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ORGANIZER;CN=ESTAD 2023:mailto:info@metec-estad.com
LOCATION:Room 8
SUMMARY:Optimization and performance improving in metal industry by digital technologies
DESCRIPTION:The INEVITABLE project applies digital technologies for an optimized and improved performance of different metalmaking processes with focus on steelmaking but also for nonferrous alloy casting. The aim is the development of high-level supervisory and control systems for different production plants and their demonstration in operational environments to enable an optimized operation of the processes, going hand in hand with a reduction of resource consumption and CO2 emissions. The digital transformation and upgrade of the processes include data acquisition, processing and analytics of datastreams, standardization of relevant data interfaces and storage, and application of the functionalities of smart sensor technologies, cognitive control and Industry 4.0 concepts. 
The INEVITABLE project revolved around various steelmaking processes, ranging from electric arc furnace (EAF) and secondary metallurgy up to the cold rolling mill. This talk gives an overview over the cognitive control solutions developed in the project. 
For the EAF operations, process models and optimization framework have been developed, based on both theoretical and data-driven approaches using operational data. Their aim is to allow continuous online estimation of the bath temperature and oxygen level, offline process simulation for scenario testing, and optimization of the energy consumption via improved EAF inputs.
In secondary metallurgy, ladle furnace and vacuum degassing processes have been considered. Based on vibration sensors and image data, the stirring behavior has been monitored. Together with the evaluation of other process data, model-based advisory systems for process control and decision support as well as predictive models for cleanliness and castability of liquid steel have been developed. 
Furthermore, a system for supervision, optimisation, and condition monitoring of cold rolling mills has been developed. Strip speed sensors and an x-ray thickness measurement have been introduced, and an overall upgrade of the databases has been implemented, including communication interfaces between sensors, edge devices and cloud database. 
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DTSTART:20230615T115000
DTEND:20230615T121000
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