The new plant is scheduled to be commissioned in the second half of 2028 and will process crushed steel and iron scrap, commonly referred to as shear scrap. The project is designed to improve the quality of scrap through advanced sorting technologies that allow for the wider use of recycled materials in steel production, which requires strict quality standards.
Artificial intelligence systems and X-rays to improve scrap quality
A key feature of the project is the integration of artificial intelligence-based object recognition technologies and X-ray analysis into the scrap recycling workflow. These systems will help identify and separate impurities from incoming scrap streams, especially traces of free copper, which are one of the biggest challenges in producing high-quality steel from recycled materials.
According to ROGESA, this technology is expected to reduce copper contamination in cut scrap by about 30 percent. This improvement will allow steel manufacturers to increase the scrap utilization rate while maintaining the product quality requirements imposed on premium steel grades.
Higher scrap usage is expected to lead to lower demand for raw materials
The project supports the growing transition to circular steel production and increased scrap consumption in production using electric arc furnaces. By improving scrap quality, ROGESA strives to maximize the use of recycled steel and reduce dependence on direct reduction iron (DRI) and other primary raw materials. The company estimates that this facility will save about 63,000 tons of direct recovery and other primary raw materials annually.
In addition to saving materials, the project is expected to bring significant environmental benefits. ROGESA estimates that the advanced processing plant will reduce annual energy consumption by up to 16 gigawatt-hours compared to traditional processing methods. In addition, annual carbon emissions are expected to be reduced by as much as 76,000 tons, which will contribute to the achievement of both corporate and national




