Advanced Analytics for Sustainable Steel Ladle Logistics

Advanced analytics are an important tool to advance the digitalization and sustainability of the steel industry. One possible lever is improving the energy efficiency of steel ladle logistics. This paper introduces an optimization framework leveraging mathematical programming and data-driven models. First, it discusses its scope, requirements and potential benefits. Next, it discusses its application in a decision-support system developed for the Tata Steel Netherlands use case. Historical data evaluation reveals how ladle deployment decisions influence costs and CO₂ emissions. The results highlight the expected contributions to more sustainable operations. Finally, it discusses the framework’s implementation challenges and future improvement directions.

Authors:

Victor Ruela | TU Wien
 

Dr. Paul Van Beurden | Tata Steel Netherlands
 

Dr. Felix Birkelbach | TU Wien
 

Prof. Dr. Rene Hofmann | TU Wien
 

Session Chairs:

Tanya Manibusan | NALCO Water, An Ecolab Company
 

Chris Krechting | Everguard.ai