CN105116850B - A kind of pelletizing burnup control method and device - Google Patents
A kind of pelletizing burnup control method and device Download PDFInfo
- Publication number
- CN105116850B CN105116850B CN201510423408.4A CN201510423408A CN105116850B CN 105116850 B CN105116850 B CN 105116850B CN 201510423408 A CN201510423408 A CN 201510423408A CN 105116850 B CN105116850 B CN 105116850B
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- weight
- parameter preset
- mtd
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005453 pelletization Methods 0.000 title claims abstract description 55
- 238000000034 method Methods 0.000 title claims abstract description 39
- 239000000446 fuel Substances 0.000 claims abstract description 54
- 238000004519 manufacturing process Methods 0.000 claims abstract description 19
- 239000011159 matrix material Substances 0.000 claims description 39
- 238000006243 chemical reaction Methods 0.000 claims description 8
- 238000010219 correlation analysis Methods 0.000 claims description 7
- 238000005265 energy consumption Methods 0.000 abstract description 2
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 229910000278 bentonite Inorganic materials 0.000 description 3
- 239000000440 bentonite Substances 0.000 description 3
- SVPXDRXYRYOSEX-UHFFFAOYSA-N bentoquatam Chemical compound O.O=[Si]=O.O=[Al]O[Al]=O SVPXDRXYRYOSEX-UHFFFAOYSA-N 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 229910052742 iron Inorganic materials 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 239000008188 pellet Substances 0.000 description 3
- 239000002253 acid Substances 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 230000009897 systematic effect Effects 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 239000002817 coal dust Substances 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
-
- 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
- Y02P80/00—Climate change mitigation technologies for sector-wide applications
- Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
-
- 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]
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Monitoring And Testing Of Nuclear Reactors (AREA)
Abstract
The embodiments of the invention provide a kind of pelletizing burnup control method and device, method therein includes：Wherein n parameter is chosen from pelletizing production procedure parameter as parameter preset；The parameter value of the n parameter preset is gathered in real time；The parameter value of the n parameter preset is input in default computation model, fuel value needed for obtaining；Fuel usage amount is adjusted according to the required fuel value in real time.By the computation model pre-established, realize some parameters by regulation and pelletizing fuel consumption is control effectively, optimize pelletizing production, reduce energy consumption level.
Description
Technical field
The present invention relates to acid pellet technology, more particularly to a kind of pelletizing burnup control method and device.
Background technology
In steel and iron industry, iron ore acid pellet production technology is the developing direction of current optimization Bf Burden.It is raw
The pelletizing that output is come needs to consume the fuel such as substantial amounts of coal dust or natural gas in burning, is become more and more popular in cost idea
Today, reduction energy resource consumption have become major iron company's cost efficiency stand in the breach need solve the problem of.
Generally, the production process of pelletizing determines the combustion process of pelletizing, but the pelletizing of influence pelletizing fuel consumption is given birth to
Produce procedure parameter a lot, for example have pelletizing production scale, operating rate, bentonite consumption, finished ball nodulizing FeO contents, pelletizing into
Product ore deposit basicity, pelletizing crushing strength etc., not yet have a kind of prior art to utilize pelletizing production procedure parameter pair at present
Pelletizing fuel consumption is effectively controlled.
The content of the invention
To overcome problems of the prior art, the present invention provides a kind of pelletizing burnup control method and device, with reality
Now pelletizing fuel consumption is control effectively using pelletizing production procedure parameter.
First aspect according to embodiments of the present invention includes there is provided a kind of pelletizing burnup control method, methods described：
Wherein n parameter is chosen from pelletizing production procedure parameter as parameter preset；
The parameter value of the n parameter preset is gathered in real time；
The parameter value of the n parameter preset is input in default computation model, fuel value needed for obtaining；
Fuel usage amount is adjusted according to the required fuel value in real time；
Wherein, the computation model is：
Y fuel values for needed for described, yjFor j-th of parameter preset and the relationship equation of fuel consumption, wjFor yjPower
Weight.
Optionally, the w is obtained in the following wayj：
M sample data of each parameter preset is obtained, m * n matrix is constituted；
First weight is obtained from the matrix according to compositive power coefficient method and entropy assessment；
Second weight is obtained according to the input of user；
Determine the proportionality coefficient of first weight and the second weight；
First weight and second weight are added according to the proportionality coefficient and obtain wj, j=1,2...n.
Optionally, w is obtained especially by following mannerj：
Obtain each parameter preset xiM sample data, constitute m * n matrix X=(xij)m×n, wherein xij(i=1,2,
3,...,m；J=1,2,3 ..., n) it is parameter preset xiJ-th of sample data；
The matrix is changed according to following rule：
Wherein I1=it is required that the smaller the better parameter preset, I2=it is required that the parameter preset being the bigger the better, I3={ will
Aspire for stability in the parameter preset of ideal value }；
The order of magnitude of unified parameter preset simultaneously eliminates dimension, and the matrix is carried out into such as down conversion：
xij=100 × (xij-xjmin)/(xjmax-xjmin) (i=1,2 ..., m；J=1,2 ..., n)
The element of the matrix is calculated as below again：
Pass through
Obtain the first weight beta=(β1,β2,...,βn)T, wherein
Second weight α=(α is determined according to the input of user1,α2,...,αn)T, wherein
The proportionality coefficient μ of first weight and the second weight is determined, wherein 0≤μ ＜ 1；
Pass through
wj=μ αj+(1-μ)βj(j=1,2 ..., n)
Obtain wj, wherein
Optionally, the yjObtain in the following way：
Correlation analysis is carried out with fuel consumption to j-th of parameter preset, the phase is drawn by the method for curve matching
Closing property equation yj, j=1,2...n.
Second aspect according to embodiments of the present invention includes there is provided a kind of pelletizing burn-up control assembly, described device：
Selecting module, for choosing wherein n parameter from pelletizing production procedure parameter as parameter preset；
Acquisition module, the parameter value for gathering the n parameter preset in real time；
Computing module, for the parameter value of the n parameter preset to be input in default computation model, needed for obtaining
Fuel value；
Control module, for adjusting fuel usage amount in real time according to the required fuel value；
Wherein, the computation model is：
Y fuel values for needed for described, yjFor j-th of parameter preset and the relationship equation of fuel consumption, wjFor yjPower
Weight.
Optionally, the w is obtained in the following wayj：
M sample data of each parameter preset is obtained, m * n matrix is constituted；
First weight is obtained from the matrix according to compositive power coefficient method and entropy assessment；
Second weight is obtained according to the input of user；
Determine the proportionality coefficient of first weight and the second weight；
First weight and second weight are added according to the proportionality coefficient and obtain wj, j=1,2...n.
Optionally, w is obtained especially by following mannerj：
Obtain each parameter preset xiM sample data, constitute m * n matrix X=(xij)m×n, wherein xij(i=1,2,
3,...,m；J=1,2,3 ..., n) it is parameter preset xiJ-th of sample data；
The matrix is changed according to following rule：
Wherein I1=it is required that the smaller the better parameter preset, I2=it is required that the parameter preset being the bigger the better, I3={ will
Aspire for stability in the parameter preset of ideal value }；
The order of magnitude of unified parameter preset simultaneously eliminates dimension, and the matrix is carried out into such as down conversion：
xij=100 × (xij-xjmin)/(xjmax-xjmin) (i=1,2 ..., m；J=1,2 ..., n)
The element of the matrix is calculated as below again：
Pass through
Obtain the first weight beta=(β1,β2,...,βn)T, wherein
Second weight α=(α is determined according to the input of user1,α2,...,αn)T, wherein
The proportionality coefficient μ of first weight and the second weight is determined, wherein 0≤μ ＜ 1；
Pass through
wj=μ αj+(1-μ)βj(j=1,2 ..., n)
Obtain wj, wherein
Optionally, the yjObtain in the following way：
Correlation analysis is carried out with fuel consumption to j-th of parameter preset, the phase is drawn by the device of curve matching
Closing property equation yj, j=1,2...n.
The technical scheme that embodiments of the invention are provided can include the following benefits：
In embodiments of the present invention, by the computation model pre-established, realize by adjust some parameters to pelletizing
Fuel consumption control effectively, and optimizes pelletizing production, reduces energy consumption level.
It should be appreciated that the general description of the above and detailed description hereinafter are only exemplary and explanatory, not
Can the limitation present invention.
Brief description of the drawings
Accompanying drawing herein is merged in specification and constitutes the part of this specification, shows the implementation for meeting the present invention
Example, and for explaining principle of the invention together with specification.
Fig. 1 is a kind of flow chart of pelletizing burnup control method according to an exemplary embodiment；
Fig. 2 is the systematic schematic diagram according to an exemplary embodiment；
Fig. 3 is a kind of flow chart of pelletizing burnup control method according to an exemplary embodiment；
Fig. 4 is a kind of schematic diagram of pelletizing burn-up control assembly according to an exemplary embodiment.
Embodiment
Here exemplary embodiment will be illustrated in detail, its example is illustrated in the accompanying drawings.Following description is related to
During accompanying drawing, unless otherwise indicated, the same numbers in different accompanying drawings represent same or analogous key element.Following exemplary embodiment
Described in embodiment do not represent and the consistent all embodiments of the present invention.On the contrary, they be only with as appended
The example of the consistent apparatus and method of some aspects be described in detail in claims, the present invention.
Fig. 1 is a kind of flow chart of pelletizing burnup control method according to an exemplary embodiment.Referring to Fig. 1 institutes
Show, this method includes：
S101, chooses wherein n parameter as parameter preset from pelletizing production procedure parameter.Wherein n is natural number.
S102, gathers the parameter value of the n parameter preset in real time.
S103, the parameter value of the n parameter preset is input in default computation model, fuel value needed for obtaining.
S104, fuel usage amount is adjusted according to the required fuel value in real time.
Wherein, the computation model is：
Y fuel values for needed for described, yjFor j-th of parameter preset and the relationship equation of fuel consumption, wjFor yjPower
Weight.
As an example, the yjIt can obtain in the following way：
Correlation analysis is carried out with fuel consumption to j-th of parameter preset, the phase is drawn by the method for curve matching
Closing property equation yj, j=1,2...n.
Pelletizing production procedure parameter (hereinafter referred to as candidate parameter) might have a lot, for example, have pelletizing production scale, work
Industry rate, bentonite consumption, finished ball nodulizing FeO contents, pelletizing finished product ore deposit basicity, pelletizing crushing strength etc..In order to carry out
These candidate parameters and pelletizing fuel consumption can be carried out correlation research analysis respectively, draw each candidate parameter by screening
The relationship equation influenceed on pelletizing fuel consumption.
If it find that certain candidate parameter influences notable to pelletizing fuel consumption, then by the parameter be added to parameter preset it
Row, and record the relationship equation y of the parameter and fuel consumptionj, it is on the contrary then exclude the candidate parameter.
Fig. 2 is the systematic schematic diagram according to an exemplary embodiment.It is shown in Figure 2：
Sampling unit：It is responsible for the sampling to candidate parameter, is used for dependency analysis unit and model computing unit；
Dependency analysis unit：The sampled data of each candidate parameter is read from database, by candidate parameter and pelletizing
Fuel consumption carries out correlation analysis, judges whether have an impact, and whether notable influences, is joined selected candidate according to influence degree
Number is transmitted to Modeling Calculation unit as parameter preset；
Modeling Calculation unit：The sampled data of each parameter preset is read from database, passes through compositive power coefficient method
The parameter preset that dependency analysis unit is determined is calculated and optimized mathematical model is set up, model and model parameter is defeated
Go out to give Optimized model unit, Modeling Calculation unit is constantly calculated sampled data, constantly update the model in Optimized model
Parameter, model will not be altered once setting up, and simply according to the sampled data of renewal, recalculate model parameter, and update excellent
Change model parameter；
Optimized model unit：Model and model parameter are calculated from Modeling Calculation unit, after model is set up, according to
Tracking, calculating, the assessment of Real-time Production Process parameter, according toPelletizing fuel consumption y values are calculated, ball is provided
Group's fuel optimization standard, is exported to control unit；
Control unit：It is responsible for the control and regulation to pelletizing fuel, pelletizing fuel optimization standard is passed into controller, with
Complete the control to pelletizing injecting coal quantity and jet amount.
It is shown in Figure 3, in the present embodiment or some other embodiments of the invention, institute can be obtained in the following way
State wj：
S301, obtains m sample data of each parameter preset, constitutes m * n matrix；
S302, the first weight is obtained according to compositive power coefficient method and entropy assessment from the matrix；
S303, the second weight is obtained according to the input of user；
S304, determines the proportionality coefficient of first weight and the second weight；
S305, first weight and second weight are added according to the proportionality coefficient and obtain wj, j=1,
2...n。
Further, in the present embodiment or of the invention some other embodiments, specifically can with as follows 1)~
8) w is obtainedj：
1) each parameter preset x is obtainediM sample data, constitute m * n matrix X=(xij)m×n, wherein xij(i=1,
2,3,...,m；J=1,2,3 ..., n) it is parameter preset xiJ-th of sample data.
As an example, under certain scene, parameter preset includes (i.e. x1、x2、x3、x4、x5、x6)：
Tag_QTSCGM：Pelletizing plant's production scale (ten thousand t)；
Tag_ZYL：Pelletizing plant's operating rate (%)；
Tag_PRTYL：Bentonite usage amount (kg/t pellets)；
Tag_FeO：Pelletizing finished product ore deposit FeO contents (%)；
Tag_R：Pelletizing finished product ore deposit basicity (again)；
Tag_KYQD：Pelletizing crushing strength (N/).
The sample collected is as shown in table 1 below：
Table 1
The matrix then set up is
2) matrix is changed according to following rule：
Wherein I1=it is required that the smaller the better parameter preset, I2=it is required that the parameter preset being the bigger the better, I3={ will
Aspire for stability in the parameter preset of ideal value }.xjmin=min { xij| j=1,2 ..., n }, xjmax=max { xij| j=1,2 ...,
n}。
Example in undertaking, the matrix after conversion is changed into
3) unify the order of magnitude of parameter preset and eliminate dimension, the matrix is subjected to such as down conversion：
xij=100 × (xij-xjmin)/(xjmax-xjmin) (i=1,2 ..., m；J=1,2 ..., n).
Example in undertaking, the matrix after conversion is changed into
4) element of the matrix is calculated as below：
Example in undertaking, is obtained by formula above
5) pass through
Obtain the first weight beta=(β1,β2,...,βn)T, wherein
Note working as xijWhen=0, x is providedijlnxij=0 (i=1,2 ..., m；J=1,2 ..., n).
First weight is alternatively referred to as objective weight.
Example in undertaking, can obtain
β=(0.063 0.155 0.078 0.199 0.118 0.172 0.215)T
6) the second weight α=(α is determined according to the input of user1,α2,...,αn)T, wherein
Second weight is alternatively referred to as subjective weight, and the determination of subjective weight can be dug according to pelletizing production Process History data
Pick and analysis are got.
Example in undertaking, user determines
α=(0.1 0.05 0.1 0.15 0.1 0.2 0.3)T
7) the proportionality coefficient μ of first weight and the second weight is determined, wherein 0≤μ ＜ 1.
Proportionality coefficient μ is alternatively referred to as preference coefficient, and it reflects Bu Tong weight of the analyst to subjective weight and objective weight
Visual range degree, is set according to specific needs.
8) pass through
wj=μ αj+(1-μ)βj(j=1,2 ..., n)
Obtain wj, wherein
Wj can be described as comprehensive weight again.
Example in undertaking, if μ=0.6, can be obtained
W=(0.085 0.092 0.091 0.170 0.107 0.189 0.266)T。
So, after having obtained w, by w and each yiSubstitute intoFinally it can obtain computation model.
For example, it is assumed that the relationship equation fitted under another scene is y1=0.0006x2- 0.25x+46.4, y2=-
4.5x2+ 27.5x+6.6, and calculate to obtain w1=0.17, w2=0.83, then it can obtain computation model
Y=w1y1+w2y2=0.17 (0.0006x2-0.25x+46.4)+0.83(-4.5x2+27.5x+6.6)
Fig. 4 is a kind of schematic diagram of pelletizing burn-up control assembly according to an exemplary embodiment.Referring to Fig. 4 institutes
Show, device 400 can include：
Selecting module 401, for choosing wherein n parameter from pelletizing production procedure parameter as parameter preset；
Acquisition module 402, the parameter value for gathering the n parameter preset in real time；
Computing module 403, for the parameter value of the n parameter preset to be input in default computation model, is obtained
Required fuel value；
Control module 404, for adjusting fuel usage amount in real time according to the required fuel value；
Wherein, the computation model is：
Y fuel values for needed for described, yjFor j-th of parameter preset and the relationship equation of fuel consumption, wjFor yjPower
Weight.
In the present embodiment or some other embodiments of the invention, the w can be obtained in the following wayj：
M sample data of each parameter preset is obtained, m * n matrix is constituted；
First weight is obtained from the matrix according to compositive power coefficient method and entropy assessment；
Second weight is obtained according to the input of user；
Determine the proportionality coefficient of first weight and the second weight；
First weight and second weight are added according to the proportionality coefficient and obtain wj, j=1,2...n.
In the present embodiment or some other embodiments of the invention, w can be specifically obtained in the following wayj：
Obtain each parameter preset xiM sample data, constitute m * n matrix X=(xij)m×n, wherein xij(i=1,2,
3,...,m；J=1,2,3 ..., n) it is parameter preset xiJ-th of sample data；
The matrix is changed according to following rule：
Wherein I1=it is required that the smaller the better parameter preset, I2=it is required that the parameter preset being the bigger the better, I3={ will
Aspire for stability in the parameter preset of ideal value }；
The order of magnitude of unified parameter preset simultaneously eliminates dimension, and the matrix is carried out into such as down conversion：
xij=100 × (xij-xjmin)/(xjmax-xjmin) (i=1,2 ..., m；J=1,2 ..., n)
The element of the matrix is calculated as below again：
Pass through
Obtain the first weight beta=(β1,β2,...,βn)T, wherein
Second weight α=(α is determined according to the input of user1,α2,...,αn)T, wherein
The proportionality coefficient μ of first weight and the second weight is determined, wherein 0≤μ ＜ 1；
Pass through
wj=μ αj+(1-μ)βj(j=1,2 ..., n)
Obtain wj, wherein
In the present embodiment or some other embodiments of the invention, the yjObtain in the following way：
Correlation analysis is carried out with fuel consumption to j-th of parameter preset, the phase is drawn by the device of curve matching
Closing property equation yj, j=1,2...n.
On the device in above-described embodiment, wherein modules perform the concrete mode of operation in relevant this method
Embodiment in be described in detail, explanation will be not set forth in detail herein.
Those skilled in the art will readily occur to its of the present invention after considering specification and putting into practice invention disclosed herein
Its embodiment.The application be intended to the present invention any modification, purposes or adaptations, these modifications, purposes or
Person's adaptations follow the general principle of the present invention and including undocumented common knowledge in the art of the invention
Or conventional techniques.Description and embodiments are considered only as exemplary, and true scope and spirit of the invention are by appended
Claim is pointed out.
It should be appreciated that the invention is not limited in the precision architecture for being described above and being shown in the drawings, and
And various modifications and changes can be being carried out without departing from the scope.The scope of the present invention is only limited by appended claim.
Claims (6)
1. a kind of pelletizing burnup control method, it is characterised in that methods described includes：
Wherein n parameter is chosen from pelletizing production procedure parameter as parameter preset；
The parameter value of the n parameter preset is gathered in real time；
The parameter value of the n parameter preset is input in default computation model, fuel value needed for obtaining；
Fuel usage amount is adjusted according to the required fuel value in real time；
Wherein, the computation model is：
<mrow>
<mi>y</mi>
<mo>=</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msub>
<mi>w</mi>
<mi>j</mi>
</msub>
<msub>
<mi>y</mi>
<mi>j</mi>
</msub>
</mrow>
Y fuel values for needed for described, yjFor j-th of parameter preset and the relationship equation of fuel consumption, wjFor yjWeight；
The w is obtained in the following wayj：
M sample data of each parameter preset is obtained, m * n matrix is constituted；
First weight is obtained from the matrix according to compositive power coefficient method and entropy assessment；
Second weight is obtained according to the input of user；
Determine the proportionality coefficient of first weight and the second weight；
First weight and second weight are added according to the proportionality coefficient and obtain wj, j=1,2...n.
2. according to the method described in claim 1, it is characterised in that obtain w especially by following mannerj：
Obtain each parameter preset xjM sample data, constitute m * n matrix X=(xij)m×n, wherein xij(i=1,2,
3,...,m；J=1,2,3 ..., n) it is parameter preset xjI-th of sample data；
The matrix is changed according to following rule：
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mtd>
<mtd>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msub>
<mi>I</mi>
<mn>1</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mi>max</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msub>
<mi>I</mi>
<mn>2</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>|</mo>
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>j</mi>
<mo>*</mo>
</msubsup>
</mrow>
<mo>|</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msub>
<mi>I</mi>
<mn>3</mn>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein I1=it is required that the smaller the better parameter preset, I2=it is required that the parameter preset being the bigger the better, I3={ to seek to stable
It is scheduled on the parameter preset of ideal value }；
The order of magnitude of unified parameter preset simultaneously eliminates dimension, and the matrix is carried out into such as down conversion：
xij=100 × (xij-xjmin)/(xjmax-xjmin) (i=1,2 ..., m；J=1,2 ..., n)
The element of the matrix is calculated as below again：
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>/</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
Pass through
<mrow>
<msub>
<mi>h</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mo>-</mo>
<msup>
<mrow>
<mo>(</mo>
<mi>ln</mi>
<mi> </mi>
<mi>m</mi>
<mo>)</mo>
</mrow>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mi>ln</mi>
<mi> </mi>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<mrow>
<msub>
<mi>&beta;</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>h</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>/</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>h</mi>
<mi>k</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
Obtain the first weight beta=(β1,β2,...,βn)T, wherein
Second weight α=(α is determined according to the input of user1,α2,...,αn)T, wherein
The proportionality coefficient μ of first weight and the second weight is determined, wherein 0≤μ ＜ 1；
Pass through
wj=μ αj+(1-μ)βj(j=1,2 ..., n)
Obtain wj, wherein
3. according to the method described in claim 1, it is characterised in that the yjObtain in the following way：
Correlation analysis is carried out with fuel consumption to j-th of parameter preset, the correlation is drawn by the method for curve matching
Equation yj, j=1,2...n.
4. a kind of pelletizing burn-up control assembly, it is characterised in that described device includes：
Selecting module, for choosing wherein n parameter from pelletizing production procedure parameter as parameter preset；
Acquisition module, the parameter value for gathering the n parameter preset in real time；
Computing module, for the parameter value of the n parameter preset to be input in default computation model, fuel needed for obtaining
Value；
Control module, for adjusting fuel usage amount in real time according to the required fuel value；
Wherein, the computation model is：
<mrow>
<mi>y</mi>
<mo>=</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msub>
<mi>w</mi>
<mi>j</mi>
</msub>
<msub>
<mi>y</mi>
<mi>j</mi>
</msub>
</mrow>
Y fuel values for needed for described, yjFor j-th of parameter preset and the relationship equation of fuel consumption, wjFor yjWeight；
The w is obtained in the following wayj：
M sample data of each parameter preset is obtained, m * n matrix is constituted；
First weight is obtained from the matrix according to compositive power coefficient method and entropy assessment；
Second weight is obtained according to the input of user；
Determine the proportionality coefficient of first weight and the second weight；
First weight and second weight are added according to the proportionality coefficient and obtain wj, j=1,2...n.
5. device according to claim 4, it is characterised in that obtain w especially by following mannerj：
Obtain each parameter preset xjM sample data, constitute m * n matrix X=(xij)m×n, wherein xij(i=1,2,
3,...,m；J=1,2,3 ..., n) it is parameter preset xjI-th of sample data；
The matrix is changed according to following rule：
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mtd>
<mtd>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msub>
<mi>I</mi>
<mn>1</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mi>max</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msub>
<mi>I</mi>
<mn>2</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>|</mo>
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msubsup>
<mi>x</mi>
<mi>j</mi>
<mo>*</mo>
</msubsup>
</mrow>
<mo>|</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msub>
<mi>I</mi>
<mn>3</mn>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein I1=it is required that the smaller the better parameter preset, I2=it is required that the parameter preset being the bigger the better, I3={ to seek to stable
It is scheduled on the parameter preset of ideal value }；
The order of magnitude of unified parameter preset simultaneously eliminates dimension, and the matrix is carried out into such as down conversion：
xij=100 × (xij-xjmin)/(xjmax-xjmin) (i=1,2 ..., m；J=1,2 ..., n)
The element of the matrix is calculated as below again：
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>/</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
2
Pass through
<mrow>
<msub>
<mi>h</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mo>-</mo>
<msup>
<mrow>
<mo>(</mo>
<mi>ln</mi>
<mi> </mi>
<mi>m</mi>
<mo>)</mo>
</mrow>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mi>ln</mi>
<mi> </mi>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<mrow>
<msub>
<mi>&beta;</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>h</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>/</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>h</mi>
<mi>k</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
Obtain the first weight beta=(β1,β2,...,βn)T, wherein
Second weight α=(α is determined according to the input of user1,α2,...,αn)T, wherein
The proportionality coefficient μ of first weight and the second weight is determined, wherein 0≤μ ＜ 1；
Pass through
wj=μ αj+(1-μ)βj(j=1,2 ..., n)
Obtain wj, wherein
6. device according to claim 4, it is characterised in that the yjObtain in the following way：
Correlation analysis is carried out with fuel consumption to j-th of parameter preset, the correlation is drawn by the device of curve matching
Equation yj, j=1,2...n.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510423408.4A CN105116850B (en) | 2015-07-17 | 2015-07-17 | A kind of pelletizing burnup control method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510423408.4A CN105116850B (en) | 2015-07-17 | 2015-07-17 | A kind of pelletizing burnup control method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105116850A CN105116850A (en) | 2015-12-02 |
| CN105116850B true CN105116850B (en) | 2017-08-22 |
Family
ID=54664868
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510423408.4A Active CN105116850B (en) | 2015-07-17 | 2015-07-17 | A kind of pelletizing burnup control method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105116850B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107037787B (en) * | 2016-02-03 | 2019-01-25 | 中冶长天国际工程有限责任公司 | A kind of grate-kiln pelletizing burnup control method and device |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2048553A1 (en) * | 2007-10-12 | 2009-04-15 | Powitec Intelligent Technologies GmbH | Control circuit for regulating a process, in particular a combustion process |
| CN101561148A (en) * | 2009-05-08 | 2009-10-21 | 上海颖科计算机科技有限公司 | Boiler combustion control system and method |
| CN102252343A (en) * | 2011-05-05 | 2011-11-23 | 浙江宜景环保科技有限公司 | Method for optimizing combustion of porous medium combustor |
| CN102261671A (en) * | 2010-05-28 | 2011-11-30 | 王荣虎 | Boiler combustion multi-constraint and multi-object optimization expert system and optimization method thereof |
| CN103672948A (en) * | 2013-12-13 | 2014-03-26 | 聚光科技(杭州)股份有限公司 | Combustion control system and method of industrial furnace |
| JP5577568B2 (en) * | 2008-01-17 | 2014-08-27 | Ｊｆｅスチール株式会社 | Impeller desulfurization control apparatus and method |
| CN104313306A (en) * | 2014-10-15 | 2015-01-28 | 中冶长天国际工程有限责任公司 | Method and device for measuring specific consumption of sintering ore solid fuel |
| CN104328276A (en) * | 2014-10-15 | 2015-02-04 | 中冶长天国际工程有限责任公司 | Method of controlling solid fuel in sintering process, device and system |
-
2015
- 2015-07-17 CN CN201510423408.4A patent/CN105116850B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2048553A1 (en) * | 2007-10-12 | 2009-04-15 | Powitec Intelligent Technologies GmbH | Control circuit for regulating a process, in particular a combustion process |
| JP5577568B2 (en) * | 2008-01-17 | 2014-08-27 | Ｊｆｅスチール株式会社 | Impeller desulfurization control apparatus and method |
| CN101561148A (en) * | 2009-05-08 | 2009-10-21 | 上海颖科计算机科技有限公司 | Boiler combustion control system and method |
| CN102261671A (en) * | 2010-05-28 | 2011-11-30 | 王荣虎 | Boiler combustion multi-constraint and multi-object optimization expert system and optimization method thereof |
| CN102252343A (en) * | 2011-05-05 | 2011-11-23 | 浙江宜景环保科技有限公司 | Method for optimizing combustion of porous medium combustor |
| CN103672948A (en) * | 2013-12-13 | 2014-03-26 | 聚光科技(杭州)股份有限公司 | Combustion control system and method of industrial furnace |
| CN104313306A (en) * | 2014-10-15 | 2015-01-28 | 中冶长天国际工程有限责任公司 | Method and device for measuring specific consumption of sintering ore solid fuel |
| CN104328276A (en) * | 2014-10-15 | 2015-02-04 | 中冶长天国际工程有限责任公司 | Method of controlling solid fuel in sintering process, device and system |
Non-Patent Citations (1)
| Title |
|---|
| 基于多源异构信息融合的回转窑球团焙烧过程操作模式优化;任秀东;《中国优秀硕士学位论文全文数据库工程科技I辑》;20150715(第7期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105116850A (en) | 2015-12-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gao et al. | Pathways towards regional circular economy evaluated using material flow analysis and system dynamics | |
| Chen et al. | Generalized thermodynamic optimization for iron and steel production processes: Theoretical exploration and application cases | |
| Wang et al. | Multi-objective optimization of synergic energy conservation and CO2 emission reduction in China's iron and steel industry under uncertainty | |
| Mitra et al. | Multiobjective optimization of top gas recycling conditions in the blast furnace by genetic algorithms | |
| Hao et al. | De-capacity policy effect on China’s coal industry | |
| Zhou et al. | A technical framework for integrating carbon emission peaking factors into the industrial green transformation planning of a city cluster in China | |
| Liu et al. | System dynamics analysis on characteristics of iron-flow in sintering process | |
| Li et al. | Study on an implementation scheme of synergistic emission reduction of CO2 and air pollutants in China’s steel industry | |
| CN104975118A (en) | Method for optimizing ratio of raw materials before iron making | |
| Steckel et al. | To end coal, adapt to regional realities | |
| Rao et al. | Forecasting the carbon emissions in Hubei Province under the background of carbon neutrality: A novel STIRPAT extended model with ridge regression and scenario analysis | |
| Xu et al. | Reducing the fluctuation of oxygen demand in a steel plant through optimal production scheduling | |
| Perpiñán et al. | CO2 recycling in the iron and steel industry via power-to-gas and oxy-fuel combustion | |
| Adilson de Castro et al. | The mini blast furnace process: An efficient reactor for green pig iron production using charcoal and hydrogen-rich gas: A study of cases | |
| Wang et al. | Industrial structure optimization and low-carbon transformation of Chinese industry based on the forcing mechanism of CO2 emission peak target | |
| Zhang et al. | Intelligent natural gas and hydrogen pipeline dispatching using the coupled thermodynamics-informed neural network and compressor boolean neural network | |
| Li et al. | A novel seasonal grey model for forecasting the quarterly natural gas production in China | |
| Widl et al. | Combined optimal design and control of hybrid thermal-electrical distribution grids using co-simulation | |
| Malone et al. | Industrial ecosystems and food webs: An ecological-based mass flow analysis to model the progress of steel manufacturing in China | |
| CN105116850B (en) | A kind of pelletizing burnup control method and device | |
| Choi et al. | Machine Learning-Based Tap Temperature Prediction and Control for Optimized Power Consumption in Stainless Electric Arc Furnaces (EAF) of Steel Plants | |
| Song et al. | Analysis, evaluation and optimization strategy of China thermal power enterprises’ business performance considering environmental costs under the background of carbon trading | |
| Fan et al. | A system dynamics based model for coal investment | |
| García García et al. | A mixed integer linear programming model for the optimization of steel waste gases in cogeneration: A combined coke oven and converter gas case study | |
| Liu et al. | Multi-objective optimization for oil-gas production process based on compensation model of comprehensive energy consumption using improved evolutionary algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |