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

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Summary
This summary is machine-generated.

This study developed a real-time support system to predict top-freezing events in continuous casting, enhancing plant safety. The system uses Big Data and a digital twin for improved control and defect prevention in steel production.

Keywords:
Big DataComputational Fluid DynamicsContinuous CastingDigital Twin

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Area of Science:

  • Materials Science
  • Process Engineering
  • Data Science

Background:

  • Manual control of continuous casting (CC) is challenging due to numerous factors.
  • Manual top-freezing controls impact strand quality and failure risk.
  • Undetected top-freezing events pose a significant plant safety risk.

Purpose of the Study:

  • To digitalize and optimize continuous casting machines.
  • To develop a real-time support system for predicting top-freezing events.
  • To improve control and enhance plant safety during continuous casting.

Main Methods:

  • Utilized offline material tracking, data stream synchronization, and Big Data analytics.
  • Developed an offline 3D digital twin of the mold, considering heat transfer and solidification.
  • Integrated continuous caster input variables, evaluated by CFD simulations, into an online support system.

Main Results:

  • Identified defect-promoting scenarios by correlating statistical results with surface defect detection.
  • Created a 3D digital twin to study influential factors in top-freezing.
  • Developed and trained an online support system connected to the plant's database.

Conclusions:

  • Real-time support system enables prediction of top-freezing events throughout casting.
  • Significantly increases plant safety and allows for targeted inspections.
  • Contributes to optimized steel production with improved reliability and defect prevention.