CN107716560A - A kind of new Hot Strip Rolling load distribution method - Google Patents

A kind of new Hot Strip Rolling load distribution method Download PDF

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CN107716560A
CN107716560A CN201710958271.1A CN201710958271A CN107716560A CN 107716560 A CN107716560 A CN 107716560A CN 201710958271 A CN201710958271 A CN 201710958271A CN 107716560 A CN107716560 A CN 107716560A
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msub
mrow
load
roll
sharing
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CN107716560B (en
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刘新忠
徐双
田华
刘承宝
刘雅超
武凯
刘菊
狄敏
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Beijing Aritime Intelligent Control Co Ltd
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Beijing Aritime Intelligent Control Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
    • B21B37/58—Roll-force control; Roll-gap control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
    • B21B37/16—Control of thickness, width, diameter or other transverse dimensions
    • B21B37/165—Control of thickness, width, diameter or other transverse dimensions responsive mainly to the measured thickness of the product

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Control Of Metal Rolling (AREA)

Abstract

The invention discloses a kind of new Hot Strip Rolling load distribution method, belong to control technology field.The present invention obtains typical case's band other roll-force distribution coefficient of steel layer to multi-goal optimizing function offline optimization using Hybrid Particle Swarm Optimization, joint Energy dissipation method and standard reduction ratio distribution method obtain sharing of load initial value, are iterated and realized in line computation using equilibrium iteration method in roll-force distribution model.The sharing of load new method that the present invention is combined using " offline optimization+offline optimization+On-line Control ", in line computation and the application verification validity of this method, it is very fast with good convergence and calculating speed, meet, in line computation requirement, to have taken into account required power and Strip Shape Control requirement again.

Description

A kind of new Hot Strip Rolling load distribution method
Technical field
The invention belongs to control technology field, is related to the control method of hot strip rolling finishing stands, specifically, refers to A kind of Hot Strip Rolling load distribution method.
Background technology
Sharing of load calculating is the core content of milling train rolling production technique.Rational sharing of load is product thickness precision And the basic guarantee of strip shape quality, it is the production capacity that can give full play to milling train, steady production process, ensures that product is good The premise of thickness plate shape and physical property., can be with by the optimized algorithm of multiple target because the factor of influence of rolled code is more Each physical quantity of the operation of rolling is considered, under the conditions of field apparatus is met, the exit thickness of each frame of reasonable distribution, Rational sharing of load is obtained, meets the indices of technological requirement.
The course of hot rolling control system that domestic steel mill introduces at present uses the sharing of load in traditional load distribution method more Y-factor method Y, mainly include 3 kinds of patterns：Energy consumption curve pattern, pressure pattern and rolling force mode.Wherein, energy consumption curve pattern and Pressure pattern can directly calculate each rack outlet thickness of finish rolling, and rolling force mode then needs just obtain into excessively multiple iterative calculation Obtain each rack outlet thickness.The above two advantages are that each frame reduction ratio fluctuation for obtaining is smaller, and shortcoming is when rolling operating mode changes It is unreasonable easily to there is roll-force allocation proportion (after such as renewing roller) during change；The latter's advantage is that each frame roll-force ratio is basic Keep constant, shortcoming is when rolling force model prediction error is larger, and the reduction ratio fluctuation of each frame is also larger.
At present, also there are some disclosed associated documents in the establishing method about fine-rolling strip steel sharing of load, such as applies Disclosed in numbers 2014101747322 Chinese invention patent " a kind of to take into account required power and the hot-strip of good profile is born Lotus distribution method ", it comprises the following steps：Step S1, the offline optimization of finish rolling sharing of load coefficient, it is other to obtain typical case's band steel layer Finish rolling sharing of load scheme Pareto optimal solution sets；Step S2, the offline decision-making of finish rolling sharing of load coefficient：Using based on The decision-making technique of polymerization is weighted, from the Pareto optimal solution sets obtained by step 1, final Pareto optimization solutions is selected, makees For typical case's band other sharing of load coefficient of steel layer；Step S3, the distribution of each frame belt steel rolling thickness of finish rolling calculates and in line traffic control System, obtains the drafts and its exit thickness of each frame of finish rolling.The program takes into account the hot-strip of required power and good profile Load distribution method, sharing of load coefficient is determined using intelligent optimization method, avoid Conventional wisdom method need repeatedly trial and error, The defects of experimentation cost is higher.Its deficiency is first, the selection of multi-goal optimizing function is not comprehensive enough, the intelligence of offline optimization Optimization method is more complicated, calculates time length；Second, it is larger using the sharing of load initial value deviation for depressing mode computation, to rolling Force mode processed is calculated and had an impact；Third, roll-force mode computation process is cumbersome, code is long, computationally intensive.
The content of the invention
The present invention is directed to problems of the prior art, proposes a kind of " energy consumption curve pattern+pressure pattern+roll-force The sharing of load new method that pattern " is combined, in line computation and the application verification validity of this method, there is good convergence Property and calculating speed is very fast, meets in line computation requirement.
The present invention provides a kind of Hot Strip Rolling load distribution method, is a kind of heat for taking into account required power and good profile Strip load distribution method is rolled, typical strip is obtained to multi-goal optimizing function offline optimization using Hybrid Particle Swarm Optimization The other roll-force distribution coefficient of layer, combines Energy dissipation method and standard reduction ratio distribution method obtains sharing of load initial value, rolls Realization is iterated in line computation using equilibrium iteration method in power distribution model.
A kind of Hot Strip Rolling load distribution method provided by the invention, comprises the following steps：
Step S201：By gathering each parameter of mm finishing mill unit, main mathematical models are established, obtain multiple single-goal functions, Comprehensive multiple single-goal functions, obtain multi-goal optimizing function, determine that optimized algorithm is mixed according to multi-goal optimizing function characteristic Close particle swarm optimization algorithm；
Step S202：Hybrid Particle Swarm Optimization model is established, collection primary data is carried out to multi-goal optimizing function Optimization obtains optimal solution, i.e. roll-force distribution coefficient；
Step S203：Experience rolling schedule model is established, using standard reduction ratio distribution method+energy consumption curve distribution method to first Beginning data obtain sharing of load initial value after being handled；
Step S204：Roll-force distribution model is established, and the roll-force obtained to sharing of load initial value according to S202 is divided Distribution coefficient is iterated using equilibrium iteration method is calculated one group of sharing of load value and reduction ratio.
Step S205：Amplitude limit amendment is carried out to reduction ratio obtained by step S204, finally gives allocation result, so as to complete pair The on line real time control of the belt steel rolling thickness of each frame of finishing mill.
The present invention also provides a kind of Load Distribution System of the Hot Strip Rolling load distribution method described in realize, described Load Distribution System mainly includes roll-force distribution coefficient computing module, initial load distribution computing module and roll-force distribution Model on-line control module.Roll-force distribution coefficient computing module is right using Hybrid Particle Swarm Optimization collection primary data Multi-goal optimizing function optimizes, and obtains one group of roll-force distribution coefficient；Initial load distribution computing module is born using experience Lotus distribution model is handled the primary data of input, obtains sharing of load initial value；Roll-force distribution model On-line Control Module is iterated calculating to two groups of initial data of sharing of load initial value and roll-force distribution coefficient, and result is limited Width amendment is so that it is determined that allocation result.
Compared with the prior art, it is an advantage of the invention that：
1st, the present invention " offline optimization+offline optimization+On-line Control " is combined, using Hybrid Particle Swarm Optimization from Line optimizes to obtain typical case's band other sharing of load coefficient of steel layer；Initial depression rate is carried out using experience rolling schedule model excellent Change, obtain relatively reliable sharing of load initial value；Realized using the iterative calculation of roll-force distribution model in line computation and progress Real-time online controls, and can both meet the time requirement in line computation/control, and take into account required power and Strip Shape Control requirement again.
2nd, use Hybrid Particle Swarm Optimization to calculate constraint multi-goal optimizing function to determine sharing of load coefficient, overcome Artificial experience method and traditional intelligence method during files of constrained multi-objective function Solve problems for being not readily available a component Cloth is uniform and the shortcomings that spreading extensive noninferior solution, fine-rolling strip steel rolling procedure setting accuracy and rolling stabilization are improved so as to reach The purpose of property.
3rd, the reduction ratio under the distribution of comprehensive energy consumption curve, reduction ratio distribution and roll-force distribution Three models, to pressure Rate carries out clipping operation twice, and so as to obtain final allocation result, this mode can not only overcome reduction ratio under single-mode Unbalanced problem is distributed, more ensure that assignment accuracy and the good glacing flatness of plate shape.
Brief description of the drawings
Fig. 1 is Load Distribution System structural representation provided by the invention.
Fig. 2 is load distribution method flow chart of steps provided by the invention.
Fig. 3 is intelligent optimization method flow chart provided by the invention.
Fig. 4 is that sharing of load initial value provided by the invention determines steps flow chart schematic diagram.
Fig. 5 is rolling force mode sharing of load calculating process flow chart provided by the invention.
Embodiment
For the object, technical solutions and advantages of the present invention are more clearly understood, below in conjunction with specific embodiment, and reference Accompanying drawing, the present invention is described in more detail.
Present invention firstly provides a kind of Hot Strip Rolling load distribution method, required power and good profile can be taken into account, " offline optimization+offline optimization+On-line Control " is combined by the present invention, is obtained using Hybrid particle swarm optimization method offline optimization Typical case's band other roll-force distribution coefficient of steel layer；Offline optimization is carried out to initial depression rate using experience rolling schedule model, obtained To relatively reliable sharing of load initial value；Realized in line computation and exist in real time using the iterative calculation of roll-force distribution model Line traffic control, can both meet the time requirement in line computation/control, and take into account required power and Strip Shape Control requirement again, and gram While taking artificial experience method deficiency, reach the purpose for improving fine-rolling strip steel Strip Shape Control precision and rolling stability.
As shown in Fig. 2 Hot Strip Rolling load distribution method provided by the invention, is comprised the following steps that：
Step S201：By gathering each parameter of mm finishing mill unit, main mathematical models are established, obtain multiple single-goal functions, Comprehensive all single-goal functions, obtain multi-goal optimizing function, optimized algorithm are determined according to multi-goal optimizing function characteristic.
Each parameter of described mm finishing mill unit includes steel grade, thickness and temperature etc., and described mathematical modeling can select power Proportional object function, energy consumption minimum target function and plate shape well-targeted function etc., combine all Mathematical Models Multi-goal optimizing function, and determine that optimized algorithm is Hybrid Particle Swarm Optimization.
The multi-goal optimizing function of finish rolling sharing of load is established according to following methods：
Roll-force proportional object function, energy consumption minimum target function and plate shape well-targeted function is chosen to bear as finish rolling The object function of lotus allocation optimization problems.
The proportional object function J of described roll-force1Stated using following expression formula：
Described required power minimum target function J2Stated using following expression formula：
Wherein NiFor the rolling power (KW) of the i-th frame；hiFor the i-th rack outlet thickness (mm)；N is frame number.
Described belt plate shape well-targeted function J3Stated using following expression formula：
Wherein P1、P2、P3Respectively first, second, third frame roll-force (N), CRiFor the outlet convexity (μ of the i-th frame M), ΔiFor optimizing regulation amount, a1、a2For proportionality coefficient, n is rolling mills frame number, and i is frame label and 1≤i≤n.
The multi-goal optimizing function that belt restraining is established by above three object function is as follows：
Constraints is：
Wherein PiIt is the roll-force of the i-th frame, PmFor maximum rolling force (KW)；IiTo flow through the electric current of i-th of frame, Im For maximum current (A), hnFor product objective thickness (mm), and the exit thickness of the n-th frame, h0For product original depth.
Step S202：Hybrid Particle Swarm Optimization model is established, collection primary data is carried out to multi-goal optimizing function Optimization obtains optimal solution, i.e. roll-force distribution coefficient；Described primary data showing including bending roller force, roll gap, temperature etc. The slab data and equipment relevant parameter of field collection.
Step S203：Experience rolling schedule model is established, using standard reduction ratio distribution method+energy consumption curve distribution method to first Beginning data obtain sharing of load initial value after being handled；It is initial that described sharing of load initial value includes each rack outlet thickness Value and drafts initial value.
Described experience rolling schedule model carries out load using energy consumption curve distribution method combined standard reduction ratio distribution method Distribute the calculating of initial value.
Energy consumption curve distribution method empirical equation：
In formula：h0For product original depth；anFor the total energy consumption of n frame；To add up Energy dissipation coefficient；K1, K2For Energy consumption equation coefficients.Thus empirical equation can draw one group of sharing of load value { hi(i=1,2 ... n) and the first reduction ratio Breadth coefficient
Second reduction ratio breadth coefficient also can obtain according to standard reduction ratio distribution methodUtilizeIt is rightCarry out amplitude limit Processing, and the 3rd final reduction ratio breadth coefficient is obtained by relative processingSo that it is determined that at the beginning of each frame sharing of load Initial value.
Step S204：Roll-force distribution model is established, and sharing of load initial value is distributed according to default roll-force and is Number is iterated calculating using equilibrium iteration method, and judges whether each frame rolling ratio of finish rolling meets the condition of convergence, to determine Whether terminate iteration, most export at last one group meet the sharing of load value of roll-force distribution coefficient that is drawn in step S202 and 4th reduction ratio breadth coefficient
Step S205：To the 4th reduction ratio breadth coefficient obtained by step S204Amplitude limit amendment is carried out, finally gives distribution As a result, so as to completing the on line real time control of the belt steel rolling thickness to each frame of finishing mill.
As shown in figure 3, Hybrid Particle Swarm Optimization model is established in step S202, using the model to the more of belt restraining Objective optimization function optimizes calculating, produces one group of extensive optimal solution that is evenly distributed and spreads along Pareto forward positions, institute The optimization stated, which calculates, to be comprised the following steps：
Step S301：Given population scale N, speed and the position of primary are randomly selected as defined in the range of, is produced Initial population pop (t), t=0, Pareto optimal solutions in initial population are found out, is deposited into external memory storage and forms noninferior solution Collect I；
Step S302：The desired positions that single particle undergoes are designated as Pbest (t) and are set to current location, are selected The desired positions Gbest (t) that whole colony is undergone is selected, for each particle, Gbest (t) is selected at random from Noninferior Solution Set I Take, for each particle in colony, the speed of more new particle and position, obtain new colony
Step S303：For new colonyIn each particle, according to setting probability become twice Different, the population after order variation is pop (t+1) so that the position of all particles makes t all as defined in the range of in pop (t+1) =t+1；
Step S304：The Noninferior Solution Set I in external memory storage is updated with pop (t), if the particle number in set I exceedes During given scale, the crowding of each particle is calculated, retains the larger particle of crowding；
Step S305：To all particles in pop (t), according to the individual extreme value of each particle of comparison criterion renewal；
Step S306：Judge whether the maximum iteration of optimized algorithm meets, if meeting to turn to step S307, otherwise turn To step S302；
Step S307：Pareto optimal solution of all particles in Noninferior Solution Set I as problem after output renewal, optimization Algorithm stops.
As shown in figure 4, the core of the determination of sharing of load initial value described in step S203 is distributed using reduction ratio Method and energy consumption curve distribution method carry out calculation of initial value to sharing of load, and an amplitude limit amendment is included in calculating process, so this Fig. 4 is also the process of an offline optimization.Its main contents is as follows：
Step S401：One group of each frame sharing of load value and can be drawn according to energy consumption curve distribution method empirical equation One reduction ratio breadth coefficient
Step S402：One group of second reduction ratio breadth coefficient is obtained according to standard reduction ratio distribution method
Step S403：UtilizeIt is rightAmplitude limiting processing is carried out, and the 3rd final reduction ratio is obtained by relative processing Breadth coefficient
Step S404：According to the 3rd obtained reduction ratio breadth coefficientEach rack outlet thickness value is obtained, as institute The sharing of load initial value stated.
It is illustrated in figure 5 roll-force distribution model in step S204 and carries out sharing of load calculating process flow chart, the process In, using the sharing of load initial value obtained, establish equation and be iterated calculating, be met one group of pressure of the condition of convergence Rate, it is the process in line computation, specifically includes following steps：
Step S501：Selecting step S202 obtains sharing of load coefficient, for rolling force mode sharing of load in line computation；
Step S502：Combined according to energy consumption curve distribution method and standard reduction ratio distribution method and determine that each rack outlet of finish rolling is thick Spend initial value；
Step S503：Equilibrium iteration method iterative formula is determined according to S501 and S502, and is iterated；
Step S504：Calculate roll-force correction value；
Step S505：Calculate each frame reduction ratio of mm finishing mill unit and exit thickness updated value；
Step S506：Judge whether each frame roll-force ratio meets the condition of convergence, step S503 gone to if being unsatisfactory for, Final result is exported if meeting.
Using method provided by the invention, certain steel grade is taken to carry out the sharing of load of hot continuous rolling, specific implementation step is as follows：
Determine first including strip width, workpiece thickness, finish to gauge target thickness, target convexity, finishing stand number Slab data, equipment relevant parameter is as roll-force distribution coefficient computation model, experience rolling schedule model and roll-force The primary data of distribution model.
Foundation includes multi-goal optimizing function, standard reduction ratio distribution method formula, energy consumption curve distribution method empirical equation, rolls Mathematical modeling including power iterative equation formula processed.Wherein by analyzing the influence of each parameter, the optimization aim for determining this steel grade is Power is proportional, energy consumption minimum and good profile glacing flatness.
Input initial data obtains roll-force point respectively by particle swarm optimization algorithm model and experience rolling schedule model Distribution coefficient { αiAnd sharing of load initial valueFurther try to achieve the 3rd reduction ratio breadth coefficient now
By roll-force distribution model with roll-force distribution coefficient { αiIt is to constrain to sharing of load initial valueCarry out Iteration, finally met one group of sharing of load of the condition of convergenceFurther try to achieve the 4th reduction ratio of each frame now Breadth coefficient
The correction factor of each frame is finally determined, is usedIt is rightAmplitude limit amendment is carried out, it is final after relative processing { ε is distributed to one group of reduction ratioi, then can obtain final sharing of load according to first frame inlet thickness and last rack outlet THICKNESS CALCULATION {hi}。
The same of the required power of fine-rolling strip steel, power-balance and good profile can be being taken into account using the method for the invention When, overcome artificial experience method and traditional intelligence method deficiency, reach and improve fine-rolling strip steel Strip Shape Control precision and rolling surely Qualitatively purpose.It can be widely used for production process control and the fine-rolling strip steel control of product quality field of band steel of hot strip mill.
As shown in figure 1, the realization device of Hot Strip Rolling load distribution method provided by the invention, including roll-force distribution Coefficients calculation block 141, initial load distribution computing module 142 and roll-force distribution model on-line control module 143.Its In：
The roll-force distribution coefficient computing module 141 is to be adopted by Hybrid Particle Swarm Optimization model 112 using system What the primary data 111 of collection optimized to multi-goal optimizing function 102 obtains one group of roll-force distribution coefficient 113；Described The influence factor 101 of multi-goal optimizing function 102 is to influence the external factor of hot-tandem unit sharing of load, including steel grade The parameter such as (chemical composition), physical dimension (thickness) and supplied materials temperature, constraints include production equipment constraint, production technology about Beam and the constraint of produced on-site experience.Collection in worksite of the described primary data 111 including bending roller force, roll gap, temperature etc. Slab data and equipment relevant parameter.
The initial load distribution computing module 142 is that input data 121 is carried out via experience rolling schedule model 122 Processing, obtains sharing of load initial value 123；Described experience rolling schedule model 122 uses standard reduction ratio distribution method and energy The Optimized model that consumption curve distribution method is combined；The input data 121 is standard reduction ratio distribution method and energy consumption curve distribution Data needed for method, including workpiece thickness, finish rolling end rack outlet thickness, Energy dissipation equation coefficients etc.；Sharing of load is initial Value 123 is the initial value of each frame pressure value calculated to roll-force allocation model.
The roll-force distribution model on-line control module 143 is using at the beginning of sharing of load as roll-force distribution model 131 Two groups of initial data of initial value and roll-force distribution coefficient are iterated calculating, and carry out amplitude limit amendment to iterative calculation result 132 so that it is determined that allocation result 133.Described allocation result 133 is to determine the final drafts of each frame of Hot Strip Rolling.It is described Roll-force distribution model 131 using equilibrium iteration method be iterated calculate obtained meet roll-force distribution coefficient constraint pressure Lower rate breadth coefficient.The amplitude limit amendment 132 is to reduce each frame pressure when the error of roll-force distribution model 131 is larger The fluctuation of rate to rolling force mode allocation result, it is necessary to carry out amplitude limiting processing.
It is to be used to realize that the present invention and embodiment, the scope of the present invention should not necessarily be limited by this description to be described above, this Field it is to be understood by the skilled artisans that do not departing from any modification or partial replacement of the scope of the present invention, belong to the present invention Claim is come the scope that limits.

Claims (7)

  1. A kind of 1. new Hot Strip Rolling load distribution method, it is characterised in that：Comprise the following steps,
    Step S201：By gathering each parameter of mm finishing mill unit, founding mathematical models obtain multiple single-goal functions, and synthesis is multiple Single-goal function, multi-goal optimizing function is obtained, determine that optimized algorithm is hybrid particle swarm according to multi-goal optimizing function characteristic Optimized algorithm；
    Step S202：Hybrid Particle Swarm Optimization model is established, collection primary data optimizes to multi-goal optimizing function Obtain optimal solution, i.e. roll-force distribution coefficient；
    Step S203：Experience rolling schedule model is established, using standard reduction ratio distribution method+energy consumption curve distribution method to initial number According to obtaining sharing of load initial value after being handled；
    Step S204：Roll-force distribution model is established, and the roll-force obtained to sharing of load initial value according to step S202 is divided Distribution coefficient is iterated using equilibrium iteration method is calculated one group of sharing of load value and reduction ratio breadth coefficient；
    Step S205：Amplitude limit amendment is carried out to reduction ratio breadth coefficient obtained by step S204, allocation result is finally given, so as to complete The on line real time control of the belt steel rolling thickness of each frame of paired finishing mill.
  2. A kind of 2. new Hot Strip Rolling load distribution method according to claim 1, it is characterised in that：Described finish rolling Each parameter of unit includes steel grade, thickness and temperature, the proportional object function of described mathematical modeling selection power, energy consumption minimum mesh Scalar functions and plate shape well-targeted function.
  3. A kind of 3. new Hot Strip Rolling load distribution method according to claim 1, it is characterised in that：Step S201 roots The multi-goal optimizing function of finish rolling sharing of load is established according to following methods：
    Roll-force proportional object function, energy consumption minimum target function and plate shape well-targeted function are chosen as finish rolling load point Object function with optimization problem；
    The proportional object function J of described roll-force1Stated using following expression formula：
    <mrow> <msub> <mi>J</mi> <mn>1</mn> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>N</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>-</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow>
    Described required power minimum target function J2Stated using following expression formula：
    <mrow> <msub> <mi>J</mi> <mn>2</mn> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>N</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>h</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>h</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow>
    Wherein NiFor the rolling power of the i-th frame；hiIt is frame number for the i-th rack outlet thickness n；
    Described belt plate shape well-targeted function J3Stated using following expression formula：
    <mrow> <msub> <mi>J</mi> <mn>3</mn> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mi>n</mi> <mo>-</mo> <mn>3</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <msub> <mi>a</mi> <mn>1</mn> </msub> <msub> <mi>P</mi> <mn>2</mn> </msub> </mrow> <msub> <mi>P</mi> <mn>1</mn> </msub> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <msub> <mi>a</mi> <mn>2</mn> </msub> <msub> <mi>P</mi> <mn>3</mn> </msub> </mrow> <msub> <mi>P</mi> <mn>2</mn> </msub> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <msub> <mi>CR</mi> <mi>i</mi> </msub> </mrow> <msub> <mi>h</mi> <mi>i</mi> </msub> </mfrac> <mo>/</mo> <mfrac> <mrow> <msub> <mi>CR</mi> <mi>n</mi> </msub> </mrow> <msub> <mi>h</mi> <mi>n</mi> </msub> </mfrac> <mo>+</mo> <msub> <mi>&amp;Delta;</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow>
    Wherein P1、P2、P3Respectively first, second, third frame roll-force, CRiFor the outlet convexity of the i-th frame, ΔiFor optimization Regulated quantity, a1、a2For proportionality coefficient, n is rolling mills frame number, and i is frame label and 1≤i≤n；
    The multi-goal optimizing function that belt restraining is established by above three object function is as follows：
    <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mi>min</mi> <mi> </mi> <msub> <mi>J</mi> <mn>1</mn> </msub> <mo>=</mo> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>N</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>-</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>min</mi> <mi> </mi> <msub> <mi>J</mi> <mn>2</mn> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <msub> <mi>N</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>h</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>h</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>min</mi> <mi> </mi> <msub> <mi>J</mi> <mn>3</mn> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mi>n</mi> <mo>-</mo> <mn>3</mn> </mrow> <mi>n</mi> </munderover> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <msub> <mi>a</mi> <mn>1</mn> </msub> <msub> <mi>P</mi> <mn>2</mn> </msub> </mrow> <msub> <mi>P</mi> <mn>1</mn> </msub> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <msub> <mi>a</mi> <mn>2</mn> </msub> <msub> <mi>P</mi> <mn>3</mn> </msub> </mrow> <msub> <mi>P</mi> <mn>2</mn> </msub> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <msub> <mi>CR</mi> <mi>i</mi> </msub> </mrow> <msub> <mi>h</mi> <mi>i</mi> </msub> </mfrac> <mo>/</mo> <mfrac> <mrow> <msub> <mi>CR</mi> <mi>n</mi> </msub> </mrow> <msub> <mi>h</mi> <mi>n</mi> </msub> </mfrac> <mo>+</mo> <msub> <mi>&amp;Delta;</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced>
    Constraints is：
    <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msub> <mi>P</mi> <mi>i</mi> </msub> <mo>&amp;le;</mo> <msub> <mi>P</mi> <mi>m</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msub> <mi>I</mi> <mi>i</mi> </msub> <mo>&amp;le;</mo> <msub> <mi>I</mi> <mi>m</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>h</mi> <mi>n</mi> </msub> <mo>&lt;</mo> <msub> <mi>h</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>&lt;</mo> <msub> <mi>h</mi> <mi>i</mi> </msub> <mo>&lt;</mo> <msub> <mi>h</mi> <mn>0</mn> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced>
    Wherein PiIt is the roll-force of the i-th frame, PmFor maximum rolling force；IiTo flow through the electric current of i-th of frame, ImFor maximum electricity Stream, hnFor product objective thickness, and the exit thickness of the n-th frame, h0For product original depth.
  4. A kind of 4. new Hot Strip Rolling load distribution method according to claim 1, it is characterised in that：In step S203 The determination of described sharing of load initial value, content are as follows：
    The empirical equation of energy consumption curve distribution method：
    In formula：h0For product original depth；anFor the total energy consumption of n frame；To add up Energy dissipation coefficient；K1, K2For energy consumption Equation coefficients；Thus empirical equation draws one group of sharing of load value { hi, i=1,2 ... n, and the first reduction ratio breadth coefficient
    One group of second reduction ratio breadth coefficient is also obtained according to standard reduction ratio distribution methodUtilizeIt is rightCarry out amplitude limiting processing, And the 3rd final reduction ratio breadth coefficient is obtained by relative processingEach rack outlet thickness value is obtained, so that it is determined that Each frame sharing of load initial value.
  5. A kind of 5. new Hot Strip Rolling load distribution method according to claim 1, it is characterised in that：In step S202 Hybrid Particle Swarm Optimization model is established, calculating is optimized to the multi-goal optimizing function of belt restraining using the model, is produced Raw one group of extensive optimal solution that is evenly distributed and spreads along Pareto forward positions, described Hybrid particle swarm optimization calculate include with Lower step：
    Step S301：Given population scale N, speed and the position of primary are randomly selected as defined in the range of, produced initial Colony pop (t), t=0, Pareto optimal solutions in initial population are found out, be deposited into external memory storage and form Noninferior Solution Set I；
    Step S302：The desired positions that single particle undergoes are designated as Pbest (t) and are set to current location, are selected whole The desired positions Gbest (t) that individual colony is undergone, for each particle, Gbest (t) randomly selects from Noninferior Solution Set I, For each particle in colony, the speed of more new particle and position, new colony is obtained
    Step S303：For new colonyIn each particle, according to setting probability made a variation twice, order change Population after different is pop (t+1) so that the position of all particles makes t=t+1 all as defined in the range of in pop (t+1)；
    Step S304：The Noninferior Solution Set I in external memory storage is updated with pop (t), if the particle number in Noninferior Solution Set I exceedes During given scale, the crowding of each particle is calculated, retains the larger particle of crowding；
    Step S305：To all particles in pop (t), according to the individual extreme value of each particle of comparison criterion renewal；
    Step S306：Judge whether the maximum iteration of optimized algorithm meets, if meeting to turn to step S307, otherwise turn to step Rapid S302；
    Step S307：Pareto optimal solution of all particles in Noninferior Solution Set I as problem after output renewal, optimized algorithm Stop.
  6. A kind of 6. new Hot Strip Rolling load distribution method according to claim 1, it is characterised in that：In step S204 Roll-force distribution model carries out sharing of load calculating, using the sharing of load initial value obtained, establishes equation and is iterated calculating, One group of reduction ratio of the condition of convergence is met, is the process in line computation, specifically includes following steps：
    Step S501：Selecting step S202 obtains sharing of load coefficient, for rolling force mode sharing of load in line computation；
    Step S502：According to energy consumption curve distribution method and standard reduction ratio distribution method with reference at the beginning of determining each rack outlet thickness of finish rolling Initial value；
    Step S503：Equilibrium iteration method iterative formula is determined according to S501 and S502, and is iterated；
    Step S504：Calculate roll-force correction value；
    Step S505：Calculate each frame reduction ratio of mm finishing mill unit and exit thickness updated value；
    Step S506：Judge whether each frame roll-force ratio meets the condition of convergence, step S503 is gone to if being unsatisfactory for, if full It is sufficient then export final result.
  7. 7. it is a kind of realize described in Hot Strip Rolling load distribution method Load Distribution System, it is characterised in that：Described is negative Lotus distribution system includes roll-force distribution coefficient computing module, initial load distribution computing module and roll-force distribution model and existed Line traffic control module；Roll-force distribution coefficient computing module is using Hybrid Particle Swarm Optimization collection primary data, to multiple target Majorized function optimizes, and obtains one group of roll-force distribution coefficient；Initial load distribution computing module uses experience sharing of load Model is handled the primary data of input, obtains sharing of load initial value；Roll-force distribution model on-line control module pair Two groups of initial data of sharing of load initial value and roll-force distribution coefficient are iterated calculating, and carry out amplitude limit amendment to result So that it is determined that allocation result.
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