On-line dynamic model of solidification
The on-line model solves for the temperature fields in real time, which requires high performance computational hardware synchronised with the employed software. As a consequence, the model is capable of monitoring the temperature field development in the crystallizer area as well as in regions of secondary and tertiary cooling and apply the gathered information to optimise control of continuous casting facility as a whole and/or of its key parts/nodes/joints/components .
Based on instantaneous evaluation of measured and computed data, the model adjusts parameters of casting in real time; the parameters being casting speed, crystallizer cooling, cooling intensity of cooling spraying nozzles and others.
The system also allows for the simulations of real heats to be archived for further inspection. Should the heat be defective, the system not only enables inspection of the solidification process but also its off-line re-simulation thus providing feedback data (casting speed, change to spraying plan etc.) to free the next similar heat from the defects.
Another goal of the on-line system lies in improvement of quality and accuracy of the input data, mutual relationship among them and other qualitative parameters, including determination of their limiting values. The model is made ready for direct integration into second control level.
The model functionality is conditioned by availability of on-line input data from the first control level at frequency of 10 seconds minimum and some other data from the second control level.
The input data are fed into the temperature field model via a newly developed interface based on industrial standards such as SQL, OPC or DataSocket. The interface corresponds to the actual control state and its source codes are available to the customer so it can be user modified when the control changes.
The on-line model solves for the temperature fields in real time, which requires high performance computational hardware synchronised with the employed software. As a consequence, the model is capable of monitoring the temperature field development in the crystallizer area as well as in regions of secondary and tertiary cooling and apply the gathered information to optimise control of continuous casting facility as a whole and/or of its key parts/nodes/joints/components.
Basic parameters evaluated:
surface temperature profile along the continuous casting line (CCL)
solid shell thickness of the blank while passing CCL
length and shape of the blank liquid core
isosolid and isoliquid surface profiles along CCL
temperature distribution over an arbitrary cross section and at particular time.
These can be provided to the second control level via the mentioned interface.
The on-line model can automatically adjust technological parameters of an operating CCL to reach the required qualitative parameters of the steel cast.
A general estimate of benefits expected after implementation to CCL
By knowing the temperature field of the continuously cast blank at any location along CCL passage (especially at the straightening point) up to the cooling after passing the withdrawal unit, along with the temperature field shape including the blank enthalpy, these may be achieved:
improved blank (billet) quality – reduction of central incompactness and number of segregations especially. This has a particularly positive impact on increased quality of the inner surface of seamless tubing rolled of circular blanks of middle- and high-carbon steels
reduction of breakout danger, when the breakout is due to the thermal stress within the blank shell
introduction and effective operation of electromagnetic mixing of the liquid core because of the exact knowledge of the core shape and its changes with casting speed, which improves the quality in the blank central
speeding up the introduction process of new steel grades or solution of common problems of CCL operation such as sudden appearance of defects of internal incompactness type, erroneous crystallizer function – cooling – due to unsuitable casting powder application etc.
increase of casting production of CCL because of the knowledge of the temperature field xxx formation at the increased casting speed and determination of its maxima in relationship with the liquid core length
|Dále pro zjišťovány asynchoní informace, s centrální databáze ocelárny (FLS), pomocí rozhraní ADO/ODBC. Na základě těchto komunikací sestaví v pravidelných intervalech (5 až 10 sekund na požadavek on-line modelu) vektor reálných čísel. Vektor čísel prostřednictvím protokolu TCP/IP předává do on-line modelu. Komunikace je v ekvidistantním časovék kroku do 10 sekund.|
The data acquisition programme was written in Delphi and it runs on a server. It reads the configuration from a local Firebird database and communicates with the first control level, i.e. PLC, employing one of the interfaces listed: