Using dynamic life cycle assessment to evaluate the effects of industry digitalization: A steel case study

Abstract

Decarbonization of steelmaking has stagnated while it has a considerable share of global greenhouse gas emissions and a growing demand. Digitalization is seen as a viable option to reduce emissions and costs of the sector in the near term and life cycle assessment (LCA) as a comprehensive framework to evaluate changes in production practices. In this study, we analyze the potential impact of using optimization algorithms to improve the operation of a steelmaking plant in Spain. Specifically, we study the potential effects of optimizing the sequence in which steel is produced to minimize losses during casting. The global warming (GW) impacts and economic costs are quantified using a dynamic LCA model, considering uncertainty and temporal variability using an open-source LCA framework. The results indicate, on average, modest savings in costs and are inconclusive regarding GW emissions. Most of the cost savings come from a reduction in the use of additives and electricity, which are wasted when the steel is scrapped during casting. The methodological framework has proven useful in quantifying and interpreting the potential effects of digitalization. The implemented solution, tested in an industrial setting, allows an automated evaluation of production at the plant using the LCA model, facilitating the use of sustainability criteria in decision-making.