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Multi-resolution Networks Decomposition, Optimisation and Control for Dynamically Adaptive Water Supply Networks


Department of Civil & Environmental Engineering

Applications accepted all year round Funded PhD Project (Students Worldwide)
London United Kingdom Civil Engineering Environmental Engineering Machine Learning

About the Project

Supervisor Imperial College London: Dr Ivan Stoianov

Supervisor Severn Trent Water: David Morrell

One PhD scholarship funded by Severn Trent Water and Imperial College London to investigate, formalise and validate an analytical and computational framework for the multi-resolution decomposition, optimisation and control of District Meter Areas (DMAs) in water supply networks (WSNs). The PhD will be based in the Dept. of Civil and Environmental Engineering, in the InfraSense Labs research group working with Severn Trent Water, offering a wide range of training and development opportunities in a highly stimulating environment, as well as access to world-leading academics and water utility experts, facilities and networks.

Project details:

The overall research goal for this PhD project is to investigate, formalise and validate an analytical and computational framework for the multi-resolution decomposition, optimisation and control of District Meter Areas (DMAs) in water supply networks (WSNs). This will enable the optimal evolution of existing network topologies (e.g. sectors with single water inlets) into networks, which dynamically adapt their connectivity and operational conditions in order to improve both the redundancy in connectivity and the pressure control; and therefore, improve their resilience (the ability of networks to tolerate and recover from failures and/or respond to extreme demand).

We refer to this framework as Design-for-Control of Adaptive Networks (DCAN). The DCAN framework simultaneously optimises the design (e.g. opening kept shut valves, valve placements and network connectivity modifications) and the operational control (e.g. control functions and settings for valves and pumps for a given configuration). This co-design approach for large-scale WSNs takes into account the hydraulic dynamics and associated uncertainties, and the development of robust mathematical optimisation and control methods that enable multiple operational objectives.

Academic requirements and experience:

•        A good First Class Degree (or International equivalent) Applied Mathematics, Control and Systems Engineering, Chemical Process Engineering, Civil Engineering or a course with strong emphasis on mathematical optimisation, control and systems engineering.

•        A Masters level degree qualification in any of these subjects/courses (Applied Mathematics, Control engineering, Chemical Process Engineering, Civil Engineering or a course with strong emphasis on mathematical optimisation, control and systems engineering) will be highly beneficial.

•        Solid background in applied mathematics (linear algebra), mathematical optimisation or control engineering.

•        Good knowledge of Matlab and/or Python.

How to apply:

Applicants wishing to be considered for these opportunities should send the following application documents to Ivan Stoianov ()

1.      Current CV including details of their academic record

2.      Covering letter making explaining their motivation and suitability

3.      Contact details of two academic referees

Application via the Imperial College Registry is not necessary at this stage.

Applications will continue to be accepted until a suitable candidate is appointed and the Scholarship is available to start straightaway.


Funding Notes

The studentship will provide funding for 3 years including tuition fees and a tax-free stipend at the standard UKRI London rate, ~ £17,285 (tax free) for the 2020/21 academic year. In addition, allowance is provided for research consumables and conference attendance.
Full funding is available to Home students. The funding can also be used to partly support an international student.

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