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Control of Metallurgical Processes with indirect measurements and Machine Learning

MetMaskin

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
Jernkontoret
KUNGLIGA TEKNISKA HÖGSKOLAN
Luleå tekniska universitet
OVAKO SWEDEN AB
Outokumpu Stainless AB
SSAB AB
UDDEHOLMS AB

Funding agency

Vinnova

Beginning and End Dates

November 2018 - October 2022

Contact person

Björn Glaser
Björn Glaser associate professor bjoerng@kth.se +4687908339 Profile

Other information

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

www.vinnova.se/en/p/control-of-metallurgical-processes-with-indirect-measurements-and-machine-learning-metmaskin/

Vinnova
Page responsible:webmaster@mse.kth.se
Belongs to: Materials Science and Engineering
Last changed: Nov 26, 2024
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