Thesis subject

Investigating Waste Behaviours in Urban Environments

Urbanisation is surging, with over half of the human population predicted to live in an urban environment in the near future. This growth will place a strain on critical infrastructure distribution networks (water, energy, food, healthcare), which already operate in a state that is complex and intertwined within society. To transition towards a sustainable society, there needs to be a change in both societal behaviours (e.g. reducing water/energy/food waste activities) and the technology currently in place (e.g. greater use of green energy, digital twins, preventative maintenance solutions and precision technology).

This project will allow you to:

  1. Explore the challenge of understanding and modelling waste behaviours for a selected amenity (water, energy, food, healthcare) that will further the understanding of waste behaviours in society
  2. Model the reliability/availability/resilience of critical infrastructures

You will also have the opportunity to use (or propose the use of) advanced technologies (e.g. AI/ML/DL, Digital Twins) as a solution to reducing waste behaviours.


    Objectives

    1. Review current processes and state-of-the-art for waste behaviour modelling, and what current approaches are used to adapt to waste reduction.
    2. Investigate which sectors (e.g. water, energy, food) are most impacted by waste behaviour and how this affects production/service provision.
    3. Identify solutions/applications that are suitable for supporting critical infrastructure technology.

      Literature

      • Shooshtarian, S.; Caldera, S.; Maqsood, T.; Ryley, T. Using Recycled Construction and Demolition Waste Products: A Review of Stakeholders’ Perceptions, Decisions, and Motivations. Recycling 2020, 5, 31. https://doi.org/10.3390/recycling5040031
      • Barreiro, J.; Lopes, R.; Ferreira, F.; Brito, R.; Telhado, M.J.; Matos, J.S.; Matos, R.S. Assessing Urban Resilience in Complex and Dynamic Systems: The RESCCUE Project Approach in Lisbon Research Site. Sustainability 2020, 12, 8931. https://doi.org/10.3390/su12218931

      Requirements (optional)

        Theme(s): Water, Energy and Food Network, Waste Behaviours, Artificial intelligence and machine learning, Digital Twins

        Contact person(s)

        Will Hurst (will.hurst@wur.nl)