Water infrastructure fit for the 21st century
The centralised model for water infrastructure established in the 19th century has served us well, but it comes with high energy, capital and maintenance costs that are not compatible with a global net-zero carbon and sustainable economy.
Our vision is a future for off-grid water and wastewater biological treatment systems that operate with the ease, convenience and reliability of commercial domestic appliances (or community-scale versions of them).
Our Objectives
- Reliability
New systems must be reliable so that they are dependable for end users. - Ease of maintenance
New solutions require the development of new maintenance approaches. - Safety
Safety is paramount. End users will be able to take control of their water-related health. - Desirable to end users
Technologies must be desirable to end users. We will work to overcome barriers to adoption. - Affordable
We will work to ensure new solutions are economically viable for communities. - Effective
New off-grid test solutions to ensure efficacy will be developed. - Desirable to legislators
New approaches must be acceptable to regulators and legislators.
Updates
- EPSRC Off-grid Water Technologies Plenary Meeting -Dec 2024
December 9, 2024
EPSRC Off-grid Water Technologies Plenary Meeting -Dec 2024
The EPSRC Programme Grant on Off-grid Water Technologies Plenary Meeting was held on the 9th December 2024. The event was held at the University of Glasgow with others joining online via Zoom. The agenda included a welcome from our Principal Investigator, Professor Bill Sloan. From the Research Team presentations were received from Dr Tania Gomez-Borraz ...
- IAEA Circular Economy Technical Meeting
November 11, 2024
IAEA Circular Economy Technical Meeting
Dr Siming You was invited to attend the circular economy technical meeting held by the International Atomic Energy Agency (IAEA) in the United Nations Office at Vienna, Austria, between 11th Nov and 15th Nov 2024. Dr You shared his research on the environmental impact assessment of waste treatment and the machine learning-based process modelling of ...
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