Skip to main content

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 +4687908339

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: Apr 28, 2021
Optimization of the ingot casting process by minimising macrosegregation and porosity
Avoiding cracking during casting of a duplex stainless steel
Development and Application of an Operator Vision Assistance System for Enhanced Direct Process Control in Foundries
FerroSilva - fossil-free virgin steel from iron ore and biogenic reduction gas
Optimisation of the oxygen use in EAF steelmaking by direct process monitoring of the chemical melt reactions
Variable nozzle height in AOD converter, stage 2
Control of nitrogen content in the production of stainless steel
Raw Ideas for Materials Projects
A multiscale and multifunction Cascade Catalytic Fast Pyrolysis of lignocellulose for the production of gasoline- diesel range fuel for transportation section
Sustainable technology for the staged recovery of an agricultural water from high moisture fermentation products
Recycling plastic wastes to valuable chemicals of monoaromatics and metals through catalytic-pyrolysis
Optimized biofuel-production via two-step upgrading via catalytic pyrolysis and hydrotreatment
Electrified-Catalytic Reforming using 3D printed catalysts for Biomethane production from biomass pyrolysis
Gradient control in thin film solar cells
Flexible Ladle Preheating Procedures using Plasma Heated Refractory
Control of Metallurgical Processes with indirect measurements and Machine Learning
Optimization and Performance Improving in Metal Industry by Digital Technologies
BLast furnace stack density Estimation through on-line Muons ABsorption measurements
INEVITABLE
Digitalisation of Atomisation
Direct reduction of alloy metals
Advanced design, monitoring , development and validation of novel HIgh PERformance MATerials and components