Raw materials represent one of the biggest cost factors in the production of crude steel. Implementing the right strategy for allocating materials opens up vast potential for cost savings in production. In electric steelmaking, producers are facing a particular challenge: operators need to maximize the amount of low-priced scrap in a melt while at the same time ensuring that steel quality meets the requisite production goals.
To make things more challenging, there is a lack of knowledge of the chemical composition of the input materials. This introduces a process variability, causing unnecessary large amounts of expensive raw materials being used, because low-cost scrap with unwanted tramp elements puts the product quality at risk. 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 is.
To deal with the process variability, the Metallics Optimizer uses machine learning techniques to predict the chemical concentrations of different elements in the available commodities. The chemical concentrations of different elements vary over time as different layers of the scrap piles on the scrapyard are consumed. The prediction of the chemical properties gives operators a better idea on how to use charge materials. The figure shows the estimated copper content of commodity 3 fluctuates between 0.05 and 0.20 percentage points between September 2019 and July 2020. Depending on how much copper is aimed for with casted steel grades during that time span, it is necessary to adjust how much of commodity 3 can be used in the charge mixes.
On top of these machine learning-based commodity characterization, the Metallics Optimizer employs physical (mass and energy balance) models to predict the chemical properties of different charges in future sequences. Those proven mass and energy balance equations further decrease the process variability because operators can reliably forecast the chemical properties of heats in a future sequences.
There are many factors, such as scrap costs, electric energy, wear on electrodes, or tap to tap time, to name just a few, which influence the costs of producing a heat. Based on its commodity characterization and its physical models, the metallics optimizer also employs an optimizer, considering many of such factors. With it, the solution will pick the cheapest charge mixes that fulfill product specifications over a whole future sequence. This optimization allows production at lowest cost without sacrificing quality.