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Integrated approaches of systems engineering and graph analytics for mineral supply chains analysis
Aydogdu, Kemalcan
Aydogdu, Kemalcan
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2025
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2026-11-11
Abstract
The global push for decarbonization and supply chain resilience has placed increasing pressure on the mining industry to reduce its environmental impact while ensuring a steady supply of critical minerals. This dissertation integrates systems engineering and graph analytics to analyze three key aspects of mineral supply chains: (1) decarbonization of mining haulage systems, (2) emission impacts of iron ore processing, and (3) supply chain transparency and resiliency assessments using graph-based methods.
The first study applies system dynamics modeling (SDM) to quantify greenhouse gas (GHG) emissions and economic trade-offs in open-pit copper mining haulage systems. It compares a diesel truck fleet with an electric overland conveyor system under different electricity grid emission factors. Results show that electrification can significantly reduce emissions when powered by low-carbon electricity sources. However, high capital expenditure (CAPEX) and regional energy mix variability present key challenges to widespread adoption.
The second study focuses on iron ore processing systems in Canada and Peru, examining how geographical location and ore type influence emissions. Using system dynamics modeling, this study models the iron ore processing systems and quantifies the GHG emissions of those plants while considering the regional energy grids and the type of ores processed. Results indicate that processing magnetite ore in fossil fuel-reliant grids (e.g., Peru) generates significantly higher emissions than in hydropower-dominated grids (e.g., Canada). Additionally, the study assesses the long-term effects of Canada's carbon tax policy and energy transition strategies on iron ore processing emissions. Findings reveal that while carbon pricing effectively reduces emissions over time, its success depends on the availability of low-emission electricity and industry adaptation to regulatory changes.
The third study reconstructs a complete mineral supply chain network using global trade transaction data and employs graph analytics to assess transparency and resiliency. This study provides a novel methodology to assess the transparency of the supply chain networks by a graph-based methodology. Furthermore, the study applies various graph analytics metrics to identify the critical nodes, bottlenecks, and geopolitical risks in supply chain operations and applies stress testing to assess the resiliency of the network by applying random disruptions to the supply chain network. Results show that supply chain disruptions can be quantified using graph metrics, providing a scalable method for enhancing risk management and emission tracking in global mineral supply chains.
By integrating systems engineering and graph analytics, this dissertation provides a comprehensive data-driven decision-support framework for policymakers and industry stakeholders. The findings offer practical strategies for reducing mining emissions, optimizing processing systems, and enhancing supply chain resiliency and transparency, contributing to a more sustainable and transparent mineral industry.
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