CN105116850A - Pellet fuel consumption control method and device - Google Patents

Abstract

Embodiments of the invention provide a pellet fuel consumption control method and device. The method comprises selecting n parameters from pellet production process parameters, taking the n parameters as preset parameters, acquiring parameter values of the n preset parameters in real time, inputting the parameter values of the n preset parameters into a preset calculation model to obtain a needed fuel value, and adjusting a fuel usage amount in real time according to the needed fuel value. Through the preset established calculation model, effective control for pellet fuel consumption by means of adjustments of the some parameters is achieved, pellet production is optimized, and the energy consumption level is reduced.

Description

A kind of pelletizing burnup control method and device

Technical field

The present invention relates to acid pellet technology, particularly relate 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.The pelletizing produced needs when burning to consume the fuel such as a large amount of coal dusts or rock gas, in today that cost idea becomes more and more popular, reduces the problem needing to solve of standing in the breach that energy resource consumption has become each big steel company cost efficiency.

Usually, the production run of pelletizing determines the combustion process of pelletizing, but the pelletizing production procedure parameter affecting pelletizing fuel consumption is a lot, such as there are pelletizing production scale, operating rate, bentonite consumption, finished ball nodulizing FeO content, pelletizing finished product ore deposit basicity, pelletizing crushing strength etc., not yet have a kind of prior art that pelletizing production procedure parameter can be utilized effectively to control pelletizing fuel consumption at present.

Summary of the invention

For overcoming problems of the prior art, the invention provides a kind of pelletizing burnup control method and device, to realize utilizing pelletizing production procedure parameter to control effectively to pelletizing fuel consumption.

According to the first aspect of the embodiment of the present invention, provide a kind of pelletizing burnup control method, described method comprises:

Choose from pelletizing production procedure parameter wherein n parameter as parameter preset;

The parameter value of the parameter preset of n described in Real-time Collection;

The parameter value of a described n parameter preset is input in default computation model, obtains required fuel value;

According to the described required real-time fuel metering use amount of fuel value;

Wherein, described computation model is:

$y=\underset{j=1}{\overset{n}{\Σ}}{w}_j{y}_j$

Y is described required fuel value, y _jfor the relationship equation of a jth parameter preset and fuel consumption, w _jfor y _jweight.

Optionally, described w is obtained in the following way _j:

Obtain m sample data of each parameter preset, form m * n matrix;

The first weight is obtained from described matrix according to compositive power coefficient method and entropy assessment;

The second weight is obtained according to the input of user;

Determine the scale-up factor of described first weight and the second weight;

Described first weight and described second weight are obtained w according to described scale-up factor addition _j, j=1,2...n.

Optionally, w is obtained especially by such as under type _j:

Obtain each parameter preset x _im sample data, form m * n matrix X=(x _ij) _{m × n}, wherein x _ij(i=1,2,3 ..., m; J=1,2,3 ..., n) be parameter preset x _ia jth sample data;

Described matrix is changed according to following rule:

$x_{ij}=\left\{\begin{array}{cc}{x}_{ij}& j\∈I_1\\ x_{j\max }-x_{ij}& j\∈I_2\\ |x_{ij}-x_j^{*}|& j\∈I_3\end{array}\right.$

Wherein I ₁={ requiring the smaller the better parameter preset }, I ₂={ parameter preset that requirement is the bigger the better }, I ₃={ requirement is stabilized in the parameter preset of ideal value };

Unify the order of magnitude of parameter preset and eliminate dimension, described matrix is carried out as down conversion:

x _ij＝100×(x _ij-x _jmin)/(x _jmax-x _jmin)(i＝1,2,...,m；j＝1,2,...,n)

Again described entry of a matrix element is calculated as follows:

$x_{ij}=x_{ij}/\underset{i=1}{\overset{m}{\Σ}}{x}_{ij}$

Pass through

$h_j=-{\left(\ln m\right)}^{-1}\underset{i=1}{\overset{m}{\Σ}}{x}_{ij}{\mathrm{lnx}}_{ij}$

${\mathrm{\β}}_j=(1-h_j)/\underset{k=1}{\overset{n}{\Σ}}(1-h_k)$

Obtain the first weight beta=(β ₁, β ₂..., β _n) ^t, wherein

The second weight α=(α is determined according to the input of user ₁, α ₂..., α _n) ^t, wherein

Determine the scale-up factor μ of described first weight and the second weight, wherein 0≤μ < 1;

Pass through

w _j＝μα _j+(1-μ)β _j(j＝1,2,...,n)

Obtain w _j, wherein $\underset{j=1}{\overset{n}{\&Sigma;}}{w}_j=1,w_j\&GreaterEqual;0(j=1,2,\mathrm{...},n).$

Optionally, described y _jobtain in the following way:

Correlation analysis is carried out to a jth parameter preset and fuel consumption, draws described relationship equation y by the method for curve _j, j=1,2...n.

According to the second aspect of the embodiment of the present invention, provide a kind of pelletizing burn-up control assembly, described device comprises:

Select module, for choose from pelletizing production procedure parameter wherein n parameter as parameter preset;

Acquisition module, for the parameter value of the parameter preset of n described in Real-time Collection;

Computing module, for being input in default computation model by the parameter value of a described n parameter preset, obtains required fuel value;

Control module, for according to the described required real-time fuel metering use amount of fuel value;

Wherein, described computation model is:

$y=\underset{j=1}{\overset{n}{\&Sigma;}}{w}_j{y}_j$

Y is described required fuel value, y _jfor the relationship equation of a jth parameter preset and fuel consumption, w _jfor y _jweight.

Optionally, described w is obtained in the following way _j:

Obtain m sample data of each parameter preset, form m * n matrix;

The first weight is obtained from described matrix according to compositive power coefficient method and entropy assessment;

The second weight is obtained according to the input of user;

Determine the scale-up factor of described first weight and the second weight;

Described first weight and described second weight are obtained w according to described scale-up factor addition _j, j=1,2...n.

Optionally, w is obtained especially by such as under type _j:

Obtain each parameter preset x _im sample data, form m * n matrix X=(x _ij) _{m × n}, wherein x _ij(i=1,2,3 ..., m; J=1,2,3 ..., n) be parameter preset x _ia jth sample data;

Described matrix is changed according to following rule:

$x_{ij}=\left\{\begin{array}{cc}{x}_{ij}& j\&Element;I_1\\ x_{j\max }-x_{ij}& j\&Element;I_2\\ |x_{ij}-x_j^{*}|& j\&Element;I_3\end{array}\right.$

Wherein I ₁={ requiring the smaller the better parameter preset }, I ₂={ parameter preset that requirement is the bigger the better }, I ₃={ requirement is stabilized in the parameter preset of ideal value };

Unify the order of magnitude of parameter preset and eliminate dimension, described matrix is carried out as down conversion:

x _ij＝100×(x _ij-x _jmin)/(x _jmax-x _jmin)(i＝1,2,...,m；j＝1,2,...,n)

Again described entry of a matrix element is calculated as follows:

$x_{ij}=x_{ij}/\underset{i=1}{\overset{m}{\&Sigma;}}{x}_{ij}$

Pass through

$h_j=-{\left(\ln m\right)}^{-1}\underset{i=1}{\overset{m}{\&Sigma;}}{x}_{ij}{\mathrm{lnx}}_{ij}$

${\mathrm{\&beta;}}_j=(1-h_j)/\underset{k=1}{\overset{n}{\&Sigma;}}(1-h_k)$

Obtain the first weight beta=(β ₁, β ₂..., β _n) ^t, wherein

The second weight α=(α is determined according to the input of user ₁, α ₂..., α _n) ^t, wherein

Determine the scale-up factor μ of described first weight and the second weight, wherein 0≤μ < 1;

Pass through

w _j＝μα _j+(1-μ)β _j(j＝1,2,...,n)

Obtain w _j, wherein $\underset{j=1}{\overset{n}{\&Sigma;}}{w}_j=1,w_j\&GreaterEqual;0(j=1,2,\mathrm{...},n).$

Optionally, described y _jobtain in the following way:

Correlation analysis is carried out to a jth parameter preset and fuel consumption, draws described relationship equation y by the device of curve _j, j=1,2...n.

The technical scheme that embodiments of the invention provide can comprise following beneficial effect:

In embodiments of the present invention, by the computation model set up in advance, achieve and by some parameters of adjustment, pelletizing fuel consumption is control effectively, optimize pelletizing production, reduce energy consumption level.

Should be understood that, it is only exemplary and explanatory that above general description and details hereinafter describe, and can not limit the present invention.

Accompanying drawing explanation

Accompanying drawing to be herein merged in instructions and to form the part of this instructions, shows embodiment according to the invention, and is used from instructions one and explains principle of the present invention.

Fig. 1 is the process flow diagram of a kind 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 the process flow diagram of a kind of pelletizing burnup control method according to an exemplary embodiment;

Fig. 4 is the schematic diagram of a kind of pelletizing burn-up control assembly according to an exemplary embodiment.

Embodiment

Here will be described exemplary embodiment in detail, its sample table shows in the accompanying drawings.When description below relates to accompanying drawing, unless otherwise indicated, the same numbers in different accompanying drawing represents same or analogous key element.Embodiment described in following exemplary embodiment does not represent all embodiments consistent with the present invention.On the contrary, they only with as in appended claims describe in detail, the example of apparatus and method that aspects more of the present invention are consistent.

Fig. 1 is the process flow diagram of a kind of pelletizing burnup control method according to an exemplary embodiment.Shown in Figure 1, the method comprises:

S101, choose from pelletizing production procedure parameter wherein n parameter as parameter preset.Wherein n is natural number.

The parameter value of S102, n described in a Real-time Collection parameter preset.

S103, is input in default computation model by the parameter value of a described n parameter preset, obtains required fuel value.

S104, according to the described required real-time fuel metering use amount of fuel value.

Wherein, described computation model is:

$y=\underset{j=1}{\overset{n}{\&Sigma;}}{w}_j{y}_j$

Y is described required fuel value, y _jfor the relationship equation of a jth parameter preset and fuel consumption, w _jfor y _jweight.

Exemplarily, described y _jcan obtain in the following way:

Correlation analysis is carried out to a jth parameter preset and fuel consumption, draws described relationship equation y by the method for curve _j, j=1,2...n.

Pelletizing production procedure parameter (hereinafter referred to as candidate parameter) may have a lot, such as, have pelletizing production scale, operating rate, bentonite consumption, finished ball nodulizing FeO content, pelletizing finished product ore deposit basicity, pelletizing crushing strength etc.In order to screen, correlation research analysis can be carried out respectively to these candidate parameter and pelletizing fuel consumption, draw the relationship equation that each candidate parameter affects pelletizing fuel consumption.

If find that certain candidate parameter on the impact of pelletizing fuel consumption significantly, then join the row of parameter preset, and record the relationship equation y of this parameter and fuel consumption by this parameter _jotherwise, then this candidate parameter is got rid of.

Fig. 2 is the systematic schematic diagram according to an exemplary embodiment.Shown in Figure 2:

Sampling unit: be responsible for the sampling to candidate parameter, for dependency analysis unit and model computing unit;

Dependency analysis unit: the sampled data reading each candidate parameter from database, candidate parameter and pelletizing fuel consumption are carried out correlation analysis, judged whether impact, whether impact is remarkable, according to influence degree, selected candidate parameter passed to Modeling Calculation unit as parameter preset;

Modeling Calculation unit: the sampled data reading each parameter preset from database, by compositive power coefficient method the parameter preset that dependency analysis unit is determined calculated and set up optimized mathematical model, model and model parameter are exported to Optimized model unit, Modeling Calculation unit constantly calculates sampled data, constantly update the model parameter in Optimized model, model is once set up, to not change, just according to the sampled data upgraded, recalculate model parameter, and upgrade Optimized model parameter;

Optimized model unit: model and model parameter calculate from Modeling Calculation unit, after model is set up, according to the tracking of Real-time Production Process parameter, calculating, assessment, according to calculate pelletizing fuel consumption y value, provide pelletizing fuel optimization standard, export to control module;

Control module: be responsible for the regulating and controlling to pelletizing fuel, pelletizing fuel optimization standard passed to controller, to complete the control to pelletizing injecting coal quantity and jet amount.

Shown in Figure 3, in the present embodiment or the present invention's some other embodiments, described w can be obtained in the following way _j:

S301, obtains m sample data of each parameter preset, forms m * n matrix;

S302, obtains the first weight according to compositive power coefficient method and entropy assessment from described matrix;

S303, obtains the second weight according to the input of user;

S304, determines the scale-up factor of described first weight and the second weight;

S305, obtains w by described first weight and described second weight according to described scale-up factor addition _j, j=1,2...n.

Further, in the present embodiment or the present invention's some other embodiments, specifically can as follows 1) ~ 8) obtain w _j:

1) each parameter preset x is obtained _im sample data, form m * n matrix X=(x _ij) _{m × n}, wherein x _ij(i=1,2,3 ..., m; J=1,2,3 ..., n) be parameter preset x _ia jth sample data.

Exemplarily, under certain scene, parameter preset comprises (i.e. x ₁, x ₂, x ₃, x ₄, x ₅, x ₆):

Tag_QTSCGM: (ten thousand t) for pelletizing plant's production scale;

Tag_ZYL: pelletizing plant's operating rate (%);

Tag_PRTYL: bentonitic clay use amount (kg/t pellet);

Tag_FeO: pelletizing finished product ore deposit FeO content (%);

Tag_R: pelletizing finished product ore deposit basicity (doubly);

Tag_KYQD: pelletizing crushing strength (N/).

The sample collected is as shown in table 1 below:

Table 1

The matrix then set up is

$X=\left[\begin{array}{ccccccc}75& 48.01& 9.57& 0.28& 0.05& 2673& 15.5\\ 240& 87.23& 14.48& 0.33& 0.05& 2683& 15.3\\ 158& 73.07& 18.6& 1.4& 0.19& 2494& 16.5\\ 27& 89.75& 24.9& 2.83& 0.13& 2088& 44.1\\ 111& 87.72& 23.25& 2.35& 0.09& 2743& 28.04\\ 500& 95.86& 18.92& 0.23& 0.21& 2584& 26.5\end{array}\right]$

2) described matrix is changed according to following rule:

$x_{ij}=\left\{\begin{array}{cc}{x}_{ij}& j\&Element;I_1\\ x_{j\max }-x_{ij}& j\&Element;I_2\\ |x_{ij}-x_j^{*}|& j\&Element;I_3\end{array}\right.$

Wherein I ₁={ requiring the smaller the better parameter preset }, I ₂={ parameter preset that requirement is the bigger the better }, I ₃={ requirement is stabilized in the parameter preset of ideal value }.x _jmin＝min{x _ij|j＝1,2,...,n},x _jmax＝max{x _ij|j＝1,2,...,n}。

Example in undertaking, the matrix after conversion becomes

$X=\left\{\begin{array}{ccccccc}425& 47.85& 9.57& 0.28& 0.12& 70& 15.5\\ 260& 8.63& 14.48& 0.33& 0.12& 60& 15.3\\ 342& 22.79& 18.6& 1.4& 0.02& 249& 16.5\\ 473& 6.11& 24.9& 2.83& 0.04& 655& 44.1\\ 389& 8.14& 23.25& 2.35& 0.08& 0& 28.04\\ 0& 0& 18.92& 0.23& 0.04& 159& 26.5\end{array}\right\}$

3) unify the order of magnitude of parameter preset and eliminate dimension, described matrix is carried out as down conversion:

x _ij＝100×(x _ij-x _jmin)/(x _jmax-x _jmin)(i＝1,2,...,m；j＝1,2,...,n)。

Example in undertaking, the matrix after conversion becomes

$X=\left\{\begin{array}{ccccccc}89.85& 100.00& 0.00& 1.92& 100.00& 10.69& 0.69\\ 54.97& 18.04& 32.03& 3.85& 100.00& 9.16& 0.00\\ 72.30& 47.63& 58.90& 45.00& 0.00& 38.02& 4.17\\ 100.00& 12.77& 100.00& 100.00& 20.00& 100.00& 100.00\\ 82.24& 17.01& 89.24& 81.54& 60.00& 0.00& 44.24\\ 0.00& 0.00& 60.99& 0.00& 20.00& 24.27& 38.89\end{array}\right\}$

4) described entry of a matrix element is calculated as follows:

$x_{ij}=x_{ij}/\underset{i=1}{\overset{m}{\&Sigma;}}{x}_{ij}.$

Example in undertaking, is obtained by formula above

$X=\left\{\begin{array}{ccccccc}0.22& 0.51& 0.00& 0.01& 0.33& 0.06& 0.00\\ 0.14& 0.09& 0.09& 0.02& 0.33& 0.05& 0.00\\ 0.18& 0.24& 0.17& 0.19& 0.00& 0.21& 0.02\\ 0.25& 0.07& 0.29& 0.43& 0.07& 0.55& 0.53\\ 0.21& 0.09& 0.26& 0.35& 0.20& 0.00& 0.24\\ 0.00& 0.00& 0.18& 0.00& 0.07& 0.13& 0.21\end{array}\right\}$

5) pass through

$h_j=-{\left(lnm\right)}^{-1}\underset{i=1}{\overset{m}{\&Sigma;}}{x}_{ij}{\mathrm{lnx}}_{ij}$

${\mathrm{\&beta;}}_j=(1-h_j)/\underset{k=1}{\overset{n}{\&Sigma;}}(1-h_k)$

Obtain the first weight beta=(β ₁, β ₂..., β _n) ^t, wherein

Note working as x _ijwhen=0, regulation x _ijlnx _ij=0 (i=1,2 ..., m; J=1,2 ..., n).

First weight also can be described as objective weight.

In undertaking, example, can obtain

β＝(0.0630.1550.0780.1990.1180.1720.215) ^T

6) the second weight α=(α is determined according to the input of user ₁, α ₂..., α _n) ^t, wherein

Second weight also can be described as subjective weight, and the determination of subjective weight can get according to the data mining of pelletizing production Process History and analysis.

Example in undertaking, user determines

α＝(0.10.050.10.150.10.20.3) ^T

7) the scale-up factor μ of described first weight and the second weight is determined, wherein 0≤μ < 1.

Scale-up factor μ also can be described as preference coefficient, it reflects the different attention degrees of analyst to subjective weight and objective weight, sets according to specific needs.

8) pass through

w _j＝μα _j+(1-μ)β _j(j＝1,2,...,n)

Obtain w _j, wherein $\underset{j=1}{\overset{n}{\&Sigma;}}{w}_j=1,w_j\&GreaterEqual;0(j=1,2,\mathrm{...},n).$

Wj can be described as comprehensive weight again.

In undertaking, example, if μ=0.6, then can obtain

w＝(0.0850.0920.0910.1700.1070.1890.266) ^T。

Like this, after obtaining w, by w and each y _isubstitute into finally computation model can be obtained.

Such as, suppose that the relationship equation simulated under another scene is y ₁=0.0006x ²-0.25x+46.4, y ₂=-4.5x ²+ 27.5x+6.6, and calculate to obtain w ₁=0.17, w ₂=0.83, then can obtain computation model

y＝w ₁y ₁+w ₂y ₂＝0.17(0.0006x ²-0.25x+46.4)+0.83(-4.5x ²+27.5x+6.6)

Fig. 4 is the schematic diagram of a kind of pelletizing burn-up control assembly according to an exemplary embodiment.Shown in Figure 4, device 400 can comprise:

Select module 401, for choose from pelletizing production procedure parameter wherein n parameter as parameter preset;

Acquisition module 402, for the parameter value of the parameter preset of n described in Real-time Collection;

Computing module 403, for being input in default computation model by the parameter value of a described n parameter preset, obtains required fuel value;

Control module 404, for according to the described required real-time fuel metering use amount of fuel value;

Wherein, described computation model is:

$y=\underset{j=1}{\overset{n}{\&Sigma;}}{w}_j{y}_j$

Y is described required fuel value, y _jfor the relationship equation of a jth parameter preset and fuel consumption, w _jfor y _jweight.

In the present embodiment or the present invention's some other embodiments, described w can be obtained in the following way _j:

Obtain m sample data of each parameter preset, form m * n matrix;

The first weight is obtained from described matrix according to compositive power coefficient method and entropy assessment;

The second weight is obtained according to the input of user;

Determine the scale-up factor of described first weight and the second weight;

Described first weight and described second weight are obtained w according to described scale-up factor addition _j, j=1,2...n.

In the present embodiment or the present invention's some other embodiments, specifically can obtain w in the following way _j:

Obtain each parameter preset x _im sample data, form m * n matrix X=(x _ij) _{m × n}, wherein x _ij(i=1,2,3 ..., m; J=1,2,3 ..., n) be parameter preset x _ia jth sample data;

Described matrix is changed according to following rule:

$x_{ij}=\left\{\begin{array}{cc}{x}_{ij}& j\&Element;I_1\\ x_{j\max }-x_{ij}& j\&Element;I_2\\ |x_{ij}-x_j^{*}|& j\&Element;I_3\end{array}\right.$

Wherein I ₁={ requiring the smaller the better parameter preset }, I ₂={ parameter preset that requirement is the bigger the better }, I ₃={ requirement is stabilized in the parameter preset of ideal value };

Unify the order of magnitude of parameter preset and eliminate dimension, described matrix is carried out as down conversion:

x _ij＝100×(x _ij-x _jmin)/(x _jmax-x _jmin)(i＝1,2,...,m；j＝1,2,...,n)

Again described entry of a matrix element is calculated as follows:

$x_{ij}=x_{ij}/\underset{i=1}{\overset{m}{\&Sigma;}}{x}_{ij}$

Pass through

$h_j=-{\left(\ln m\right)}^{-1}\underset{i=1}{\overset{m}{\&Sigma;}}{x}_{ij}{\mathrm{lnx}}_{ij}$

${\mathrm{\&beta;}}_j=(1-h_j)/\underset{k=1}{\overset{n}{\&Sigma;}}(1-h_k)$

Obtain the first weight beta=(β ₁, β ₂..., β _n) ^t, wherein

The second weight α=(α is determined according to the input of user ₁, α ₂..., α _n) ^t, wherein

Determine the scale-up factor μ of described first weight and the second weight, wherein 0≤μ < 1;

Pass through

w _j＝μα _j+(1-μ)β _j(j＝1,2,...,n)

Obtain w _j, wherein $\underset{j=1}{\overset{n}{\&Sigma;}}{w}_j=1,w_j\&GreaterEqual;0(j=1,2,\mathrm{...},n).$

In the present embodiment or the present invention's some other embodiments, described y _jobtain in the following way:

Correlation analysis is carried out to a jth parameter preset and fuel consumption, draws described relationship equation y by the device of curve _j, j=1,2...n.

About the device in above-described embodiment, wherein the concrete mode of modules executable operations has been described in detail in about the embodiment of the method, will not elaborate explanation herein.

Those skilled in the art, at consideration instructions and after putting into practice invention disclosed herein, will easily expect other embodiment of the present invention.The application is intended to contain any modification of the present invention, purposes or adaptations, and these modification, purposes or adaptations are followed general principle of the present invention and comprised the undocumented common practise in the art of the present invention or conventional techniques means.Instructions and embodiment are only regarded as exemplary, and true scope of the present invention and spirit are pointed out by appended claim.

Should be understood that, the present invention is not limited to precision architecture described above and illustrated in the accompanying drawings, and can carry out various amendment and change not departing from its scope.Scope of the present invention is only limited by appended claim.

Claims (8)

1. a pelletizing burnup control method, is characterized in that, described method comprises:

Choose from pelletizing production procedure parameter wherein n parameter as parameter preset;

The parameter value of the parameter preset of n described in Real-time Collection;

The parameter value of a described n parameter preset is input in default computation model, obtains required fuel value;

According to the described required real-time fuel metering use amount of fuel value;

Wherein, described computation model is:

$y=\underset{j=1}{\overset{n}{\&Sigma;}}{w}_j{y}_j$

Y is described required fuel value, y _jfor the relationship equation of a jth parameter preset and fuel consumption, w _jfor y _jweight.

2. method according to claim 1, is characterized in that, obtains described w in the following way _j:

Obtain m sample data of each parameter preset, form m * n matrix;

The first weight is obtained from described matrix according to compositive power coefficient method and entropy assessment;

The second weight is obtained according to the input of user;

Determine the scale-up factor of described first weight and the second weight;

Described first weight and described second weight are obtained w according to described scale-up factor addition _j, j=1,2...n.

3. method according to claim 2, is characterized in that, obtains w especially by such as under type _j:

Obtain each parameter preset x _im sample data, form m * n matrix X=(x _ij) _{m × n}, wherein x _ij(i=1,2,3 ..., m; J=1,2,3 ..., n) be parameter preset x _ia jth sample data;

Described matrix is changed according to following rule:

$x_{ij}=\left\{\begin{array}{cc}{x}_{ij}& j\&Element;I_1\\ x_{j\max }-x_{ij}& j\&Element;I_2\\ \left|x_{ij}-x_j^{*}\right|& j\&Element;I_3\end{array}\right.$

Wherein I ₁={ requiring the smaller the better parameter preset }, I ₂={ parameter preset that requirement is the bigger the better }, I ₃={ requirement is stabilized in the parameter preset of ideal value };

Unify the order of magnitude of parameter preset and eliminate dimension, described matrix is carried out as down conversion:

x _ij＝100×(x _ij-x _jmin)/(x _jmax-x _jmin)(i＝1,2,...,m；j＝1,2,...,n)

Again described entry of a matrix element is calculated as follows:

$x_{ij}=x_{ij}/\underset{i=1}{\overset{m}{\&Sigma;}}{x}_{ij}$

Pass through

$h_j=-{\left(\ln m\right)}^{-1}\underset{i=1}{\overset{m}{\&Sigma;}}{x}_{ij}{\mathrm{lnx}}_{ij}$

${\mathrm{\&beta;}}_j=(1-h_j)/\underset{k=1}{\overset{n}{\&Sigma;}}(1-h_k)$

Obtain the first weight beta=(β ₁, β ₂..., β _n) ^t, wherein

The second weight α=(α is determined according to the input of user ₁, α ₂..., α _n) ^t, wherein

Determine the scale-up factor μ of described first weight and the second weight, wherein 0≤μ < 1;

Pass through

w _j＝μα _j+(1-μ)β _j(j＝1,2,...,n)

Obtain w _j, wherein $\underset{j=1}{\overset{n}{\&Sigma;}}{w}_j=1,w_j\&GreaterEqual;0,(j=1,2,...,n).$

4. method according to claim 1, is characterized in that, described y _jobtain in the following way:

Correlation analysis is carried out to a jth parameter preset and fuel consumption, draws described relationship equation y by the method for curve _j, j=1,2...n.

5. a pelletizing burn-up control assembly, is characterized in that, described device comprises:

Select module, for choose from pelletizing production procedure parameter wherein n parameter as parameter preset;

Acquisition module, for the parameter value of the parameter preset of n described in Real-time Collection;

Computing module, for being input in default computation model by the parameter value of a described n parameter preset, obtains required fuel value;

Control module, for according to the described required real-time fuel metering use amount of fuel value;

Wherein, described computation model is:

$y=\underset{j=1}{\overset{n}{\&Sigma;}}{w}_j{y}_j$

Y is described required fuel value, y _jfor the relationship equation of a jth parameter preset and fuel consumption, w _jfor y _jweight.

6. device according to claim 5, is characterized in that, obtains described w in the following way _j:

Obtain m sample data of each parameter preset, form m * n matrix;

The first weight is obtained from described matrix according to compositive power coefficient method and entropy assessment;

The second weight is obtained according to the input of user;

Determine the scale-up factor of described first weight and the second weight;

Described first weight and described second weight are obtained w according to described scale-up factor addition _j, j=1,2...n.

7. device according to claim 6, is characterized in that, obtains w especially by such as under type _j:

Obtain each parameter preset x _im sample data, form m * n matrix X=(x _ij) _{m × n}, wherein x _ij(i=1,2,3 ..., m; J=1,2,3 ..., n) be parameter preset x _ia jth sample data;

Described matrix is changed according to following rule:

$x_{ij}=\left\{\begin{array}{cc}{x}_{ij}& j\&Element;I_1\\ x_{j\max }-x_{ij}& j\&Element;I_2\\ \left|x_{ij}-x_j^{*}\right|& j\&Element;I_3\end{array}\right.$

Wherein I ₁={ requiring the smaller the better parameter preset }, I ₂={ parameter preset that requirement is the bigger the better }, I ₃={ requirement is stabilized in the parameter preset of ideal value };

Unify the order of magnitude of parameter preset and eliminate dimension, described matrix is carried out as down conversion:

x _ij＝100×(x _ij-x _jmin)/(x _jmax-x _jmin)(i＝1,2,...,m；j＝1,2,...,n)

Again described entry of a matrix element is calculated as follows:

$x_{ij}=x_{ij}/\underset{i=1}{\overset{m}{\&Sigma;}}{x}_{ij}$

Pass through

$h_j=-{\left(\ln m\right)}^{-1}\underset{i=1}{\overset{m}{\&Sigma;}}{x}_{ij}{\mathrm{lnx}}_{ij}$

${\mathrm{\&beta;}}_j=(1-h_j)/\underset{k=1}{\overset{n}{\&Sigma;}}(1-h_k)$

Obtain the first weight beta=(β ₁, β ₂..., β _n) ^t, wherein

The second weight α=(α is determined according to the input of user ₁, α ₂..., α _n) ^t, wherein

Determine the scale-up factor μ of described first weight and the second weight, wherein 0≤μ < 1;

Pass through

w _j＝μα _j+(1-μ)β _j(j＝1,2,...,n)

Obtain w _j, wherein $\underset{j=1}{\overset{n}{\&Sigma;}}{w}_j=1,w_j\&GreaterEqual;0(j=1,2,...,n).$

8. device according to claim 5, is characterized in that, described y _jobtain in the following way:

Correlation analysis is carried out to a jth parameter preset and fuel consumption, draws described relationship equation y by the device of curve _j, j=1,2...n.