Embedded real-time analysis of continuous casting for machine-supported quality optimisation

Manual process control of the continuous casting (CC) process is difficult due to the big number of influencing factors. During continuous casting, manual top-freezing controls must be carried out. Every manual performed mould control can affect the strand quality and even increase the risk of failure. Therefore, regular top-freezing controls are performed after a certain casting duration. However, top-freezing events between the regular controls cannot be detected and are a major risk for plant safety.

In the RFCS (Research Fund for Coal and Steel) project RealTimeCastSupport, the aim of the research was the digitalisation and optimised control of continuous casting machines. A real-time support system was developed to predict quality-relevant top-freezing events and thus achieve improved control. This was reached by offline material tracking, synchronisation of data streams and statistical analysis by application of Big Data technologies, the development of a digital twin and the exploitation of various CC data and surface inspection to predict reliability of steel production. Results The following results were achieved: Identification of defect promoting scenarios by correlation of statistical results and surface defect detection.Realisation of an offline 3D digital twin of the mould with two different casting sizes, different geometries, and a varying immersion depth of the submerged entry nozzle (SEN), considering heat transfer, inert gas feeding, and solidification for parameter studies to identify the most influential factors in top-freezing as a defect promoting scenario. Input variables from the continuous casters were evaluated by CFD simulations and afterwards used to develop and train an online support system which was connected to the existing database in the plant. The system will be finetuned offline to internal specifications. This will further allow an optimized system with increased recall and precision parameter.

The application of a real-time support system enables the prediction of top-freezing events during the whole casting process. Subsequently, this significantly increases the plant safety and offers to carry out top-freezing inspections in a more targeted manner in the future. This publication is part of a series of papers in the frame of the dissemination project METACAST.