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Process-time Optimization of Vacuum Degassing Using a Genetic Alloy Design Approach
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Computational Thermodynamics.
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Computational Thermodynamics. Thermocalc Software AB, Sweden.ORCID iD: 0000-0002-8493-9802
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2014 (English)In: Materials, ISSN 1996-1944, E-ISSN 1996-1944, Vol. 7, no 12, 7997-8011 p.Article in journal (Refereed) Published
Abstract [en]

This paper demonstrates the use of a new model consisting of a genetic algorithm in combination with thermodynamic calculations and analytical process models to minimize the processing time during a vacuum degassing treatment of liquid steel. The model sets multiple simultaneous targets for final S, N, O, Si and Al levels and uses the total slag mass, the slag composition, the steel composition and the start temperature as optimization variables. The predicted optimal conditions agree well with industrial practice. For those conditions leading to the shortest process time the target compositions for S, N and O are reached almost simultaneously.

Place, publisher, year, edition, pages
2014. Vol. 7, no 12, 7997-8011 p.
Keyword [en]
genetic algorithm, computational thermodynamics, vacuum degassing, materials by design, steelmaking
National Category
Materials Engineering
Identifiers
URN: urn:nbn:se:kth:diva-159128DOI: 10.3390/ma7127997ISI: 000346796800023Scopus ID: 2-s2.0-84921302555OAI: oai:DiVA.org:kth-159128DiVA: diva2:783708
Note

QC 20150127

Available from: 2015-01-27 Created: 2015-01-22 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Profitability = f(G): Computational Thermodynamics, Materials Design and Process Optimization
Open this publication in new window or tab >>Profitability = f(G): Computational Thermodynamics, Materials Design and Process Optimization
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The thesis starts by giving a motivation to materials modeling as a way to increase profitability but also a possibility decrease the environmental impact. Fundamental concepts of relevance for this work are introduced, this include the materials genome, ICME and of course the CALPHAD method. As a demonstration promising results obtained by an ICME approach using genetic algorithms and CALPHAD on the vacuum degassing process are presented. In order to make good predictive calculations and process models it is important to have good thermodynamic descriptions. Thus most part of the work has concerned the thermodynamic assessments of systems of importance for steelmaking, corrosion and similar processes. The main focus has been the assessment of sulfur-containing systems and thermodynamic descriptions of the Fe-Mn-Ca-Mg-S, Fe-Ca-O-S, Fe-Mg-O and Mg-Mn-O systems are presented. In addition, heat capacity measurements of relevance for the Mg-Mn-O system have been performed. To summarize the efforts some application examples concerning thermodynamic calculations related to steelmaking and inclusion formation are shown.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2016. 64 p.
Keyword
CALPHAD, steel, sulfide, steelmaking, thermodynamics, ICME, CALPHAD, stål, sulfid, ståltillverkning, termodynamik, ICME
National Category
Metallurgy and Metallic Materials
Research subject
Materials Science and Engineering
Identifiers
urn:nbn:se:kth:diva-191243 (URN)978-91-7729-084-1 (ISBN)
External cooperation:
Public defence
2016-09-23, B3, Brinellvägen 23, Stockholm, 10:00 (English)
Opponent
Supervisors
Projects
COMPASS
Note

QC 20160829

Available from: 2016-08-29 Created: 2016-08-25 Last updated: 2016-08-29Bibliographically approved

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Mao, HuahaiSelleby, Malin

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