Mathematical Optimization meets LCA: a methodological framework for the Steelmaking sector

19th International Conference on Society & Materials, SAM19, 2025 1 Mathematical Optimization meets LCA: a methodological framework for the Steelmaking sector Federico Rossi1,2, Monia Niero1,2, Valentina Colla3,1, Antonella Zaccara3,1, Stefano Dettori3,1, Laura Laid3,1, Teresa Annunziata Branca3,1, Silvia Cateni3,1, Lorenzo Vannini3,1, Fabio Iraldo2,1 1Sant’Anna School of Advanced Studies, Sustainability and Climate Interdisciplinary Center, Via Santa Cecilia 24, Pisa 56126 Italy, 2Sant’Anna School of Advanced Studies, Institute of Management, Sustainability Management (SuM) Lab, Piazza Martiri della Libertà 24, Pisa 56126 Italy, 3Sant’Anna School of Advanced Studies, TeCIP Institute Via G. Moruzzi 1, Pisa 56124 Italy. feder[email protected] Abstract The steelmaking industry faces significant challenges in balancing environmental sustainability with the production of high-quality materials. Secondary steel production in Electric Arc Furnaces (EAFs) and Ladle Furnaces (LFs) present opportunities for reduced environmental impacts through optimized recycling and operational strategies. The European project ALCHIMIA “Data and Decentralized Artificial Intelligence for a Competitive and Green European Metallurgy Industry” addresses these challenges by integrating mathematical optimization and Life Cycle Assessment (LCA) methodologies to develop an "Optimized LCA" approach tailored to the steel industry. This approach allows for the systematic determination of the optimal mix of steel scraps, ferroalloys, and other inputs, minimizing environmental burdens while adhering to technical and quality requirements. The Optimized LCA model proposed in this study is grounded in the ISO 14040 and ISO 14044 standards, adapting the LCA methodological framework to include optimization variables alongside traditional LCA parameters. By converting scenario-based comparative assessments into algorithmic optimization problems, this method identifies solutions that traditional LCA might overlook. Key components of the methodology include defining environmental objective functions (e.g., minimizing Global Warming Potential), establishing optimization parameters and variables from the Life Cycle Inventory, and using mathematical constraints to ensure process feasibility and material quality. We present a case study from the ALCHIMIA project to illustrate the application of this approach. Data from CELSA Group facility in France are used to benchmark traditional LCA against the optimized model. Results indicate that the Optimized LCA method not only enhances decision-making efficiency but also reduces the effort required for scenario modeling. The case study highlights the model’s ability to improve environmental performance by refining input compositions and operational parameters, with direct implications for reducing energy consumption and emissions. This study concludes that integrating optimization with LCA offers a robust framework for advancing sustainability assessment in steelmaking. Future work will focus on extending this methodology to include economic objectives, thereby creating a multi-criteria decision-making tool for more environmentally sustainable and more competitive metallurgical processes. Keywords: Life Cycle Assessment, Electric Arc Furnace, Ladle Furnace, steel scrap, optimization