CN105525248A - Method for plating thickness feedforward optimal control of galvanization production line - Google Patents

Method for plating thickness feedforward optimal control of galvanization production line Download PDF

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CN105525248A
CN105525248A CN201510853736.8A CN201510853736A CN105525248A CN 105525248 A CN105525248 A CN 105525248A CN 201510853736 A CN201510853736 A CN 201510853736A CN 105525248 A CN105525248 A CN 105525248A
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air knife
thickness
coating
feedforward
value
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CN105525248B (en
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王绍亮
陈鹏
周玄昊
潘再生
施一明
王天林
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ZHEJIANG SUPCON RESEARCH Co Ltd
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ZHEJIANG SUPCON RESEARCH Co Ltd
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    • C—CHEMISTRY; METALLURGY
    • C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C2/00—Hot-dipping or immersion processes for applying the coating material in the molten state without affecting the shape; Apparatus therefor
    • C23C2/14—Removing excess of molten coatings; Controlling or regulating the coating thickness
    • C23C2/16—Removing excess of molten coatings; Controlling or regulating the coating thickness using fluids under pressure, e.g. air knives
    • C23C2/18—Removing excess of molten coatings from elongated material
    • C23C2/20—Strips; Plates

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  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Engineering & Computer Science (AREA)
  • Materials Engineering (AREA)
  • Mechanical Engineering (AREA)
  • Metallurgy (AREA)
  • Organic Chemistry (AREA)
  • Coating With Molten Metal (AREA)

Abstract

The invention discloses a method for plating thickness feedforward optimal control of a galvanization production line. A plating thickness set value presetting module is adopted to smoothly carry out switching without producing off-grade goods rejects; an air knife height feedforward setting module is adopted to avoid the accident of galvanizing zinc splashing and ensure the transverse uniformity of strip steel; a plating thickness neural network prediction module is adopted to accurately predict the plating thickness so as to provide basis for adjusting control variable; a control variable multi-target optimizing module is adopted for carrying out iterative search on the optimal air knife distance and air knife pressure setting in current working conditions; a distance distribution module is adopted to overcome the defect of inconsistent plating thickness of upper and lower surfaces of the strip steel caused by errors of a center line of the strip belt and abrasion of a stabilizing roll and a tower top roll. The method provided by the invention solves the control difficulties that plating thickness deviation is over large and plating thickness detection is hysteretic due to product specification switching and strip steel speed change, and can realize automatic presetting control on the plating thickness and effectively reduce plating quality fluctuation.

Description

A kind of galvanization production line thickness of coating feedforward optimizing and controlling method
Technical field
The invention belongs to commercial run Optimized-control Technique field, automatic control technology is optimized in the feedforward particularly relating to cold rolling continuous galvanizing process air knife distance and air pressure.
Background technology
Zinc-plated: to refer to pot galvanize here, be also galvanizing and galvanizing, be a kind of effective anti-corrosion of metal mode, be mainly used on the metal construction facility of every profession and trade.Pot galvanize is immersed in the zinc liquid melted rust cleaning steel part after annealing process, makes steel beam column surface attachment zinc layers, thus play rot-resistant object.
" little air knife, little air pressure " principle: in galvanizing production process, air knife is apart from excessive, easily cause the gas significant divergence that air knife sprays, thus affect coating surface homogeneity transversely, and air knife is apart from time constant, air pressure is larger, scattering degree is corresponding aggravation also, on the contrary, when the value of air knife Distance geometry air pressure is all less, jet flow stream scattering degree is little, also little on the impact of coating surface horizontal homogeneity, and Here it is controls " little air knife, little air pressure " principle of coating surface horizontal homogeneity.
Galvanizing production is widely used in building, household electrical appliances, automobile and other industries because of its good corrosion resistance nature, the important technology index weighing galvanizing production quality comprises thickness of coating and coating uniformity, namely the thickness of coating of galvanizing production not only will meet specification requirements, and will ensure that coating surface is smooth.The main processing parameter of galvanizing process comprises strip speed, air knife height, air knife Distance geometry air pressure, wherein air knife height is determined by strip speed, under normal circumstances, belt steel surface institute is zinc-plated is liquid, air knife height can not have an impact to zinc is thick, is cause spray nozzle clogging in order to zinc liquid when preventing that belt speed is too high, cutter presses through large splashes to the adjustment of knife up.The principal element affecting thickness of coating and surface uniformity is strip speed, air knife Distance geometry air pressure.Strip speed, by the impact of annealing furnace throughput, belt steel thickness and heating cycle, cannot independently regulate, as surveying and uncontrollable variable.Therefore, adopt in usually producing and thickness of coating and the most direct air knife Distance geometry air pressure of uniformity coefficient impact are controlled production process.
Zinc-plated be one typical time become the production process of large time delay, non-linear, strong disturbance.Owing to lacking the control techniques means effectively overcoming above-mentioned difficult point, when coating becomes specification or strip speed generation larger change, thickness of coating and coating uniformity control to be the difficult problem that the zinc-plated production product quality of puzzlement improves always, the experience that current most domestic iron and steel enterprise depends on operative employee adopts manual hand manipulation to control in conjunction with the mode of bottom loop PID, its control accuracy is low, product specification change is long for transit time, zinc consumption is large, quality fluctuation is large, what even can not ensure thickness of coating and coating surface homogeneity meets product quality indicator requirement, often there will be off-specification material.
Summary of the invention
The present invention proposes a kind of galvanization production line coating feedforward optimizing and controlling method, when can tackle thickness of coating switching or strip speed change fast, make rapidly the adjustment of the controlled variable such as air knife height, air knife distance, air pressure, realize the quick tracking of thickness of coating switching or the large saltus step of speed, improve the control accuracy of thickness of coating, improve the homogeneity of coating surface quality, reduce the quality fluctuation of galvanizing production, reduce the generation of substandard product.
In order to realize foregoing invention object, the invention provides a kind of galvanization production line coating feedforward optimizing and controlling method, in its each control cycle, system cloud gray model step comprises:
S101. judge whether a upper control cycle system is in feed forward control state according to the value of zone bit, if zone bit is false, then going up a control cycle is Feedback control, enters S102; Otherwise a upper control cycle is feed forward control state, enters S113;
S102. inquire about secondary machine, detect next coil volume thickness information, whether unanimously judge that current coil of strip and next coil roll up thickness of coating information, if unanimously, then enter S103, otherwise enter S109;
S103. read the speed S (t) of current coil of strip from first-level machine, if large saltus step appears in speed, then enter S104; Otherwise terminate this control cycle feed forward control to calculate;
S104. read current coil of strip thickness of coating set(ting)value Rcw (t) from first-level machine, in conjunction with the speed S (t) of current coil of strip, adopt thickness of coating neural network prediction model and multi-objective optimization algorithm to calculate air knife distance feedforward control amount D ffc(t), air pressure feedforward control amount P ffc(t); Obtain optimum cutter apart from feedforward control amount and optimum air pressure feedforward control amount, then enter S105;
S105. be true by mark position, the distance calculated value L_calc zero setting of t feedforward control action point after air knife, enters S107;
S106. with strip speed S (t) for search terms, from air knife height feedforward setting table inquiry air knife height feedforward set(ting)value H ffct (), enters S107;
S107. according to air knife distance allocation algorithm, air knife distance set(ting)value D before and after calculating ffc_ back (t) and D ffc_ front (t);
S108. air knife height feedforward control amount H is assigned ffc(t), air knife distance feedforward control amount D ffc(t), air pressure feedforward control amount P ffct (), to PLC control system, this control cycle calculates and terminates;
S109. read next coil volume thickness of coating set(ting)value Rcw_next from first-level machine, in conjunction with the speed S (t) of current coil of strip, adopt multi-objective optimization algorithm to calculate air knife distance feedforward control amount D ffc(t), air pressure feedforward control amount P ffct (), then enters S110;
S110. read the speed of current coil from first-level machine, from the machine-readable thickness of coating set(ting)value of taking off coil of secondary, if zinc coat thickness control target increases, then enter S111; If thickness of coating target reduces, then enter S112;
S111. read range information L (t) of weld seam and air knife position from first-level machine in real time, calculate Δ P=P ffc(t)-P (t), to table look-up the distance allowance L_set determining that coating set(ting)value is presetting according to the value of Δ P, if meet L (t) < L_set, then enter S105, otherwise, terminate this control cycle feed forward control and calculate;
S112. read range information L (t) of weld seam and air knife position from first-level machine in real time, when L (t)=0, enter S105, otherwise, terminate this control cycle feed forward control and calculate;
S113. according to the distance L of strip speed and air knife, thickness tester and adopt cycle T, judge whether feedforward action point reaches thickness tester, if arrival thickness tester, then enter S114; If do not arrive thickness tester, then enter S115;
S114. be false by zone bit mark position, enter feedback control and calculate;
If S115. speed has large saltus step, then enter S116; Otherwise, terminate this control cycle feed forward control and calculate;
S116. read current coil of strip thickness of coating set(ting)value Rcw (t) from first-level machine, in conjunction with the speed S (t) of current coil of strip, adopt multi-objective optimization algorithm to calculate air knife distance feedforward control amount D ffc(t), air pressure feedforward control amount P ffct (), then enters S106.
Further, in step S104, when carrying out thickness of coating neural network prediction, with strip speed, air knife distance, air pressure for input, thickness of coating is export the neural network prediction model set up, this model learns based on the historical data of galvanizing process, makes prediction online according to the sampled value of current strip speed, air knife distance, air pressure to thickness of coating.
Further, in step S104, when carrying out multi-objective optimization algorithm, with the deviation between thickness of coating and set(ting)value for input, air knife Distance geometry air pressure is for exporting, adopt thickness of coating neural network prediction module to guarantee the control accuracy of thickness of coating, guarantee coating surface horizontal homogeneity according to " pocket knife distance, little air pressure " principle simultaneously, optimum air knife Distance geometry air pressure setting under the current strip speed of continuous iterative search.
Further, in step s 106, the principle that described air knife height feedforward setting table, according to zinc liquid, does not occur to splash and minimize in zinc liquid air the time of dispelling the heat is formulated.
Further, in step s 107, described air knife distance allocation algorithm is according to air knife distance before and after the change of air knife distance, belt steel thickness change calculations, described air knife distance distribution module only just works when change of lap, first need to detect spot welds information in real time, when spot welds crossed by air knife, judge whether that carrying out front and back air knife distance distributes: when not changing specification, only need on existing air knife distance basis, evenly distribute air knife distance variable quantity; When changing specification, first consider belt steel thickness compensating for variations, when belt steel thickness changes, the corresponding compensation thickness change of rear air knife, then, after total air knife distance variable quantity deducts compensation rate, evenly distribute air knife distance variable quantity.
Further, in step s 110, when thickness of coating is large from little change, when spot welds is through desired location, to air knife height, air knife distance, air pressure need correspondingly adjust, and namely starts the thickness of coating set(ting)value of being rolled up by next coil in advance zinc coat thickness control target as the current end of reel; When thickness of coating is from when diminishing greatly, then cross spot welds and carry out again making corresponding adjustment to air knife height, air knife Distance geometry air pressure.
Further, in step s 103, if meet | S (t)-S (t-1) | > S θthen enter S104; If do not meet, then terminate this control cycle feed forward control and calculate;
In step sl 15, if meet | S (t)-S (t-1) | > S θthen enter S116; If do not meet, then terminate this control cycle feed forward control and calculate.
Further, in step S104, the computation process of described multi-objective optimization algorithm is as follows:
Objective function: absolute value min|CWp-Rcw (t) of the difference of the thickness of coating set(ting)value of coating weight neural network prediction value and t feedforward controller |, the minimum value minP of t air pressure feedforward set(ting)value ffc(t), the minimum value minD of t air knife distance feedforward set(ting)value ffc(t)
Decision variable: P ffc(t), D ffc(t)
Constraint condition:
D ffc(t)∈[Dmin,Dmax](2)
P ffc(t)∈[Pmin,Pmax](3)
NNp(D ffc(t),S(t),P ffc(t))＝CWp(4)
Wherein, formula (2), (3) represent the technological process constraint of two operational variables, and formula (4) represents the strip speed of neural network prediction module reaction, air knife distance, mapping relations between air pressure and thickness of coating.
Further, in step s 110, if meet t last volume coil of strip thickness of coating set(ting)value to be greater than t current coil of strip thickness of coating set(ting)value, namely formula Rcw_next>Rcw_now, then enter S111; Otherwise, then S112 is entered.
Further, in step S113, make L_calc=L_calc+S (t) * T, if the distance calculated value of t feedforward control action point after air knife is more than or equal to the distance measure between air knife and thickness tester, namely formula L_calc >=L_measure, then enter S114; If do not meet, then enter S115.
The Advantageous Effects of technique scheme is as follows:
(1) when product specification switches or strip speed changes, the present invention adopts nerual network technique Modling model, reflect the nonlinear mapping relation between each controling parameters of full working zone and thickness of coating exactly, substantially increase the control accuracy of thickness of coating.
(2) the present invention adopts controlled variable multi-objective optimization algorithm, can when coating specification switches or strip speed changes, according to thickness of coating set(ting)value and strip speed, when ensureing zinc coat thickness control precision, ensure coating surface horizontal homogeneity to greatest extent, provide air knife Distance geometry air pressure set(ting)value fast, overcome the large dead time of system, non-linear and strong jamming, compensating disturbance is on the impact of thickness of coating, decrease the transit time of system, improve the quality of thickness of coating and coating horizontal homogeneity simultaneously.
(3) the present invention adopts air knife distance apportioning method in feed forward control, and the impact that the displacement effectively between pre-compensation band steel medullary ray and air knife medullary ray brings, ensure that the homogeneity with steel upper and lower surface thickness of coating in feed forward control.
In sum, adopt Controlling System of the present invention, can when product specification switches and strip speed changes, effectively overcome production process time become large dead time, non-linear and strong jamming, overcome topworks can not fast tracking fixed valure and band steel medullary ray and air knife medullary ray be subjected to displacement brought impact, ensure the surface quality of coil of strip coating and the homogeneity of band steel upper and lower surface coating, realize production status and switch fast.
Accompanying drawing explanation
Fig. 1 is the system architecture diagram that the embodiment of the present invention adopts a kind of galvanization production line thickness of coating feedforward optimizing and controlling method;
Fig. 2 is the feed forward control operational flow diagram of the embodiment of the present invention;
Fig. 3 controls design sketch when being the thickness of coating set(ting)value change of the embodiment of the present invention;
Fig. 4 controls design sketch when being the line speed change of the embodiment of the present invention.
Embodiment
In order to make those skilled in the art can understand feature of the present invention and technology contents further, refer to following detailed description for the present invention and accompanying drawing.
Below in conjunction with accompanying drawing, the specific embodiment of the invention is further described.
The meaning of each symbol of the present invention sees table:
As shown in Figure 1, this Controlling System is primarily of five part compositions such as thickness of coating neural network prediction module, controlled variable multi-objective optimization module, the presetting module of thickness of coating set(ting)value, air knife height feedforward setting module, air knife distance distribution module.
In figure, I represents thickness of coating neural network prediction module, be one with strip speed, air knife distance, air pressure for input, thickness of coating is export the neural network prediction model set up, this model learns based on the historical data of galvanizing process, accurate prediction can be made to thickness of coating online according to the detected value of current operation operating mode (strip speed S (t), air knife distance D (t), air pressure P (t)), the nonlinear mapping relation of this neural network is designated as NNp (*), and its expression formula is as follows:
NNp(D(t),S(t),P(t))＝CWp(t)
This module is the basis that controlled variable multi-objective optimization module is run.
In figure, II represents controlled variable multi-objective optimization module, this module to reduce the deviation between thickness of coating actual value and set(ting)value, little air knife and little air pressure for optimization aim, optimum air knife Distance geometry air pressure set(ting)value under iterative search current working (strip speed, set(ting)value).Specifically, what this module solved is a multi-objective optimization problem, its objective function comprises deviation, air knife distance value, air pressure value three between thickness of coating and set(ting)value, and decision variable is air knife distance feedforward set(ting)value and air pressure feedforward set(ting)value, constraint condition is strip speed, air knife distance, the mapping relations between air pressure and thickness of coating of the reaction of neural network prediction module, and air pressure, air knife retrain apart from the technological process of two operational variables.By solving above-mentioned multi-objective optimization problem, obtain optimum air knife distance feedforward set(ting)value D ffc_best(t), air pressure feedforward set(ting)value P ffc_bestt (), makes zinc coat thickness control deviation minimum, takes into account coating surface horizontal homogeneity simultaneously.
In figure, III represents thickness of coating set(ting)value coiling temperature setup, because feed forward control is to air pressure, air knife height, the adjustment process of air knife distance all needs the time, especially air pressure regulates is a slow process, it is adjusted to the right place needs the longer time, therefore, for the quality of volume next when guaranteeing that thickness of coating set(ting)value changes meets the demands, need this thickness of coating set(ting)value coiling temperature setup module determination air knife height, air knife distance, the feedforward such as air pressure set(ting)value assigns the opportunity to PLC, this presetting mechanism starts when the information of next coil of strip being detected (now weld seam not yet crosses air knife position).Wherein, when thickness of coating is from (Rcw_now>Rcw_next) when diminishing greatly, after the weld seam between current volume and next volume crosses air knife position, assign air knife height, air knife distance, air pressure feedforward set(ting)value; When thickness of coating set(ting)value is large from little change (Rcw_now<Rcw_next), for guaranteeing that the thickness of coating of next steel coil head is not less than its set(ting)value, must need correspondingly adjust air knife height, air knife distance, air pressure in advance.Specifically, the distance allowance L_set of the presetting needs of size determination coating set(ting)value of the air pressure amplitude of accommodation that this module calculates according to controlled variable multi-objective optimization module, as distance L (t) the < L_set of weld seam and air knife position, assign the feedforward set amount of air knife height, air knife distance, air pressure immediately; Otherwise, illustrate that welding seam distance air knife position is still far away, do not need to assign feedforward set amount immediately.
In figure, IV represents air knife height feedforward setting module, air knife height feedforward setting module is to be with steel horizontal homogeneity for control objectives, under there is not the prerequisite of splashing accident in guarantee zinc liquid, reduce zinc liquid aerial heat radiation time before entering air blowing link as far as possible, improve zinc liquid temperature, particularly steel edge portion zinc liquid temperature, to reduce zinc fluid viscosity, reduce edge zinc liquid adhesion amount, improve band steel horizontal homogeneity.Specifically, there is not based on zinc liquid the principle splashing and minimize in zinc liquid air the time of dispelling the heat in this module, formulates air knife height feedforward setting table; When needs carry out feedforward setting to air knife height, this module with strip speed S (t) and thickness of coating set(ting)value for search terms, from air knife height feedforward setting table inquiry air knife height feedforward set(ting)value H ffc(t).The air knife height feedforward setting table of current application is as shown in the table:
In figure, V represents air knife distance distribution module, and this module mainly solves because band steel medullary ray itself exists the inconsistent problem of band steel upper and lower surface thickness of coating that the factors such as the wearing and tearing of error, stabilizing roller and tower top roller, front and back coil of strip thickness change cause.Specifically, this module is according to observed value D_back (t), the D_front (t) of current front and back air knife distance, and variable quantity D_delta (t) of the total distance of air knife and belt steel thickness variable quantity TH_delta (t), calculate front and back air knife distance and to feedover set(ting)value D ffc_ back (t), D ffc_ front (t).
Based on above-mentioned five modules, in each control cycle, system cloud gray model step is as follows:
For without loss of generality, make current control cycle be T, the allowance length of the presetting distance of coating set(ting)value is L_set, and air knife is L to the distance of thickness tester.
S1. read the speed of current coil from first-level machine, from the machine-readable relevant information of taking off coil of secondary, comprise belt steel thickness, thickness of coating set(ting)value, judge the change of thickness of coating, if zinc coat thickness control target increases, then enter S2; If zinc coat thickness control target reduces, then enter S3; If large saltus step appears in speed, then enter S4, thickness of coating set(ting)value is current coil of strip zinc coat thickness control target.
S2. read spot welds information in real time from first-level machine, as spot welds distance air knife L_set, open coiling temperature setup, namely thickness of coating set(ting)value is set to the zinc coat thickness control target of lower coil, then enters S4.
S3. read spot welds information in real time from first-level machine, when spot welds crosses air knife, open coiling temperature setup, namely system zinc coat thickness control target value is set to the coil of strip thickness of coating set(ting)value being about to enter zinc coating operations, then enters S4.
S4. according to strip speed, by air knife height feedforward table inquiry air knife height feedforward set(ting)value.
S5. by current air knife distance, air pressure is as initial solution, and assign it to the contemporary optimum solution of controlled variable multi-objective optimization module, strip speed, air knife distance, air pressure input thickness of coating neural network prediction module are obtained thickness of coating predictor, this value and set(ting)value are compared calculation control deviation, using controlled deviation square, air pressure, air knife distance as three dimensional attribute of multiple-objection optimization, build Model for Multi-Objective Optimization.
The Model for Multi-Objective Optimization of artificial intelligence approach to above-mentioned structure is adopted to be optimized, concrete steps are: carry out cross and variation to contemporary optimum solution, obtain new alternative solution, alternative solution input thickness of coating neural network prediction module is obtained thickness of coating predictor, this value and set(ting)value are compared calculation control deviation, and the contemporary optimum solution of alternative solution and controlled variable multi-objective optimization module is compared, if in alternative solution any dimension property value (controlled deviation square, air pressure, air knife is apart from one of them) larger than contemporary optimum solution, then contemporary optimum solution remains unchanged, if have at least in alternative solution, the property value of a dimension (controlled deviation square, air pressure, air knife be apart from one of them) is less than contemporary optimum solution and other dimensions are not more than contemporary optimum solution, then think that alternative solution is better than contemporary optimum solution, the value of alternative solution is assigned to contemporary optimum solution, repeat S5 step, until meet predetermined optimization end condition.
S6. according to air knife distance allocation algorithm, air knife distance set(ting)value before and after calculating.
S7. according to the distance L of strip speed and air knife, thickness tester and adopt cycle T, judge whether the point of feedforward action reaches thickness tester, arrive at this point of application and be called the feed forward control time during this period of time before thickness tester, during feed forward control, judge spot welds real-time information, if spot welds crosses air knife, then according to air knife distance allocation algorithm; Rear air knife set(ting)value compensates respective belt steel amounts of thickness variation, if speed changes, then air pressure respective change carrys out compensation speed variable quantity, if all do not change during this period, then lock air knife height value, air knife distance value, air pressure value, until the feedforward time.
Based on above-mentioned feedforward control system framework, in each control cycle, as shown in Figure 2, in figure, details are as follows for each step for operational flow diagram of the present invention:
S101. judge whether a upper control cycle system is in feed forward control state according to the value of Flag_ffc, if Flag_ffc=false, then going up a control cycle is Feedback control, enters S102,
Otherwise a upper control cycle is feed forward control state, enters S113;
S102. inquire about first-level machine, detect next coil volume thickness information, whether unanimously judge that current coil of strip and next coil roll up thickness of coating information, if unanimously, then enter S103, otherwise enter S109;
S103. read the speed S (t) of current coil of strip from first-level machine, judge whether inequality meets below
|S(t)-S(t-1)|＞S θ(1)
If met, then enter S104; If do not met, then terminate this control cycle feed forward control and calculate.
S104. read current coil of strip thickness of coating set(ting)value Rcw (t) from first-level machine, in conjunction with the speed S (t) of current coil of strip, adopt multi-objective optimization algorithm to calculate air knife distance feedforward control amount D ffc(t), air pressure feedforward control amount P ffc(t).This multi-objective optimization process specifically describes as follows:
Objective function: min|CWp-Rcw (t) |, minP ffc(t), minD ffc(t)
Decision variable: P ffc(t), D ffc(t)
Constraint condition:
D ffc(t)∈[Dmin,Dmax](2)
P ffc(t)∈[Pmin,Pmax](3)
NNp(D ffc(t),S(t),P ffc(t))＝CWp(4)
Wherein, formula (2), (3) represent the technological process constraint of two operational variables, and formula (4) represents the strip speed of neural network prediction module reaction, air knife distance, mapping relations between air pressure and thickness of coating.Above-mentioned multi-objective optimization question can adopt PSO algorithm, genetic algorithm, simulated annealing, ant colony optimization for solving, can obtain optimum cutter apart from feedforward control amount D ffc_best(t), air pressure feedforward control amount P ffc_bestt (), makes D ffc(t)=D ffc_best(t), P ffc(t)=P ffc_bestt () then enters S105.
S105. zone bit Flag_ffc is set to true.L_calc zero setting, enters S107.
S106. with strip speed S (t) for search terms, from air knife height feedforward setting table inquiry air knife height feedforward set(ting)value H ffc(t).Enter S106.
S107. according to air knife distance allocation algorithm, air knife distance set(ting)value D before and after calculating ffc_ back (t) and D ffc_ front (t).First, read as front and back air knife distance D_back (t), front air knife distance D_front (t), current coil of strip belt steel thickness TH_now and last volume coil of strip belt steel thickness TH_next from first-level machine.Then, the total distance D of front and back air knife that rear air knife distance D_back (t) and front air knife distance D_front (t) sum and optimized algorithm are calculated ffct () is made comparisons, calculate to obtain air knife distance variable quantity D_delta (t), that is:
D_delta(t)＝D ffc(t)-(D_back(t)+D_front(t))(5)
Air knife distance variable quantity D_delta (t) is evenly distributed to front and back air knife, then can be regarded as front and back air knife distance is:
{ D f f c _ b a c k ( t ) = D _ d e l t a ( t ) / 2 + D _ b a c k ( t ) D f f c _ f r o n t ( t ) = D _ d e l t a ( t ) / 2 + D _ f r o n t ( t ) - - - ( 6 )
When range information L (t) of weld seam and air knife position meets formula (13), then need to carry out corresponding compensation to rear air knife distance to belt steel thickness change.According to current coil of strip belt steel thickness TH_now and last volume coil of strip belt steel thickness TH_next, calculate to obtain belt steel thickness variable quantity TH_delta (t), that is:
TH_delta(t)＝TH_next(t)-TH_now(t)(7)
Belt steel thickness is compensated to rear air knife distance, then can be regarded as rear air knife distance D_back (t) is:
D ffc_back(t)＝D ffc_back(t)+TH_delta(t)(8)
S108. air knife height feedforward control amount H is assigned ffc(t), air knife distance feedforward control amount D ffc(t), air pressure feedforward control amount P ffct () is to PLC control system.This control cycle calculates and terminates.
S109. read next coil volume thickness of coating set(ting)value Rcw_next from first-level machine, in conjunction with the speed S (t) of current coil of strip, adopt multi-objective optimization algorithm to calculate air knife distance feedforward control amount D ffc(t), air pressure feedforward control amount P ffc(t).This computation process is identical with S104, repeats no more here.Enter S110.
S110. read current coil of strip thickness of coating set(ting)value Rcw_now from first-level machine, and judge the relation between Rcw_now and Rcw_next:
Rcw_next＞Rcw_now(9)
Rcw_next≤Rcw_now(10)
If the relation between Rcw_now and Rcw_next meets formula (9), then enter S111;
If the relation between Rcw_now and Rcw_next meets formula (10), then enter S112;
S111. read range information L (t) of weld seam and air knife position from first-level machine in real time, calculate
ΔP＝P ffc(t)-P(t)(11)
To table look-up the distance allowance L_set determining that coating set(ting)value is presetting according to the value of Δ P, then following formula judged:
L(t)＜L_set(12)
If meet formula (5), then enter S105, otherwise, terminate this control cycle feed forward control and calculate.
S112. range information L (t) of weld seam and air knife position is read in real time from first-level machine, when
L(t)＝0(13)
Time, enter S105, otherwise, terminate this control cycle feed forward control and calculate.
S113. according to strip speed S (t) and control cycle T, accumulation calculating feed forward control assigns the distance implemented rear coil of strip and moved.
L_calc＝L_calc+S(t)*T(14)
Following formula is judged:
L_calc≥L_measure(15)
If meet (15) formula, then enter S114, otherwise, enter S115.
S114. zone bit Flag_ffc is set to false, enters feedback control and calculate.
S115. judge whether speed has large saltus step, namely judge whether inequality meets below
|S(t)-S(t-1)|＞S θ(16)
If meet formula (16), then enter S116; If do not met, then terminate this control cycle feed forward control and calculate.
S116. read current coil of strip thickness of coating set(ting)value Rcw (t) from first-level machine, in conjunction with the speed S (t) of current coil of strip, adopt multi-objective optimization algorithm to calculate air knife distance feedforward control amount D ffc(t), air pressure feedforward control amount P ffc(t).This computation process is identical with S104, repeats no more here.Then S106 is entered.
In addition, often at regular intervals, system online acquisition runs the new production data produced during this period of time, filters out sample, carries out continuation training, upgrade neural network to prediction neural network weight.Specifically, adopt back-propagation method to upgrade the weight of neural network, enable prediction neural network learn new work information, can the change of adaptive system feature automatically.
Below for zinc-plated actual production process, the beneficial effect acquired by the present invention is described:
List before adopting the feedforward control system of the inventive method design to put into operation in Fig. 3 and the effect comparison after putting into operation, production status during thickness of coating set point change switched fast.As we can see from the figure, thickness of coating set(ting)value is from 85g/m 2rise to 125g/m 2time, feedforward control system of the present invention, by regulating air knife Distance geometry air pressure value in advance, makes thickness of coating reach 125g/m when specification switches 2, meet manufacturing technique requirent, meanwhile, the present invention regulates also significantly shorten compared with Artificial Control settling time used.By comparison, Artificial Control cannot regulate when coating specification switches and put in place, causes 125g/m 2volume head thickness of coating is less than set(ting)value, and quality product is not up to standard.And when thickness of coating set(ting)value is about to from 125g/m 2drop to 85g/m 2time, feedforward control system of the present invention regulates air knife Distance geometry air pressure immediately when coating specification changes, and regulate reaction to upgrade rapidly than Artificial Control, the time of its transient process also significantly reduces than Artificial Control situation.This comparative illustration adopts the method for the invention, when thickness of coating set point change, precisely can regulate air knife Distance geometry air pressure value, reduces settling time, guarantee that the thickness of coating of next volume is up to standard.
To list in Fig. 4 before adopting the Controlling System of the inventive method design to put into operation and after putting into operation for the effect comparison can surveyed when uncontrolled variable (strip speed) changes.Can as seen from the figure, when no matter speed rises or declines, compared with Artificial Control, feedforward control system of the present invention can make the overshoot of zinc coat thickness control significantly reduce, and then reduces settling time, and production status immediate stability is got off.Illustrate and adopt the method for the invention, effectively the thickness of coating that causes of inhibiting band steel speed can fluctuate, improve the quality of products.
Above to invention has been exemplary description; obvious specific implementation of the present invention is not subject to the restrictions described above; as long as have employed method of the present invention design and the various improvement carried out of technical scheme, or directly apply to other occasion, all within protection scope of the present invention without improvement.

Claims (10)

1. a galvanization production line coating feedforward optimizing and controlling method, it is characterized in that, in each control cycle, system cloud gray model step comprises:
S101. judge whether a upper control cycle system is in feed forward control state according to the value of zone bit, if zone bit is false, then going up a control cycle is Feedback control, enters S102; Otherwise a upper control cycle is feed forward control state, enters S113;
S102. inquire about secondary machine, detect next coil volume thickness information, whether unanimously judge that current coil of strip and next coil roll up thickness of coating information, if unanimously, then enter S103, otherwise enter S109;
S103. read the speed S (t) of current coil of strip from first-level machine, if large saltus step appears in speed, then enter S104; Otherwise terminate this control cycle feed forward control to calculate;
S104. read current coil of strip thickness of coating set(ting)value Rcw (t) from first-level machine, in conjunction with the speed S (t) of current coil of strip, adopt thickness of coating neural network prediction model and multi-objective optimization algorithm to calculate air knife distance feedforward control amount D ffc(t), air pressure feedforward control amount P ffc(t); Obtain optimum cutter apart from feedforward control amount and optimum air pressure feedforward control amount, then enter S105;
S105. be true by mark position, the distance calculated value L_calc zero setting of t feedforward control action point after air knife, enters S107;
S106. with strip speed S (t) for search terms, from air knife height feedforward setting table inquiry air knife height feedforward set(ting)value H ffct (), enters S107;
S107. according to air knife distance allocation algorithm, air knife distance set(ting)value D before and after calculating ffc_ back (t) and D ffc_ front (t);
S108. air knife height feedforward control amount H is assigned ffc(t), air knife distance feedforward control amount D ffc(t), air pressure feedforward control amount P ffct (), to PLC control system, this control cycle calculates and terminates;
S109. read next coil volume thickness of coating set(ting)value Rcw_next from first-level machine, in conjunction with the speed S (t) of current coil of strip, adopt multi-objective optimization algorithm to calculate air knife distance feedforward control amount D ffc(t), air pressure feedforward control amount P ffct (), then enters S110;
S110. read the speed of current coil from first-level machine, from the machine-readable thickness of coating set(ting)value of taking off coil of secondary, if zinc coat thickness control target increases, then enter S111; If thickness of coating target reduces, then enter S112;
S111. read range information L (t) of weld seam and air knife position from first-level machine in real time, calculate Δ P=P ffc(t)-P (t), to table look-up the distance allowance L_set determining that coating set(ting)value is presetting according to the value of Δ P, if meet L (t) < L_set, then enter S105, otherwise, terminate this control cycle feed forward control and calculate;
S112. read range information L (t) of weld seam and air knife position from first-level machine in real time, when L (t)=0, enter S105, otherwise, terminate this control cycle feed forward control and calculate;
S113. according to the distance L of strip speed and air knife, thickness tester and adopt cycle T, judge whether feedforward action point reaches thickness tester, if arrival thickness tester, then enter S114; If do not arrive thickness tester, then enter S115;
S114. be false by zone bit mark position, enter feedback control and calculate;
If S115. speed has large saltus step, then enter S116; Otherwise, terminate this control cycle feed forward control and calculate;
S116. read current coil of strip thickness of coating set(ting)value Rcw (t) from first-level machine, in conjunction with the speed S (t) of current coil of strip, adopt multi-objective optimization algorithm to calculate air knife distance feedforward control amount D ffc(t), air pressure feedforward control amount P ffct (), then enters S106.
2. a kind of galvanization production line coating feedforward optimizing and controlling method as claimed in claim 1, is characterized in that:
In step S104, when carrying out thickness of coating neural network prediction, with strip speed, air knife distance, air pressure for input, thickness of coating is export the neural network prediction model set up, this model learns based on the historical data of galvanizing process, makes prediction online according to the sampled value of current strip speed, air knife distance, air pressure to thickness of coating.
3. a kind of galvanization production line coating feedforward optimizing and controlling method as claimed in claim 1, is characterized in that:
In step S104, when carrying out multi-objective optimization algorithm, with the deviation between thickness of coating and set(ting)value for input, air knife Distance geometry air pressure is for exporting, thickness of coating neural network prediction module is adopted to guarantee the control accuracy of thickness of coating, guarantee coating surface horizontal homogeneity according to " pocket knife distance, little air pressure " principle, optimum air knife Distance geometry air pressure setting under the current strip speed of continuous iterative search simultaneously.
4. a kind of galvanization production line coating feedforward optimizing and controlling method as claimed in claim 1, is characterized in that:
In step s 106, the principle that described air knife height feedforward setting table, according to zinc liquid, does not occur to splash and minimize in zinc liquid air the time of dispelling the heat is formulated.
5. a kind of galvanization production line coating feedforward optimizing and controlling method as claimed in claim 1, is characterized in that:
In step s 107, described air knife distance allocation algorithm is according to air knife distance before and after the change of air knife distance, belt steel thickness change calculations, described air knife distance distribution module only just works when change of lap, first need to detect spot welds information in real time, when spot welds crossed by air knife, judge whether that carrying out front and back air knife distance distributes: when not changing specification, only need on existing air knife distance basis, evenly distribute air knife distance variable quantity; When changing specification, first consider belt steel thickness compensating for variations, when belt steel thickness changes, the corresponding compensation thickness change of rear air knife, then, after total air knife distance variable quantity deducts compensation rate, evenly distribute air knife distance variable quantity.
6. a kind of galvanization production line coating feedforward optimizing and controlling method as claimed in claim 1, it is characterized in that: in step s 110, when thickness of coating is large from little change, when spot welds is through desired location, to air knife height, air knife distance, air pressure need correspondingly adjust, and namely starts the thickness of coating set(ting)value of being rolled up by next coil in advance zinc coat thickness control target as the current end of reel; When thickness of coating is from when diminishing greatly, then cross spot welds and carry out again making corresponding adjustment to air knife height, air knife Distance geometry air pressure.
7. a kind of galvanization production line coating feedforward optimizing and controlling method as claimed in claim 1, is characterized in that: in step s 103, if meet | S (t)-S (t-1) | and > S θthen enter S104; If do not meet, then terminate this control cycle feed forward control and calculate;
In step sl 15, if meet | S (t)-S (t-1) | > S θthen enter S116; If do not meet, then terminate this control cycle feed forward control and calculate.
8. a kind of galvanization production line coating feedforward optimizing and controlling method as claimed in claim 1, it is characterized in that, in step S104, the computation process of described multi-objective optimization algorithm is as follows:
Objective function: absolute value min|CWp-Rcw (t) of the difference of the thickness of coating set(ting)value of coating weight neural network prediction value and t feedforward controller |, the minimum value minP of t air pressure feedforward set(ting)value ffc(t), the minimum value minD of t air knife distance feedforward set(ting)value ffc(t)
Decision variable: P ffc(t), D ffc(t)
Constraint condition:
D ffc(t)∈[Dmin,Dmax](2)
P ffc(t)∈[Pmin,Pmax](3)
NNp(D ffc(t),S(t),P ffc(t))＝CWp(4)
Wherein, formula (2), (3) represent the technological process constraint of two operational variables, and formula (4) represents the strip speed of neural network prediction module reaction, air knife distance, mapping relations between air pressure and thickness of coating.
9. a kind of galvanization production line coating feedforward optimizing and controlling method as claimed in claim 1, it is characterized in that: in step s 110, if meet t last volume coil of strip thickness of coating set(ting)value to be greater than t current coil of strip thickness of coating set(ting)value, namely formula Rcw_next>Rcw_now, then enter S111; Otherwise, then S112 is entered.
10. a kind of galvanization production line coating feedforward optimizing and controlling method as claimed in claim 1, it is characterized in that: in step S113, make L_calc=L_calc+S (t) * T, if the distance calculated value of t feedforward control action point after air knife is more than or equal to the distance measure between air knife and thickness tester, namely formula L_calc >=L_measure, then enter S114; If do not meet, then enter S115.
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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106167887A (en) * 2016-07-04 2016-11-30 浙江中控研究院有限公司 Based on the cutter hot dip galvanizing coating thickness fast switch over method away from dynamic compensation and system
CN106435427A (en) * 2016-08-08 2017-02-22 浙江中控研究院有限公司 Air knife distance optimization control method applied to galvanization production
CN108396275A (en) * 2017-02-05 2018-08-14 鞍钢股份有限公司 A kind of continuous hot galvanizing air knife jetting pressure autocontrol method
CN110629149A (en) * 2019-10-21 2019-12-31 中冶南方工程技术有限公司 Zinc layer thickness control device of hot galvanizing unit
CN110846641A (en) * 2019-12-03 2020-02-28 山西潞安太阳能科技有限责任公司 Early warning device for monitoring coating time of tubular PECVD (plasma enhanced chemical vapor deposition) process
CN111270182A (en) * 2020-03-20 2020-06-12 攀钢集团攀枝花钢铁研究院有限公司 Hot-dip Zn-Al-Mg alloy coated steel plate and preparation method thereof
CN111850450A (en) * 2019-04-29 2020-10-30 上海梅山钢铁股份有限公司 Zinc layer control method for differential thickness coating strip steel
CN113667918A (en) * 2021-07-27 2021-11-19 首钢京唐钢铁联合有限责任公司 Control method for switching thickness of hot-dip galvanized strip steel coating
CN115094364A (en) * 2022-06-20 2022-09-23 首钢京唐钢铁联合有限责任公司 Method and device for controlling thickness of zinc layer of strip steel of galvanizing production line

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH03173757A (en) * 1989-11-30 1991-07-29 Kawasaki Steel Corp Method for controlling sticking rate of plating
JPH04263056A (en) * 1991-02-18 1992-09-18 Nippon Steel Corp Method for controlling plating weight in hot dip metal plating steel strip
CN103469137A (en) * 2013-09-07 2013-12-25 鞍钢股份有限公司 Preset control method for thickness flying gauge change of hot-dip zinc coating
CN103695830A (en) * 2013-12-20 2014-04-02 鞍钢股份有限公司 Coating thickness control method in hot galvanizing production process

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH03173757A (en) * 1989-11-30 1991-07-29 Kawasaki Steel Corp Method for controlling sticking rate of plating
JPH04263056A (en) * 1991-02-18 1992-09-18 Nippon Steel Corp Method for controlling plating weight in hot dip metal plating steel strip
CN103469137A (en) * 2013-09-07 2013-12-25 鞍钢股份有限公司 Preset control method for thickness flying gauge change of hot-dip zinc coating
CN103695830A (en) * 2013-12-20 2014-04-02 鞍钢股份有限公司 Coating thickness control method in hot galvanizing production process

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CN106167887A (en) * 2016-07-04 2016-11-30 浙江中控研究院有限公司 Based on the cutter hot dip galvanizing coating thickness fast switch over method away from dynamic compensation and system
CN106167887B (en) * 2016-07-04 2018-07-06 浙江中控研究院有限公司 Hot dip galvanizing coating thickness fast switch over method and system away from dynamic compensation based on knife
CN106435427A (en) * 2016-08-08 2017-02-22 浙江中控研究院有限公司 Air knife distance optimization control method applied to galvanization production
CN106435427B (en) * 2016-08-08 2018-10-02 浙江中控研究院有限公司 Zinc-plated production air knife is apart from optimal control method
CN108396275A (en) * 2017-02-05 2018-08-14 鞍钢股份有限公司 A kind of continuous hot galvanizing air knife jetting pressure autocontrol method
CN108396275B (en) * 2017-02-05 2019-10-29 鞍钢股份有限公司 A kind of continuous hot galvanizing air knife jetting pressure autocontrol method
CN111850450A (en) * 2019-04-29 2020-10-30 上海梅山钢铁股份有限公司 Zinc layer control method for differential thickness coating strip steel
CN111850450B (en) * 2019-04-29 2022-06-14 上海梅山钢铁股份有限公司 Zinc layer control method for differential thickness coating strip steel
CN110629149A (en) * 2019-10-21 2019-12-31 中冶南方工程技术有限公司 Zinc layer thickness control device of hot galvanizing unit
CN110846641A (en) * 2019-12-03 2020-02-28 山西潞安太阳能科技有限责任公司 Early warning device for monitoring coating time of tubular PECVD (plasma enhanced chemical vapor deposition) process
CN111270182A (en) * 2020-03-20 2020-06-12 攀钢集团攀枝花钢铁研究院有限公司 Hot-dip Zn-Al-Mg alloy coated steel plate and preparation method thereof
CN113667918A (en) * 2021-07-27 2021-11-19 首钢京唐钢铁联合有限责任公司 Control method for switching thickness of hot-dip galvanized strip steel coating
CN113667918B (en) * 2021-07-27 2023-08-15 首钢京唐钢铁联合有限责任公司 Control method for thickness switching of hot dip galvanized strip steel coating
CN115094364A (en) * 2022-06-20 2022-09-23 首钢京唐钢铁联合有限责任公司 Method and device for controlling thickness of zinc layer of strip steel of galvanizing production line

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