SmartLadle - Smart consideration of actual ladle status monitored by novel sensors for secondary metallurgy process parameters and ladle maintenance strategies

Initial situation:

The steelmaking ladle has a strong influence on the success of secondary metallurgical treatment during liquid steel production:

  • Thermal ladle state influences temperature evolution of the melt,
  • History and ladle lining influence the steel quality,
  • Sufficient refractory thickness that decreases over ladle lifetime must be guaranteed for safety reasons,
  • Status of the stirring plug and the stirring strategy significantly influence the stirring efficiency and consequently reaching the metallurgical aims.

Throughout the ladle life, alterations such as changes in refractory material properties and ladle/plug geometry due to wear will occur. However, the ladle status is not considered in dynamic adjustment of process parameters or for optimum maintenance (relining) – both are performed according to defined static practices and schedules.

Project targets:

The overall objective of this project is the online monitoring of the ladle status using a soft sensor supported by a new smart sensor and a data-based solution for the dynamic consideration of the actual ladle status in process control.

Innovative approaches:

  • Dynamic adaption of process parameters to actual ladle status
  • Realisation of a smart ladle by applying smart sensors at a ladle, which is well in accordance with Industry 4.0 paradigm
  • A soft sensor to calculate the ladle status parameters using diverse data sources including special measuring signals like refractory temperature and conventional dynamic signals from the process system
  • A smart sensor enabling access to the actual ladle status: The new data from the smart sensor, e.g. regarding thermal state of the ladle or tundish, can then be used as additional input parameter for online process models, e.g. regarding steel temperature evolution during steelmaking processes, and improve their accuracy
  • Advanced ML model application to determine ladle wear rate instead of linear regression approaches that have been used with a simpler dataset

Benefits for the industry:

  • Improvement in liquid steel temperature understanding and modelling is expected to help saving energy.
  • Improving the measuring and modelling outputs will help adapting to unexpected conditions in steel industry (e.g. employ more electricity instead of gas, or high refractory costs).
  • An improvement in refractory materials use will have a direct impact on the increase of refractory life-time and in refractory cost reductions.
  • Knowledge of actual ladle status and dynamic adaption of operational process parameters will result in improvement of the steelmaking process and prevention of ladle break-through.
  • Reduced refractory need (costs and CO2 emissions), reduced natural gas demand, flexible steel production, increased safety

More detailed information about the project: SmartLadle

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