Should plastic waste be recycled locally or regionally? A European optimisation model

Masterarbeit Details

Background

Plastics are both a challenge and an opportunity for the circular economy. Today, large volumes of end-of-life plastics are incinerated, wasting the embedded carbon and releasing CO₂. Yet this same carbon can be kept in the loop: through chemical recycling, mixed plastic waste can be turned into pyrolysis oil and reused in industrial plants such as steam crackers and gasifiers. In this way, waste plastics become a secondary carbon feedstock, replacing crude oil while reducing Europe’s dependence on fossil resources.

To realise this potential, recycling must be seen as a cross-boarder system of waste flows. Waste may be collected in one country but processed in another, raising the key question: under what conditions is it economically and environmentally sensible to transport plastic waste across borders?

This thesis addresses that question by developing a European optimisation model that maps waste flows, recycling plants, and industrial customers to support actionable decisions for the circular economy.

 

Content

The work combines data collection, model building, and scenario analysis, with the following main steps: 

• Build a data foundation: Collect data on waste generation, plant locations and capacities, costs, emissions, and process performance.
• Scale the optimisation approach: Extend an existing Germany-specific model to a European-wide perspective where plastic waste, recycling plants, and industrial customers are linked across borders.
• Explore scenarios and sensitivities: Test how results change under different assumptions (e.g. changes in costs and credits, higher or lower transport prices, or stricter climate targets and policies).
• Analyse cross-border flows: Identify in which scenarios it is beneficial for waste to move from one country to another, and what this implies for Europe’s circular economy.

 

Requirements

• Degree in industrial engineering, chemical engineering, or related fields with an interest in circular economy and sustainable technologies.
• Motivation to work with data and to structure complex systems clearly.
• Advanced Python skills and interest optimisation libraries such as CPLEX/Docplex for solving MILPs.
• Application should include: current transcript, CV, and a brief cover letter.