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Prediction of forever chemical concentrations in real-world drinking water treatment systems

Cranfield University Faculty of Engineering and Applied Science
✓ Fully Funded 🎓 Chemistry 🎓 Engineering 🎓 Environmental Sciences hydrology analytical chemistry environmental chemistry pollution environmental sciences climate science microbiology biochemistry

Develop a predictive model to forecast PFAS behaviour in granular activated carbon filters, enabling water utilities to optimize treatment processes. Collaborate with leading water companies and EPSRC in a fully funded project enhancing sustainability and compliance in drinking water safety.

AI-generated overview

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Why This Research Matters

This research delivers tools to reduce operational costs and environmental impact in water treatment by optimizing PFAS removal. It supports strategic planning for water utilities to meet evolving regulations, ultimately contributing to public health and sustainable resource use.

Per- and Polyfluoroalkyl Substances Granular Activated Carbon Water Treatment Environmental Chemistry Predictive Modelling Sustainability

Project Description

Project Overview

This exciting fully funded PhD, sponsored by Anglian Water, Thames Water, Yorkshire Water, Northumbrian Water, and EPSRC, addresses the challenge of PFAS (forever chemicals) in drinking water. The project aims to develop a predictive model using experimental data from testing to support utilities’ evidence-based decision-making to improve the resilience and cost-effectiveness of forever chemicals removal from water.

PFAS are micropollutants of growing concern globally, with new regulations requiring effective removal in drinking water. Granular Activated Carbon (GAC), a common treatment, faces cost and sustainability challenges due to shortened regeneration cycles with PFAS compared to other pollutants.

What You Will Do

  • Produce experimental data characterising PFAS removal in GAC systems.
  • Build a mechanistic-driven multicomponent model representing PFAS removal in GAC filters.
  • Validate the model with data from live treatment plants.

Expected Outcomes

The model will enable water companies to forecast GAC bed life under varying water qualities, reduce monitoring burdens, and support proactive compliance with future PFAS standards. The project will deliver cost savings, optimized GAC operation strategies, reduced experimental testing, and minimized PFAS analysis costs.

Why It Matters

This research provides an evidence-based approach contributing to strategic planning and future-proofing water infrastructure against PFAS regulations. It promotes efficient GAC use, reduces carbon footprint and operating expenses, and supports sustainability in the water industry. Collaboration with leading water companies and EPSRC ensures access to training, experimental facilities, and international placement opportunities.

Entry Requirements

Applicants should have a first- or second-class UK honours degree or international equivalent in chemical engineering, chemistry, environmental engineering, environmental science or related disciplines. Candidates should have some understanding of water science, be self-motivated with good communication skills, and modelling skills are desirable.

How to Apply

Contact Dr Irene Carra via email at rene.carra@cranfield.ac.uk for further information. Eligible applicants should complete the online application form. Early submission is encouraged as applications are reviewed on receipt and the position may fill before the deadline.

Eligibility

UK/Home
EU
International

Supervisor Profile

DI
Dr Irene Carra
Cranfield University, Faculty of Engineering and Applied Science

Dr Irene Carra focuses on applied research in water treatment technologies, particularly in removing micropollutants such as PFAS. Her approach integrates experimental data with predictive modelling to improve water treatment resilience and sustainability. She collaborates extensively with water utilities and contributes to advancing regulations and practical solutions in water science.

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