WO2014185810A1 - Method for adjusting final steel properties at steel mill facility - Google Patents
Method for adjusting final steel properties at steel mill facility Download PDFInfo
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- WO2014185810A1 WO2014185810A1 PCT/RU2013/000396 RU2013000396W WO2014185810A1 WO 2014185810 A1 WO2014185810 A1 WO 2014185810A1 RU 2013000396 W RU2013000396 W RU 2013000396W WO 2014185810 A1 WO2014185810 A1 WO 2014185810A1
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- steel
- parameters
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- determined
- computing unit
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- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 66
- 239000010959 steel Substances 0.000 title claims abstract description 66
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000004519 manufacturing process Methods 0.000 claims abstract description 13
- 238000012417 linear regression Methods 0.000 claims description 14
- 238000013528 artificial neural network Methods 0.000 description 5
- 238000000137 annealing Methods 0.000 description 4
- HTYIXCKSEQQCJO-UHFFFAOYSA-N phenaglycodol Chemical compound CC(C)(O)C(C)(O)C1=CC=C(Cl)C=C1 HTYIXCKSEQQCJO-UHFFFAOYSA-N 0.000 description 4
- 238000005096 rolling process Methods 0.000 description 4
- 238000005275 alloying Methods 0.000 description 3
- 238000005097 cold rolling Methods 0.000 description 3
- 238000005098 hot rolling Methods 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000005266 casting Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000007688 edging Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 239000012467 final product Substances 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000000465 moulding Methods 0.000 description 1
- 238000005121 nitriding Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000005496 tempering Methods 0.000 description 1
- 238000009864 tensile test Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- 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
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B38/00—Methods or devices for measuring, detecting or monitoring specially adapted for metal-rolling mills, e.g. position detection, inspection of the product
-
- 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
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
-
- 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
Definitions
- the invention relates to a method for adjusting properties in the production of steel, in which by means of a computing unit values for parameters characterizing for desired properties of the steel are determined with the aid of an experimental data set.
- the invention relates to a facility for producing steel, comprising a facility control device, which comprises a computing unit, by means of which parameters characterizing values for desired properties of the steel are capable of being determined with the aid of an experimental data set.
- Steel rolling is a complicated technological process consisting of many stages, which are e.g. casting, hot rolling, edging, cold rolling, annealing or temper-rolling. On each stage the process is controlled by certain process parameters. The overall number of parameters may reach several hundreds.
- process parameters include chemical composition, geometrical parameters of the slab, the rolling speed, the strip coiling temperature (during hot rolling), the strip finishing temperature (during hot rolling), the reduction in thickness during cold rolling and the annealing temperatures.
- influence parameters reflect only a fraction of all the parameters influencing the properties of rolled steel, which is the final product of the steel production process.
- the yield stress (Re), the ultimate stress (Rm) and the elongation (A80) belong to the most important properties.
- the yield stress (Re) the ultimate stress (Rm) and the elongation (A80) belong to the most important properties.
- the yield stress (Rm) the ultimate stress (Rm) and the elongation (A80) belong to the most important properties.
- Such requirements are formulated as lower and upper limit values.
- For example, for a certain steel grade it may be desirable to get into an interval from 240 MPa to 280 MPa for the yield stress.
- the total variation of the yield stress for corresponding steel grade can reach from 190 to 310 MPa, since the process parameters that allow reaching desirable values for the yield stress are often unknown due to complexity of the steel rolling process.
- the creation of a physical model, which considers the influence of process parameters on the yield stress is difficult.
- the determination of the values for parameters characterizing for desired properties of the steel corresponds to a simultaneous adjustment of the parameters which influence the steel grade.
- the simultaneous adjustments of parameters the time consuming method of changing only one factor at a time to determine its single influence upon the steel quality can be avoided.
- the simultaneous adjustment of the parameters is a very quick method of setting parameter values in order to reach desired properties of the steel.
- Determining a minimum value for a desired property of the steel can result in saving resources during the steel manufacturing. If e.g. a certain hardness of the steel is required, the amount of alloying elements can be metered such that the minimum requirements to the steel are met precisely, or that the requirements to steel are met by means of the cheapest of the alloying continuances that can be used. Furthermore the thermal process that requires the least energy consumption can be chosen based on the values of the parameters.
- determining a maximum value for at least one of the desired properties of the steel by means of the computing unit it can be evaluated most effective for which operational demands the steel can be established. If the requirements for the steel outreach the achievable maximum value, one can abstain from e.g. extensive hardness tests or tensile tests. Instead by means of a computing unit can be determined in an early stage that to achieve even higher steel quality further procedures need to be performed not during the steel manufacturing, but rather subsequently. These procedures would include e.g. a nitriding process in conjunction with a subsequent tempering. In other words by means of the computing unit it can be determined in a particularly early stage of the steel production, whether further procedures need to be performed subsequent to the steel manufacturing, to meet the requirements, i.e. the desired properties of the steel.
- the initial set of parameters that influence the desired properties of the steel comprises a quantity of several hundred. As an example some of these several hundred parameters are listed in table 1 : Table 1
- the parameters that are listed in table 1 represent a subset of the several hundreds of parameters that have influenced on the desired properties of the steel.
- other parameters from the several hundreds of parameters can be chosen whereat the other parameters can replace the parameters shown in table 1 as well as they can be added to the parameters shown in table 1.
- the number of parameters shown in table 1 can also be reduced.
- the parameters illustrated in table 1 correspond to the most important parameters to influence the desired properties of the steel. From this most important parameters according to table 1, a subset of parameters, which correspond to process parameters, can be used to adjust the desired properties of the steel.
- the computing unit determines the values of the parameters by means of linear regression.
- the linear regression is a particularly simple method, and thus especially appropriate for a statistical model. In other words by means of the linear regression the minimum and maximum values for the desired properties of the steel can be determined extremely easy and fast.
- the linear regression is expressed by eq. 1 : where a, are tuneable parameters which are concerning the subset of parameters x, characterizing for desired properties of the steel. Furthermore the linear regression provides a simple and very fast learning procedure as well as the absence of overfitting, since the latter is only a problem for mathematical equations of higher order.
- each of the parameters x is parameterized by real-valued parameters y, ranging from 0 to 1.
- the desired Re value which corresponds to one of the desired properties of the steel is outside the interval [Re mm , Re max ] it cannot be reached. If the desired Re value is inside this interval, then it may be reached by adjusting the process parameters x,. To calculate the required values Xj one has to first calculate values of y, (see eq. 3), taking into account the restriction, which is expressed by eq. 4. This is done by substituting to the left part of eq. 7 the desired Re value and solving it with respect to y.
- the facility for producing steel according to the invention comprises a facility control device, which comprises a computing unit, by means of which parameters characterizing values for desired properties of the steel are capable of being determined with the aid of an experimental data set.
- the values are capable of being simultaneously determined by means of the computing unit and are capable of being transmitted by means of the facility control device to associated sections of the facility.
- the mechanical properties of the steel which correspond to the desired properties of the steel can be controlled particularly fast and efficient.
- the values to associated sections which are responsible of e.g. thermal processes or molding processes or alloying processes, the production of steel with desired properties can also be controlled particularly efficient.
- FIG 1 a histogram of different yield stresses, whereat each of the yield stresses occurs with a certain probability due to the usual scattering of steel production;
- FIG 2 a diagram, which illustrates the determination of a yield stress by means of a process parameter.
- FIG 1 illustrates the frequency distribution of a yield stress 1 by means of a histogram.
- the yield stress 1 is plotted on the abscissa and increases according to an arrow direction of an arrow 8.
- the frequency 2 is plotted on the ordinate of the histogram and increases according to an arrow direction of an arrow 9.
- the histogram illustrates, that different yield stresses 1 can be achieved during the manufacturing of steel according to a frequency distribution. In other words one has to take into account that the yield stress 1 which can be reached during the manufacturing lies within a scattering region.
- FIG 2 illustrates the dependency of the yield stress 1 of a parameter 3, which is determined by a computing unit.
- the parameter 3 is plotted on the abscissa and increases to an arrow direction of an arrow 10.
- the yield stress 1 is plotted on the ordinate and increases according to an arrow direction of an arrow 1 1.
- a parameter 3 which is e.g. a strip velocity at annealing or e.g. a strip thickness after cold rolling, whereat a predicted value 7 of the yield stress 1 for this parameter 3 is determined.
- FIG 2 also illustrates the linear dependency of the yield stress 1 from the parameter 3 within a minimum boundary, which corresponds to a minimum value 4 and a maximum boundary, which corresponds to a maximum value 5.
Abstract
The invention relates to a method for adjusting properties in the production of steel, in which by means of a computing unit values (7) for parameters (3) characterizing for desired properties (6) of the steel are determined with the aid of an experimental data set, whereat the values (7) are determined simultaneously. Furthermore the invention relates to a facility for producing steel.
Description
METHOD FOR ADJUSTING FINAL STEEL PROPERTIES AT STEEL
MILL FACILITY
DESCRIPTION The invention relates to a method for adjusting properties in the production of steel, in which by means of a computing unit values for parameters characterizing for desired properties of the steel are determined with the aid of an experimental data set.
Moreover, the invention relates to a facility for producing steel, comprising a facility control device, which comprises a computing unit, by means of which parameters characterizing values for desired properties of the steel are capable of being determined with the aid of an experimental data set.
Presently many efforts are given to improve the final production quality of steel. Steel rolling is a complicated technological process consisting of many stages, which are e.g. casting, hot rolling, edging, cold rolling, annealing or temper-rolling. On each stage the process is controlled by certain process parameters. The overall number of parameters may reach several hundreds. The examples of process parameters include chemical composition, geometrical parameters of the slab, the rolling speed, the strip coiling temperature (during hot rolling), the strip finishing temperature (during hot rolling), the reduction in thickness during cold rolling and the annealing temperatures. The before mentioned influence parameters reflect only a fraction of all the parameters influencing the properties of rolled steel, which is the final product of the steel production process. Among the properties that characterize the steel quality, the yield stress (Re), the ultimate stress (Rm) and the elongation (A80) belong to the most important properties. Depending on steel grade, costumer wishes and other conditions, it is often required to achieve certain values of output properties. Usually, such requirements are formulated as lower and upper limit values. For example, for a certain steel grade it may be desirable to get into an interval from 240 MPa to 280 MPa for the yield stress. The total variation of the yield stress for corresponding steel grade can reach from 190 to 310 MPa, since the process parameters that allow reaching desirable values for the yield stress are often unknown due to complexity of the steel rolling process. For the same
reason, the creation of a physical model, which considers the influence of process parameters on the yield stress is difficult. For this reason, several approaches have been published to calculate the processing parameters e.g. by neural networks. Thus, for example the usage of neural networks with mathematical approaches of higher order is known. The major disadvantage of using neural networks as a prediction model for searching for the process parameter values that lead to desired Re values is, that using neural networks results in complicated and computationally expensive search processes.
Therefore, it is the object of the present invention to provide a particularly effective and at the same time particularly fast method and a facility of the initially mentioned kind.
This task is solved by a method having the features of patent claim 1 and by a facility having the features of patent claim 8. Advantageous embodiments with expedient further developments of the invention are indicated in the dependent patent claims.
In the method according to the invention the values are determined simultaneously.
The determination of the values for parameters characterizing for desired properties of the steel corresponds to a simultaneous adjustment of the parameters which influence the steel grade. By means of the simultaneous adjustments of parameters the time consuming method of changing only one factor at a time to determine its single influence upon the steel quality can be avoided. In other words and especially in contrast to the determination of values by means of neural networks, the simultaneous adjustment of the parameters is a very quick method of setting parameter values in order to reach desired properties of the steel.
It has turned out to be advantageous, if by means of the computing unit a minimum value is determined for at least one of the desired properties, which is reached by means of the values of the parameters.
Determining a minimum value for a desired property of the steel can result in saving resources during the steel manufacturing. If e.g. a certain hardness of the steel is
required, the amount of alloying elements can be metered such that the minimum requirements to the steel are met precisely, or that the requirements to steel are met by means of the cheapest of the alloying continuances that can be used. Furthermore the thermal process that requires the least energy consumption can be chosen based on the values of the parameters.
It is further advantageous, if by means of the computing unit a maximum value for at least one of the desired properties is determined, which is reached by means of the values of the parameters.
By determining a maximum value for at least one of the desired properties of the steel by means of the computing unit it can be evaluated most effective for which operational demands the steel can be established. If the requirements for the steel outreach the achievable maximum value, one can abstain from e.g. extensive hardness tests or tensile tests. Instead by means of a computing unit can be determined in an early stage that to achieve even higher steel quality further procedures need to be performed not during the steel manufacturing, but rather subsequently. These procedures would include e.g. a nitriding process in conjunction with a subsequent tempering. In other words by means of the computing unit it can be determined in a particularly early stage of the steel production, whether further procedures need to be performed subsequent to the steel manufacturing, to meet the requirements, i.e. the desired properties of the steel.
It is further advantageous, if by the computing unit from the parameters relevant process parameters are determined, which are required for adjusting the desired properties of the steel.
The initial set of parameters that influence the desired properties of the steel comprises a quantity of several hundred. As an example some of these several hundred parameters are listed in table 1 :
Table 1
In other words the parameters that are listed in table 1 represent a subset of the several hundreds of parameters that have influenced on the desired properties of the steel. As an
alternative to the listed parameters according to table 1 also other parameters from the several hundreds of parameters can be chosen whereat the other parameters can replace the parameters shown in table 1 as well as they can be added to the parameters shown in table 1. Furthermore the number of parameters shown in table 1 can also be reduced. The parameters illustrated in table 1 correspond to the most important parameters to influence the desired properties of the steel. From this most important parameters according to table 1, a subset of parameters, which correspond to process parameters, can be used to adjust the desired properties of the steel. Furthermore it has to be taken into account, that a certain subset of these process parameters is required for adjusting at least one desired property of the steel, whilst a different subset of process parameters is required for adjusting at least one different desired property of the steel. Since only a subset of certain parameters, which corresponds to the process parameters, is determined by the computing unit, whereat this certain process parameters are required for adjusting a certain desired properties of the steel, the time for adjusting the property can be significantly reduced.
It has turned out to be particularly advantageous if the computing unit determines the values of the parameters by means of linear regression. The linear regression is a particularly simple method, and thus especially appropriate for a statistical model. In other words by means of the linear regression the minimum and maximum values for the desired properties of the steel can be determined extremely easy and fast. The linear regression is expressed by eq. 1 :
where a, are tuneable parameters which are concerning the subset of parameters x, characterizing for desired properties of the steel. Furthermore the linear regression provides a simple and very fast learning procedure as well as the absence of overfitting, since the latter is only a problem for mathematical equations of higher order. The advantage of linear regression models originates from the fact, that a linear function is
defined on a bounded area, whereat it has a minimum respectively maximum value on the respective boundary of this area. Therefore, it is quite straight forward to determine a minimum achievable Re value (Remin) and a maximum achievable Re value (Remax). This is done by parameterization of eq. 1 , whereby eq. 2 arises:
whereat eq. 3 is defined as: xI(y1)=x.mm+ y.-(xI max-xI m,n) (3)
15<i<19,0< y,<l
As is shown in eq. 3, each of the parameters x, is parameterized by real-valued parameters y, ranging from 0 to 1.
Furthermore the parameters y, have to satisfy eq. 4: sign(a15) yi5=sign(a16) yi6=...=sign(ai9) yig (4) whereat the signum-conditions of eq. 5 have to be taken into account: sign(x)=-l for x<0; sign(x)=0 for x=0; and
sign(x)=0 for x=0; (5) When, after defining the new adjustable parameter y, eq. 6 can be established:
whereby the linear regression parameterization takes the form of eq. 7:
Now, by varying a single parameter y from 0 to 1, one can calculate Remin and Remax by taking into account the conditions of eq. 8:
Remm=Re(0)
Remax=Re(l) (8)
If the desired Re value, which corresponds to one of the desired properties of the steel is outside the interval [Remm, Remax] it cannot be reached. If the desired Re value is inside this interval, then it may be reached by adjusting the process parameters x,. To calculate the required values Xj one has to first calculate values of y, (see eq. 3), taking into account the restriction, which is expressed by eq. 4. This is done by substituting to the left part of eq. 7 the desired Re value and solving it with respect to y.
As an example, the following assumptions are made to explain the determination of predicted values, whereat table 2 shows a subset of parameters, which correspond to the process parameters, derived from the most important parameters that influence the properties of steel:
- there is a set of process parameters, which are listed in table 2, and the predicted value of Re for theses process parameters is 229 MPa
- the desired value of Re is 240 MPa
- the calculated values for Remin and Remax derived by means of eq. 8 are Remin = 215 MPa and Remax = 247 MPa
If a desired value of Re = 240 MPa has to be achieved, then one has to take a parameter value of y = 0,78. The values of the process parameters, which can e.g. be annealing parameters, are calculated using eq. 3 and eq. 6.
Table 2
It is further advantageous, if by means of the experimental data set a data set concerning the linear regression is determined, which is utilized for adjusting the values of the parameters.
Due to the fact that experimental data are used for adjusting the values of the parameters by means of the linear regression, it can be ensured, that the results of the linear regression reflect the real connection between the parameters and the desired properties of the steel particularly accurate. The more experimental data are available to determine a data set concerning the linear regression, the more accurate the linear regression can adjust the values of the parameters. Furthermore it is advantageous, if the parameters concerning the steel are independent of each other.
With independent variables it is particularly easy to determine each parameters influence on the steel properties. If the parameters are independent of each other, changing the value of one parameter will not affect the other parameters. Thus the influence of each single parameter to the steel quality can be evaluated particularly accurate.
The facility for producing steel according to the invention comprises a facility control device, which comprises a computing unit, by means of which parameters
characterizing values for desired properties of the steel are capable of being determined with the aid of an experimental data set. The values are capable of being simultaneously determined by means of the computing unit and are capable of being transmitted by means of the facility control device to associated sections of the facility.
By the deployment of the facility the mechanical properties of the steel, which correspond to the desired properties of the steel can be controlled particularly fast and efficient. By transmitting the values to associated sections, which are responsible of e.g. thermal processes or molding processes or alloying processes, the production of steel with desired properties can also be controlled particularly efficient.
The advantages and preferred embodiments described for the method according to the invention also apply to the facility according to the invention and vice versa. The features and feature combinations mentioned above in the description as well as the features and feature combinations mentioned below in the description of figures and/or shown in the figures alone are usable not only in the respectively specified combination, but also in other combinations or alone without departing from the scope of the invention.
Further advantages, features and details of the invention are apparent from the claims, the following description of preferred embodiments as well as based on the drawings.
The drawings show in:
FIG 1 a histogram of different yield stresses, whereat each of the yield stresses occurs with a certain probability due to the usual scattering of steel production; and
FIG 2 a diagram, which illustrates the determination of a yield stress by means of a process parameter.
FIG 1 illustrates the frequency distribution of a yield stress 1 by means of a histogram.
The yield stress 1 is plotted on the abscissa and increases according to an arrow direction of an arrow 8. The frequency 2 is plotted on the ordinate of the histogram and increases according to an arrow direction of an arrow 9. The histogram illustrates, that different yield stresses 1 can be achieved during the manufacturing of steel according to a frequency distribution. In other words one has to take into account that the yield stress 1 which can be reached during the manufacturing lies within a scattering region.
FIG 2 illustrates the dependency of the yield stress 1 of a parameter 3, which is determined by a computing unit. The parameter 3 is plotted on the abscissa and increases to an arrow direction of an arrow 10. The yield stress 1 is plotted on the ordinate and increases according to an arrow direction of an arrow 1 1. As an example it is supposed, that a parameter 3, which is e.g. a strip velocity at annealing or e.g. a strip thickness after cold rolling, whereat a predicted value 7 of the yield stress 1 for this parameter 3 is determined. FIG 2 also illustrates the linear dependency of the yield stress 1 from the parameter 3 within a minimum boundary, which corresponds to a minimum value 4 and a maximum boundary, which corresponds to a maximum value 5. One can recognize, that if a desired value 6 which corresponds to the desired properties of the steel has to be reached, then the parameter 3 has to be set to a target value 12. In other words, by setting the parameter 3 to the target value 12, it can be achieved, that the desired value 6 and the predicted value 7 at least substantially agree with each other.
Claims
1. A method for adjusting properties in the production of steel, in which by means of a computing unit values (7) for parameters (3) characterizing for desired properties (6) of the steel are determined with the aid of an experimental data set,
characterized in that,
the values (7) are determined simultaneously.
2. The method according to claim 1 ,
characterized in that,
by means of the computing unit a minimum value (4) is determined for at least one of the desired properties (6), which is reached by means of the values (7) of the parameters (3)· 3. The method according to claims 1 or 2,
characterized in that,
by means of the computing unit a maximum value (5) for at least one of the desired properties (6) is determined, which is reached by means of the values (7) of the parameters
(3).
4. The method according to any one of the preceding claims,
characterized in that,
by the computing unit from the parameters (3) relevant process parameters are determined, which are required for adjusting the desired properties (6) of the steel.
5. The method according to any one of the preceding claims,
characterized in that,
the computing unit determines the values (7) of the parameters (3) by means of linear regression.
6. The method according to claim 5,
characterized in that,
by means of the experimental data set a data set concerning the linear regression is determined, which is utilized for adjusting the values (7) of the parameters (3).
7. The method according to any one of the preceding claims,
characterized in that,
the parameters (3) concerning the steel are independent of each other.
8. A facility for producing steel, comprising a facility control device, which comprises a computing unit, by means of which parameters (3) characterizing values (7) for desired properties (6) of the steel are capable of being determined with the aid of an experimental data set,
characterized in that,
the values (7) are capable of being simultaneously determined by means of the computing unit and are capable of being transmitted by means of the facility control device to associated sections of the facility.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/RU2013/000396 WO2014185810A1 (en) | 2013-05-13 | 2013-05-13 | Method for adjusting final steel properties at steel mill facility |
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| Application Number | Priority Date | Filing Date | Title |
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| PCT/RU2013/000396 WO2014185810A1 (en) | 2013-05-13 | 2013-05-13 | Method for adjusting final steel properties at steel mill facility |
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| WO2014185810A1 true WO2014185810A1 (en) | 2014-11-20 |
| WO2014185810A8 WO2014185810A8 (en) | 2015-06-11 |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112149272A (en) * | 2020-08-12 | 2020-12-29 | 唐山钢铁集团高强汽车板有限公司 | Cold-rolled steel strip mechanical property prediction model based on multiple linear regression analysis |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109847916B (en) * | 2018-12-26 | 2021-01-12 | 厦门邑通软件科技有限公司 | Energy-saving optimization method of cement raw material vertical mill system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4840051A (en) * | 1987-06-01 | 1989-06-20 | Ipsco Inc. | Steel rolling using optimized rolling schedule |
| US6430461B1 (en) * | 1996-10-30 | 2002-08-06 | Voest-Alpine Industrieanlagenbau Gmbh | Process for monitoring and controlling the quality of rolled products from hot-rolling processes |
| US20060259176A1 (en) * | 2005-04-20 | 2006-11-16 | Omron Corporation | Manufacture condition setting system, manufacture condition setting method, control program, and computer-readable record medium recording control program therein |
| JP2010033536A (en) * | 2007-12-20 | 2010-02-12 | Nippon Steel Corp | Method, device for predicting product material value, method, program for determining handling condition, and computer readable recording medium |
-
2013
- 2013-05-13 WO PCT/RU2013/000396 patent/WO2014185810A1/en active Application Filing
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4840051A (en) * | 1987-06-01 | 1989-06-20 | Ipsco Inc. | Steel rolling using optimized rolling schedule |
| US6430461B1 (en) * | 1996-10-30 | 2002-08-06 | Voest-Alpine Industrieanlagenbau Gmbh | Process for monitoring and controlling the quality of rolled products from hot-rolling processes |
| US20060259176A1 (en) * | 2005-04-20 | 2006-11-16 | Omron Corporation | Manufacture condition setting system, manufacture condition setting method, control program, and computer-readable record medium recording control program therein |
| JP2010033536A (en) * | 2007-12-20 | 2010-02-12 | Nippon Steel Corp | Method, device for predicting product material value, method, program for determining handling condition, and computer readable recording medium |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112149272A (en) * | 2020-08-12 | 2020-12-29 | 唐山钢铁集团高强汽车板有限公司 | Cold-rolled steel strip mechanical property prediction model based on multiple linear regression analysis |
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| WO2014185810A8 (en) | 2015-06-11 |
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