Obtaining a Multi-Factor Optimum Blend Using Scrap within the Scope of Sustainable and Environmentally Friendly Steel Production: Application in a Steel-Casting Company
Aydoğan Baş (),
Burak Birgören and
Ümit Sami Sakalli
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Aydoğan Baş: Department of Industrial Engineering, Kirikkale University, Kirikkale 71450, Türkiye
Burak Birgören: Department of Industrial Engineering, TOBB Economics and Technology University, Ankara 06560, Türkiye
Ümit Sami Sakalli: Department of Industrial Engineering, Kirikkale University, Kirikkale 71450, Türkiye
Sustainability, 2024, vol. 16, issue 11, 1-18
Abstract:
This study tackles the challenge of optimizing scrap blends in steel production to achieve sustainability and environmental consciousness. Focusing on a steel-casting company as a case study, we develop a mathematical model that minimizes cost, emissions, and energy consumption while maximizing scrap utilization. This model considers the specific elemental composition of various scrap piles and pure elements, alongside their associated costs and environmental impacts in the production of GS52 steel in a foundry company. Through the GAMS program and further verification with Microsoft Excel, we demonstrate that the optimal blend significantly reduces raw material costs by prioritizing scrap (99.7%) over pure elements. Moreover, this optimized blend minimizes energy consumption and associated carbon emissions, thus contributing to a more sustainable and environmentally friendly steel production process. This study offers valuable insights and a practical framework for the steel industry to adopt cost-effective and eco-conscious practices, aligning with global efforts towards sustainable manufacturing.
Keywords: sustainable production; steel scrap blending; mathematical modeling; reuse of scrap; carbon emissions in production; cost-effective blending (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:11:p:4446-:d:1400872
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