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The metallurgical sector is one of the main pillars of European industry. Steel is important not only for modern economies of developed countries but is an essential part of building infrastructure in developing countries, and thus in the coming decades, global demand for steel is expected to increase to meet growing social and economic needs. Currently, however, the steel industry is among the top three producers of CO2 emissions. Steel producers have a great opportunity to achieve greenhouse gas emission reductions and improve energy consumption efficiency, thereby increasing their competitiveness, mainly through optimizing, digitalization and innovating the production process by introducing low-carbon technologies and increasing the efficiency of raw material and energy resources. This scientific article is focused on developing advanced mathematical prediction models and analysis of steel production processes. By utilizing modern BAT and ITs technologies, this research aims to improve the competitiveness of the steel industry by optimizing production processes and through the integration of datasets and analysis techniques contribute to reducing the carbon emissions in steel production through energy consumption optimization and the implementation of sustainable practices. Through the utilization of mathematical prediction models and advanced analysis techniques, the article aims to optimize steel production processes and enhance the competitiveness of the metallurgical sector.
Keywords: Decarbonization, metallurgy, emissions, steel industry, digitalization© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.