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ENERGIX-Stort program energi

Electrical Conditions in Submerged Arc Furnaces - Identification and Improvement (SAFECI)

Alternative title: Identifikasjon og forbedring av elektriske forhold i smelteovner (SAFECI)

Awarded: NOK 14.0 mill.

SAFECI is a knowledge-building project for Industry that aims at improving the energy efficiency of submerged arc smelting furnaces (SAFs) by increasing the understanding of the electrical conditions and enabling the refinement and automation of the control systems. Design and operation of smelting furnaces have been gradually improved through industrial experience, research, modern process control, new and/or improved measurements, etc. Nevertheless, due to the extreme conditions in the core of the furnace and difficulties to obtain reliable measurements, several process variations are not properly understood. The SAFECI project aims at establishing methods to reliably identify the inner electrical states (current paths, etc.). In SAFECI, we aim at combining various mathematical modelling with suitable measurements. If successful, the project will establish new tools to identify, and hence control, the electrical conditions within SAFs. Improved electrical conditions lead directly to more stabilized operation, more suitable energy distribution and enhanced furnace efficiency (lower kWh/kg product), with corresponding energy savings. In the start-up phase, the project has concentrated on meeting with key personnel of the industrial partners to discuss how the electrical conditions in the furnaces are currently monitored, what are the knowledge gaps and what are the most pressing issues that should be prioritized by the project. Due to the current situation, on-site visits have been postponed to 2022. Concerning the modelling work, the finite element method models for Silicon and FerroSilicon furnaces have been expanded and improved to better represent the furnace of the industrial partner and to allow for the study of unbalanced electrical conditions. On the experimental front, laboratory experiments conducted at NTNU gave micro-CT scans for packed reduction material, these have been investigated with software tool developed by NORCE researchers. SAFECI is financially supported by The Research Council of Norway (Pr. No. 326802) and the companies Elkem, Eramet Norway, Finnfjord and Wacker Chemicals Norway.

The project will address the following priority set out by ENERGIX: • Improved automation and control systems for achieving major gains in energy efficiency The industrial partners (Elkem AS, Eramet Norway AS, Finnfjord AS and Wacker Chemicals Norway AS, major players in the production of silicon and ferroalloys) have identified that improved electrical conditions in the Submerged Arc Furnaces (SAFs) lead directly to more stabilized operation, more optimal energy distribution and enhanced furnace efficiency (lower kWh/kg product), with corresponding energy savings. Electrical Conditions in smelting furnaces have been studied in the KPN project "Electrical Conditions and their Process Interactions in High Temperature Metallurgical Reactors (ElMet)". This project has provided very good insight based on a wide range of first-principle models. It was also tested how the results from several large-scale FEM (Finite Element Method) simulations can be "concentrated" into metamodels. These are surrogate models that retains the same generalization capabilities as the original FEM models, while being computationally lightweight. The project intends to explore such metamodels and combine them with data-driven modelling into "Digital Siblings" (a major step towards appropriate future Digital Twins), mirroring the typical electric behaviour of SAFs. This tool will then apply existing operational data, combined with some required new measurements, to identify the inner, hidden, electrical states within the furnaces. To succeed, the project needs to be highly interdisciplinary, putting together: • Metallurgical knowledge at university level • Metallurgical know-how from metallurgists, operators, etc. from the partner companies • Physics based mathematical modelling of SAFs, especially electrical conditions • Data based modelling of processes • Big Data Cybernetics, including artificial intelligence (AI) and machine leaning (ML) • Measurement technology

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ENERGIX-Stort program energi