Model-based methodologies for optimal sensor placement
Development of data-driven sensor placement methodologies to maximise the quantity and quality of information obtained by a system and enable operational decision making.
Start date1 October 2022
Funding sourceFaculty of Engineering and Physical Sciences - Doctoral Training Partnership
UKRI stipend (e.g. £16,062 for 2022/23), home fees covered, £3,000 Research Training Support Grant and £3,000 Bench fees.
The advancement of complex engineered systems - from micro to large scale - has increased the demand for dynamic monitoring and reliable performance. Optimal sensor placement is a well-known challenge in all these systems to acquire real-time information of a system’s response at minimum cost whilst detecting predetermined performance criteria. The type, number, and position of sensors, in conjunction with system-specific operational constraints can generate challenging design problems.
The scope of this project is to develop data-driven sensor placement methodologies to maximise the quantity and quality of information obtained by a system and enable operational decision making. Predictive models will be built to enable the analysis of alternative monitoring and control strategies. Optimization based design methods will be developed to identify sensor network configurations with inherently improved operability characteristics. The designs will include process steps and monitoring aspects based on process requirements and the ability to generate the data required for model predictive control.
The methodologies can find applications that span across a wide range of industry sectors such as pharmaceutical, process, and clean energy.
The successful candidate will be supervised by Dr Dimitrios Tsaoulidis (University of Surrey) and co-supervised by Professor Eric Fraga (University College London) and Professor Panagiota Angeli (University College London). The successful candidate will receive extensive research training and be given opportunities to participate and present their work in conferences, workshops, and seminars to develop professional skills and a research network.
The post is offered to students/graduates with UK, settled, or pre-settled status for a start in October 2022.
This studentship is open to candidates who pay UK/home rate fees.
Applicants are expected to hold a first or upper-second class degree in a relevant discipline (or equivalent overseas qualification), or a lower second plus a good masters degree (distinction normally required).
English language requirements
IELTS Academic: 6.5 or above (or equivalent) with 6.0 in each individual category.
How to apply
To apply, please send your CV to Dr Dimitrios Tsaoulidis (email@example.com) and start an application via the process through the Chemical and Process Engineering Research PhD programme page. Please clearly state the studentship title and supervisor (Dr Dimitrios Tsaoulidis) on your application.
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