Raw materials account for the highest costs by far in the production of one ton of crude steel. Implementing the right strategy for using these charge materials can generate the greatest potential for cost savings during production. Producers in the electric steelmaking industry are facing a particular challenge here: They need to maximize the amount of low-priced scrap in a melt while at the same time ensuring that it has the quality needed to meet the requisite production goals. In terms of the scrap, special emphasis is placed here on unwanted tramp elements such as copper or tin. In many cases, only an analysis that is carried out after the feedstock has been melted can show how high the proportion of these tramp elements in the scrap actually is.

The Metallics Optimizer makes up for this deficit. This application uses artificial intelligence techniques to forecast the amount of undesired tramp elements in the scrap before it is melted. The Metallics Optimizer uses this forecast to calculate the lowest-cost composition for the melt's charge mix by means of an optimization algorithm. Here, the Metallics Optimizer takes account not only of the costs of the material but also of all costs related to the production of the melt, such as component wear and tear or energy consumption during the melting.