PhD in Plastics Analysis, Sorting & Recycling Technologies Through Intelligent Classification

Project Overview

Plastic remains an invaluable material for human society in many key areas, but the challenge of sustainable end-of-life disposal routes continues to exist. Accurate sorting and the production of high quality recyclate is a key target in order to ensure confidence and security in recycled plastic supply chains and subsequent manufacturing industries. This is essential for society to reach targets for the inclusion of recycled and recyclable content across sectors such as automotive and packaging. To meet this challenge, an infrastructure of knowledge-led and digitally-enabled systems that underpin future manufacturing needs to be developed.

Our previous work on AI & machine learning (ML) for sorting highlighted a need for practical solutions and we were the first to demonstrate the potential of deep learning methods to solve the sorting problem, followed up by demonstrating that using multiple data sources (e.g. IR, Raman and LIBS) can improve overall performance.

While classification accuracy has improved significantly with our work, the quality and quantity of recyclate use is still a challenging problem. The aim of this project is to enhance the previous work on sorting with an improved understanding of extrusion compounding through in-line rheometry for real-time monitoring of shear viscosity. The integration of real-time rheological data with ML principles will form the basis of an intelligent recycling system, backed up by measurable sustainability credentials with the ultimate aim of delivering a new approach that delivers on both recycled content and net zero targets.

Essential and desirable student background criteria

Students must have at least a 2.1 in a STEM subject that can be applied to the field of sustainable polymer engineering. A keen interest in sustainability is also essential.

To apply

To apply please complete our online enquiry form and upload your CV.

Please ensure you meet the minimum requirements before filling in the online form.

Key Information:

Funding Source: EPSRC DTP

Funding Duration: 3.5 years

Stipend: Standard UKRI stipend rate

Supporting Company: N/A

Supervisor: Dr Stuart Coles, Prof. Kurt Debattista, Prof. Ton Peijs

Eligibility: Available to eligible Home fee status

Start date: October 2024