MSc theses
Potential future topics
Artificial Intelligence related topics:
- Reverse engineering and investigative approaches for LCA
- How to go from product disassembly to LCA
- Material recognition methods applied to industrial ecology
- Artificial Intelligence and digital technology applications in industrial ecology
- FAIR and Data Quality Assessment in industrial ecology
- Environmental Impacts of AI and Digital Twins
- Collaborative approaches for AI (e.g., citizen science) in industrial ecology
Circular economy related topics:
- Information Systems for the circular economy
- Modelling circular economy scenarios for products and business models, and Design for X practices at the macro-economic level or at the border between Micro-Meso-Macro levels
- Hydrogen scenarios for steel manufacturing using EEIOA
- Rebound effects of CE using EEIOA
- Software tool development for CE assessment
Send me a message if any of these topics speak to you ✋
Current and past theses
As 1st supervisor:
Current
Optimizing Packaging Waste Sorting through Computer Vision Systems (Aleks Žitkevičs)
Does circular economy create environmental injustice: A global and systematic assessment of the impact of circular economy on ecologically unequal exchanges (Gael Croué)
Structural Decomposition Analysis regarding Costs of Sourcing embedded in Global Trade and Changes in Production Recipe in the 21st Century (Bram Honig)
The environmental impacts of current and future biofuel consumption by the Dutch maritime sector (Kevin Heideman)
Past
PeDALI: A Pedigree matrix approach for data-driven Data quality Assessment for Life cycle Inventories (Floor Bagchus)
Circular Economy Strategies for Metals: Assessing the Impact of Circularity Interventions on the Dutch Economy by 2050 (Reginold Jesuratnam)
As 2nd/3rd supervisor
Past
From Sewage to Coal: New insights in Char Production: Performance evaluation with a combined Process Simulation and ex-ante Life Cycle Assessment (Heiko Rossdeutscher)
Machine learning in ex-ante LCA: assessing enhancements of forecasting algorithms through systematic literature review and case studies (Nils Pauliks)