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Control of Metallurgical Processes with indirect measurements and Machine Learning


The project aims to optimise steel manufacturing process steps through prediction of stirring intensity, which is expected to provide a more efficient steel production that takes place on time and with reduced energy consumption. Prediction of stirring intensity in metallurgical processes is realised by combining the use of measurement technology with signal processing along with machine learning. Better process control is expected to optimise time spent in process steps such as pouring and carbon control in converters.

Metalliska material genom Programkontoret
Swerim AB
Sandvik Materials Technology
Luleå tekniska universitet
Outokumpu Stainless AB

Funding agency


Beginning and End Dates

November 2018 - October 2022

Contact person

Björn Glaser
Björn Glaser
associate professor +4687908339

Other information

More information about MetMaskin project is available on Vinnova’s webpage:

Belongs to: Materials Science and Engineering
Last changed: Apr 28, 2021
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