CN107245540B - A kind of control strategy of blast furnace material distribution process radial direction thickness of feed layer distribution - Google Patents
A kind of control strategy of blast furnace material distribution process radial direction thickness of feed layer distribution Download PDFInfo
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B5/00—Making pig-iron in the blast furnace
- C21B5/006—Automatically controlling the process
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B2300/00—Process aspects
- C21B2300/04—Modeling of the process, e.g. for control purposes; CII
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Abstract
The present invention gives a kind of control strategies based on the distribution of the blast furnace material distribution process radial direction thickness of feed layer of iterative learning and multi-model.The present invention is distributed as being controlled target with blast furnace material distribution process radial direction thickness of feed layer, using burden distribution matrix as performance variable, constructs a kind of control strategy towards the distribution of blast furnace material distribution process radial direction thickness of feed layer.Write the distribution of bed of material radial thickness as weight and basic function form first, the expression method of thickness of feed layer target distribution and controlled distribution is given on the basis of basic function model formulation, a kind of control strategy of blast furnace material distribution process radial direction thickness of feed layer distribution is given on the basis of iterative learning and multi-model, multi-model is responsible for integer field cloth feed ring number vectorκSelection and optimization, and iterative learning is mainly used for real number field chute dip vectorαControl.The present invention provides theoretical foundation for the adjustment and setting of burden distribution matrix in burden distribution system, for promoting the process of blast furnace industrial process automation, improve the blast furnace operating smelted towards high-performance, and realizes that energy conservation, emission reduction and the performance indicator optimization of blast furnace ironmaking process have a very important significance to a greater extent.
Description
Technical field
The present invention relates to a kind of controls based on the distribution of the blast furnace material distribution process radial direction thickness of feed layer of iterative learning and multi-model
System strategy, is related to the modeling and control of distributed system, is related to metallurgy, computer science, mathematics, scientific and random point of control
The intersection and fusion of the technical fields such as cloth control.
Background technique
Blast furnace material distribution is an important operation system in blast furnace operating, is blast furnace stable smooth operation, blast furnace stable yields, reduction accident
Rate and the key link for reducing fuel consumption.The practice of many years and experience have shown that, blast furnace material distribution process is formed by the bed of material
Original depth is distributed the distribution for not only influencing initial shape of charge level and temperature field, while being also blast furnace stable yields, and blast furnace is stablized suitable
Row, blast furnace accident rate and blast furnace fuel consumption key link (Liu Yuncai, blast furnace material distribution rule [M], metallurgical industry publishing house,
2012).Due to lacking the valid model of complex three-dimensional solid charge level output shape inside description blast furnace, the system of blast furnace material distribution system
Fixed and adjustment is still executed by veteran section chief, brings more negative effect to blast furnace steady production.
Chinese patent 201410336893.7 provides a kind of control method of blast furnace material distribution process radial direction ore coke ratio, establishes
Relationship model of the blast furnace material distribution control parameter to charge level, for describing blast furnace material distribution model, blanking process model has certain
Positive effect can not achieve due to lacking effective description of burden distribution matrix and thickness of feed layer output distribution to thickness of feed layer point
The control of cloth.Patent application document 201510586609.6 provides between a kind of description charge level output shape and performance variable
Relationship model, but the control program of thickness of feed layer distribution is not provided.For existing blast furnace material distribution system, the present invention is proposed
A kind of blast furnace distribution control system being distributed as controlled parameter with bed of material output thickness, and give a kind of based on iterative learning
The control strategy being distributed with the blast furnace material distribution process thickness of feed layer of multi-model.The present invention is for promoting blast furnace industrial process automation
Process, improve the blast furnace operating smelted towards high-performance, and energy conservation, the emission reduction of realization blast furnace ironmaking process to a greater extent
And performance indicator optimization has a very important significance.The formulation and adjustment of existing blast furnace material distribution system are still by veteran
Section chief execute, lack effective theory support, bring more negative effect to blast furnace steady production, this be it is generally existing in the industry and
Technical problem urgently to be resolved.
Summary of the invention
The invention patent proposes one kind on the basis of patent application document 201510586609.6 with blast furnace material distribution mistake
Journey radial direction thickness of feed layer is distributed as control target, using burden distribution matrix as the distributed parameter control system of performance variable, and gives
A kind of control strategy based on the distribution of the blast furnace material distribution process radial direction thickness of feed layer of iterative learning and multi-model.
The blast furnace material distribution mistake based on iterative learning and multi-model that in order to solve the above-mentioned technical problems, the present invention provides a kind of
The control strategy of journey radial direction thickness of feed layer distribution, which is characterized in that be distributed as being controlled with blast furnace material distribution process radial direction thickness of feed layer
Target processed constructs the control strategy being distributed towards blast furnace material distribution process radial direction thickness of feed layer using burden distribution matrix as performance variable；Institute
Stating control strategy includes:
(1) distribution of controlled device bed of material radial thickness is write as weight and basic function form using separate variables；
(2) on the basis of the bed of material radial thickness described in weight and basic function is distributed, weight is manually adjusted to set
The control target of thickness of feed layer distribution；
(3) according to the control target and the real-time radial thickness of the bed of material of the blast furnace material distribution process thickness of feed layer distribution set
Distribution define thickness of feed layer distributed controll performance indicator criterion function, based on iterative learning and multi-model process to blast furnace material distribution
Process performance variable burden distribution matrix automatically adjusts.
In above-mentioned as a preferred technical solution, (1), controlled device bed of material radial thickness distribution h (y, u) meets cylindricality product
Point constraint, is a bivariate distribution function relevant to decision variable, performance variable burden distribution matrix include chute inclination angle sequence with
Two parts of rotating cycle sequence, wherein chute dip vector α belongs to real number field, and cloth feed ring number vector κ belongs to nature number field,
Causing blast furnace material distribution process radial direction thickness of feed layer distributed controll is a kind of special COMPLEX MIXED control system.
Above-mentioned as a preferred technical solution, (1) specifically:
1) bed of material radial thickness distribution h (y, u) is write as weight w under integral constrainti(u) and basic function Bi(y) form,
And determine the number n+1 of basic function and weight:
Wherein, bed of material radial thickness distribution h (y, u) is the position y and decision variable burden distribution matrix u apart from blast furnace center
Two-dimensional function, VtFor furnace charge total volume, biFor basic function Bi(y) volume integral；Basic function BiIt (y) is B-spline function, n 5-
Integer between 20；
2) relationship between n dimension weight vector W (u) and the distribution of bed of material radial thickness is described using dimensionality reduction mode:
H (y, u)=C (y) W (u)+L (y),
W (u)=[w1(u),w2(u),…,wn(u)]T∈Rn×1,
Wherein, W (u) is that n relevant to decision variable u ties up weight vector, and C (y) is n Wiki function composition after dimension-reduction treatment
Matrix, L (y) be dimension-reduction treatment after bound variable.
In above-mentioned as a preferred technical solution, (2), controlled variable bed of material radial thickness target distribution function g (y) is by base
Function and weight determine, on the basis of the bed of material radial thickness described in weight as claimed in claim 3 and basic function is distributed,
Manually adjust weight WgTo set the control target of thickness of feed layer distribution, specifically: g (y)=C (y) Wg+L(y)。
In above-mentioned as a preferred technical solution, (3), according to the control of the blast furnace material distribution process thickness of feed layer distribution set
Thickness of feed layer distributed controll performance indicator criterion function is determined in the distribution of target processed and the real-time radial thickness of the bed of material, and provides
A kind of control strategy based on the blast furnace material distribution process of iterative learning and multi-model process automatic adjustment performance variable burden distribution matrix,
Specifically:
1) constraint followed according to cloth feed ring Number Sequence κThe maximum number of rings m and blast furnace material distribution of cloth
Multicenter few cloth principle in process edge determines M alternative κjFinite aggregate K={ the κ of composition1,κ2,…κM}；
2) according to target distribution g (y), cloth feed ring number vector κ in limited countably infinite set is definedjCriterion function:
Wherein
3) according to cloth feed ring number vector κ in limited countably infinite setjCorresponding criterion function is slipped with the method for Gradient Iteration
The control law of groove tilt angle vector α:
Wherein (k) indicates the number of iterative learning；
4) maximum number of iterations and stopping criterion for iteration are set, according to the integer field cloth feed ring number vector κ's having determined
Limited countably infinite set successively calculates cloth feed ring number vector κjCriterion function, from M limited countably infinite set K={ κ1,κ2,…κMIn choosing
Select the minimum value min of criterion function corresponding to vector κAnd it is provided accordingly according to the performance indicator of minimum
Decision variable α and κ.
More specifically, the present invention also provides a kind of blast furnace material distribution process radial direction bed of material based on iterative learning and multi-model
The control strategy of thickness distribution, the specific steps are as follows:
Step 1: obtaining blast furnace material distribution process blast-furnace body parameter, including furnace throat radius, stockline height, chute length, larynx
Pipe height, chute fascinate away from, chute coefficient of friction, furnace charge angle of rest (repose), furnace charge heap density, charge batch weight, and provide performance variable cloth
Expect the vector description of matrix:
α=[α1,…,αm]T∈Rm×1,αi∈[αmin,αmax], (1)
U=[α, κ], (3)
Wherein αminAnd αmaxIndicate the boundary of chute tilt adjustable section, m indicates maximum cloth number of rings, chute dip vector α
Belong to real number field, and rotating cycle vector κ belongs to nature number field.
Step 2: obtaining blast furnace material distribution process stockline radial distribution γ (y), i.e. cloth process bottom distribution shape, wherein y
Indicate the distance apart from blast furnace center.
Step 3: furnace charge volume V is calculated according to charge batch weight and furnace charge heap densityt, and assume furnace charge heap density constant, root
According to conservation of mass principle, volume and furnace charge of the furnace charge in feed bin are equal in the volume of blast furnace throat punishment cloth, and then we mention
The isometric principle of blast furnace material distribution process furnace charge out:
Wherein, f (y, u) indicates that burden distribution is formed by radial top profile on the basis of stockline γ (y), and u is indicated
Burden distribution matrix constitutes κ by chute dip vector α and rotating cycle vector and constitutes.
Step 4: thickness of feed layer distribution is calculated according to the distribution shape of blast furnace material distribution process radial direction bottom and top:
H (y, u)=f (y, u)-γ (y). (5)
Step 5: according to the isometric principle of blast furnace material distribution process furnace charge and the separation of variable, bed of material radial thickness being distributed h
(y, u) is write as weight wi(u) and basic function Bi(y) form, specific implementation including the following steps:
Step 5-1: the number n+1 of basic function and weight is determined:
This patent basic function Bi(y), B-spline basic function, integer of the n between 5-10 are selected as.
Step 5-2: basic function B is determinedi(y) volume integral:
B=[b1,b2,…,bn]T∈Rn×1。 (9)
Step 5-3: the vector description of weight W (u) is determined:
W (u)=[w1(u),w2(u),…,wn(u)]T∈Rn×1, (10)
Wherein vector b and W (u), dimension be n.
Step 5-4: the relationship between weight vector W (u) and the distribution of dynamic radial thickness of feed layer is described using dimensionality reduction mode:
H (y, u)=C (y) W (u)+L (y), (13)
Step 6: according to the description of the isometric principle of blast furnace material distribution process furnace charge and above-mentioned weight and basic function, setting
The target g (y) of thickness of feed layer distribution:
G (y)=C (y) Wg+ L (y), (15)
Step 7: according to blast furnace material distribution expertise, determining the limited countably infinite set of integer field cloth feed ring number vector κ: K=
{κ1,κ2,…κM, wherein M indicates cardinality of a set.
Step 8: the target g (y) being distributed according to the thickness of feed layer of setting defines cloth feed ring number vector κ in limited countably infinite setj
Criterion function:
Wherein
Step 9: according to cloth feed ring number vector κ in limited countably infinite setjCorresponding criterion function is given with the method for Gradient Iteration
It slips the control law of groove tilt angle vector α:
Wherein (k) indicates the number of iterative learning.
Step 10: setting maximum number of iterations and stopping criterion for iteration, according to the integer field cloth feed ring number having determined to
The limited countably infinite set of amount κ successively calculates cloth feed ring number vector κjCriterion function, from M limited countably infinite set K={ κ1,κ2,…κM}
The minimum value min of criterion function corresponding to middle selection vector κAnd provide corresponding decision variable α and κ.
The present invention achieves significant technical effect, is embodied in: 1) giving a kind of radial with blast furnace material distribution process
Thickness of feed layer is distributed as control target, using burden distribution matrix as the distributed parameter control system of performance variable, and gives a kind of base
In the control strategy of the blast furnace material distribution process radial direction thickness of feed layer of iterative learning and multi-model distribution.2) blast furnace material distribution process, by
Adjustable parameter in performance variable burden distribution matrix belongs to different number fields, and blast furnace material distribution process radial direction thickness of feed layer is caused to be distributed
Control is a kind of special COMPLEX MIXED control system.This patent is distributed as being controlled with blast furnace material distribution process radial direction thickness of feed layer
Target constructs a kind of control plan towards the distribution of blast furnace material distribution process radial direction thickness of feed layer using burden distribution matrix as performance variable
Slightly.3) present invention provides theoretical foundation and practical operation level for the adjustment and setting of burden distribution matrix in burden distribution system
Method gives the concrete measure for manually adjusting thickness of feed layer distribution, is conducive to the reality for promoting blast furnace material distribution process control
It is existing, while the thought of this patent distribution parameter control can also be used for solving the control problem of other complex objects.
Detailed description of the invention
Fig. 1 is the radial schematic diagram of blast furnace material distribution process:
Detailed description of the invention: VtIndicate the volume of furnace charge charge；α is chute inclination angle；ω is angular velocity of rotation；F (y, u) indicates material
Distribution shape at the top of face, y indicate the distance apart from furnace center, and r indicates furnace throat radius；γ (y) indicates charge level bottom distribution shape
(also referred to as stockline distribution)；H (y, u) indicates thickness distribution；
Fig. 2 is blast furnace material distribution process radial direction thickness of feed layer distribution control system structure chart；
Fig. 3 is the blast furnace material distribution process radial direction thickness of feed layer distributed controll flow chart based on iterative learning and multi-model；
Fig. 4 is the effect picture of the blast furnace material distribution process radial direction thickness of feed layer distributed controll based on iterative learning and multi-model；
Specific embodiment
Technical solution of the present invention is further described with specific implementation with reference to the accompanying drawing.
A kind of control strategy based on the distribution of the blast furnace material distribution process radial direction thickness of feed layer of iterative learning and multi-model, specifically
Steps are as follows:
Step 1: obtaining blast furnace material distribution process blast-furnace body parameter, including furnace throat radius, stockline height, chute length, larynx
Pipe height, chute fascinate away from, chute coefficient of friction, furnace charge angle of rest (repose), furnace charge heap density, charge batch weight, and provide performance variable cloth
Expect the vector description of matrix:
α=[α1,…,αm]T∈Rm×1,αi∈[αmin,αmax], (1)
U=[α, κ], (3)
Wherein αminAnd αmaxIndicate the boundary of chute tilt adjustable section, m indicates maximum cloth number of rings, chute dip vector α
Belong to real number field, and rotating cycle vector κ belongs to nature number field.
Step 2: obtaining blast furnace material distribution process stockline radial distribution γ (y), i.e. cloth process bottom distribution shape, wherein y
Indicate the distance apart from blast furnace center.
Step 3: furnace charge volume V is calculated according to charge batch weight and furnace charge heap densityt, and assume furnace charge heap density constant, root
According to conservation of mass principle, volume and furnace charge of the furnace charge in feed bin are equal in the volume of blast furnace throat punishment cloth, and then we mention
The isometric principle of blast furnace material distribution process furnace charge out:
Wherein, f (y, u) indicates that burden distribution is formed by radial top profile on the basis of stockline γ (y), and u is indicated
Burden distribution matrix constitutes κ by chute dip vector α and rotating cycle vector and constitutes.
Step 4: thickness of feed layer distribution is calculated according to the distribution shape of blast furnace material distribution process radial direction bottom and top:
H (y, u)=f (y, u)-γ (y). (5)
Step 5: according to the isometric principle of blast furnace material distribution process furnace charge and the separation of variable, bed of material radial thickness being distributed h
(y, u) is write as weight wi(u) and basic function Bi(y) form, specific implementation including the following steps:
Step 5-1: the number n+1 of basic function and weight is determined:
This patent basic function Bi(y), B-spline basic function, integer of the n between 5-10 are selected as.
Step 5-2: basic function B is determinedi(y) volume integral:
B=[b1,b2,…,bn]T∈Rn×1。 (9)
Step 5-3: the vector description of weight W (u) is determined:
W (u)=[w1(u),w2(u),…,wn(u)]T∈Rn×1, (10)
Wherein vector b and W (u), dimension be n.
Step 5-4: the relationship between weight vector W (u) and the distribution of dynamic radial thickness of feed layer is described using dimensionality reduction mode:
H (y, u)=C (y) W (u)+L (y), (13)
Step 6: according to the description of the isometric principle of blast furnace material distribution process furnace charge and above-mentioned weight and basic function, setting
The target g (y) of thickness of feed layer distribution:
G (y)=C (y) Wg+ L (y), (15)
Step 7: according to blast furnace material distribution expertise, determining the limited countably infinite set of integer field cloth feed ring number vector κ: K=
{κ1,κ2,…κM, wherein M indicates cardinality of a set.
Step 8: the target g (y) being distributed according to the thickness of feed layer of setting defines cloth feed ring number vector κ in limited countably infinite setj
Criterion function:
Wherein
Step 9: according to cloth feed ring number vector κ in limited countably infinite setjCorresponding criterion function is given with the method for Gradient Iteration
It slips the control law of groove tilt angle vector α:
Wherein (k) indicates the number of iterative learning.
Step 10: setting maximum number of iterations and stopping criterion for iteration, according to the integer field cloth feed ring number having determined to
The limited countably infinite set of amount κ successively calculates cloth feed ring number vector κjCriterion function, from M limited countably infinite set K={ κ1,κ2,…κM}
The minimum value min of criterion function corresponding to middle selection vector κAnd provide corresponding decision variable α and κ.
For Fig. 1 and 2500m shown in Fig. 23And tank is without clock-type steel plant blast furnace, furnace throat radius 4.3m, furnace charge volume
30m3, basic function number is your n+1=6, the weight vector W of target distributiong=[0.91,0.90,0.7,0.45,0.24]T, standby
The limited countably infinite set is selected to be
γ (y) is fitted by live data-oriented and is obtained.
For above-mentioned specific blast furnace material distribution process, level is embodied:
(1) step 5 sets basic function B1(y),B2(y),…,B6(y)。
(2) according to step 6 and target distribution weight vector Wg=[0.91,0.90,0.7,0.45,0.24]TCalculate target point
Cloth g (y).
(3) blast furnace burden drop point site (Liu Yuncai, blast furnace material distribution rule [M], metallurgical work are calculated according to material flow track model
Industry publishing house, 2012), f (y, u) is calculated according to patent application document 201510586609.6, and calculate according to this patent step 4
Thickness distribution h (y, u).
(4) maximum number of iterations is set as 50, and control flow chart according to Fig.3, calculates decision variable in real timeAnd criterion functionAnd final decision variable α and κ are calculated according to step 10.
(5) relativity for providing initial distribution and being finally distributed, as shown in Figure 4.
The present invention gives the concrete operations methods for manually adjusting thickness of feed layer distribution, from the ore and coke thickness of optimization
The control based on the distribution of the blast furnace material distribution process radial direction thickness of feed layer of iterative learning and multi-model that the present invention provides is seen in degree distribution
The optimization that the performance variable burden distribution matrix of bed of material target thickness distribution may be implemented in strategy calculates, and has visual strong, operation letter
All there is highly important guidance to anticipate for feature single, result is accurate, the realization controlled for operation optimization of distribution and cloth process
Justice.
This patent gives the concrete operations method for manually adjusting thickness of feed layer distribution, based on iteration in terms of control effect
It practises and the control strategy of the blast furnace material distribution process radial direction thickness of feed layer of multi-model distribution can arbitrarily track given bed of material thickness
Degree distribution has the characteristics that visuality is strong, easy to operate, result is accurate, and operation optimization of distribution and cloth process are controlled
Realizing all has highly important directive significance.
Claims (6)
1. a kind of control strategy based on the distribution of the blast furnace material distribution process bed of material radial thickness of iterative learning and multi-model, feature
Be, be distributed as being controlled target with blast furnace material distribution process bed of material radial thickness, using burden distribution matrix as performance variable, building towards
The control strategy of blast furnace material distribution process bed of material radial thickness distribution, the control strategy include:
(1) distribution of controlled device bed of material radial thickness is write as weight and basic function form using separate variables；
(2) on the basis of the bed of material radial thickness described in weight and basic function is distributed, weight is manually adjusted to set the bed of material
The control target of thickness distribution；
(3) according to the control target of the blast furnace material distribution process thickness of feed layer distribution set and point of the real-time radial thickness of the bed of material
Cloth defines thickness of feed layer distributed controll performance indicator criterion function, based on iterative learning and multi-model process to blast furnace material distribution process
Performance variable burden distribution matrix automatically adjusts.
2. a kind of blast furnace material distribution process bed of material radial thickness based on iterative learning and multi-model according to claim 1 point
The control strategy of cloth, it is characterised in that: controlled device bed of material radial thickness distribution h (y, u) meets cylindricality integral constraint, is one
Bivariate distribution function relevant to decision variable, performance variable burden distribution matrix include chute inclination angle sequence and rotating cycle sequence two
A part, wherein chute dip vector α belongs to real number field, and cloth feed ring number vector κ belongs to nature number field.
3. a kind of blast furnace material distribution process bed of material radial thickness based on iterative learning and multi-model according to claim 1 point
The control strategy of cloth, which is characterized in that (1) is write as the distribution of controlled device bed of material radial thickness using separate variables
Weight and basic function form, specifically:
1) bed of material radial thickness distribution h (y, u) is write as weight w under integral constrainti(u) and basic function Bi(y) form, and really
Determine the number n+1 of basic function and weight: Wherein, bed of material radial thickness distribution h (y, u) be position y apart from blast furnace center and
The two-dimensional function of decision variable burden distribution matrix u, VtFor furnace charge total volume, biFor basic function Bi(y) volume integral；Basic function Bi
It (y) is B-spline function, integer of the n between 5-20；
2) relationship between n dimension weight vector W (u) and the distribution of bed of material radial thickness is described using dimensionality reduction mode:
H (y, u)=C (y) W (u)+L (y),
W (u)=[w1(u),w2(u),…,wn(u)]T∈Rn×1,
Wherein, W (u) is that n relevant to decision variable u ties up weight vector, and C (y) is the square of n Wiki function composition after dimension-reduction treatment
Battle array, L (y) are the bound variable after dimension-reduction treatment.
4. a kind of blast furnace material distribution process bed of material radial thickness based on iterative learning and multi-model according to claim 3 point
The control strategy of cloth, which is characterized in that in (2), controlled variable bed of material radial thickness target distribution function g (y) is by base letter
Several and weight determines, on the basis of the bed of material radial thickness described in weight and basic function is distributed, manually adjusts weight WgWith
The control target of thickness of feed layer distribution is set, specifically: g (y)=C (y) Wg+L(y)。
5. a kind of blast furnace material distribution process bed of material radial thickness based on iterative learning and multi-model according to claim 4 point
The control strategy of cloth, which is characterized in that the control target that described (3) are distributed according to the blast furnace material distribution process thickness of feed layer set
And the distribution of the real-time radial thickness of the bed of material defines thickness of feed layer distributed controll performance indicator criterion function, based on iterative learning and
Multi-model process automatically adjusts to blast furnace material distribution process performance variable burden distribution matrix, specifically:
1) constraint followed according to cloth feed ring Number Sequence κThe maximum number of rings m of cloth and blast furnace material distribution process side
The few cloth principle of edge multicenter determines M alternative κjFinite aggregate K={ the κ of composition1,κ2,…κM}；
2) according to target distribution g (y), cloth feed ring number vector κ in limited countably infinite set is definedjCriterion function:
Wherein
3) according to cloth feed ring number vector κ in limited countably infinite setjCorresponding criterion function provides chute with the method for Gradient Iteration and inclines
The control law of angular amount α:
Wherein (k) indicates the number of iterative learning；
4) maximum number of iterations and stopping criterion for iteration are set, according to the limited of the integer field cloth feed ring number vector κ having determined
Countably infinite set successively calculates cloth feed ring number vector κjCriterion function, from M limited countably infinite set K={ κ1,κ2,…κMIn select to
Measure the minimum value of criterion function corresponding to κAnd corresponding decision is provided according to the performance indicator of minimum
Variable α and κ.
6. a kind of control strategy based on the distribution of the blast furnace material distribution process bed of material radial thickness of iterative learning and multi-model, feature
It is, includes the following steps:
Step 1: obtaining blast furnace material distribution process blast-furnace body parameter, including furnace throat radius, stockline height, chute length, trunnion height
Degree, chute fascinate away from, chute coefficient of friction, furnace charge angle of rest (repose), furnace charge heap density, charge batch weight, and provide performance variable cloth square
The vector description of battle array:
α=[α1,…,αm]T∈Rm×1,αi∈[αmin,αmax], (1)
U=[α, κ], (3)
Wherein αminAnd αmaxIndicate the boundary of chute tilt adjustable section, m indicates maximum cloth number of rings, and chute dip vector α belongs to
Real number field, and rotating cycle vector κ belongs to nature number field；
Step 2: obtaining blast furnace material distribution process stockline radial distribution γ (y), i.e. cloth process bottom distribution shape, wherein y is indicated
Distance apart from blast furnace center；
Step 3: furnace charge volume V is calculated according to charge batch weight and furnace charge heap densityt, and assume furnace charge heap density constant, according to quality
Conserva-tion principle, volume and furnace charge of the furnace charge in feed bin are equal in the volume of blast furnace throat punishment cloth, obtain:
Wherein, f (y, u) indicates that burden distribution is formed by radial top profile on the basis of stockline γ (y), and u indicates cloth
Matrix constitutes κ by chute dip vector α and rotating cycle vector and constitutes；
Step 4: thickness of feed layer distribution is calculated according to the distribution shape of blast furnace material distribution process radial direction bottom and top:
H (y, u)=f (y, u)-γ (y)； (5)
Step 5: according to the isometric principle of blast furnace material distribution process furnace charge and the separation of variable, bed of material radial thickness being distributed h (y, u)
Write as weight wi(u) and basic function Bi(y) form specifically includes following sub-step:
Step 5-1: bed of material radial thickness distribution h (y, u) is write as weight w under integral constrainti(u) and basic function Bi(y) shape
Formula, and determine the number n+1 of basic function and weight:
Wherein, bed of material radial thickness distribution h (y, u) is the two dimension of the position y and decision variable burden distribution matrix u apart from blast furnace center
Function, VtFor furnace charge total volume, biFor basic function Bi(y) volume integral；Basic function Bi(y) be B-spline function, n be 5-20 it
Between integer；
Step 5-2: the relationship between n dimension weight vector W (u) and the distribution of bed of material radial thickness is described using dimensionality reduction mode:
H (y, u)=C (y) W (u)+L (y), (9)
W (u)=[w1(u),w2(u),…,wn(u)]T∈Rn×1, (11)
Wherein, W (u) is that n relevant to decision variable u ties up weight vector, and C (y) is the square of n Wiki function composition after dimension-reduction treatment
Battle array, L (y) are the bound variable after dimension-reduction treatment；
Step 6: according to the description of the isometric principle of blast furnace material distribution process furnace charge and above-mentioned weight and basic function, the bed of material is set
The target g (y) of thickness distribution:
G (y)=C (y) Wg+ L (y), (13)
Step 7: the constraint followed according to cloth feed ring Number Sequence κThe maximum number of rings m and blast furnace material distribution mistake of cloth
The few cloth principle of Cheng Bianyuan multicenter determines M alternative κjFinite aggregate K={ the κ of composition1,κ2,…κM}；
Step 8: the target g (y) being distributed according to the thickness of feed layer of setting defines cloth feed ring number vector κ in limited countably infinite setjStandard
Then function:
Wherein
Step 9: according to cloth feed ring number vector κ in limited countably infinite setjCorresponding criterion function is slipped with the method for Gradient Iteration
The control law of groove tilt angle vector α:
Wherein (k) indicates the number of iterative learning；
Step 10: setting maximum number of iterations and stopping criterion for iteration, according to the integer field cloth feed ring number vector κ's having determined
Limited countably infinite set successively calculates cloth feed ring number vector κjCriterion function, from M limited countably infinite set K={ κ1,κ2,…κMIn choosing
Select criterion function corresponding to vector κ minimum value min (J (α) |κj), and corresponding determine is provided according to the performance indicator of minimum
Plan variable α and κ.
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