CN109127739B

CN109127739B - Method and device for detecting and processing temperature of rolled piece of wire controlled cooling system

Info

Publication number: CN109127739B
Application number: CN201811236607.4A
Authority: CN
Inventors: 潘巍; 王云波; 温志强; 郭巨众
Original assignee: Ceristar Electric Co ltd; Capital Engineering & Research Inc Ltd
Current assignee: Ceristar Electric Co ltd; Capital Engineering & Research Inc Ltd
Priority date: 2018-10-23
Filing date: 2018-10-23
Publication date: 2021-04-16
Anticipated expiration: 2038-10-23
Also published as: CN109127739A

Abstract

The invention provides a method and a device for detecting and processing the temperature of a rolled piece of a wire controlled cooling system, comprising the following steps: acquiring historical temperature of an inlet of a finishing mill and historical temperature of an outlet of the finishing mill; determining historical temperature rise data according to the historical temperature of the inlet of the finishing mill and the historical temperature of the outlet of the finishing mill; determining a relation curve of the inlet temperature and the temperature rise data according to the historical inlet temperature and the historical temperature rise data of the finishing mill; acquiring an actual measurement temperature of an inlet of a finishing mill and an actual measurement temperature of an outlet of the finishing mill; and determining the water supply quantity of the cooling water tank according to the measured temperature of the inlet of the finishing mill, the measured temperature of the outlet of the finishing mill and the relation curve of the inlet temperature and the temperature rise data. This scheme uses multiple temperature can realize controlling cold rolling manufacturing automated control well, has promoted temperature control's precision and reliability.

Description

Method and device for detecting and processing temperature of rolled piece of wire controlled cooling system

Technical Field

The invention relates to the technical field of controlled rolling and controlled cooling, in particular to a method and a device for detecting and processing the temperature of a rolled piece of a wire controlled cooling system.

Background

The core of the controlled rolling and controlled cooling technology is that the obdurability of the steel is further improved and reasonable comprehensive performance is obtained by controlling the process parameters such as heating temperature, rolling process, cooling condition and the like in the rolling process, the content of alloy elements and carbon can be reduced, precious alloy elements are saved, and the production cost is reduced. When high-grade and high-added-value steel grades (such as cold heading steel, spring steel, bearing steel and the like) are rolled, the requirement on the control of the cooling temperature of the steel grades is extremely high and is often required to be controlled within +/-10 ℃, and the rolled material can be separated from an optimal phase change area due to slightly more and less water, so that the final mechanical property of the product is influenced, and quality disputes are generated. In the automatic control cold rolling process, a control system always uses the measured temperature data of a temperature detection point as the basis for adjusting the cooling water amount, which means that the temperature value of a rolled piece must be accurately measured, and then the water supply amount can be accurately controlled, so that the cooling effect is ensured.

In the field of high-speed wire rod water-cooling control, temperature signals are measured by a finishing mill inlet pyrometer, a finishing mill outlet pyrometer, and a laying head outlet pyrometer, and are respectively a finishing mill inlet temperature T2, a finishing mill outlet temperature T3, and a laying head outlet temperature T4, as shown in fig. 1. The pyrometer is generally a thermometer used for measuring a temperature higher than 500 ℃, and commonly used pyrometers include an optical pyrometer, a colorimetric pyrometer, a radiation pyrometer and the like, and the temperature of the pyrometer is mainly measured by using the spectral radiance (namely spectral radiance) of an object.

In practical application, the outlet temperature T3 of the finishing mill is the temperature of a rolled piece entering a cooling water tank, the outlet of the finishing mill is close to the inlet of the cooling water tank, a pyrometer is arranged at a reasonable position, and T3 (if reliable) can directly reflect the real-time temperature of the measuring point, but the measuring point is easily influenced by factors such as water vapor and a cooling water film, so that the fluctuation of the detected temperature signal is severe, and the actual temperature value cannot be truly reflected; meanwhile, the sectional area of the rolled piece is small, the speed is high (the speed can reach 110 m/s), and the rolled piece is influenced by impact of cooling water, tension change and the like to cause the rolled piece to jump up, down, left and right, so that the updating period of a detection signal is short, the time of a data window is narrow, the signal cannot be fast and stable, severe fluctuation occurs, and a specific temperature curve is shown in fig. 2 and 3.

T2 is the temperature of the inlet side of the finishing mill, the rolled piece is low in speed and large in sectional area, meanwhile, the rolled piece is far away from water tanks at two ends and is not influenced by water vapor and the like, the temperature detection at the position is very reliable, but the rolled piece is long in distance from the inlet of the water tank and lags behind a lot, and the temperature of the rolled piece can be changed (temperature rise or temperature drop) through rolling of the finishing mill, so that the temperature of the rolled piece entering the water tank cannot be directly reflected by T2.

T4 is the rolled piece temperature after water cooling, and the rolled piece temperature after rolling is finished, and the hysteresis effect is generated by using T4 to regulate the water supply amount.

Therefore, it is not ideal to use any single temperature to realize automatic control of cold rolling, and the accuracy and reliability of temperature control are not high.

Disclosure of Invention

The embodiment of the invention provides a method and a device for detecting and processing the temperature of a rolled piece of a wire controlled cooling system, which can perfectly realize automatic control of cold rolling by using various temperatures and improve the precision and the reliability of temperature control.

The method for detecting and processing the temperature of the rolled piece of the wire rod cooling control system comprises the following steps:

acquiring historical temperature of an inlet of a finishing mill and historical temperature of an outlet of the finishing mill;

determining historical temperature rise data according to the historical temperature of the inlet of the finishing mill and the historical temperature of the outlet of the finishing mill;

determining a relation curve of the inlet temperature and the temperature rise data according to the historical inlet temperature and the historical temperature rise data of the finishing mill;

acquiring an actual measurement temperature of an inlet of a finishing mill and an actual measurement temperature of an outlet of the finishing mill;

and determining the water supply quantity of the cooling water tank according to the measured temperature of the inlet of the finishing mill, the measured temperature of the outlet of the finishing mill and the relation curve of the inlet temperature and the temperature rise data.

This wire rod accuse cold system rolled piece temperature detection processing apparatus includes:

the temperature acquisition module is used for acquiring the historical temperature of the inlet of the finishing mill and the historical temperature of the outlet of the finishing mill;

the historical temperature rise data determining module is used for determining historical temperature rise data according to the historical temperature of the inlet of the finishing mill and the historical temperature of the outlet of the finishing mill;

the relation curve determining module is used for determining a relation curve between the inlet temperature and the temperature rise data according to the historical temperature and the historical temperature rise data of the finishing mill inlet;

the temperature acquisition module is further configured to: acquiring an actual measurement temperature of an inlet of a finishing mill and an actual measurement temperature of an outlet of the finishing mill;

and the water supply quantity determining module is used for determining the water supply quantity of the cooling water tank according to the measured temperature of the inlet of the finishing mill, the measured temperature of the outlet of the finishing mill and a relation curve of the inlet temperature and the temperature rise data.

The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method when executing the computer program.

The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium stores a computer program for executing the method.

In the embodiment of the invention, historical temperature rise data is determined according to the historical temperature of the inlet of the finishing mill and the historical temperature of the outlet of the finishing mill, a relation curve of the inlet temperature and the temperature rise data is determined according to the historical temperature of the inlet of the finishing mill and the historical temperature rise data, and the water supply quantity of the cooling water tank is determined according to the obtained relation curves of the actually measured temperature of the inlet of the finishing mill, the actually measured temperature of the outlet of the finishing mill and the inlet temperature and the temperature rise data. This scheme uses multiple temperature can realize controlling cold rolling manufacturing automated control well, has promoted temperature control's precision and reliability.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic view of a temperature sensing element arrangement;

FIG. 2 is a graph of a finishing mill exit temperature profile;

FIG. 3 is a graph of measured temperature at the outlet of a finishing mill;

FIG. 4 is a flowchart of a method for detecting and processing a temperature of a rolled piece in a wire cooling control system according to an embodiment of the present invention;

FIG. 5 is a flow chart of an outlet temperature process provided by an embodiment of the present invention;

FIG. 6 is a graph of inlet temperature versus temperature data (i.e., temperature change curve) according to an embodiment of the present invention;

FIG. 7 is a flow chart of an inlet temperature process provided by an embodiment of the present invention;

FIG. 8 is a flow chart of a data processing method before entering a self-learning optimization process provided by an embodiment of the present invention;

FIG. 9 is a flow chart of a self-learning optimization process for temperature rise data corresponding to a current measured temperature at an inlet of a finishing mill according to an embodiment of the present invention;

FIG. 10 is a flow chart of a self-learning optimization process for temperature rise data for points other than the current measured temperature at the finishing mill inlet provided by an embodiment of the present invention;

FIG. 11 is a Gaussian weight distribution diagram for each steel type provided by an embodiment of the present invention;

FIG. 12 is a flow chart of temperature acquisition and self-learning (top half) according to an embodiment of the present invention;

FIG. 13 is a flow chart of temperature acquisition and self-learning (bottom half) according to an embodiment of the present invention;

FIG. 14 is a block diagram of a rolled piece temperature detection processing apparatus of a wire cooling control system according to an embodiment of the present invention;

FIG. 15 is a block diagram of a rolled piece temperature detection processing apparatus of a wire cooling control system according to an embodiment of the present invention;

FIG. 16 is a block diagram of a rolled piece temperature detection processing apparatus of a wire cooling control system according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In an embodiment of the present invention, a method for detecting and processing a temperature of a rolled piece of a wire controlled cooling system is provided, as shown in fig. 4, the method includes:

step 401: acquiring historical temperature of an inlet of a finishing mill and historical temperature of an outlet of the finishing mill;

step 402: determining historical temperature rise data according to the historical temperature of the inlet of the finishing mill and the historical temperature of the outlet of the finishing mill;

determining historical temperature rise data according to the following formula:

ΔT_mea＝T3_mea-T2_mea；

wherein, Delta T_meaRepresenting historical temperature rise data; t3_meaRepresenting the acquired historical outlet temperature of the finishing mill; t2_meaRepresenting the acquired finishing mill inlet historical temperature.

Step 403: determining a relation curve of the inlet temperature and the temperature rise data according to the historical inlet temperature and the historical temperature rise data of the finishing mill;

step 404: acquiring an actual measurement temperature of an inlet of a finishing mill and an actual measurement temperature of an outlet of the finishing mill;

step 405: and determining the water supply quantity of the cooling water tank according to the measured temperature of the inlet of the finishing mill, the measured temperature of the outlet of the finishing mill and the relation curve of the inlet temperature and the temperature rise data.

In the embodiment of the present invention, after the historical inlet temperature and the historical outlet temperature of the finishing mill are obtained, the historical inlet temperature and the historical outlet temperature of the finishing mill need to be processed, as shown in fig. 5, the specific processing is as follows:

step 501: judging whether the historical outlet temperature of the finishing mill is greater than the minimum value of the preset outlet temperature of the finishing mill or not:

step 502: when the finishing mill outlet historical temperature T3_meaLess than or equal to the preset minimum value T3 of outlet temperature of finishing mill_minWhen the historical temperature of the outlet of the finishing mill is invalid, the historical temperature of the outlet of the finishing mill is obtained again;

step 503: when the historical outlet temperature of the finishing mill is greater than the minimum value of the preset outlet temperature of the finishing mill, judging whether the change rate of the historical outlet temperature of the finishing mill is smaller than a preset proportion:

judging whether the change rate of the historical temperature of the outlet of the finishing mill is smaller than a preset proportion according to the following formula:

|(T3_lastmea-T3_mea)/T3_lastmea|<10％；

wherein, T3_lastmeaRepresenting the historical temperature of the outlet of the finishing mill obtained in the last temperature acquisition; t3_meaAnd the preset proportion is 10 percent, and represents the historical temperature of the outlet of the finishing mill acquired by current temperature acquisition.

Step 504: when the change rate of the historical outlet temperature of the finishing mill is larger than or equal to a preset proportion, the historical outlet temperature of the finishing mill is invalid, and the historical outlet temperature of the finishing mill is obtained again;

step 505: and when the change rate of the historical outlet temperature of the finishing mill is smaller than a preset proportion, the historical outlet temperature of the finishing mill is effective.

After the temperature data processing, step 402: determining historical temperature rise data according to the historical temperature of the inlet of the finishing mill and the historical temperature of the outlet of the finishing mill, and specifically comprising the following steps:

and determining historical temperature rise data according to the historical temperature of the inlet of the finishing mill and the historical temperature of the outlet of the finishing mill judged to be effective.

Specifically, the temperature data processing described above is actually analog input signal processing (AI), Peak picking calculation (Peak) and Ramp amplitude limiting (Ramp) processing performed on the finishing mill entrance history temperature and the finishing mill exit history temperature. In the processing process, errors such as overrun of temperature values, excessive temperature change and the like are effectively inhibited.

The analog input signal processing (AI) receives bottom thermometer feedback signals (finishing mill inlet historical temperature T2 and finishing mill outlet historical temperature T3, including but not limited to 4-20 mA types), performs functions of validity check, engineering value conversion, abnormal elimination, alarm and the like on feedback values, ensures stability and credibility of the input bottom signal, and gives an alarm when a signal feedback channel is abnormal, if the signal feedback channel is in a temperature closed-loop control process, the temperature closed loop can be maintained by using a last correct value according to the design of an operator, or the temperature closed loop is automatically exited after the alarm, and the flow closed-loop mode is automatically entered.

Peak picking calculation (Peak) is used for quickly eliminating the low detection of the thermometer caused by water vapor, iron scale, sun and shade surfaces and the like, and picking up the temperature Peak value in the set Peak picking interval. The peak picking area is set to be adjustable, the shorter the peak picking length is set, the higher the sampling density is, and the better the real-time property of the temperature value is. The function can effectively and quickly remove the obvious low temperature value out of the control system.

Ramp clipping (Ramp) is used to suppress the magnitude of the change in the sampled temperature value. Due to the existence of the thermal inertia of the rolled piece, the temperature value of the rolled piece cannot be suddenly changed, the slope amplitude limiting limits the influence on the feedback of the thermometer caused by the bouncing and the shaking of the rolled piece, other irregular heat sources (such as flame cutting and electrogas welding) on the spot and the like by inhibiting the change rate of the slope amplitude limiting, and the function can effectively and quickly remove the obvious low temperature value or high temperature value out of the control system. The rate of change is derived from process specifications and is determined during the commissioning phase.

In the embodiment of the present invention, the relationship curve between the inlet temperature and the temperature rise data is different for different rolled steel varieties, specifications, speeds, and other parameters, so step 403 specifically includes:

determining layer parameters according to the historical temperature of the finish rolling mill inlet, wherein the layer parameters comprise a steel gauge layer, a rolling speed layer and a number of layers of commissioning racks;

and determining a relation curve of the inlet temperature and the temperature rise data according to the layer parameters and the historical temperature rise data.

For example: the temperature change curve of the rolling SWRCH 22A-phi 6.5 specification, the outlet speed of the finishing mill is 40.75-44.34m/s, and when the finishing mill is used for 6 frames is shown in figure 6. As can be seen from fig. 6, the temperature rise data gradually decreased as the inlet temperature gradually increased.

In the embodiment of the present invention, as shown in fig. 7, step 405 specifically determines the water supply amount of the cooling water tank as follows:

step 4051: determining whether the finishing mill inlet measured temperature T2mea is present on the inlet temperature (T2) versus temperature rise data (Δ T) (dtT):

step 4052: if the measured inlet temperature T2mea of the finishing mill exists on the relation curve of the inlet temperature and the temperature rise data, searching the temperature rise data delta T corresponding to the measured inlet temperature of the finishing mill from the relation curve of the inlet temperature and the temperature rise data_oldDetermining theoretical temperature of an outlet of the finishing mill according to the actually measured temperature of the inlet of the finishing mill and corresponding temperature rise data, and determining water supply quantity of a cooling water tank according to the theoretical temperature of the outlet of the finishing mill;

step 4053: if the measured temperature of the finishing mill inlet does not exist on the relation curve of the inlet temperature and the temperature rise data, judging whether at least two corresponding points exist in a preset range, wherein the two corresponding points represent two groups of corresponding inlet temperature and temperature rise data, the preset range is [ inlet temperature-preset value, inlet temperature + preset value ], and the preset value can be 5 degrees, namely [ T2-5, T2+5 ]:

step 4054: if at least two corresponding points exist, calculating temperature rise data delta T corresponding to the measured temperature at the inlet of the finishing mill according to the temperature rise data corresponding to the two corresponding points by using an interpolation method_oldDetermining theoretical temperature of an outlet of a finishing mill according to the actually measured temperature of the inlet of the finishing mill and the calculated temperature rise data, determining water supply quantity of a cooling water tank according to the theoretical temperature of the outlet of the finishing mill, and adding the actually measured temperature of the inlet of the finishing mill and the calculated temperature rise data to a relation curve of the inlet temperature and the temperature rise data;

step 4055: and if the two corresponding points do not exist, determining temperature rise actual data according to the measured temperature of the finishing mill inlet and the measured temperature of the finishing mill outlet, determining the water supply quantity of the cooling water tank according to the measured temperature of the finishing mill outlet, and adding the measured temperature of the finishing mill inlet and the temperature rise actual data into a relation curve of the inlet temperature and the temperature rise data.

In the embodiment of the invention, in the actual rolling process, sometimes the final rolling effect obtained by the water supply amount of the cooling water tank determined according to the actually measured outlet temperature of the finishing mill may be better than the final rolling effect obtained by the water supply amount of the cooling water tank determined according to the actually measured inlet temperature of the finishing mill on the relation curve of the inlet temperature and the temperature rise data and the theoretical outlet temperature of the finishing mill determined according to the corresponding temperature rise data, and at this moment, the self-learning optimization processing needs to be carried out on the temperature rise data on the temperature change curve corresponding to the actually measured inlet temperature of the finishing mill obtained by current measurement. Specifically, as shown in fig. 8, the self-learning optimization processing procedure includes:

step 601: obtaining a current measured temperature of a first laying head outlet and a current measured temperature of a second laying head outlet, wherein the current measured temperature of the first laying head outlet is determined according to the current measured temperature of the finishing mill inlet and corresponding temperature rise data, or the current measured temperature of the first laying head outlet is determined according to the current measured temperature of the finishing mill inlet and temperature rise data calculated by an interpolation method; the measured temperature of the outlet of the second laying head is determined according to the current measured temperature of the outlet of the finishing mill;

step 602: obtaining a first difference absolute value by subtracting the current measured temperature of the first laying head outlet from the optimal laying head outlet temperature;

step 603: obtaining a second difference absolute value by subtracting the current measured temperature of the second laying head outlet from the optimal laying head outlet temperature;

step 604: comparing the first difference absolute value and the second difference absolute value:

step 605: and when the first difference absolute value is larger than the second difference absolute value, performing self-learning optimization processing on temperature rise data corresponding to the current measured temperature of the inlet of the finishing mill according to the current measured temperature of the outlet of the finishing mill to obtain optimized temperature rise data.

Step 606: and when the first difference absolute value is smaller than the second difference absolute value, not performing self-learning optimization processing on the temperature rise data corresponding to the current measured temperature of the finishing mill inlet.

Specifically, as shown in fig. 9, step 605 is specifically executed as follows:

step 6051: determining the current theoretical temperature of the outlet of the finishing mill, wherein the current theoretical temperature of the outlet of the finishing mill is determined according to the current measured temperature of the inlet of the finishing mill and corresponding temperature rise data, or according to the current measured temperature of the inlet of the finishing mill and temperature rise data calculated by an interpolation method;

step 6052: determining a finishing mill outlet temperature difference value according to the current measured temperature of the finishing mill outlet and the current theoretical temperature of the finishing mill outlet;

step 6053: determining a change coefficient according to the temperature difference value of the outlet of the finishing mill and the type of the steel profile;

step 6054: determining current temperature rise actual data according to the current measured temperature of the inlet of the finishing mill and the current measured temperature of the outlet of the finishing mill;

step 6055: determining temperature rise change data, wherein the temperature rise data are temperature rise data, current temperature rise actual data and a change coefficient corresponding to the current measured temperature of the finishing mill inlet, or calculated temperature rise data, current temperature rise actual data and a change coefficient corresponding to the current measured temperature of the finishing mill inlet;

temperature rise change data was determined according to the following formula:

ΔT_new＝aΔT_mea+(1-a)ΔT_old；

wherein, Delta T_newRepresenting temperature rise change data; a represents a variation coefficient; delta T_meaRepresenting the actual data of the current temperature rise; delta T_oldAnd representing temperature rise data corresponding to the current measured temperature of the finishing mill inlet or calculated temperature rise data corresponding to the current measured temperature of the finishing mill inlet.

Specifically, practice shows that when a is taken as a value according to table 1, rapid self-adaptation and stable output of a curve can be realized.

TABLE 1

Step 6056: and when the temperature rise change data is determined, judging whether the change rate of the temperature rise change data exceeds a preset limit value, and if the change rate of the temperature rise change data exceeds the preset limit value, carrying out amplitude limiting adjustment on the temperature rise change data.

Step 6057: and updating the temperature rise data corresponding to the current measured temperature of the finish rolling mill inlet by using the temperature rise change data, or updating the calculated temperature rise data corresponding to the current measured temperature of the finish rolling mill inlet by using the temperature rise change data.

In the embodiment of the invention, after the self-learning optimization processing is carried out on the temperature rise data on the temperature change curve corresponding to the measured temperature of the inlet of the finishing mill obtained by current measurement, the self-learning optimization processing is also required to be carried out on other temperature rise data on the temperature change curve. As shown in fig. 10, the optimization process steps are as follows:

step 1001: determining a temperature rise data change amount, wherein the temperature rise data change amount is determined according to temperature rise change data and temperature rise data corresponding to the current measured temperature of the finish rolling mill inlet, or the temperature rise data change amount is determined according to the temperature rise change data and calculated temperature rise data corresponding to the current measured temperature of the finish rolling mill inlet;

the temperature rise data change amount is determined according to the following formula:

deltaT＝ΔT_new-ΔT_old；

wherein deltaT represents the amount of change in temperature rise data, Δ T_newRepresenting temperature rise change data; delta T_oldAnd representing temperature rise data corresponding to the current measured temperature of the finishing mill inlet or calculated temperature rise data corresponding to the current measured temperature of the finishing mill inlet.

Step 1002: judging whether the temperature rise data change is larger than 0:

step 1003: if the temperature is greater than 0, traversing all inlet temperatures (points on the left side of the temperature change curve corresponding to T2 mea) corresponding to the current actually measured temperature of the finish rolling mill inlet on the inlet temperature and temperature rise data relation curve in sequence, and setting a control variable (ct1) to be 0;

step 1004: if the temperature is not more than 0, sequentially traversing all inlet temperatures (points on the temperature change curve on the right side corresponding to T2 mea) corresponding to the current measured temperature of the finish rolling mill inlet on the inlet temperature and temperature rise data relation curve, and setting a control variable (ct1) to be 1;

step 1005: judging whether a first inlet temperature Ti is not equal to the current measured temperature of the finish rolling mill inlet, wherein the first inlet temperature is any one of traversed inlet temperatures:

step 1006: if not, calculating a first self-learning weight of the first inlet temperature by using a Gaussian function, and determining first temperature rise updating data of the first inlet temperature according to the temperature rise data change amount, the first self-learning weight and the temperature rise data corresponding to the first inlet temperature;

determining first temperature rise update data according to the following formula:

ΔT_inew＝ΔT_iold+K₁×deltaT；

wherein, Delta T_inewRepresenting temperature rise change data corresponding to the ith inlet temperature on the inlet temperature and temperature rise data relation curve, wherein i is equal to 1, 2 and … … n, n represents the number of all inlet temperatures which are smaller than the current measured temperature of the finishing mill inlet on the inlet temperature and temperature rise data relation curve, or n represents the number of all inlet temperatures which are larger than the current measured temperature of the finishing mill inlet on the inlet temperature and temperature rise data relation curve; delta T_ioldTemperature rise data corresponding to the ith inlet temperature on a relation curve of the inlet temperature and the temperature rise data is represented; k₁Denotes a first self-learning weight, K₁<1；

And if so, directly using the temperature rise change data of the current measured temperature at the inlet of the finishing mill.

Step 1007: judging whether a control variable corresponding to the first inlet temperature is 0:

step 1008: if the value is 0 (it is indicated that the left point on the temperature change curve corresponds to T2 mea), increasing the value of the temperature rise data corresponding to the first inlet temperature, and determining whether the temperature rise data corresponding to the first inlet temperature is smaller than the temperature rise data corresponding to the second inlet temperature (i.e., determining a variation trend of the temperature rise curve), where the second inlet temperature is an inlet temperature located after the first inlet temperature on the inlet temperature and temperature rise data relationship curve:

step 1009: if the temperature rise data corresponding to the first inlet temperature delta Ti is less than the second inlet temperature

Δ T (i +1) corresponding temperature rise data (i.e., the latter point rises faster than the former point, which is not in accordance with the change rule of the temperature rise curve of fig. 6), so that the second self-learning weight of the first inlet temperature is calculated using a gaussian function, and the second temperature rise update data of the first inlet temperature is determined according to the first temperature rise update data and the second self-learning weight of the second inlet temperature;

determining second temperature rise update data for the first inlet temperature according to the following equation:

ΔT′_inew＝K₂×ΔT_(i+1)new；

wherein, delta T'_inewRepresenting second temperature rise update data; k₂Representing a second self-learning weight; delta T_(i+1)newFirst temperature rise update data indicative of a second inlet temperature;

step 1010: if the temperature rise data corresponding to the first inlet temperature is not smaller than the temperature rise data corresponding to the second inlet temperature, updating the temperature rise data corresponding to the first inlet temperature according to the first temperature rise updating data of the first inlet temperature;

step 1011: if not 0 (i.e. 1, which means that the value is the right point on the temperature change curve corresponding to T2 meas), reducing the value of the temperature rise data corresponding to the first inlet temperature, and determining whether the temperature rise data corresponding to the first inlet temperature is greater than the temperature rise data corresponding to a third inlet temperature, where the third inlet temperature is an inlet temperature before the first inlet temperature on the inlet temperature and temperature rise data relationship curve:

step 1012: if the temperature rise data Δ Ti corresponding to the first inlet temperature is greater than the temperature rise data corresponding to the third inlet temperature Δ T (i-1) (that is, the later point rises faster than the former point and does not conform to the change rule of the temperature rise curve of fig. 6), calculating a third self-learning weight of the first inlet temperature by using a gaussian function, and determining third temperature rise update data of the first inlet temperature according to the first temperature rise update data and the third self-learning weight of the third inlet temperature;

ΔT′_inew＝K₃×ΔT_(i-1)new；

wherein, delta T'_inewRepresenting third temperature rise update data; k₃Representing a third self-learning weight; delta T_(i-1)newFirst temperature rise update data indicative of a third inlet temperature;

step 1013: and if the temperature rise data corresponding to the first inlet temperature is not greater than the temperature rise data corresponding to the third inlet temperature, updating the temperature rise data corresponding to the first inlet temperature according to the first temperature rise updating data of the first inlet temperature.

In actual production, the learning data of the point can affect the trends of other points on the temperature change curve, and under the condition of ensuring the stability of the system, how to quickly and stably adjust the curve trends of other points is always a difficult problem. The invention is based on the Gaussian distribution function and utilizes the differential state between other points and the measuring point to calculate the self-learning weight of other points, thereby effectively solving the problem. The gaussian function is as follows:

after continuous optimization on site and the combination of actual control effect, it is determined that the self-learning effect is optimal when a' is 1, b is 0 and c is valued according to table 2 in the gaussian function.

TABLE 2 typical Steel grade values

The self-learning weight curves of other points when different steel grades are rolled are shown in FIG. 11.

The flow chart of the rolled piece temperature detection processing method of the whole wire controlled cooling system is shown in fig. 12 and 13.

Based on the same inventive concept, the embodiment of the invention also provides a rolled piece temperature detection processing device of the wire controlled cooling system, which is described in the following embodiment. Because the principle of solving the problems of the rolled piece temperature detection processing device of the wire rod cooling control system is similar to the rolled piece temperature detection processing method of the wire rod cooling control system, the implementation of the rolled piece temperature detection processing device of the wire rod cooling control system can refer to the implementation of the rolled piece temperature detection processing method of the wire rod cooling control system, and repeated parts are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.

Fig. 14 is a block diagram of a rolled piece temperature detection processing device of a wire rod cooling control system according to an embodiment of the present invention, and as shown in fig. 14, the rolled piece temperature detection processing device includes:

a temperature acquisition module 1401 for acquiring a finishing mill inlet historical temperature and a finishing mill outlet historical temperature;

a historical temperature rise data determination module 1402, configured to determine historical temperature rise data according to the historical temperature at the inlet of the finishing mill and the historical temperature at the outlet of the finishing mill;

a relation curve determining module 1403, configured to determine a relation curve between the inlet temperature and the temperature rise data according to the historical temperature of the finishing mill inlet and the historical temperature rise data;

the temperature acquisition module 1401 is further configured to: acquiring an actual measurement temperature of an inlet of a finishing mill and an actual measurement temperature of an outlet of the finishing mill;

and a water supply amount determining module 1404, configured to determine a water supply amount of the cooling water tank according to the measured finishing mill inlet temperature, the measured finishing mill outlet temperature, and a relationship curve between the inlet temperature and the temperature rise data.

This structure will be explained below.

In the embodiment of the present invention, as shown in fig. 15, the method further includes: a temperature processing module 1405 for:

after the historical inlet temperature and the historical outlet temperature of the finishing mill are obtained, comparing the historical outlet temperature of the finishing mill with the minimum value of the preset outlet temperature of the finishing mill:

when the historical outlet temperature of the finishing mill is less than or equal to the minimum value of the preset finishing mill outlet temperature, the historical outlet temperature of the finishing mill is invalid, and the historical outlet temperature of the finishing mill is obtained again;

when the historical outlet temperature of the finishing mill is greater than the minimum value of the preset outlet temperature of the finishing mill, judging whether the change rate of the historical outlet temperature of the finishing mill is smaller than a preset proportion:

when the change rate of the historical outlet temperature of the finishing mill is larger than or equal to a preset proportion, the historical outlet temperature of the finishing mill is invalid, and the historical outlet temperature of the finishing mill is obtained again;

when the change rate of the historical outlet temperature of the finishing mill is smaller than a preset proportion, the historical outlet temperature of the finishing mill is effective;

determining historical temperature rise data according to the historical temperature of the inlet of the finishing mill and the historical temperature of the outlet of the finishing mill, wherein the historical temperature rise data comprises the following steps:

In this embodiment of the present invention, the temperature processing module 1405 is specifically configured to:

|(T3_lastmea-T3_mea)/T3_lastmea|<10％；

wherein, T3_lastmeaRepresenting the historical temperature of the outlet of the finishing mill obtained in the last temperature acquisition; t3_meaFinishing mill outlet showing current temperature acquisitionThe preset proportion is 10% of the historical temperature.

In this embodiment of the present invention, the historical temperature rise data determining module 1402 is specifically configured to:

determining historical temperature rise data according to the historical temperature of the inlet of the finishing mill and the historical temperature of the outlet of the finishing mill according to the following formula:

ΔT_mea＝T3_mea-T2_mea；

In this embodiment of the present invention, the relation curve determining module 1403 is specifically configured to:

In the embodiment of the present invention, the water supply amount determining module 1404 is specifically configured to:

determining whether the measured temperature at the inlet of the finishing mill exists on a relational curve of the inlet temperature and the temperature rise data:

if the measured inlet temperature of the finishing mill exists on the relation curve of the inlet temperature and the temperature rise data, searching the temperature rise data corresponding to the measured inlet temperature of the finishing mill from the relation curve of the inlet temperature and the temperature rise data, determining the theoretical outlet temperature of the finishing mill according to the measured inlet temperature of the finishing mill and the corresponding temperature rise data, and determining the water supply quantity of the cooling water tank according to the theoretical outlet temperature of the finishing mill;

if the measured inlet temperature of the finishing mill does not exist on the relation curve of the inlet temperature and the temperature rise data, judging whether at least two corresponding points exist in a preset range, wherein the two corresponding points represent two groups of corresponding inlet temperature and temperature rise data, and the preset range is [ inlet temperature-preset value, inlet temperature + preset value ]:

if at least two corresponding points exist, calculating temperature rise data corresponding to the measured temperature of the finishing mill inlet according to the temperature rise data corresponding to the two corresponding points by using an interpolation method, determining the theoretical temperature of the finishing mill outlet according to the measured temperature of the finishing mill inlet and the calculated temperature rise data, determining the water supply quantity of a cooling water tank according to the theoretical temperature of the finishing mill outlet, and adding the measured temperature of the finishing mill inlet and the calculated temperature rise data to a relation curve of the inlet temperature and the temperature rise data;

and if the two corresponding points do not exist, determining temperature rise actual data according to the measured temperature of the finishing mill inlet and the measured temperature of the finishing mill outlet, determining the water supply quantity of the cooling water tank according to the measured temperature of the finishing mill outlet, and adding the measured temperature of the finishing mill inlet and the temperature rise actual data into a relation curve of the inlet temperature and the temperature rise data.

In the embodiment of the present invention, as shown in fig. 16, the method further includes: a self-learning optimization process 1406 for:

obtaining a current measured temperature of a first laying head outlet and a current measured temperature of a second laying head outlet, wherein the current measured temperature of the first laying head outlet is determined according to the current measured temperature of the finishing mill inlet and corresponding temperature rise data, or the current measured temperature of the first laying head outlet is determined according to the current measured temperature of the finishing mill inlet and temperature rise data calculated by an interpolation method; the measured temperature of the outlet of the second laying head is determined according to the current measured temperature of the outlet of the finishing mill;

obtaining a first difference absolute value by subtracting the current measured temperature of the first laying head outlet from the optimal laying head outlet temperature;

obtaining a second difference absolute value by subtracting the current measured temperature of the second laying head outlet from the optimal laying head outlet temperature;

and comparing the first difference absolute value with the second difference absolute value, and when the first difference absolute value is larger than the second difference absolute value, performing self-learning optimization processing on temperature rise data corresponding to the current measured temperature at the inlet of the finishing mill according to the current measured temperature at the outlet of the finishing mill to obtain optimized temperature rise data.

In the embodiment of the present invention, the self-learning optimization processing 1406 is specifically configured to:

performing self-learning optimization processing on temperature rise data corresponding to the current measured temperature of the inlet of the finishing mill according to the current measured temperature of the outlet of the finishing mill in the following mode to obtain optimized temperature rise data:

determining the current theoretical temperature of the outlet of the finishing mill, wherein the current theoretical temperature of the outlet of the finishing mill is determined according to the current measured temperature of the inlet of the finishing mill and corresponding temperature rise data, or according to the current measured temperature of the inlet of the finishing mill and temperature rise data calculated by an interpolation method;

determining a finishing mill outlet temperature difference value according to the current measured temperature of the finishing mill outlet and the current theoretical temperature of the finishing mill outlet;

determining a change coefficient according to the temperature difference value of the outlet of the finishing mill and the type of the steel profile;

determining current temperature rise actual data according to the current measured temperature of the inlet of the finishing mill and the current measured temperature of the outlet of the finishing mill;

determining temperature rise change data, wherein the temperature rise data are temperature rise data, current temperature rise actual data and a change coefficient corresponding to the current measured temperature of the finishing mill inlet, or calculated temperature rise data, current temperature rise actual data and a change coefficient corresponding to the current measured temperature of the finishing mill inlet;

and updating the temperature rise data corresponding to the current measured temperature of the finish rolling mill inlet by using the temperature rise change data, or updating the calculated temperature rise data corresponding to the current measured temperature of the finish rolling mill inlet by using the temperature rise change data.

In an embodiment of the present invention, the self-learning optimization process 1406 is further configured to:

and judging whether the change rate of the temperature rise change data exceeds a preset limit value or not, and if the change rate of the temperature rise change data exceeds the preset limit value, carrying out amplitude limiting adjustment on the temperature rise change data.

temperature rise change data was determined according to the following formula:

ΔT_new＝aΔT_mea+(1-a)ΔT_old；

after self-learning optimization processing is carried out on temperature rise data corresponding to the current measured temperature at the inlet of the finishing mill, self-learning optimization processing is carried out on other temperature rise data on the inlet temperature and temperature rise data relation curve;

the method comprises the following steps:

determining a temperature rise data change amount, wherein the temperature rise data change amount is determined according to temperature rise change data and temperature rise data corresponding to the current measured temperature of the finish rolling mill inlet, or the temperature rise data change amount is determined according to the temperature rise change data and calculated temperature rise data corresponding to the current measured temperature of the finish rolling mill inlet;

judging whether the temperature rise data change is larger than 0:

if the temperature is greater than 0, traversing all inlet temperatures corresponding to temperatures smaller than the current measured temperature of the finish rolling mill inlet on the inlet temperature and temperature rise data relation curve in sequence, and setting a control variable to be 0;

if the temperature is not more than 0, sequentially traversing all inlet temperatures corresponding to temperatures larger than the current measured temperature of the finish rolling mill inlet on the inlet temperature and temperature rise data relation curve, and setting a control variable to be 1;

judging whether a first inlet temperature is not equal to the current measured temperature of the finish rolling mill inlet, wherein the first inlet temperature is any one of traversed inlet temperatures:

if not, calculating a first self-learning weight of the first inlet temperature by using a Gaussian function, and determining first temperature rise updating data of the first inlet temperature according to the temperature rise data change amount, the first self-learning weight and the temperature rise data corresponding to the first inlet temperature;

judging whether a control variable corresponding to the first inlet temperature is 0:

if the temperature is 0, increasing the value of the temperature rise data corresponding to the first inlet temperature, and judging whether the temperature rise data corresponding to the first inlet temperature is smaller than the temperature rise data corresponding to a second inlet temperature, wherein the second inlet temperature is the inlet temperature which is positioned after the first inlet temperature on the relation curve of the inlet temperature and the temperature rise data:

if the temperature rise data corresponding to the first inlet temperature is smaller than the temperature rise data corresponding to the second inlet temperature, calculating a second self-learning weight of the first inlet temperature by using a Gaussian function, and determining second temperature rise updating data of the first inlet temperature according to the first temperature rise updating data and the second self-learning weight of the second inlet temperature;

if the temperature rise data corresponding to the first inlet temperature is not smaller than the temperature rise data corresponding to the second inlet temperature, updating the temperature rise data corresponding to the first inlet temperature according to the first temperature rise updating data of the first inlet temperature;

if not, reducing the value of the temperature rise data corresponding to the first inlet temperature, and judging whether the temperature rise data corresponding to the first inlet temperature is greater than the temperature rise data corresponding to a third inlet temperature, wherein the third inlet temperature is the inlet temperature before the first inlet temperature on the relation curve of the inlet temperature and the temperature rise data:

if the temperature rise data corresponding to the first inlet temperature is larger than the temperature rise data corresponding to a third inlet temperature, calculating a third self-learning weight of the first inlet temperature by using a Gaussian function, and determining third temperature rise updating data of the first inlet temperature according to the first temperature rise updating data and the third self-learning weight of the third inlet temperature;

and if the temperature rise data corresponding to the first inlet temperature is not greater than the temperature rise data corresponding to the third inlet temperature, updating the temperature rise data corresponding to the first inlet temperature according to the first temperature rise updating data of the first inlet temperature.

ΔT_inew＝ΔT_iold+K₁×deltaT；

wherein, Delta T_inewRepresenting temperature rise change data corresponding to the ith inlet temperature on the inlet temperature and temperature rise data relation curve, wherein i is equal to 1, 2 and … … n, n represents the number of all inlet temperatures which are smaller than the current measured temperature of the finishing mill inlet on the inlet temperature and temperature rise data relation curve, or n represents the number of all inlet temperatures which are larger than the current measured temperature of the finishing mill inlet on the inlet temperature and temperature rise data relation curve; delta T_ioldTemperature rise data corresponding to the ith inlet temperature on a relation curve of the inlet temperature and the temperature rise data is represented; k₁Representing a first self-learning weight; deltaT represents the amount of change in temperature data, deltaT_new-ΔT_old，ΔT_newRepresenting temperature rise change data; delta T_oldAnd representing temperature rise data corresponding to the current measured temperature of the finishing mill inlet or calculated temperature rise data corresponding to the current measured temperature of the finishing mill inlet.

In conclusion, the method and the device for detecting and processing the temperature of the rolled piece of the wire rod cooling control system solve the problem of reliability of a temperature detection signal in the existing high-speed wire rod cooling control system, can effectively inhibit or even eliminate the influence of severe external factors such as the shade surface (generated by uneven cooling), water vapor, a cooling water film, the bounce of the rolled piece and the like on the surface of the rolled piece, can process the actual temperature value signal accurately and quickly, and lays a good data foundation for further control and application of related advanced algorithms.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made to the embodiment of the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A temperature detection processing method for a rolled piece of a wire controlled cooling system is characterized by comprising the following steps:

determining the water supply quantity of a cooling water tank according to the measured inlet temperature of the finishing mill, the measured outlet temperature of the finishing mill and a relation curve of the inlet temperature and the temperature rise data;

after the historical temperature of the inlet of the finishing mill and the historical temperature of the outlet of the finishing mill are obtained, the method further comprises the following steps:

judging whether the historical outlet temperature of the finishing mill is greater than the minimum value of the preset outlet temperature of the finishing mill or not:

2. The rolled piece temperature detection processing method for the wire rod cooling control system according to claim 1, characterized by judging whether the change rate of the historical outlet temperature of the finishing mill is smaller than a preset proportion according to the following formula:

|(T3_lastmea-T3_mea)/T3_lastmea|<10％；

3. The method for detecting and processing the temperature of a rolled piece of a wire rod cooling control system according to claim 1, wherein the historical temperature rise data is determined according to the historical temperature at the inlet of the finishing mill and the historical temperature at the outlet of the finishing mill according to the following formula:

ΔT_mea＝T3_mea-T2_mea；

wherein, Delta T_meaRepresenting historical temperature rise data; t3_meaIndicating the essence of acquisitionThe historical temperature of the outlet of the rolling mill; t2_meaRepresenting the acquired finishing mill inlet historical temperature.

4. The method for inspecting and processing the temperature of a rolling stock for a wire rod cooling control system according to claim 1, wherein determining an inlet temperature versus temperature rise data relationship curve based on the historical inlet temperature and the historical temperature rise data for the finishing mill comprises:

5. The method for detecting and processing the temperature of a rolled piece of a wire rod cooling control system according to claim 1, wherein determining the water supply amount of a cooling water tank according to the measured temperature at the inlet of the finishing mill, the measured temperature at the outlet of the finishing mill and the relation curve of the inlet temperature and the temperature rise data comprises:

6. The wire controlled cooling system rolled piece temperature detection processing method of claim 5, further comprising:

7. The rolled piece temperature detection processing method for the wire rod cooling control system according to claim 6, wherein the self-learning optimization processing is performed on the temperature rise data corresponding to the current measured temperature at the inlet of the finishing mill according to the current measured temperature at the outlet of the finishing mill, so as to obtain the optimized temperature rise data, and the method comprises the following steps:

8. The wire controlled cooling system rolled piece temperature detection processing method of claim 7, further comprising:

9. The rolled piece temperature detection processing method of the wire controlled cooling system according to claim 7, characterized in that the temperature rise change data is determined according to the following formula:

ΔT_new＝aΔT_mea+(1-a)ΔT_old；

10. The wire controlled cooling system rolled piece temperature detection processing method of claim 7, further comprising:

the method comprises the following steps:

judging whether the temperature rise data change is larger than 0:

11. The wire controlled cooling system product temperature sensing processing method of claim 10, wherein the first temperature rise update data is determined according to the following equation:

ΔT_inew＝ΔT_iold+K₁×deltaT；

12. The utility model provides a wire rod controlled cooling system rolled piece temperature detection processing apparatus which characterized in that includes:

the water supply quantity determining module is used for determining the water supply quantity of the cooling water tank according to the measured temperature of the inlet of the finishing mill, the measured temperature of the outlet of the finishing mill and a relation curve of the inlet temperature and the temperature rise data;

further comprising: a temperature processing module to:

after the historical inlet temperature and the historical outlet temperature of the finishing mill are obtained, whether the historical outlet temperature of the finishing mill is greater than the minimum value of the preset outlet temperature of the finishing mill is judged:

13. The wire rod cooling control system rolled piece temperature detection processing apparatus of claim 12, wherein the temperature processing module is specifically configured to:

|(T3_lastmea-T3_mea)/T3_lastmea|<10％；

14. The rolled piece temperature detection processing apparatus for a wire rod cooling control system according to claim 12, wherein the historical temperature rise data determination module is specifically configured to:

ΔT_mea＝T3_mea-T2_mea；

15. The wire rod cooling control system rolled piece temperature detection processing apparatus of claim 12, wherein the relationship curve determining module is specifically configured to:

16. The wire rod cooling control system rolled piece temperature detection processing apparatus of claim 12, wherein the feedwater quantity determination module is specifically configured to:

17. The wire controlled cooling system product temperature sensing processing apparatus of claim 16, further comprising: self-learning optimization processing for:

18. The wire rod cooling control system rolled piece temperature detection processing apparatus of claim 17, wherein the self-learning optimization processing is specifically configured to:

19. The wire controlled cooling system product temperature sensing processing apparatus of claim 18, wherein the self-learning optimization process is further configured to:

20. The wire rod cooling control system rolled piece temperature detection processing apparatus of claim 18, wherein the self-learning optimization process is specifically configured to:

temperature rise change data was determined according to the following formula:

ΔT_new＝aΔT_mea+(1-a)ΔT_old；

wherein, Delta T_newRepresenting temperature rise change data; a represents a variation coefficient; delta T_meaRepresenting the actual data of the current temperature rise; delta T_oldTemperature rise data corresponding to the current measured temperature of the finishing mill inlet or a meter corresponding to the current measured temperature of the finishing mill inletCalculated temperature rise data.

21. The wire controlled cooling system product temperature sensing processing apparatus of claim 18, wherein the self-learning optimization process is further configured to:

the method comprises the following steps:

judging whether the temperature rise data change is larger than 0:

22. The wire rod cooling control system rolled piece temperature detection processing apparatus of claim 21, wherein the self-learning optimization processing is specifically configured to:

ΔT_inew＝ΔT_iold+K₁×deltaT；

23. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 11 when executing the computer program.

24. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 11.