a priority greater than the second alloy and the third alloy,
wherein: the first alloy refers to an alloy that affects only the main element and the main element is not affected by the addition of other alloys;
the second alloy refers to an alloy that affects a plurality of elements, and an alloy that affects a plurality of elements refers to an alloy that affects other elements in addition to the main element;
the third alloy refers to an alloy in which a plurality of alloys affect the same element;
the same class of alloys is considered to be priced in a priority that is higher for low prices than for high prices.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109359723A (en) * | 2018-11-20 | 2019-02-19 | 北京科技大学 | Based on the converter terminal manganese content prediction technique for improving regularization extreme learning machine |
| CN110502781A (en) * | 2019-07-05 | 2019-11-26 | 武汉科技大学 | A kind of ferroalloy production blending optimization method based on priori knowledge |
| CN110516402A (en) * | 2019-09-05 | 2019-11-29 | 中冶南方工程技术有限公司 | A method of optimization electric arc furnaces waste steel ingredient |
| KR20200021247A (en) * | 2018-08-20 | 2020-02-28 | 주식회사 포스코 | Method of operating converter |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US7668702B2 (en) * | 2002-07-19 | 2010-02-23 | Applied Materials, Inc. | Method, system and medium for controlling manufacturing process using adaptive models based on empirical data |
| CN105483310B (en) * | 2015-11-23 | 2017-05-10 | 东北大学 | Steelmaking batch grouping and production scheduling method for whole process production |
| CN110490672B (en) * | 2019-09-18 | 2023-05-30 | 中冶南方工程技术有限公司 | Method for controlling input amount of scrap steel and alloy in electric furnace smelting |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20200021247A (en) * | 2018-08-20 | 2020-02-28 | 주식회사 포스코 | Method of operating converter |
| CN109359723A (en) * | 2018-11-20 | 2019-02-19 | 北京科技大学 | Based on the converter terminal manganese content prediction technique for improving regularization extreme learning machine |
| CN110502781A (en) * | 2019-07-05 | 2019-11-26 | 武汉科技大学 | A kind of ferroalloy production blending optimization method based on priori knowledge |
| CN110516402A (en) * | 2019-09-05 | 2019-11-29 | 中冶南方工程技术有限公司 | A method of optimization electric arc furnaces waste steel ingredient |
Non-Patent Citations (1)
| Title |
|---|
| LF精炼成分预报模型的开发;李广帮;赵成林;赵素华;王丽娟;张维维;;鞍钢技术(第04期);全文 * |
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