CN111250545B - Control system and method for reducing swing steel coil over-thickness rate - Google Patents
Control system and method for reducing swing steel coil over-thickness rate Download PDFInfo
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- CN111250545B CN111250545B CN201811455530.XA CN201811455530A CN111250545B CN 111250545 B CN111250545 B CN 111250545B CN 201811455530 A CN201811455530 A CN 201811455530A CN 111250545 B CN111250545 B CN 111250545B
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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
- B21B37/16—Control of thickness, width, diameter or other transverse dimensions
- B21B37/18—Automatic gauge control
- B21B37/20—Automatic gauge control in tandem mills
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B38/00—Methods or devices for measuring, detecting or monitoring specially adapted for metal-rolling mills, e.g. position detection, inspection of the product
- B21B38/006—Methods or devices for measuring, detecting or monitoring specially adapted for metal-rolling mills, e.g. position detection, inspection of the product for measuring temperature
Abstract
The invention belongs to the technical field of hot rolling methods, and particularly relates to a control system and a control method for reducing the over-thickness rate of a swinging steel coil, wherein the control system and the control method are used for carrying out real-time online judgment on whether strip steel generates swinging or not, and filing and classifying according to swinging time; selecting a corresponding weight proportion according to the actual rolling information, thereby accurately calculating the finish rolling inlet temperature setting; feeding back the accuracy of temperature setting according to the thickness of the finish rolling outlet strip steel, and performing subsequent weight proportion self-learning; the method also considers the intervention of operators in the calculation, and can predict whether the temperature of the strip steel is lower than an alarm value on the middle roller way, so that the method is more accurate and effective; the system and the method are realized on the basis of intellectualization without manual operation.
Description
Technical Field
The invention belongs to the technical field of hot rolling methods, and particularly relates to a control system and method for reducing the super-thickness rate of a swinging steel coil.
Background
The accuracy of the temperature model calculation in the hot rolling production line is very important, and the temperature model calculation is directly related to the setting calculation of model parameters such as the rolling force of a roller and the speed, the tension, the forward slip coefficient and the like of the strip steel. If the temperature calculation setting of the strip steel entering the finish rolling is not accurate, the temperature calculation of the 7 frames for finish rolling is not accurate, and the rolling force setting calculation of the 7 frames is also inaccurate, so that the final thickness of the strip steel cannot reach the expected value; when the temperature of the finish rolling outlet is not accurately predicted due to the temperature of the inlet, the pre-control of the cooling water and the temperature between the racks for finish rolling is also not accurate.
The super-thick head of the strip steel influences the quality of a steel coil, and simultaneously, the hit rate of plate types such as convexity, straightness and the like is reduced. When the temperature of the head of a piece of strip steel is higher than the calculated temperature, the phenomena of smaller set rolling force, smaller strip steel tension and the like can be caused, and finally the head thickness of the strip steel is over thick, and the hit rate of the thickness is reduced. The hit rate of the thickness of the strip steel of the hot rolling production line does not reach the standard, and the common phenomenon is that the head part is over thick. In order to solve the problem of the super-thick thickness of the head of the strip steel, on-site data needs to be collected and analyzed, and a solution can be made only by finding the reason.
Chinese patent CN103406369A discloses a method for improving the accuracy of the rolling force of the head of a strip steel by using a temperature function, which introduces the function of calculating the temperature drop of the head of the strip steel by optimizing and improving the calculation of a temperature drop model, and obtains a relatively accurate calculated value of the temperature of the head of the strip steel by calculating the temperature drop from rough rolling to finish rolling in a way of dividing the strip steel into a plurality of sections along the length direction; however, the scheme cannot predict and judge the steel coil which is swung at the middle roller way in real time and accurately classify the steel coil, so that the prediction of the inlet temperature of the hot continuous rolling is not accurate enough, and the accurate control of the thickness of the steel coil cannot be realized.
Disclosure of Invention
The invention solves the technical problems in the prior art and provides a control system and a control method for reducing the extra-thick rate of a swinging steel coil.
In order to solve the problems, the technical scheme of the invention is as follows:
a control system for reducing the over-thickness rate of swinging steel coils comprises,
the steel coil swinging temperature weight distribution module: according to the quality number, the band steel species, the thickness and the swing time, establishing a weight distribution storage table in a database, and storing weight distribution parameters obtained by different steel species according to the swing time;
swinging the steel coil temperature weight self-learning module: updating and storing the learning coefficient of the temperature weight according to the quality number, the band steel species, the temperature and the thickness feedback, and establishing a weight self-learning storage table;
the temperature detection and calculation module: the device comprises a temperature detection module and a temperature calculation module; the temperature detection module detects the actual temperature of the surface of the strip steel according to the pyrometer; the temperature calculation module predicts the temperature of the strip steel reaching a finish rolling inlet and calculates the temperature of each layer in the thickness direction according to the detected actual temperature of the surface of the strip steel;
measuring module of temperature at the inlet of finish rolling: according to the surface temperature of the strip steel at the finish rolling inlet, calling a temperature calculation module to calculate the temperature TF of each layer in the thickness directionactEstablishing a cache matrix for recording the temperatures of the sections and the layers;
the intermediate blank swinging judging module: judging whether the steel coil generates oscillation or not according to the actual time required from the rough rolling outlet pyrometer to the finish rolling inlet pyrometer;
finish rolling temperature setting module: selecting a weight distribution parameter reading way according to the judgment result of the intermediate billet swinging judgment module so as to calculate the finish rolling inlet temperature;
the thickness feedback control module: and calculating the difference value between the actual thickness and the target thickness of the strip steel at the finish rolling outlet to determine a learning coefficient for replacing the learning coefficient of the swinging steel coil temperature weight self-learning module.
Preferably, the control system for reducing the excessive thickness rate of the swaying steel coil further comprises an operator intervention module: when the strip steel needs to stay in the middle roller way in case of emergency on site and an operator knows the stay time, calling an operator intervention module; the operator intervention module calls the temperature calculation module to calculate the strip steel head temperature TR of the strip steel reaching the finish rolling inlet according to the retention time of the intermediate roller table input by the operatorcal(ii) a Judging the temperature of the head of the strip steel calculated, and judging the temperature when the temperature is TRcalAnd carrying out model alarm when the temperature is less than 900 ℃.
A control method for reducing the over-thickness rate of swinging steel coils comprises the following steps:
step 1, establishing a model configuration table;
step 2, detecting the actual temperature of the strip steel according to a pyrometer at the rough rolling outlet, and calculating the temperature TR of each section and layer at the rough rolling outletactPredicting the temperature TR of the strip steel reaching each section and layer at the finish rolling inletcal；
Step 3, a pyrometer is arranged at a finish rolling inlet to detect the surface temperature of the strip steel, and the temperature TF of each layer in the thickness direction is calculatedactEstablishing a cache matrix for recording the temperatures of the sections and the layers;
step 4, judging whether the steel coil generates oscillation or not according to the actual time required from the rough rolling outlet pyrometer to the finish rolling inlet pyrometer;
step 5, selecting a weight distribution parameter reading path according to the judgment result of the step 4, thereby calculating a set issued value of the finish rolling inlet temperature;
and 6, calculating the difference value between the actual thickness and the target thickness of the strip steel at the finish rolling outlet to determine a learning coefficient, and using the learning coefficient to replace the learning coefficient in the model configuration table.
Preferably, the establishing of the model configuration table comprises:
the establishment and maintenance of the steel coil swinging temperature weight distribution module: according to the quality number, the band steel species, the thickness and the swing time, establishing a weight distribution storage table in a database, and storing weight distribution parameters obtained by different steel species according to the swing time;
the establishment and maintenance of the swinging steel coil temperature weight self-learning module: updating and storing the learning coefficient of the temperature weight according to the quality number, the band steel species, the temperature and the thickness feedback, and establishing a weight self-learning storage table;
preferably, the specific method of step 2 is:
1) dividing the strip steel into sections with the same length, acquiring the actual surface temperature of each section of the strip steel at a rough rolling outlet, and dividing the strip steel into 5 layers in the thickness direction by using a finite element technology;
2) establishing a buffer matrix for recording the temperature of the sections and the layers;
3) calculating the temperature TR of each section and layer at the rough rolling outlet by calling a temperature difference calculation methodactAnd predicting the temperature TR of each section and layer when the temperature TR reaches the finish rolling inlet through the intermediate roller waycalAt this time, the temperature data TR of the first 5 stages is savedactAnd TRcalAnd sending the data to the finish rolling model for setting and calculating the finish rolling model.
Preferably, when there is emergency on the scene and needs belted steel to stop in middle roll table and the operative employee knows the dwell time, can call operative employee and intervene the module:
1) the operator inputs the time of the stay in the middle roller way, and the temperature calculation module is called to calculate the strip steel head temperature TR of the strip steel reaching the finish rolling inletcal；
2) Judging the temperature of the head of the strip steel calculated, and judging the temperature when the temperature is TRcalAnd carrying out model alarm when the temperature is less than 900 ℃.
Preferably, the specific method of step 4 is:
1) calculating the actual time timetake required from the rough rolling outlet pyrometer to the finish rolling inlet pyrometer;
2) and when the timetake is more than 50s, determining that the steel coil generates swing, and when the timetake is less than 50s, determining that the steel coil does not generate swing.
Preferably, the finish rolling inlet temperature in the step 5 is set to the lower value TFETThe specific calculation method comprises the following steps:
TFET＝TRcal×FETcoeff×α+TFact×(1—FETcoeff×α)
wherein, TRcalThe predicted temperature of each section and layer at the finish rolling inlet is obtained;
FETcoeff is a weight distribution parameter; when the steel coil does not swing, the weight distribution parameters are read from the configuration file, and when the steel coil swings, the weight distribution parameters are read from the weight distribution storage table;
alpha is a learning coefficient;
TFactthe temperature of each layer in the thickness direction of the strip steel at the finish rolling inlet.
Preferably, the specific method of step 6 is:
a thickness detector is arranged at the outlet of the finish rolling and is used for detecting the actual thickness of the strip steel at the outletactTarget thickness is thickPDI；
1) When | thickact—thickPDI|>When 0.1cm is reached, it shows that the strip steel rolling head is in super-thick state and learning coefficient
Wherein g is a correction factor in the range of 0-1, typically set to 0.3;
2) replacing the alpha updating in the weight self-learning storage table with the calculation result of alpha calculation in the step 1).
Compared with the prior art, the invention has the advantages that,
(1) the accuracy of the set temperature of the finish rolling inlet calculated after the strip steel swings is reduced, and the temperature of the finish rolling inlet of the strip steel cannot be accurately calculated if a fixed temperature weight proportion is adopted;
(2) the method considers the intervention of operators in the calculation, and can predict whether the temperature of the strip steel is lower than an alarm value on the middle roller way, so that the method is more accurate and effective;
(3) judging whether the strip steel generates swing on line in real time, and filing and classifying according to the swing time;
(4) selecting a corresponding weight proportion according to the actual rolling information, thereby accurately calculating the finish rolling inlet temperature setting;
(5) feeding back the accuracy of temperature setting according to the thickness of the finish rolling outlet strip steel, and performing subsequent weight proportion self-learning;
(6) the related technology is realized on the basis of intellectualization without manual operation.
Drawings
FIG. 1 is a flowchart of a control method for reducing the excess thickness rate of a swinging steel coil;
FIG. 2 is a weight assignment storage table;
fig. 3 is a weight self-learning memory table.
Detailed Description
Carrying out data statistics and analysis on the strip steel data in 1 month of the hot rolling production line to obtain a conclusion: the phenomenon of swinging of the middle roller way is commonly caused in the strip steel with the over-thick head part, and the temperature setting is higher. Therefore, the set higher temperature of the finish rolling inlet of the swaying steel coil is the root cause of the generation of the super-thick head of the strip steel.
Example 1:
a control system for reducing the over-thickness rate of swinging steel coils comprises,
the steel coil swinging temperature weight distribution module: establishing a weight distribution storage table in a database according to the quality number, the SFC (strip steel race), the thickness and the swing time, and storing weight distribution parameters obtained by different steel grades according to the swing time;
swinging the steel coil temperature weight self-learning module: updating and storing the learning coefficient of the temperature weight according to the quality number, SFC (strip steel race), temperature and thickness feedback, and establishing a weight self-learning storage table;
the temperature detection and calculation module: the device comprises a temperature detection module and a temperature calculation module; the temperature detection module detects the actual temperature of the surface of the strip steel according to the pyrometer; the temperature calculation module predicts the temperature of the strip steel reaching a finish rolling inlet and calculates the temperature of each layer in the thickness direction according to the detected actual temperature of the surface of the strip steel;
an operator intervention module is included: when the strip steel needs to stay in the middle roller way in case of emergency on site and an operator knows the stay time, calling an operator intervention module; the operator intervention module calls the temperature calculation module to calculate the strip steel head temperature TR of the strip steel reaching the finish rolling inlet according to the retention time of the intermediate roller table input by the operatorcal(ii) a Judging the temperature of the head of the strip steel calculated, and judging the temperature when the temperature is TRcalPerforming model alarm when the temperature is less than 900 ℃;
measuring module of temperature at the inlet of finish rolling: according to the surface temperature of the strip steel at the finish rolling inlet, calling a temperature calculation module to calculate the temperature TF of each layer in the thickness directionactEstablishing a cache matrix for recording the temperatures of the sections and the layers;
the intermediate blank swinging judging module: judging whether the steel coil generates oscillation or not according to the actual time required from the rough rolling outlet pyrometer to the finish rolling inlet pyrometer;
finish rolling temperature setting module: selecting a weight distribution parameter reading way according to the judgment result of the intermediate billet swinging judgment module so as to calculate the finish rolling inlet temperature;
the thickness feedback control module: and calculating the difference value between the actual thickness and the target thickness of the strip steel at the finish rolling outlet to determine a learning coefficient for replacing the learning coefficient of the swinging steel coil temperature weight self-learning module.
Example 2:
a control method for reducing the over-thickness rate of swinging steel coils comprises the following steps:
1. establishing a model configuration table:
1) establishment and maintenance of steel coil swinging temperature weight distribution module
And establishing a weight distribution storage table in a database according to the quality number, the SFC, the thickness and the swing time, and storing the weight distribution obtained by different steel grades according to the swing time, wherein the weight distribution is shown in figure 2.
2) Establishment and maintenance of swinging steel coil temperature weight self-learning module
The self-learning values of the temperature weights are updated and stored according to the temperature and thickness feedback, see fig. 3.
2. Actual detection and prediction module for temperature at rough rolling outlet
And the temperature calculation module detects the actual temperature of the strip steel according to a pyrometer at the rough rolling outlet, and predicts the temperature of the strip steel reaching the finish rolling inlet and the temperature change process of the strip steel within the period of time.
1) Dividing the strip steel into sections with the same length, acquiring the actual surface temperature of each section of the strip steel at a rough rolling outlet, and dividing the strip steel into 5 layers in the thickness direction by using a finite element technology;
2) establishing a buffer matrix for recording the temperature of the sections and the layers;
3) calculating the temperature TR of each section and layer by calling a temperature difference calculation methodactAnd predicting the temperature TR when the temperature TR reaches the finish rolling inlet through the intermediate roller waycalAt this time, the temperature data TR of the first 5 stages is savedactAnd TRcalAnd sending the data to the finish rolling model for setting and calculating the finish rolling model.
3. Operator intervention module
When the strip steel needs to stay in the middle roller way under emergency conditions on site and an operator knows the stay time, the operator intervention module can be called.
1) The operator inputs the time of the stay in the middle roller way, and the temperature calculation module is called to calculate the strip steel head temperature TR of the strip steel reaching the finish rolling inletcal；
2) Judging the temperature of the head of the strip steel calculated, and judging the temperature when the temperature is TRcalAnd carrying out model alarm when the temperature is less than 900 ℃. 4. Measuring module for temperature at finish rolling inlet
The finish rolling inlet is provided with a pyrometer bandDetecting the surface temperature of the steel, and calling a temperature calculation module to calculate the temperature TF of each layer in the thickness directionactA buffer matrix is established for recording the temperatures of the segments and layers.
5. Intermediate blank swinging judging module
And (5) calling a swing judging module when the strip steel reaches a finish rolling inlet pyrometer.
1) Calculating the actual time timetake required from the rough rolling outlet pyrometer to the finish rolling inlet pyrometer;
2) when timetake is greater than 50s, determining that the steel coil generates swing, and reading the weight distribution FETCOEFF corresponding to the strip steel in the block of the figure 2;
3) for steel coils without swing, the weight distribution parameters read FETcoeff from the configuration file
6. Finish rolling temperature setting module
The temperature at the finish rolling inlet is very important for the calculation and setting of a finish rolling setting model, and the accurate temperature can ensure that the control parameters such as rolling force, threading speed and the like are correctly issued.
1) When the steel coil is not swayed, the weight distribution parameters are read from the configuration file, so the finish rolling inlet temperature TFETThe final settings are as follows:
TFET＝TRcal×FETcoeff×α+TFact×(1—FETcoeff×α)
2) when the steel coil is swayed, the weight distribution parameters are read from FIG. 2, so
TFET＝TRcal×FETCOEFF×α+TFact×(1—FETCOEFF×α)
7. Thickness feedback control module
A thickness detector is arranged at the outlet of the finish rolling and is used for detecting the actual thickness of the strip steel at the outletactTarget thickness of thickPDI。
1) When | thickact—thickPDI|>When 0.1cm is reached, it shows that the strip steel rolling head is in super-thick state and learning coefficient
Wherein g: repair theThe positive coefficient, intermediate between 0-1, is typically set to 0.3.
2) The α update in fig. 3 is replaced with the calculation result of the α calculation in the above process.
8. And (6) ending.
It should be noted that the above-mentioned embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, and all equivalent substitutions or substitutions made on the above-mentioned embodiments are included in the scope of the present invention.
Claims (7)
1. A control system for reducing the over-thickness rate of swinging steel coils is characterized by comprising,
the steel coil swinging temperature weight distribution module: according to the quality number, the band steel species, the thickness and the swing time, establishing a weight distribution storage table in a database, and storing weight distribution parameters obtained by different steel species according to the swing time;
swinging the steel coil temperature weight self-learning module: updating and storing the learning coefficient of the temperature weight according to the quality number, the band steel species, the temperature and the thickness feedback, and establishing a weight self-learning storage table;
the temperature detection and calculation module: the device comprises a temperature detection module and a temperature calculation module; the temperature detection module detects the actual temperature of the surface of the strip steel according to the pyrometer; the temperature calculation module predicts the temperature of each section of the strip steel reaching a finish rolling inlet and calculates the temperature of each layer in the thickness direction according to the detected actual temperature of the surface of the strip steel;
measuring module of temperature at the inlet of finish rolling: according to the actual temperature of the surface of the strip steel at the finish rolling inlet, calling a temperature calculation module to calculate the temperature TF of each layer in the thickness directionactEstablishing a cache matrix for recording the temperatures of the sections and the layers;
the intermediate blank swinging judging module: judging whether the steel coil generates oscillation or not according to the actual time required from the rough rolling outlet pyrometer to the finish rolling inlet pyrometer;
finish rolling temperature setting module: selecting a weight distribution parameter reading way according to the judgment result of the intermediate billet swinging judgment module so as to calculate the finish rolling inlet temperature;
the thickness feedback control module: and calculating the difference value between the actual thickness and the target thickness of the strip steel at the finish rolling outlet to determine a learning coefficient for replacing the learning coefficient of the swinging steel coil temperature weight self-learning module.
2. The control system for reducing the over-thickness rate of swinging steel coils as claimed in claim 1, wherein the control system for reducing the over-thickness rate of swinging steel coils further comprises an operator intervention module: when the strip steel needs to stay in the middle roller way in case of emergency on site and an operator knows the stay time, calling an operator intervention module; the operator intervention module calls the temperature calculation module to calculate the temperature of the head of the strip steel at the position where the strip steel reaches a finish rolling inlet according to the staying time of the middle roller table input by an operator; and judging the temperature of the head of the calculated strip steel, and giving a model alarm when the temperature is less than 900 ℃.
3. A control method for reducing the super-thickness rate of swinging steel coils is characterized by comprising the following steps:
step 1, establishing a model configuration table;
the establishment of the model configuration table comprises the following steps:
the establishment and maintenance of the steel coil swinging temperature weight distribution module: according to the quality number, the band steel species, the thickness and the swing time, establishing a weight distribution storage table in a database, and storing weight distribution parameters obtained by different steel species according to the swing time;
the establishment and maintenance of the swinging steel coil temperature weight self-learning module: updating and storing the learning coefficient of the temperature weight according to the quality number, the band steel race, the temperature and the thickness feedback, and establishing a weight self-learning storage table;
step 2, detecting the actual temperature of the surface of the strip steel according to a pyrometer at the rough rolling outlet, and calculating the temperature TR of each section and layer at the rough rolling outletactPredicting the temperature TR of the strip steel reaching each section and layer at the finish rolling inletcal；
Step 3, a pyrometer is arranged at the finish rolling inlet to measure the surface temperature of the strip steelDetecting temperature, and calculating temperature TF of each layer in thickness directionactEstablishing a cache matrix for recording the temperatures of the sections and the layers;
step 4, judging whether the steel coil generates oscillation or not according to the actual time required from the rough rolling outlet pyrometer to the finish rolling inlet pyrometer; the specific method comprises the following steps:
1) calculating the actual time timetake required from the rough rolling outlet pyrometer to the finish rolling inlet pyrometer;
2) when the timetake is more than 50s, determining that the steel coil generates swing, and when the timetake is less than 50s, determining that the steel coil does not generate swing;
step 5, selecting a weight distribution parameter reading path according to the judgment result of the step 4, thereby calculating a set issued value of the finish rolling inlet temperature;
and 6, calculating the difference value between the actual thickness and the target thickness of the strip steel at the finish rolling outlet to determine a learning coefficient, and using the learning coefficient to replace the learning coefficient in the model configuration table.
4. The control method for reducing the extra thickness of the swaying steel coil according to claim 3, wherein the specific method in the step 2 is as follows:
1) dividing the strip steel into sections with the same length, acquiring the actual surface temperature of each section of the strip steel at a rough rolling outlet, and dividing the strip steel into 5 layers in the thickness direction by using a finite element technology;
2) establishing a buffer matrix for recording the temperature of the sections and the layers;
3) calculating the temperature TR of each section and layer at the rough rolling outlet by calling a temperature difference calculation methodactAnd predicting the temperature TR of each section and layer when the temperature TR reaches the finish rolling inlet through the intermediate roller waycalAt this time, the temperature data TR of the first 5 stages is savedactAnd TRcalAnd sending the data to the finish rolling model for setting and calculating the finish rolling model.
5. The control method for reducing the extra thickness of the swinging steel coil as claimed in claim 3, wherein when an emergency situation occurs on site and the strip steel needs to stay on the middle roller way and the operator knows the stay time, the operator intervention module is invoked:
1) inputting the time required to stay in the middle roller way by an operator, and calling a temperature calculation module to calculate the temperature of the head of the strip steel at the finish rolling inlet;
2) and judging the temperature of the head of the calculated strip steel, and giving a model alarm when the temperature is less than 900 ℃.
6. The control method for reducing the extra thickness of the swaying steel coil in claim 3, wherein the finish rolling inlet temperature in the step 5 is set to the lower value TFETThe specific calculation method comprises the following steps:
TFET＝TRcal×FETcoeff×α+TFact×(1-FETcoeff×α)
wherein, TRcalThe predicted temperature of each section and layer at the finish rolling inlet;
FETcoeff is a weight distribution parameter; when the steel coil does not swing, the weight distribution parameters are read from the configuration file, and when the steel coil swings, the weight distribution parameters are read from the weight distribution storage table;
alpha is a learning coefficient;
TFactthe temperature of each layer in the thickness direction of the strip steel at the finish rolling inlet.
7. The control method for reducing the extra thickness of the swaying steel coil according to claim 3, wherein the specific method in the step 6 is as follows:
a thickness detector is arranged at the outlet of the finish rolling and is used for detecting the actual thickness of the strip steel at the outletactTarget thickness is thickPDI；
1) When | thickact-thickPDI|>When 0.1cm is reached, it shows that the strip steel rolling head is in super-thick state and learning coefficient
Wherein g is a correction coefficient ranging from 0 to 1;
2) replacing the alpha updating in the weight self-learning storage table with the calculation result of alpha calculation in the step 1).
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| JPH1094814A (en) * | 1996-09-26 | 1998-04-14 | Kawasaki Steel Corp | Method for controlling temperature on finish outlet side of hot rolled metallic plate and device for controlling temperature on finish outlet side of hot rolled metallic plate |
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| CN103406369A (en) * | 2013-02-19 | 2013-11-27 | 新疆八一钢铁股份有限公司 | Method for improving strip steel head roll force precision by utilizing temperature function |
| CN103990653A (en) * | 2013-02-19 | 2014-08-20 | 宝山钢铁股份有限公司 | Finish rolling inlet temperature hitting precision ensuring method |
| CN105695705A (en) * | 2014-11-28 | 2016-06-22 | 宝山钢铁股份有限公司 | Basic automatic rolled steel plate rapid cooling system for online solid solution treatment |
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2018
- 2018-11-30 CN CN201811455530.XA patent/CN111250545B/en active Active
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| JPH1094814A (en) * | 1996-09-26 | 1998-04-14 | Kawasaki Steel Corp | Method for controlling temperature on finish outlet side of hot rolled metallic plate and device for controlling temperature on finish outlet side of hot rolled metallic plate |
| CN1990131A (en) * | 2005-12-27 | 2007-07-04 | 宝山钢铁股份有限公司 | Roughed plate bloom temperature control method in hot-rolled process |
| CN101954376A (en) * | 2010-08-31 | 2011-01-26 | 南京钢铁股份有限公司 | Method for medium plate of controlled rolling at two stages in non-recrystallization region |
| CN103406369A (en) * | 2013-02-19 | 2013-11-27 | 新疆八一钢铁股份有限公司 | Method for improving strip steel head roll force precision by utilizing temperature function |
| CN103990653A (en) * | 2013-02-19 | 2014-08-20 | 宝山钢铁股份有限公司 | Finish rolling inlet temperature hitting precision ensuring method |
| CN105695705A (en) * | 2014-11-28 | 2016-06-22 | 宝山钢铁股份有限公司 | Basic automatic rolled steel plate rapid cooling system for online solid solution treatment |
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