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Electrical and Automation Systems for Continuous Casting Plants
We supply level 1 and 2 automation electrical equipment for Continuous Casting machines (billet, blooms, beam blanks, slabs). The supply covers:
• Electrical equipment for motor drives and motors control.
• Synchronization of withdrawable machines’ motors in multi-motor drives.
• Basic Level 1 Automation, including control PLCs and operation/display HMIs in local and global topology networks (Ethernet).
• Level 2 (process) automation, with advanced product tracking, quality assessment, cooling water control, cut to length.
Control of technological functions: Like:
• MLC – Mold Level Control, acting on withdrawable machinesd, dummy or slide gate bar.
• TLC – Tundish car steel level control
• MWC – Mold width control.
• Break-out Control – Mold temperature control, break-out prediction.
• Control of weights in ladle turret, tundish car, product.
• Measurement of temperature and O2 activity in liquid steel.
• Optimized billet tracking up to the marking machines.
Mould Level Master is suitable for all stopper-controlled continuous casting machines. A similar package is available for tundish slide gate-controlled continuous casting machines featuring an algorithm control package with similar characteristics to guarantee a stable mould level.
Features
- Average mould level stability is accurate to within +/- 2 mm
- Automatic start-up for small billet to slab formats, including automatic protection against breakout and freezing
- The mould level remains undisturbed in the case of bulging or waving
- Automatic clogging detection and reaction (corrective action)
Applications
The control program is available for all continuous casting machines
- Billet
- Bloom
- Slab
- Thin slab
- Special caster applications
The standard MLM package includes operating routines such as automatic start-up, high-level or low-level-protection, clogging prevention, breakout detection, bulging compensation (pending patent), submerged entry nozzle, refractory wear distribution and safety features.
The staggering complexity of the continuous casting process makes it impossible to model all of these phenomena together at once. Hence, it is necessary to make reasonable assumptions and to uncouple or neglect the less-important phenomena. Quantitative modelling needs incorporation of all of the phenomena which affect the specific issue of interest. Hence each model needs a specific purpose. Once the governing equations have been chosen, they are normally discretized and solved using finite-difference or finite-element methods. It is important that adequate numerical validation be conducted.
Numerical errors normally arise from too coarse a computational domain or incomplete convergence when solving the nonlinear equations. Solving a known test problem and conducting mesh refinement studies to achieve grid independent solutions are important ways to help validate the model. Finally, a model is required to be checked against experimental measurements on both the laboratory and plant scales before it can be trusted to make quantitative predictions of the real process for a parametric study.
The final test of a model is if the results can be implemented and improvements can be achieved, such as the avoidance of defects in the steel product. Plant trials are ultimately needed for this implementation. Trials are to be conducted on the basis of insights supplied from all available sources, including physical models, mathematical models, literature and previous experience. As increasing computational power continues to advance the capabilities of numerical simulation tools, the modelling plays an increasing role in future advances to high-technology continuous casting process. Modelling can augment traditional research methods in generating and quantifying the understanding needed to improve any aspect of the process. Areas where advanced computational modelling plays a crucial role in future improvements include (i) transient flow simulation, (ii) mould flux behaviour, (iii) taper design, (iv) on-line quality prediction and control, especially for new problems and processes such as high-speed billet casting, thin slab casting, and strip casting.
Future advances in the continuous casting process are not going to come from models, experiments, or plant trials. They are going to come from ideas generated by people who understand the process and the problems. This understanding is rooted in knowledge, which can be confirmed, deepened, and quantified by tools which include computational models. As the computational tools continue to improve, their importance is increasing in fulfilling this important role, leading to future process advances.