REVaMP – Retrofitting of sensors and optimization tools for the efficient use of variable input materials in the metal-producing industry

Initial situation:

  • In metal making processes, metallic scraps from end of life goods are recycled and used as secondary raw materials, which is both ecologically and commercially beneficial.
  • The increasing amounts of scrap in the feedstock bears challenges, since their composition and meltdown energy and thus the value in use can vary significantly.
  • Metal production plants need to be retrofitted with appropriate sensors and software tools for scrap mix analysis, characterisation and optimisation, as well as control tools for melting furnace operation, to cope with the varying conditions of the feedstock.   

Project targets:

  • Application of novel sensors for characterisation of metal scrap regarding composition
  • Setup and implementation of model-based monitoring and control systems to optimise the processes for steel production at varying feedstock
  • Implementation, test and evaluation of retrofitting technologies in electric and oxygen steelmaking plants

Innovative approaches:

  • Neutron sensors and LIBS sensors for in-line analysis of metal scrap are tested
  • A web based application for scrap characterisation and scrap mix optimisation is developed
  • A dynamic EAF model is used for on-line process monitoring and model predictive control 

Benefits for the industry:

  • Reduced electrical and chemical energy consumption for meltdown and refining processes
  • Reduced costs for scrap and other raw materials for primary steelmaking
  • Increased metallic yield
  • Reduced CO2 emissions by increased production of high-quality grades from secondary raw materials  
  • Less downgrading by improved characterisation of steel scrap
  • Increased overall productivity 

Please find more detailled information in der abstract "Model-Based Decision Support System for Electric Arc Furnace (EAF) Online Monitoring and Control".

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