JPH0323260B2 - - Google Patents

Description

[Detailed description of the invention]

The present invention relates to a method for controlling the surface temperature of slabs in continuous casting equipment. Conventionally, methods for controlling secondary cooling water to further cool slabs and other slabs pulled from molds in continuous casting equipment include (1) fixed value control method by manual setting by the operator, (2) control method based on the drawing speed. (3) A speed cascade control method that determines the total amount of water and distributes it at a fixed ratio to each cooling zone. (3) A heat transfer model is used to calculate the temperature distribution of each tracking surface moment by moment, and the secondary cooling area is There was a method of determining the amount of water by modifying the above heat transfer model using the heat transfer coefficient learned from the relationship between the calculated temperature and the measured temperature at the outlet side of each divided zone. In the case of method (1), it is impossible for the change in casting speed to appropriately follow the stoppage, such as at the start and end of casting or when replacing the tundish. In addition, in the case of method (2), the cooling pattern is determined spatially, and the temporal concept of the cooling process within the slab is not taken into consideration.If the drawing speed changes suddenly, the cooling pattern is determined spatially, and if the drawing speed suddenly changes, the cooling pattern is determined spatially. Even when the condition has not changed significantly, the amount of cooling water sprayed is suddenly changed, resulting in uneven cooling and resulting quality defects in the slab. Furthermore, in the case of method (3), the temporal concept of the cooling process within the slab is taken into account, but if the temperature is predicted by thermal analysis as in the past, determining the amount of cooling water is extremely complicated. It requires calculations, and a sufficient response time cannot be obtained without the use of a large computer, and the control dead time is large and the adaptability to changing events (for example, changes in heat transfer coefficient) during slab casting is poor. There are drawbacks. The present invention has been made to eliminate such drawbacks, and the surface temperature of the slab is controlled by spraying cooling water onto the slab passing through the cooling zone in the secondary cooling zone of continuous casting equipment. In the method of
The method is characterized in that the influence coefficients a _i and b _j are determined, and the temperature of the slab surface is predicted and the amount of cooling water is controlled using the cooling water amount determining equation (2). State transition equation T〓 _o = _n 〓 ⁱ⁼⁰ a _i T _oi + _l 〓 ^j=0 b _j U _oj ……(1) Cooling water amount determination equation T〓 _o : Temperature change between time n and n+1 T _oi : Surface temperature of a certain slab at each time T _o+1 ^* : Target temperature at time n+1 U _oj : Zone where a certain slab existed at each time Water volume setting values a _i (i=o...m), b _j (j=o...l): influence coefficients obtained by an autoregressive model Hereinafter, an embodiment of the present invention will be explained based on the drawings. . First, in Fig. 1, molten steel injected from tundish 1 into mold 2 gradually forms a solidified shell while being primarily cooled by heat transfer to the mold wall surface.
It is pulled out along the guide roll 3. Spray nozzle 7 onto the surface of the slab 4 that has been pulled out.
Cooling water is sprayed through a, 7b, 7c, and 7d, and the shell is bent little by little in response to the development of a solidified shell due to forced cooling. Such a secondary cooling zone 5
is divided into zones a, b, c, d, e,
Thermometers 6 (6a, 6b, 6b,
6c, 6d, 6e) are arranged. In this case, the thermometer 6 (6a, 6b, 6
c, 6d, 6e), the slab surface temperature was measured,
When the amount of information is input to the computer (CPU) 8, the optimum amount of secondary cooling water is determined, and the cooling water is sprayed onto the slab 4 from the spray nozzles 7 (7a, 7b, 7c, 7d) and cast. Cool piece 4. Furthermore, the operating status is output to the console 9.
Next, we will briefly discuss the above-mentioned temperature prediction and optimal water flow control. First, the surface temperature of the slab is taken as a state quantity,
The surface temperature of the slab during pouring is collected, subjected to statistical processing, and analyzed using the following autoregressive model, which is expressed by the state transition equation (1). T〓 _o = _n 〓 ⁱ⁼⁰ a _i T _oi + _l 〓 ^j=0 b _j U _oj ……(1) T〓 _o : Temperature change between time n and n+1 T _oi : A certain slab at each time surface temperature a _i (i=o...m), b _j (j=o...l): influence coefficient obtained by autoregressive model Then, the slab is divided into certain tracking units, and the tracking units are Measure the temperature at representative points, and calculate the temperature prediction using equation (1). T ^* _o+1 : Target temperature at time n+1 a _i , b _j : Influence system number obtained by autoregressive model Water amount control is performed using equation (2) to realize the target temperature pattern. Therefore, the state transition equation of the control system of the continuous casting equipment at a certain time can be expressed as a matrix as an m-order multi-input multi-output system using the equivalent equation as shown below. State equation of control system 〓 _(o) =〓〓 _o +〓〓 _(o) (3) Output equation of control system 〓 _o+1 =〓 _o +〓 _o (4) = (〓+〓)〓 _o +〓 〓 _o (4′) 〓: Matrix representation of the influence coefficient due to temperature of the autoregressive model obtained by equation (1) 〓: Matrix representation of the influence coefficient due to cooling water volume of the autoregressive model obtained using equation (2) 〓 Temperature vector of slab at _o+1 , T _o , time n+1, n 〓 Set water flow vector of slab at time n In order for _this control system to be controllable, the matrix 〓=[〓,〓〓, …〓 ^n-1 〓〕 It suffices if the rank of (5) is m. RANK [〓]=m (6) Conversely, control is possible if the matrices 〓, 〓 created by the autoregressive model satisfy equation (6). Regarding the results of thermal analysis of a certain temperature pattern,
By applying this method, we verified that such 〓, 〓 exist. Therefore, assuming that there are temperature measurement values and water volume measurement values as shown in Tables 1 and 2, and creating an autoregressive model in the form of equation (7), T _o+1 = a ₀ T _o + bU _o + a ₁ T _o-1 (7) 〓 _o ＝〓〓 _o ＋〓〓 _o T _o+1 =〓 _o +〓 _o . Here, if we find the matrix 〓=[B, AB, A ² B, A ³ B, A ⁴ B] and check the controllability, Since this matrix is regular, RANK [〓] = 5, and this system satisfies controllability. Next, to explain how to determine the amount of cooling water, the target temperature at the current time n is 〓 ^*T = [1150 900 800 780 790] The measured temperature is 〓 _o ^T = [1498 1151 902 797 780] The water amount is 〓 _o ^T = [ 180 80 60 40 20], the predicted temperature value at the next point in time is from equations (3) and (4): 〓 _o+1 ^T = [1147.21 900.563 801.215 779.248
790.314], which is different from the target temperature. _{Therefore ,} ^{from the water} _{quantity determination equation : _ _ _} ^_ [179.512 80.0769 60.1907 39.9076
20.0576]. The target temperature can be achieved by controlling the amount of water using U ⁽ⁿ⁾ obtained using this method.

【table】

【table】

【table】

[Table] Since the values of a _i and b _i can be improved online using temperature measurement values, a highly adaptable control system can be realized. By improving the state transition matrix online, even if an abnormality such as cooling water nozzle clogging occurs, the state transition matrix will be created using the temperature measurement value that includes the abnormality, making it possible to control The system is now able to follow abnormal conditions. Next, referring to FIG. 2, the secondary cooling water control procedure of the present invention will be described.
a, 6b, 6c, 6d) surface temperature of the slab,
information on the casting length from the pull-in roll 10 and the pulse generator 11, the casting speed from the tacho generator 12, the steel type (physical constant) from the keyboard 13,
Information such as the slab size is input to the arithmetic unit 15 (CPU) via the interface 14. Based on this information, the CPU 15 calculates,
Determine the optimal amount of water. The determined set water volume is D/
It is transmitted via the A converter 16 and controls the regulating valve 17 of the spray nozzle. Furthermore, the CPU outputs the casting temperature, casting speed pattern, surface temperature pattern, cooling water temperature, equipment constants, etc. to the console 9 to command the operational status. Hereinafter, one embodiment of the present invention will be described in comparison with the conventional method. Temperature prediction was performed for all tracking units during casting using an electronic computer, and calculation speed was compared with the conventional method. First, regarding control using a conventional thermal model, we created a program with the configuration shown in Figure 3, considering control during actual operation. Now, assuming the tracking cycle time is 20 seconds, mesh in the thickness direction and mesh in the time direction.
The calculation time was measured by changing the number of repetitions. The calculation results are shown in the table below.

[Table] If temperature prediction is performed using the state transition matrix of the present invention, it will be the same as that tested using an electronic computer.
Looking at a 45m long continuous casting machine, 4.5 (m) / 0.5 (m) x (2 + 1 + 2 + 1 + 3) x 50 (total casting length) (required number of times to predict the state of the entire system at one time) x (3.1 x 10 ^-6 +3.7×10 ^-6 ) + 0.1 (calculation time) (time to store calculation results in array
etc)) = 0.375 (sec) [calculation time] According to this, in conventional thermal model analysis, 1
2.8 seconds per time, and in the form transition method of the present invention, it is 0.3 to 0.4 seconds, about 1/7. It was found that the response time to determine the control water amount could be up to 0.8 seconds, which is 1/4 of the estimated time. Furthermore, the relationship between the predicted value of the slab surface temperature and the casting time during steady operation according to the present invention is shown in Figure 4 in comparison with the casting speed, and the relationship during unsteady operation is shown in Figure 5. show. Furthermore, the relationship between the predicted temperature value inside the slab and the thickness direction is shown in FIG. In FIG. 6, the solid line represents the expected temperature inside the slab after 9 minutes during unsteady operation, and the solid line represents the expected temperature inside the slab after 3 minutes during steady operation. According to the present invention, calculation results can be obtained with very simple calculations, so control delay time is reduced and controllability is improved, which not only improves product quality, but also reduces the size of the computer used for control. It is fully realized and allows for an inexpensive system configuration.Furthermore, abnormal conditions (such as nozzle clogging) can be monitored by time-series analysis of measured temperature values, and the abnormal conditions are incorporated into the state transition matrix. This makes it possible to set the water amount in response to abnormal conditions.
Therefore, it is possible to improve product quality and save labor in the scarfing process in the subsequent hot rolling process.

[Brief explanation of the drawing]

Figure 1 is a schematic diagram of the secondary cooling water control system in the continuous casting equipment of the present invention, Figure 2 is the control procedure for the control system in Figure 1, and Figure 3 is for control during actual operation. Figures 4 and 5 are graphs showing the relationship between the predicted value of the slab surface temperature and the casting speed during constant pouring time operation and unsteady operation, respectively. It is a graph showing the relationship between the thickness direction and the predicted temperature value inside the slab. (Main reference number), 1...Tandetshu, 2...
...Mold, 3... Guide rail, 4... Slab, 5...
... Secondary cooling zone, 6 (6a-6e) ... Slab surface thermometer, 7 (7a-7d) ... Spray nozzle, 8
...Electronic computer (CPU), 9...Console, 1
0...Retraction roll, 11...Pulse generator,
12... Octopus generator, 13... Keyboard,
14...Interface, 15...CPU, 1
6...D/A converter, 17...control valve.

Claims (1)

[Scope of Claims] 1. In a method of controlling the surface temperature of a slab by spraying cooling water on the slab passing through the cooling zone in a secondary cooling zone of continuous casting equipment, the secondary cooling The area is divided into several zones, and the measured values of temperature and cooling water amount in each zone are analyzed using an autoregressive model to determine the influence coefficients a _i and b _j of the state transition equation (1) of the control system. Cooling water amount determination equation
(2) A method for controlling the surface temperature of a slab in continuous casting equipment, characterized by predicting the temperature of the slab surface and controlling the amount of cooling water. Form transition equation T〓 _o = _n 〓 ⁱ⁼⁰ a _i T _o-1 + _l 〓 ^j=0 b _j U _oj ……(1) Cooling water amount determination equation T〓 _o : Temperature change between time n and n+1 T _oi : Surface temperature of the slab at each time T _o+1 ^* : Target temperature at time n+1 U _oj : Temperature change in the zone where a certain slab existed at each time Water volume setting value a _i (i=o…m), b _j (j=o…l): influence coefficient obtained by self-healing model