CN106521390A - Database-based strip steel continuous hot dip galvanizing plating thickness control method - Google Patents
Database-based strip steel continuous hot dip galvanizing plating thickness control method Download PDFInfo
- Publication number
- CN106521390A CN106521390A CN201610971793.0A CN201610971793A CN106521390A CN 106521390 A CN106521390 A CN 106521390A CN 201610971793 A CN201610971793 A CN 201610971793A CN 106521390 A CN106521390 A CN 106521390A
- Authority
- CN
- China
- Prior art keywords
- thickness
- coating
- strip steel
- iterative learning
- air knife
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- 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
-
- 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
- C23C2/04—Hot-dipping or immersion processes for applying the coating material in the molten state without affecting the shape; Apparatus therefor characterised by the coating material
- C23C2/06—Zinc or cadmium or alloys based thereon
Landscapes
- 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)
- Feedback Control In General (AREA)
Abstract
The invention discloses a database-based strip steel continuous hot dip galvanizing plating thickness control method. Plating thickness automatic control and control quantity continual optimization are achieved, the problems that plating thickness deviation is too large, and the adjustment time is too long can be effectively solved, and especially the effect in the plating thickness variable-specification process is more obvious.
Description
Technical field
The invention belongs to strip steel continuous hot galvanizing technical field, more particularly to a kind of strip steel continuous hot galvanizing based on data base
Zinc coat thickness control method.
Background technology
Strip hot-dip galvanizing is complicated multi-variable system, and control object has non-linear, time-varying, large time delay, multivariate coupling
There is serious purely retarded in characteristic, especially the thickness of coating measurements such as conjunction, cause zinc coat thickness control precision universal not high.
In prior art, thickness of coating automatic control technology is mostly set up on the basis of mathematical model, to model according to
Bad degree is higher.And strip steel continuous hot galvanizing process is difficult to set up accurate mathematical model, therefore the control accuracy of thickness of coating
It is all undesirable, while control system robustness need to be improved.
The content of the invention
For drawbacks described above, the technical problem to be solved present invention proposes that a kind of strip steel based on data base connects
Continuous hot dip galvanizing coating method for controlling thickness, realizes thickness of coating and automatically controls, and effectively solving thickness of coating deviation is excessive, regulation
The problems such as overlong time.
For reaching above-mentioned purpose, following technical scheme is present invention employs：
Thickness of coating overall database scheme：
Air pressure is selected to continuously adjust means as thickness of coating；Air knife nozzle gap (distance of air knife and strip steel), air knife
Height is only becoming specification process adjusting once；On the premise of air knife safety is ensured, reduce air knife nozzle gap (air knife and strip steel away from
From) gentle knife up degree, improve product quality and the energy consumption for reducing air sword blower fan；
Thickness of coating data base is set up according to hot dip galvanizing coating thickness model and historical data first, by strip steel continuously dip coat
Zinc process is divided into multiple working conditions and is separated into operating point, it is ensured that each control point can be inquired in data base；
Require to provide one group of standard process parameters for each thickness of coating specification according to production-line technique, as the initial of strip hot-dip galvanizing
Technological parameter；
Thickness of coating data base is high including thickness of coating specification, air knife nozzle gap (distance of air knife and strip steel) setting, air knife
Degree, strip speed setting, air pressure setting, air pressure learning rate；Using mathematical model combine historical data method come
Determine the initial value of air pressure setting and air pressure learning rate in data base, different operating is calculated first with mathematical model
Air knife process set value under state, is then corrected to setting value according to historical data, sets up thickness of coating data base；According to
Thickness of coating detection carries out controlling of sampling lag time, using variable-gain p-type iterative learning controller；
After automatically controlling input, the quantity of state of present operating point is recorded first, including thickness of coating specification, air knife nozzle gap
(distance of air knife and strip steel) setting, air knife highly set, strip speed setting, and inquire in being input to data base corresponding
Controlled quentity controlled variable includes air pressure and air pressure learning rate, then puts into controlling of sampling loop；When control object working condition is sent out
During changing, controlling of sampling process is closed, and current air pressure setting and air pressure learning rate controlled quentity controlled variable are passed through into line
Property weighting method be filled in data base, realize the renewal of controlled quentity controlled variable in data base.
A sets up data base
Require according to strip steel continuous hot galvanizing productive technology, by thickness of coating specification, strip speed, air knife nozzle gap (air knife
With the distance of strip steel) quantity of state is divided into multiple grades, sets up thickness of coating data base's bar according to different permutation and combination methods
Mesh, an operating point of each entry correspondence strip steel continuous hot galvanizing process in data base；
Using galvanizing mechanism model：
P=kWaVbDc
P- air knife blast actual values；
D- air knives are with strip steel apart from actual value；
V- strip speed actual values；
W- thickness of coating actual values；
K, a, b, c model parameter；
Galvanizing production process data is collected, including thickness of coating specification, speed setting, air knife wind under different working condition
Pressure setting, air knife nozzle gap (distance of air knife and strip steel) actual Value Data, estimate model parameter using Multiple Regression Analysis Method
K, a, b, c value, sets up thickness of coating mathematical model；By thickness of coating setting value Wset, strip speed actual value V, air knife and strip steel
Mathematical model is brought into apart from actual value D, air pressure setting value P under different working condition is calculatedset；
Corresponding air pressure initial set value P of different working condition is calculated using thickness of coating mathematical modelset(0), build
Vertical air pressure set-point data storehouse, is differentiated to air pressure using thickness of coating in mathematical model and can calculate different operating
Corresponding air pressure initial learn rate setting value L (0) of state, and each controlled quentity controlled variable is proofreaded according to historical data, make
For the initial controlled quentity controlled variable of thickness of coating data base；
B controlling of sampling logics
After controlling of sampling input, first current air knife regulated quantity is sampled, while strip steel position calculation starts, according to strip steel
Linear velocity calculates sampled point run location in real time, when sampled point reaches layer thickness meter, start recording thickness of coating measured value,
Strip steel position calculation is reset when layer thickness meter s rice and stops measurement by strip steel, then calculates the coating in this period
Thickness deviation values simultaneously start iterative learning control；Regulation records air knife regulated quantity again after finishing, start controlling of sampling next time；
Weld seam crosses the control logic of air knife, and weld seam after m rice and layer thickness meter in the range of m rice, is not adopted before air knife
Sample is controlled；Weld seam during n rice, according to next strip coating specification, inquires one group of standard technology in data base before air knife
Parameter, and operative employee's manual modification is allowed, standard process parameters are issued to into air knife actuator when weld seam reaches air knife；Plating
When thickness degree becomes specification, if current strip steel flash plating, when next strip steel is thickness coating, a before current tail part of band steel weld seam
Meter Ti Qian issues technological parameter, if current strip steel thickness coating, during next strip steel flash plating, in next strip steel head weld seam
A rice is delayed and issues technological parameter afterwards；
C iterative learning control
Iterative learning control adopt variable-gain p-type iterative learning controller, iterative learning initial learn rate L (0),
Calculate the learning rate L (k+1) of the K+1 time iterative learning：
Pdelta(k+1)-iterative learning control air pressure set point change amount of kth+1 time；
Wdelta(k+1)-iterative learning control thickness of coating actual value variable quantity of kth+1 time；
Wdelta(k+1)=Wset-Wact(k+1)
Wset- thickness of coating desired value；
Wact(k+1) the-the K+1 iterative learning control thickness of coating measured values；
Calculate the K+1 time iterative learning control air pressure setting value Pset(k+1)：
Pset(k+1)=Pset(k)+Pdelta(k+1)
Pset(k)-kth iterative learning control air pressure setting value；Pset(0) plating thickness before iterative learning control input
Air pressure initial set value in degrees of data storehouse；
Iterative learning number of times is limited by thickness of coating deviation, when thickness of coating absolute value of the bias is less than 1-
1.5g/m2When or current thickness of coating absolute value of the bias more than a front controlling of sampling thickness of coating absolute value of the bias, stop
Only iterative learning is calculated, when thickness of coating absolute value of the bias is more than 1.5-2g/m2When, iterative learning is put into again；Record is current
Ratio is reduced with the thickness of coating deviation of a front controlling of sampling, selects the controlling of sampling of deviation reduction large percentage corresponding repeatedly
For learning rate as controlling of sampling next time iterative learning rate；
D database updates
When control object working condition changes, present sample control process is closed, new working condition is carried out
Controlling of sampling, and current air pressure setting and air pressure learning rate controlled quentity controlled variable are filled into by the method for linear weighted function
In data base, the renewal of controlled quentity controlled variable in data base is realized；
L (k+1)=p*L (k)+(1-p) * L (k-1)
The weights P of current air pressure learning rate takes 0.1-0.9.
A kind of strip steel continuous hot galvanizing zinc coat thickness control method proposed by the present invention, realizes thickness of coating and automatically controls
With controlled quentity controlled variable Continuous optimization, the problems such as thickness of coating deviation is excessive, regulating time is long is can effectively solve the problem that, especially in coating
Thickness becomes specification process effect and becomes apparent from.
Description of the drawings
Fig. 1 is the control flow chart of the present invention；
Fig. 2 is the control system block diagram of the present invention；
Fig. 3 is single-face plating thickness specification 40g/m of the present invention2Application effect figure；
Specific embodiment
With reference to embodiment, the present invention is described in detail.
Thickness of coating overall database scheme
Air pressure is selected to continuously adjust means as thickness of coating；Air knife nozzle gap, air knife height are only becoming specification process
Adjustment is once；On the premise of air knife safety is ensured, the gentle knife up degree of knife spacing, improve product quality and reduction air knife wind is reduced
The energy consumption of machine；
Thickness of coating data base is set up according to hot dip galvanizing coating thickness model and historical data first, by strip steel continuously dip coat
Zinc process is divided into multiple working conditions and is separated into operating point, it is ensured that each operating point can be inquired in data base；
Require one group of standard process parameters to be provided for each thickness of coating specification according to production-line technique, join as the initial process of strip steel
Number；
Thickness of coating data base, including thickness of coating specification, air knife nozzle gap setting, air knife height, strip speed setting, gas
The setting of knife pressure, air pressure learning rate；Using mathematical model with reference to historical data method determining air knife pressure in data base
Power sets the initial value with air pressure learning rate, calculates air knife technique initialization under different working condition first with mathematical model
Value, is then corrected to set-point data according to historical data, sets up thickness of coating data base；Detect stagnant according to thickness of coating
Time carries out controlling of sampling afterwards, using variable-gain p-type iterative learning controller；
The quantity of state of present operating point after automatically controlling input, is recorded first, is set including thickness of coating specification, air knife nozzle gap
Determine, air knife height, strip speed set, and in being input to data base, inquire corresponding controlled quentity controlled variable to include air pressure and air knife
Pressure learning rate, then puts into controlling of sampling；When control object working condition changes, close present sample and controlled
Journey, and current air pressure setting and air pressure learning rate controlled quentity controlled variable are filled into into data base by the method for linear weighted function
In, realize the renewal of controlled quentity controlled variable in data base；Then new working condition is controlled.
1.1 set up data base
1 requires to set up thickness of coating data base according to strip steel continuous hot galvanizing productive technology, and thickness of coating specification has one side
Thickness of coating 40g/m2、50g/m2、60g/m2、90g/m2, with 40g/m2As a example by specification；Strip speed 30-160m/min, according to
2m/min amplitudes are divided into 66 grades；Air knife nozzle gap 7-10mm, is divided into 4 grades according to 1mm amplitudes；According to different rows
Row compound mode sets up thickness of coating data base, comprising 264 process datas.
Using galvanizing mechanism model：
P=k*WaVbDc
P- air knife blast actual value mbar；
D- air knives are with strip steel apart from actual value mm；
V- strip speed actual value m/min；
W- thickness of coating actual value g/m2；
K, a, b, c model parameter.
Galvanizing production process data is collected, including thickness of coating specification, speed setting, air knife wind under different working condition
The actual Value Datas such as pressure setting, air knife nozzle gap, estimate model parameter k=1, a=- using Multiple Regression Analysis Method
0.668226, b=1.324044, c=0.935216, set up thickness of coating database table, are shown in Table 1；
The corresponding air pressure initial set value of different working condition in data base is calculated using thickness of coating mathematical model
Pset(0), air pressure is differentiated using thickness of coating in mathematical model and can calculates the corresponding air knife pressure of different working condition
Power initial learn rate setting value L (0)：
W=40 strip speed V0=100, D0=10, L (0)=- 5.438539526；
And each controlled quentity controlled variable is proofreaded according to historical data, as the initial controlled quentity controlled variable of data base.
1.2 controlling of sampling logics
After controlling of sampling input, first current air knife regulated quantity is sampled, while strip steel position calculation starts, according to strip steel
Linear velocity calculates sampled point run location in real time, when sampled point reaches layer thickness meter, start recording thickness of coating measured value,
Strip steel position calculation is reset when 5 meters of layer thickness meter and stops measurement by strip steel, then calculates the coating in this period
Thickness deviation values simultaneously start iterative learning control regulation；Regulation records air knife regulated quantity again after finishing, startup is sampled next time
Control；
Air knife crosses the control logic of weld seam, weld seam before air knife 30 meters after 30 meters and layer thickness meter in the range of, do not carry out
Controlling of sampling；Weld seam when 60 meters, according to next strip coating specification, inquires one group of standard work in data base before air knife
Skill parameter, and operative employee's manual modification is allowed, standard process parameters are issued to into air knife actuator when weld seam reaches air knife；
When thickness of coating becomes specification, if current strip steel flash plating, when next strip steel is thickness coating, can weld in current tail part of band steel
Stitch first 10 meters and issue technological parameter in advance, if current strip steel thickness coating, during next strip steel flash plating, can be in next band
Delay and issue technological parameter for 10 meters after steel head weld seam；
1.3 iterative learning control
Using variable-gain p-type iterative learning controller, iterative learning initial learn rate L (0),
Calculate the learning rate L (k+1) of the K+1 time iterative learning：
Pdelta(k+1)-iterative learning control air pressure set point change amount of kth+1 time；
W delta(k+1)-iterative learning control thickness of coating actual value variable quantity of kth+1 time；
Wdelta(k+1)=Wset-Wact(k+1)
Wset- thickness of coating desired value；
Wact(k+1) the-the K+1 iterative learning control thickness of coating measured values；
Calculate the K+1 time iteration
Study control air pressure setting value Pset(k+1)：
Pset(k+1)=Pset(k)+Pdelta(k+1)
Pset(k)-kth iterative learning control air pressure setting value；Pset(0) plating thickness before iterative learning control input
Air pressure setting value in degrees of data storehouse；
Iterative learning number of times is limited by thickness of coating deviation, when thickness of coating absolute value of the bias is less than 1g/m2
When or current thickness of coating absolute value of the bias more than a front controlling of sampling thickness of coating absolute value of the bias, stop iteration
Practise and calculating, when thickness of coating absolute value of the bias is more than 1.5g/m2When, iterative learning is put into again；Record is current once to be adopted with front
The thickness of coating deviation slip of sample control, and compare the big iterative learning rate of selection reduction rule as changing that present sample is controlled
For learning rate.1.4 database update
When control object working condition changes, present sample control process is closed, and by current air pressure
The controlled quentity controlled variable such as setting and air pressure learning rate is filled in data base by the method for linear weighted function, is controlled in realizing data base
The renewal of amount, the weights of current air pressure learning rate take 0.6.
Table 1
Claims (1)
1. a kind of strip steel continuous hot galvanizing zinc coat thickness control method based on data base, it is characterised in that comprise the steps：
A sets up data base
Require according to strip steel continuous hot galvanizing productive technology, by thickness of coating specification, strip speed, air knife and strip steel apart from shape
State amount is divided into multiple grades, sets up thickness of coating data base entries according to different permutation and combination methods, in data base each
One operating point of entry correspondence strip steel continuous hot galvanizing process；
Using galvanizing mechanism model：
P=kWaVbDc
P- air knife blast actual values；D- air knives are with strip steel apart from actual value；V- strip speed actual values；
W- thickness of coating actual values；K, a, b, c model parameter；
Galvanizing production process data is collected, is set including thickness of coating specification, speed setting, air knife blast under different working condition
Fixed, the actual Value Data of the distance of air knife and strip steel, estimates model parameter k using Multiple Regression Analysis Method, and a, b, c value is set up
Thickness of coating mathematical model；By thickness of coating setting value Wset, strip speed actual value V, air knife and strip steel be apart from actual value D bands
Enter mathematical model, calculate air pressure setting value P under different working conditionset；
Corresponding air pressure initial set value P of different working condition is calculated using thickness of coating mathematical modelset(0), set up gas
Knife pressure set points data base, is differentiated to air pressure using thickness of coating in mathematical model and can calculate different working condition
Corresponding air pressure initial learn rate setting value L (0), and each controlled quentity controlled variable is proofreaded according to historical data, as plating
The initial controlled quentity controlled variable in thickness degrees of data storehouse；
B controlling of sampling logics
After controlling of sampling input, first current air knife regulated quantity is sampled, while strip steel position calculation starts, according to strip steel linear speed
Degree calculates sampled point run location in real time, when sampled point reaches layer thickness meter, start recording thickness of coating measured value, and strip steel
Strip steel position calculation is reset when layer thickness meter s rice and stop measurement, then calculate the thickness of coating in this period
Deviation simultaneously starts iterative learning control；Regulation records air knife regulated quantity again after finishing, start controlling of sampling next time；
Weld seam crosses the control logic of air knife, and weld seam after m rice and layer thickness meter in the range of m rice, does not carry out sampling control before air knife
System；Weld seam during n rice, according to next strip coating specification, inquires one group of standard process parameters in data base before air knife,
And operative employee's manual modification is allowed, standard process parameters are issued to into air knife actuator when weld seam reaches air knife；Plating thickness
When degree becomes specification, if current strip steel flash plating, when next strip steel is thickness coating, before current tail part of band steel weld seam, a rice is carried
Before issue technological parameter, if current strip steel thickness coating, during next strip steel flash plating, a after next strip steel head weld seam
Meter Yan Hou issues technological parameter；
C iterative learning control
Iterative learning control adopt variable-gain p-type iterative learning controller, iterative learning initial learn rate L (0),
Calculate the learning rate L (k+1) of the K+1 time iterative learning：
Pdelta(k+1)-iterative learning control air pressure set point change amount of kth+1 time；
Wdelta(k+1)-iterative learning control thickness of coating actual value variable quantity of kth+1 time；
Wdelta(k+1)=Wset-Wact(k+1)
Wset- thickness of coating desired value；
Wact(k+1) the-the K+1 iterative learning control thickness of coating measured values；
Calculate the K+1 time iterative learning control air pressure setting value Pset(k+1)：
Pset(k+1)=Pset(k)+Pdelta(k+1)
Pset(k)-kth iterative learning control air pressure setting value；Pset(0) thickness of coating number before iterative learning control input
According to air pressure initial set value in storehouse；
Iterative learning number of times is limited by thickness of coating deviation, when thickness of coating absolute value of the bias is less than 1-1.5g/m2When
Or current thickness of coating absolute value of the bias stops iterative learning more than the thickness of coating absolute value of the bias of a front controlling of sampling
Calculate, when thickness of coating absolute value of the bias is more than 1.5-2g/m2When, iterative learning is put into again；Record is current once to be adopted with front
The thickness of coating deviation of sample control reduces ratio, selects the corresponding iterative learning rate of controlling of sampling that deviation reduces large percentage to make
For the iterative learning rate of controlling of sampling next time；
D database updates
When control object working condition changes, present sample control process is closed, new working condition is sampled
Control, and current air pressure setting and air pressure learning rate controlled quentity controlled variable are filled into into data by the method for linear weighted function
In storehouse, the renewal of controlled quentity controlled variable in data base is realized；
L (k+1)=p*L (k)+(1-p) * L (k-1)
The weights P of current air pressure learning rate takes 0.1-0.9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610971793.0A CN106521390B (en) | 2016-10-28 | 2016-10-28 | A kind of strip continuous hot galvanizing zinc coat thickness control method based on database |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610971793.0A CN106521390B (en) | 2016-10-28 | 2016-10-28 | A kind of strip continuous hot galvanizing zinc coat thickness control method based on database |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106521390A true CN106521390A (en) | 2017-03-22 |
| CN106521390B CN106521390B (en) | 2018-09-11 |
Family
ID=58325879
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610971793.0A Active CN106521390B (en) | 2016-10-28 | 2016-10-28 | A kind of strip continuous hot galvanizing zinc coat thickness control method based on database |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106521390B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111118432A (en) * | 2020-01-14 | 2020-05-08 | 邯郸钢铁集团有限责任公司 | Method for automatically controlling weight of zinc layer of galvanized product |
| 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 |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH04263056A (en) * | 1991-02-18 | 1992-09-18 | Nippon Steel Corp | Method for controlling plating weight in hot dip metal plating steel strip |
| US20080193655A1 (en) * | 2005-03-18 | 2008-08-14 | Hans-Georg Kloeckner | Controlled Thickness Reduction for Hot-Dip Coated, Hot-Rolled Steel Strip And Installation Used In This Process |
| CN102912275A (en) * | 2012-10-23 | 2013-02-06 | 鞍钢股份有限公司 | Automatic control system for plating thickness of hot galvanizing line |
-
2016
- 2016-10-28 CN CN201610971793.0A patent/CN106521390B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH04263056A (en) * | 1991-02-18 | 1992-09-18 | Nippon Steel Corp | Method for controlling plating weight in hot dip metal plating steel strip |
| US20080193655A1 (en) * | 2005-03-18 | 2008-08-14 | Hans-Georg Kloeckner | Controlled Thickness Reduction for Hot-Dip Coated, Hot-Rolled Steel Strip And Installation Used In This Process |
| CN102912275A (en) * | 2012-10-23 | 2013-02-06 | 鞍钢股份有限公司 | Automatic control system for plating thickness of hot galvanizing line |
Non-Patent Citations (2)
| Title |
|---|
| 张岩等: "热镀锌锌层厚度自适应控制模型的研究与应用", 《钢铁》 * |
| 秦大伟等: "连续热镀锌线镀层厚度自动控制系统研究与应用", 《第九届中国钢铁年会论文集》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111118432A (en) * | 2020-01-14 | 2020-05-08 | 邯郸钢铁集团有限责任公司 | Method for automatically controlling weight of zinc layer of galvanized product |
| 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 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106521390B (en) | 2018-09-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103054152B (en) | Tobacco redryer moisture control method based on forecast Product Improve (PI) algorithm control system | |
| CN114185265B (en) | Large-scale opening jet flow wind tunnel ultrasonic speed constant total pressure continuous variable Mach number control method | |
| CN103861879B (en) | A kind of cut deal on-line cooling device and control method | |
| CN102147613B (en) | Model predictive control method and system for moisture and temperature of tobacco redryer | |
| CN102912275A (en) | Automatic control system for plating thickness of hot galvanizing line | |
| KR20180061275A (en) | Hot melt galvanized layer thickness control system and method for continuous thickness-variable strip material | |
| CN106167887B (en) | Hot dip galvanizing coating thickness fast switch over method and system away from dynamic compensation based on knife | |
| CN106868440B (en) | Method for predicting and adjusting thickness of strip steel continuous hot-dip galvanized coating | |
| CN106521390B (en) | A kind of strip continuous hot galvanizing zinc coat thickness control method based on database | |
| CN108396275B (en) | A kind of continuous hot galvanizing air knife jetting pressure autocontrol method | |
| CN105962408A (en) | Multi-strategy composite control method for moisture of tobacco at outlet of tobacco redryer | |
| CN104846306A (en) | Zincing thickness control system and method | |
| CN113812658B (en) | Loose moisture regain and water adding control method based on neural network model and double parameter correction | |
| CN113667918A (en) | Control method for switching thickness of hot-dip galvanized strip steel coating | |
| CN110946311B (en) | Quality control method and system for charging cut stems | |
| CN107130199B (en) | A kind of continuous hot galvanizing air knife knife is away from autocontrol method | |
| CN105344720A (en) | Online control method for finish rolling temperature of precision rolling strip steel | |
| CN103861875B (en) | The optimal control method of cold-rolling mill technical cooling system | |
| CN101533265B (en) | Feedback control method for calcination process in rotary kiln | |
| CN103510032B (en) | The deviate control method of cold rolling hot dip galvanizing plating uniformity | |
| CN108546897B (en) | A kind of cold rolling hot dip galvanizing steel plate transverse direction coating uniformity control method | |
| CN109763085A (en) | A kind of temperature controlled method of hot galvanizing line alloying section | |
| CN106637026A (en) | Air knife pressure real-time optimization control method and system in galvanizing process | |
| CN106690390A (en) | Temperature control method, temperature controller and roller device | |
| CN105568197A (en) | Method for accurately controlling weight of continuously-hot-galvanized zinc layer |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 102200 Beijing Changping District Beijing future iron and Technology City North Anshan Iron and Steel Research Institute Applicant after: Angang future iron & Steel Research Institute Co., Ltd. Address before: 102200 Changping District, Beijing, the future of science and technology in the North District of the city of Beijing Applicant before: Anshan Iron and Steel Research Institute |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |