Details of Grant 

EPSRC Reference:	EP/V001663/1
Title:	Membrane-Cyber-Physical System (m-CPS) for Smart Water Treatment
Principal Investigator:	Das, Dr DB
Other Investigators:	Derakhshani, Dr M Edwards, Dr RM
Researcher Co-Investigators:
Project Partners:	Anax Technologies De Montfort University Severn Trent Plc Group University of Cyprus University of Technology Malaysia
Department:	Chemical Engineering
Organisation:	Loughborough University
Scheme:	Discipline Hopping Awards
Starts:	01 October 2020	Ends:	31 March 2024	Value (£):	181,467
EPSRC Research Topic Classifications:	Artificial Intelligence Mobile Computing Networks & Distributed Systems
EPSRC Industrial Sector Classifications:	Information Technologies
Related Grants:
Panel History:	Panel Date Panel Name Outcome 06 Jul 2020 EPSRC ICT Prioritisation Panel July 2020 Announced
Summary on Grant Application Form
Filter membranes play a critical role in providing clean drinking water, access to which is one of the most pivotal human rights. Typically, the operation of the filters has relied on manual, local monitoring of operational markers such as flow rates and contaminants' concentrations. This need for hands on expert maintenance is preventing membrane technology from reaching its full potential. To correct this, the monitoring of water filter needs to be achieved by sensors, transmitting data in real-time for centralised artificial intelligence (AI) based analysis. Such an AI driven water filter system must be scalable to meet with the global demands for clean water. There is therefore a massive global opportunity for membrane systems to benefit from being implemented as cyber-physical systems (CPS). This discipline hopping grant (DHG) will provide the PI and discipline hopper Das with an immersive information and communication technology (ICT) experience. It will enable him to bring the ICT capabilities and use of smart wireless-sensor technologies for autonomous, real-time monitoring, together with AI driven data analytics within the broader area of CPS into his home discipline relating to membrane water treatment. This will be achieved by supporting/mentoring the PI at 50% FTE for 2 years to experience ways for developing a membrane-CPS (m-CPS) based on intelligent CPS architecture, embedded with a smart wireless sensor network (WSN) for continuous real-time monitoring of the performance of a membrane-treatment unit enhanced by cloud-based AI data analytics and decision making.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Sectors submitted by the Researcher
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Project URL:
Further Information:
Organisation Website:	http://www.lboro.ac.uk