Supervision of Charge Material Properties in EAF steelmaking Utilising Advanced Statistical Methods

ended

A model relies on the quality and consistency of its input data. Normally estimations of charge material properties (such as chemical composition, specific energy consumption and yield coefficients) form the base for model-based EAF charge mix calculation and energy control. However, the material properties may vary over time resulting in decreased prediction accuracy of steel chemistry, slag chemistry, energy consumption and steel temperature. The same properties also affect the value in use of the materials. Ultimately, variations in material properties render existing material mix optimizations and process models obsolete. Furthermore, as there is no reliable method available for on-line analysis of charge material properties, existing process models can never be fully reliable. This necessitates use of comprehensive safety margins regarding chemical composition and temperature of the steel. Since raw materials are the most expensive part in electrical steelmaking with 70-90 % of the total production cost and energy consumption constitutes the second largest cost with 10-15 %, an efficient use of raw materials and energy is of the outmost importance in order to keep the production costs at a competitive level. This project intends to use advanced statistical methods to correlate systematic errors in model predictions (of steel and slag chemistry, energy consumption, etc.) to use of specific charge materials and thereby identify errors in estimated material properties. Hence, statistical methods will be applied to calculate the probability that the estimated material properties of individual materials are correct. The project will lead to a supervision system for early detection of charge materials in the EAF with incorrect properties; thereby, avoiding excessive use of alloy elements, high quality scrap and energy. Naturally, this will allow for significant savings in production cost and give a better platform for future price negotiations with suppliers.