Energy System Optimization for a Scrap Based Steel Plant Using Mixed Integer Linear Programming

Johan Riesbeck
Centre for Process Integration in Steelmaking, Swerea MEFOS, Luleå, Sweden

Philip Lingebrant
Höganäs AB, Höganäs, Sweden

Erik Sandberg
Centre for Process Integration in Steelmaking, Swerea MEFOS, Luleå, Sweden

Chuan Wang
Centre for Process Integration in Steelmaking, Swerea MEFOS, Luleå, Sweden

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp110571676

Ingår i: World Renewable Energy Congress - Sweden; 8-13 May; 2011; Linköping; Sweden

Linköping Electronic Conference Proceedings 57:26, s. 1676-1683

Visa mer +

Publicerad: 2011-11-03

ISBN: 978-91-7393-070-3

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


In this work a mathematic model to simulate and optimize the energy system of a scrap based plant has been developed. Scrap based steelmaking is an energy intense production system. The potential for energy saving by system optimization is therefore high; even if the percentage of saved energy is relatively small. The model includes scrap pre-treatment; electrical arc furnace; ladle furnace and continuous casting units. To estimate the chemical compositions of the scrap charged into the EAF a statistical model based on an existing EAF plant has been used to provide the inputs to the model. Distribution factors have been used to describe the distribution of elements and oxides between the steel; slag and off gas/dust. To calculate the energy consumption in the electrical arc furnace a combination of an empirical and theoretical energy formula has been used. The model represents a general description of the most common process in electric steelmaking. It is suited to be adapted for specific plants with adjustments to the model parameters. The model gives reasonable results which follow the chemical composition of steel and slag and yield. The model can be a powerful tool to optimize the scrap mix and injectants towards energy and costs.


Energy systems; optimization; steelmaking; EAF; MILP; linear programming


[1] www.worldsteel.org

[2] M. Larsson; and Dahl; Reduction of the specific energy use in an integrated steel plant – The effect of an optimization model; ISIJ International 43(10); 2003; pp. 1664-1673.

[3] H. Pfeifer; M. Kirschen; J.P. Simoes; Thermodynamic analysis of EAF electrical energy demand; EEC 2005; May 9-11; 2005; Birmingham; England.

[4] E. Worrel; L. Price; M. Neelis; C Galitsky; Z Nan; World Best Practice Energy Intensity Values for Selected Industrial Sector. u.o.: Ernest Orlando Lawrence Berkely National Laboratory; February; 2008.

[5] Draft Reference Document on Best Available Techniques for the Production of Iron and steel; Institute for Prospective Technological Studies; European IPPC Bureau; July; 2009.

[6] The State-Of-The-Art Clean Technologies (SOACT) for Steelmaking Handbook; Asia Pacific Partnership for Clean Development and Climate; December; 2007

[7] www.castrip.com

[8] K. Nilsson; and M. Söderström; Optimizing the Operating Strategy of A Pulp and Paper Mill using the MIND Method; Energy – The International Journal 17(10); 1992; pp. 945-953.

[9] M. Karlsson; and M. Söderström; Sensitivity analysis of investments in the pulp and paper industry - on investments in the chemical recovery cycle at a board mill; International Journal of Energy Research 26(14); 2002; pp. 1253-1267.

[10]C. Ryman; and M. Larsson; Reduction of CO2 emissions from integrated steel-making by optimized scrap strategies: Application of process integration models on the BF-BOF system; ISIJ International; 46(12); 2006; pp. 1752-1758.

[11]M. Karlsson; The MIND method: A decision support for optimization of industrial energy systems - Principles and case studies; Applied Energy 88(3); 2011; pp. 577-589.



[14]Purchase specifications; Höganäs AB; Halmstad; Sweden

[15]S. Köhle; Einflussgrößen des elektrischen Energieverbrauchs und des Elektroverbrauchs von Lichtbogenöfen; Stahl und Eisen; 112(11); 1992; pp. 59-67.

[16]W. Adams; S. Alameddine; B; Bowman; N. Lugo; S. Paege; Stafford P.; Total energy consumption in arc furnaces; MPT International 6; 2002; pp. 44-50.

[17]HSC Chemistry 6.1 (software); Outotech Research Oy; Antti Roine.

Citeringar i Crossref