Improving Energy and Resource Efficiency of Electric Steelmaking Through Simulation Tools and Process Data Analyses

The European Steel industry is ever more committed to improve the socio-economic and environmental sustainability of its processes by promoting any development, which can increase resource efficiency and lower the environmental footprint of the steel production. The European Steel Technology Platform gives the highest priority to the topic of Sustainable Steel Production within its Strategic Research Agenda since 2013. Several projects have been developed both at corporate level, as well as by associations of companies and research institutions in order to investigate new processes, retrofit actions and apply innovative combinations of existing technologies that can allow to improve the energy and resource efficiency and the management of by-products, waste and wastewater. However, it is still difficult for process managers and plant engineers to find accessible tools to both analyze the process data and to perform scenario analyses aimed at evaluating in a practical and understandable manner the effect of innovations in terms of new technologies or novel procedures and operative practices. Within the research RFCS project entitled ”Environmental Impact Evaluation and Effective Management of Resources in the EAF Steelmaking – EIRES” (where EAF stands for Electric Arc Furnace), an integrated tool has been developed, which allows to evaluate the environmental impact of current operating practices, modified operating conditions and major process variations and innovations, thanks to process simulation models for both production processes and auxiliary equipment. Also a dedicated Life Cycle Assessment (LCA) is linked to the simulation models in order to provide a further evaluation from the specific LCA perspective. The paper is focused on the description of the developed integrated scenario analysis tool, which includes both process modelling and metrics tools, and to depict some examples of its application for process data analyses and scenario simulations