A Dynamic Material Flow Model for Risk-Informed Decision-Making in Decarbonizing Global Aluminum Manufacturing

Abstract

Aluminum is the world's second most consumed metal, and its production contributes substantially to global greenhouse gas (GHG) emissions. When formulating decarbonization strategies, it is imperative to ensure their coherence and alignment with existing industrial practices and standards. A material flow analysis (MFA) is needed to gain a holistic and quantitative understanding of the flows and stocks of products/materials associated with all participants within the supply chain. To support risk-informed decision policymaking in decarbonizing aluminum manufacturing, this study develops a dynamic system model that maps global aluminum flows and computes their embedded GHG emissions. A baseline scenario is devised to reflect the current business and operation landscape, and three decarbonization strategies are proposed. Deterministic simulation is performed to generate dynamic material flows and performance metrics. Monte Carlo simulation is then implemented to evaluate the robustness of the system's performance under demand uncertainties. The results reveal the immense carbon implications of material efficiency, as well as the preponderant role of post-consumer scrap recycling in decarbonizing aluminum manufacturing. Informed by simulation outputs, macro decarbonization guidelines are formulated for various criteria. The object-oriented programming framework that underlies the dynamic MFA may be integrated with network analysis, agent-based simulation, and geospatial interfaces, which may lay the foundation for modeling more fine-grained material flows and supply chain structures.