CN109190846A - A kind of new dry process rotary kiln furnace calcination process Multipurpose Optimal Method - Google Patents

A kind of new dry process rotary kiln furnace calcination process Multipurpose Optimal Method Download PDF

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CN109190846A
CN109190846A CN201811168331.0A CN201811168331A CN109190846A CN 109190846 A CN109190846 A CN 109190846A CN 201811168331 A CN201811168331 A CN 201811168331A CN 109190846 A CN109190846 A CN 109190846A
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钱锋
钟伟民
朱远明
杜文莉
梅华
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East China University of Science and Technology
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Abstract

The invention discloses a kind of cement kiln calcination process Multipurpose Optimal Methods.The method includes the steps: (1) establish Model for Multi-Objective Optimization；(2) solving optimization model obtains optimization aim setting value and its corresponding practical operation condition.

Description

A kind of new dry process rotary kiln furnace calcination process Multipurpose Optimal Method
Technical field
The present invention relates to manufacture of cement more particularly to a kind of intelligent optimization sides of New Type Dry-process Cement Production firing system Method.
Background technique
Clinker burning process is as follows, is transported at the top of suspended preheater firstly, raw material are mentioned by material bin, and raw material are due to weight Power effect is fallen from suspended preheater top, and the hot gas that can and erupt upwards during whereabouts carries out heat exchange, reach preheating Effect.Raw material after preheating enter dore furnace, and a large amount of carbonate can be calcined decomposition in dore furnace.Raw material enter later Rotary kiln, raw material are fully calcined under rotary kiln hot conditions, and the raw material of rotary kiln can be because gravity and rotatory force act on It falls into grate-cooler, grate-cooler ultimately forms clinker by extremely cold, broken etc..
Clinker burning process energy consumption is big in cement production process energy consumption proportion, and main energy consumption is firing coal consumption and electricity Consumption；It is known as free calcium oxide (f-cao) containing a small amount of uncombined calcium oxide in clinker simultaneously, clinker f-cao contains Amount has a significant impact to cement stability, and content is capable of the good of indirect reaction cement kiln firing situation.Therefore, while it is excellent Change f-cao content and per unit area yield clinker energy consumption and is of great significance to product quality and reduction production process energy consumption is improved.
In new type nonaqueous cement, firing system energy consumption is mainly coal consumption and power consumption.In recent years, occur much such as decomposing Furnace control, Rotary Kiln Control, many advanced control methods such as grate-cooler control, while also having many meal calcining rates, clinkering zone The fining level of temperature, free calcium oxide content soft sensor model, manufacture of cement further increases, and energy consumption is declined.But It is that manufacture of cement is big inertia, non-linear and close coupling complex process, the determination of operating parameter leans on operator's experience to hold mostly Row, operation is extensive, and per unit area yield energy consumption is higher, while the fluctuation of clinker Key Quality Indicator free calcium oxide content is larger.Traditional is excellent Changing model only considered one optimization aim of per unit area yield clinker energy consumption index, but Key Quality Indicator free calcium oxide in actual production Content no less important.Consider that energy consumption and quality index are particularly important simultaneously, therefore, there is an urgent need in the art to for manufacture of cement Refinement, thus take into account improve product quality and reduce energy consumption.
Summary of the invention
The present invention is intended to provide a kind of method of cement kiln calcining multiple-objection optimization.
In the first aspect of the present invention, a kind of new dry process rotary kiln furnace calcination process Multipurpose Optimal Method is provided, The method includes the steps:
(1) Model for Multi-Objective Optimization is established；With
(2) solving optimization model obtains optimization aim setting value and its corresponding practical operation condition.
In another preferred example, the cement kiln calcining is the calcining of new dry process rotary kiln furnace.
In another preferred example, the multiple-objection optimization includes free calcium oxide content and calcination process per unit area yield clinker total energy Consumption.
In another preferred example, the practical operation condition includes decision variable value corresponding with optimization aim setting value； The decision variable includes that high-temperature blower electric current, Coaling of Decomposing Furnace, rotary kiln Main motor current and kiln hood feed coal amount.
In another preferred example, the data obtained by trade-off decision variable, state variable and constraint condition are used Partial Least Squares (PLS) establishes Optimized model；Select high-temperature blower electric current, Coaling of Decomposing Furnace, rotary kiln Main motor current Feeding coal amount with kiln hood is Optimized model decision variable；Select C1 feeder pipe temperature and kiln head cover temperature for the change of Optimized model state Amount；The constraint condition is the bound that decision variable, state variable and target variable meet process safety production；
xi,min< xi< xi,max(i=1 ..., 6)
J1min< J1< J1max J2min< J2< J2max
Wherein xiIndicate decision variable or state variable；
J1And J2Respectively indicate target variable free calcium oxide content and calcination process per unit area yield clinker total energy consumption.
Preferably, the data carry out data prediction using three rank moving average filter methods.
In another preferred example, the data filtering processing uses moving average filter, obtains stable process data.Needle To process data characteristic, filter order is selected；It more preferably, is three rank moving average filters.
In another preferred example, PLS extracts the linear combination of first composition first in independent variable, extracts in dependent variable First composition, and two ingredient correlation maximums are required, regression modeling is then carried out, until reaching satisfied precision, most Partial Least Squares Regression equation is obtained eventually.
In another preferred example, the Optimized model includes:
yf-cao=f (x1,x2,x3,x4) and
ycost=f (x1,x3,x5,x6)+ξ
Wherein x2=f (x5,x7,x8)、x4=f (x6,x9)
Optimization aim one: minJ1=yf-cao；
Optimization aim two: minJ2=ycost；
Wherein yf-caoFor free calcium oxide content, ycostFor per unit area yield clinker energy consumption cost, x1For high-temperature blower electric current, x2For C1 feeder pipe temperature, x3For rotary kiln Main motor current, x4For kiln head cover temperature, x5For Coaling of Decomposing Furnace, x6It is fed for kiln hood Coal amount, x7For kiln hood air temperature three times, x8For kiln tail smoke-box temperature, x9Pressure under combing for a Room, ξ are remaining energy consumption.
In another preferred example, using the multi-objective optimization algorithm (NSGA- with elitism strategy based on non-dominated ranking II) solving optimization model.
In the second aspect of the present invention, a kind of cement kiln calcination process Multipurpose Optimal Method, the method are provided Comprising steps of
(a) data of decision variable, state variable and constraint condition are obtained；
(b) data that pretreatment obtains；The preprocessed data filtering operation；
(c) pretreated data establish Optimized model using Partial Least Squares (PLS)；With
(d) multi-objective optimization algorithm (NSGA-II) solving optimization mould with elitism strategy based on non-dominated ranking is used The decision variable value that type obtains is as the setting value in practical operation.
In another preferred example, the decision variable is high-temperature blower electric current, Coaling of Decomposing Furnace, rotary kiln main motor electricity Stream and kiln hood feed coal amount；The state variable is C1 feeder pipe temperature and kiln head cover temperature；The constraint condition is decision change Amount, state variable and target variable meet the bound of process safety production；
xi,min< xi< xi,max(i=1 ..., 6)
J1min< J1< J1max J2min< J2< J2max
Wherein xiIndicate decision variable or state variable；
J1And J2Respectively indicate target variable free calcium oxide content and calcination process per unit area yield clinker total energy consumption.
In another preferred example, the Optimized model is:
yf-cao=f (x1,x2,x3,x4)；With
ycost=f (x1,x3,x5,x6)+ξ
Wherein x2=f (x5,x7,x8)、x4=f (x6,x9)
Optimization aim one: minJ1=yf-cao；
Optimization aim two: minJ2=ycost；
Wherein yf-caoFor free calcium oxide content, ycostFor per unit area yield clinker energy consumption cost, x1For high-temperature blower electric current, x2For C1 feeder pipe temperature, x3For rotary kiln Main motor current, x4For kiln head cover temperature, x5For Coaling of Decomposing Furnace, x6It is fed for kiln hood Coal amount, x7For kiln hood air temperature three times, x8For kiln tail smoke-box temperature, x9Pressure under combing for a Room, ξ are remaining energy consumption.
Accordingly, the present invention can improve product quality and reduction energy consumption to take into account to manufacture of cement refinement.
Detailed description of the invention
Fig. 1 is cement firing system whole process schematic diagram.
Fig. 2 is Partial Least Squares calculation flow chart.
Fig. 3 is the multi-objective optimization algorithm flow chart with elitism strategy based on non-dominated ranking.
Fig. 4 is that the multiple-objection optimization of kiln calcination process solves block diagram.
Fig. 5 shows C1 feeder pipe temperature models.
Fig. 6 shows kiln head cover temperature model.
Fig. 7 shows free calcium oxide model.
Fig. 8 shows the forward position multiple-objection optimization Pareto.
Specific embodiment
The Multipurpose Optimal Method of cement kiln calcination process provided by the invention includes: to establish free calcium oxide and calcining The Model for Multi-Objective Optimization of process per unit area yield clinker energy consumption cost obtains optimal setting using intelligent optimization algorithm solving optimization model Value instructs practical operation to produce.
The present invention provides a kind of cement kiln calcination process multiple-objection optimization strategy, while optimization process Key Quality Indicator Free calcium oxide and process per unit area yield clinker energy consumption cost, technical solution of the present invention the following steps are included:
Step 1, decision variable selection and constraint condition, the constraint condition are that decision variable, state variable and target become Amount meets the bound of process safety production:
xi,min< xi< xi,max(i=1 ..., 6)
J1min< J1< J1max J2min< J2< J2max
Wherein xiIndicate decision variable or state variable；
J1And J2Respectively indicate target variable.
In one embodiment of the invention, high-temperature blower electric current, Coaling of Decomposing Furnace, rotary kiln main motor electricity are selected It is Optimized model decision variable that stream and kiln hood, which feed coal amount,；It selects C1 feeder pipe temperature and kiln head cover temperature is state variable；Choosing It selects free calcium oxide content and calcination process per unit area yield clinker total energy consumption is target variable.
Inventors have found that high-temperature blower electric current can reflect the size of firing system ventilation quantity；C1 feeder pipe temperature shadows The raw material rung in dore furnace decompose, and decision variable Coaling of Decomposing Furnace influences C1 feeder pipe temperature；Rotary kiln main motor electricity Stream reflects the quality of sintering conditions in rotary kiln indirectly；Kiln head cover temperature reflects the quality of firing situation in kiln, decision variable kiln Head feeds coal amount and directly affects kiln head cover temperature；To sum up four decision variables influence whether the quality of clinker free calcium oxide, together The main coal consumption of Shi Fanying kiln calcination process, power consumption.
Variable bound condition is as shown in the table in one embodiment of the invention
Variable Bound
High-temperature blower electric current [100,120]
Coaling of Decomposing Furnace [32,38]
Rotary kiln Main motor current [700,900]
Kiln hood feeds coal amount [20,24]
C1 feeder pipe temperature [860,880]
Kiln head cover temperature [1100,1300]
Free calcium oxide content [0.3,1.2]
Calcination process per unit area yield energy consumption cost [110,125]
It needs to understand, these variables are obtained according to the operation conditions of field device, so different device and different Equipment bound can be different, generally need to fully consider the factors such as safe and environment-friendly, energy saving according to field device practical operation situation, Consider variable bound condition.
Step 2, data prediction mainly include that data filtering operates, using moving average filter, obtain smoothly mistake Number of passes evidence.For process data characteristic, third-order filter is selected.
In a preferred embodiment of the present invention embodiment, for cement at the big problem of data noise is produced, data are pre- Processing uses the method for moving average, and Moving Window size is 3, i.e. third-order filter is as follows:
Wherein,For filter output, y (i-1), y (i), y (i+1) are decomposed into the input value of filter different moments
Step 3, Optimized model are established, and establish Optimized model using Partial Least Squares (PLS), offset minimum binary needle has Effect solves the Problems of Multiple Synteny of variable.PLS extracts the linear combination of first composition first in independent variable, in dependent variable First composition is extracted, and requires two ingredient correlation maximums, regression modeling is then carried out, is until reaching satisfied precision Only, Partial Least Squares Regression equation is finally obtained.
Optimized model is as follows
yf-cao=f (x1,x2,x3,x4)
ycost=f (x1,x3,x5,x6)+ξ
Wherein x2=f (x5,x7,x8)、x4=f (x6,x9)
Optimization aim one: minJ1=yf-cao；
Optimization aim two: minJ2=ycost；
min J1It is to require optimization aim minimum；J1min, J1maxRefer respectively to the bound of optimization aim.Target two is similarly.
Wherein yf-caoFor free calcium oxide content, ycostFor per unit area yield clinker energy consumption cost, x1For high-temperature blower electric current, x2For C1 feeder pipe temperature, x3For rotary kiln Main motor current, x4For kiln head cover temperature, x5For Coaling of Decomposing Furnace, x6It is fed for kiln hood Coal amount, x7For kiln hood air temperature three times, x8For kiln tail smoke-box temperature, x9Pressure under combing for a Room, ξ are remaining energy consumption.
In one embodiment of the invention, pretreated data use offset minimum binary step as shown in Figure 2 Establish linear optimization model；
Optimization aim one: free calcium oxide content yf-cao=f (x1,x2,x3,x4), wherein x2=f (x5,x7,x8), x4=f (x6,x9)
Wherein yf-caoFor free calcium oxide content, x1For high-temperature blower electric current, x2For C1 feeder pipe temperature, x3For revolution Kiln owner's current of electric, x4For kiln head cover temperature, x5For Coaling of Decomposing Furnace, x6Coal amount, x are fed for kiln hood7For kiln hood wind-warm syndrome three times Degree, x8For kiln tail smoke-box temperature, x9Pressure under combing for a Room.
Free calcium oxide model parameter is obtained using PLS method:
yf-cao=-3.3480+0.0035x1+0.0040x2-0.0019x3+0.0014x4
x2=715.1491+0.3439*x5+0.0690*x7+0.05932*x8
x4=1983.6-20.2235x6-63.7511x9
Optimization aim two: calcination process per unit area yield energy consumption cost ycost=f (x1,x3,x5,x6)+ξ
Commercial power price, industrial coal price,
Calcination process per unit area yield energy consumption cost model parameter: 0.8083 yuan of commercial power price/degree, industrial coal 650 are defined Yuan/ton, 500 tons of enterprise's clinker/when, be burnt into 8600 yuan of remaining energy consumption/when.
High-temperature blower converts power consumption: y1=0.0323x1(yuan/ton/when)
Main motor converts power consumption: y2=0.0323x3(yuan/ton/when)
Dore furnace feeds coal and converts coal consumption: y3=650/500x5=1.3x5(yuan/ton/when)
Kiln hood feeds coal and converts coal consumption: y4=650/500x5=1.3x5(yuan/ton/when)
Remaining energy consumption: y5=8600/500=17.2 (yuan/ton/when)
ycost=y1+y2+y3+y4+y5
It needing to understand, above-mentioned equation is not unique in practical application, different Optimized model parameters is had for different processes, The parameter of two equations is estimated using deflected secondary air.
Step 4, multiple-objection optimization calculates, using the multi-objective optimization algorithm with elitism strategy based on non-dominated ranking (NSGA-II), solving optimization model obtains the optimization calculated value of decision variable.NSGA-II algorithm mainly include initialization of population, Individual adaptation degree evaluation, Selecting operation, crossing operation and mutation operator etc..NSGA-II algorithm main innovation point essentially consists in On Selecting operation, it first proposed quick non-dominated ranking concept, effectively reduce algorithm complexity；Secondly last use is gathered around Degree measurement is squeezed, the defect for needing specified shared parameter is overcome；It is finally added to Excellence Mechanism, makes parent population and progeny population Common competition generates new population, guarantees that acquired optimal solution is not lost.Therefore NSGA-II algorithm is fast with the speed of service, solves Collect the good advantage of convergence.
In one embodiment of the invention, the multiple-objection optimization with elitism strategy based on non-dominated ranking is calculated Method is:
Firstly, the initial population that scale is N is randomly generated, pass through the selection of genetic algorithm, intersection, change after non-dominated ranking Different three basic operations obtain first generation progeny population；
Secondly, parent population is merged with progeny population, carries out quick non-dominated ranking operation since the second generation: fast The effect of fast non-dominated ranking is that search is guided to run to Pareto optimal solution set direction, it is the adaptive value classification of a circulation Process finds out non-dominant disaggregation in group first, is denoted as first non-dominant layer of F, assigns its all individual to non-dominant sequence irank =1, and it is removed from entire population；Then it circuits sequentially, finds out irank=2, irank=3 etc., until entire population is divided Layer.
The calculating of crowding operator is carried out to the individual in each non-dominant layer simultaneously, individual crowding distance is object space Upper two the individual i+1s and i-1 the distance between adjacent with i, are obtained by calculation the crowding distance L [i] of individual i, preferential to select The biggish individual of crowding distance is selected, guarantees that calculated result can be uniformly distributed in object space, to maintain optimization algorithm population Diversity.
Elitism strategy is then used, parent and filial generation are merged and used, is selected according to non-dominant relationship and the crowding of individual Suitable individual is taken to form new parent population, the defect individual that can retain in parent enters filial generation；Finally, being calculated by heredity The basic operations such as the cross and variation of method generate new progeny population, and so on, the condition until meeting EP (end of program).
In a preferred embodiment, population scale 200, the number of iterations 200 are used in the present invention, crossover probability is 0.8, mutation probability 0.5 can guarantee optimization solution required for acquirement, select suitable decision variable to set according to the forward position Pareto Definite value.For different situations, practical value can be in an approximate range.
In one embodiment of the invention, the multi-objective optimization algorithm phase with elitism strategy based on non-dominated ranking The program flow diagram answered is as shown in Figure 3.Entire multiple-objection optimization strategic process is as shown in Figure 4.
The feature that the features described above or embodiment that the present invention mentions are mentioned can be in any combination.Disclosed in this case specification All features can be used in combination with any composition form, each feature disclosed in specification, any can provide it is identical, The alternative characteristics of impartial or similar purpose replace.Therefore, except there is special instruction, revealed feature is only impartial or similar spy The general example of sign.
Main advantages of the present invention are:
1, by establishing Optimized model, the parameter of production process is optimized, can guarantee product quality and drop simultaneously Low energy consumption.
2, it is modeled using Partial Least Squares, does not need complicated process mechanism, it is only necessary to which process input and output data are Model can be obtained.
3, the multi-objective optimization algorithm with elitism strategy based on non-dominated ranking, the algorithm optimization effect is good, calculates speed Degree is fast, can satisfy the demand of process real-time optimization.
4, method provided by the invention can be created by parameter optimization under the premise of not increasing hardware cost for enterprise Economic benefit.
Present invention will be further explained below with reference to specific examples.It should be understood that these embodiments are merely to illustrate the present invention Rather than it limits the scope of the invention.In the following examples, the experimental methods for specific conditions are not specified, usually according to conventional strip Part or according to the normal condition proposed by manufacturer.Unless otherwise defined, all professional and scientific terms as used herein and sheet Meaning known to the skilled person of field is identical.In addition, any method similar to or equal to what is recorded and material all may be used Applied in the method for the present invention.The preferred methods and materials described herein are for illustrative purposes only.
Selecting high-temperature blower electric current, Coaling of Decomposing Furnace, rotary kiln Main motor current and kiln hood to feed coal amount is Optimized model Decision variable；
It selects C1 feeder pipe temperature and kiln head cover temperature is state variable；
Selecting free calcium oxide content and calcination process per unit area yield clinker total energy consumption is target variable.
Embodiment 1
According to actual field inputoutput data, C1 feeder pipe temperature models are established (such as using deflected secondary air Shown in Fig. 5), x2=715.1491+0.3439*x5+0.0690*x7+0.05932*x8, wherein x2For C1 feeder pipe temperature, x5 For Coaling of Decomposing Furnace, x7For kiln hood air temperature three times, x8For kiln tail smoke-box temperature.
Embodiment 2
According to actual field inputoutput data, kiln head cover temperature model is established (such as Fig. 6 institute using deflected secondary air Show), x4=1983.6-20.2235x6-63.7511x9, wherein x4For kiln head cover temperature, coal amount, x are fed for kiln hood9Under combing for a Room Pressure.
Embodiment 3
According to actual field inputoutput data, free calcium oxide model is established (such as Fig. 7 institute using deflected secondary air Show) yf-cao=-3.3480+0.0035x1+0.0040x2-0.0019x3+0.0014x4, x1For high-temperature blower electric current, x2It is C1 Feeder pipe temperature, x3For rotary kiln Main motor current, x4For kiln head cover temperature.
Embodiment 4
The forward position multiple-objection optimization Pareto is as a result, selection parameter: population scale 200, the number of iterations 200, crossover probability It is 0.8, mutation probability 0.5.As shown in figure 8, selecting into crossing, per unit area yield energy consumption cost 117, free calcium oxide content can choose Decision variable value when about 0.58 is as the setting value in practical operation.
The foregoing is merely illustrative of the preferred embodiments of the present invention, the substantial technological content model being not intended to limit the invention It encloses, substantial technological content of the invention is broadly defined in the scope of the claims of application, any technology that other people complete Entity or method also or a kind of equivalent change, will if identical with defined in the scope of the claims of application It is considered as being covered by among the scope of the claims.

Claims (10)

1. a kind of new dry process rotary kiln furnace calcination process Multipurpose Optimal Method, which is characterized in that the method includes the steps:
(1) Model for Multi-Objective Optimization is established；
(2) solving optimization model obtains optimization aim setting value and its corresponding practical operation condition.
2. optimization method as described in claim 1, which is characterized in that the multiple-objection optimization include free calcium oxide content and Calcination process per unit area yield clinker total energy consumption.
3. optimization method as described in claim 1, which is characterized in that the practical operation condition includes setting with optimization aim It is worth corresponding decision variable value；The decision variable includes high-temperature blower electric current, Coaling of Decomposing Furnace, rotary kiln main motor electricity Stream and kiln hood feed coal amount.
4. optimization method as described in claim 1, which is characterized in that trade-off decision variable, state variable and constraint will be passed through Condition and the data that obtain establish Optimized model using Partial Least Squares (PLS)；High-temperature blower electric current, dore furnace is selected to feed coal It is Optimized model decision variable that amount, rotary kiln Main motor current and kiln hood, which feed coal amount,；Select C1 feeder pipe temperature and kiln head cover Temperature is Optimized model state variable；The constraint condition is that decision variable, state variable and target variable meet process safety The bound of production；
xi,min< xi< xi,max(i=1 ..., 6)
J1min< J1< J1max J2min< J2< J2max
Wherein xiIndicate decision variable or state variable；
J1And J2Respectively indicate target variable free calcium oxide content and calcination process per unit area yield clinker total energy consumption.
5. optimization method as claimed in claim 4, which is characterized in that the data using three rank moving average filter methods into Line number Data preprocess.
6. optimization method as described in any one in claim 1-5, which is characterized in that the Optimized model includes:
yf-cao=f (x1,x2,x3,x4) and
ycost=f (x1,x3,x5,x6)+ξ
Wherein x2=f (x5,x7,x8)、x4=f (x6,x9)
One: min J of optimization aim1=yf-cao；
Two: min J of optimization aim2=ycost；
Wherein yf-caoFor free calcium oxide content, ycostFor per unit area yield clinker energy consumption cost, x1For high-temperature blower electric current, x2It is C1 Feeder pipe temperature, x3For rotary kiln Main motor current, x4For kiln head cover temperature, x5For Coaling of Decomposing Furnace, x6Coal is fed for kiln hood Amount, x7For kiln hood air temperature three times, x8For kiln tail smoke-box temperature, x9Pressure under combing for a Room, ξ are remaining energy consumption.
7. optimization method as claimed in claim 6, which is characterized in that using based on non-dominated ranking with the more of elitism strategy Objective optimization algorithm (NSGA-II) solving optimization model.
8. a kind of cement kiln calcination process Multipurpose Optimal Method, which is characterized in that the method includes the steps:
(a) data of decision variable, state variable and constraint condition are obtained；
(b) data that pretreatment obtains；The preprocessed data filtering operation；
(c) pretreated data establish Optimized model using Partial Least Squares (PLS)；
(d) it is obtained using multi-objective optimization algorithm (NSGA-II) solving optimization model with elitism strategy based on non-dominated ranking To decision variable value as the setting value in practical operation.
9. optimization method as claimed in claim 8, which is characterized in that the decision variable is high-temperature blower electric current, dore furnace It feeds coal amount, rotary kiln Main motor current and kiln hood and feeds coal amount；The state variable is C1 feeder pipe temperature and kiln head cover temperature； The constraint condition is the bound that decision variable, state variable and target variable meet process safety production；
xi,min< xi< xi,max(i=1 ..., 6)
J1min< J1< J1max J2min< J2< J2max
Wherein xiIndicate decision variable or state variable；
J1And J2Respectively indicate target variable free calcium oxide content and calcination process per unit area yield clinker total energy consumption.
10. optimization method as claimed in claim 8, which is characterized in that the Optimized model is:
yf-cao=f (x1,x2,x3,x4)；With
ycost=f (x1,x3,x5,x6)+ξ
Wherein x2=f (x5,x7,x8)、x4=f (x6,x9)
One: min J of optimization aim1=yf-cao；
Two: min J of optimization aim2=ycost；
Wherein yf-caoFor free calcium oxide content, ycostFor per unit area yield clinker energy consumption cost, x1For high-temperature blower electric current, x2It is C1 Feeder pipe temperature, x3For rotary kiln Main motor current, x4For kiln head cover temperature, x5For Coaling of Decomposing Furnace, x6Coal is fed for kiln hood Amount, x7For kiln hood air temperature three times, x8For kiln tail smoke-box temperature, x9Pressure under combing for a Room, ξ are remaining energy consumption.
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