Consistent ladle tracking for optimisation of steel plant logistics and product quality

The RFCS project TrackOpt implements automated ladle tracking systems to ensure consistent factory-wide tracking of the product from steelmaking via casting to delivery.


Data Science 

Algorithmic and combinatorial Optimisation 

Domaine: Manufacturing 



The overall objective of the Pilot & Demonstration project TrackOpt is to implement consistent factory-wide tracking of the ladles in use in a steel plant from tapping to casting.
The through-process tracking of the ladle can only be accomplished with an automated system that reliably monitors the movement of the ladles and delivers online feedback about ladle positions to the process control and production scheduling systems. This is mandatory for realising a continuous and reliable tracking of the product throughout the whole production process.
The following aims were targeted by implementing the automated, wireless tracking system in the harsh steelworks environment:

  • Providing mandatory input data for further projects on digitalisation and integration of complex industrial production chains as in steelworks (“Industry 4.0”)
  • Improving the safety in steelworks by avoiding accidents due to ladle mix-up.
  • Increasing the factory output as hold-ups in the production plan or downgrading of products due to mix-up of ladles can be avoided.
  • Improvement of production planning by providing more reliable information on the actual position of ladles, either being treated, in transfer or on stock.
  • Optimisation of short-term transfer by evaluation of ladle transfer patterns and the time elapsed for transfer and treatment.

The figures below represent the roles of the TrackOpt partners, the methodology used and the interactions in the project.

TrackOpt Partners collaboration
TrackOpt work methodology


Innovative sensors and instrumentations were applied to follow the ladle along the complete production chain. A wireless tracking system based on Surface Acoustic Wave (SAW), with suitable antenna and appropriate readers (lab tests at BFI and plant trials at FENO), as well as the position of the passive sensor (SAW tag) on the steelmaking ladles and protection devices.

Layout of tracking cameras
Overview over steel plant with the six optical cameras (OC) highlighted in red, the infrared in blue

A simulation engine that uses data directly from the plant and is developed by defining the ladle logistics in the steel plant as a Discrete-Event model. The simulation of the Discrete-Event model allows the computation of many quantitative metrics on the ladles and processes that are required to be scheduled in a more dynamic way.

Discrete-Event Model for Ladle Logistics

A system for the online assessment of the ageing status of the ladles operating on the FENO plant has been designed by SSSA and implemented in the project framework. The system exploits data analysis and AI-based technologies to estimate in advance the need of maintenance operations on the above-mentioned ladles by exploiting different sources information among which the operation history of ladles (including process conditions) and the reading of the thermo-cameras installed on the plant within the project.

GUI of TrackOpt software
Graphical user interface of TrackOpt software for displaying ladle positions and ladle age status, overview (left) and ladle information (right)

Added value

For the steel-making industry, the project is a key step to improve their "Industry 4.0" readiness thanks to increased monitoring and control they will get on the flow of ladles. It will reduce risk of costly errors and increase reactiveness, flexibility and productivity of the plant.

This project has received funding from the Research Fund for Coal and Steel under grant agreement No 753592