A PhD Studentship in Optimal Monitoring of Grain Cargos
Tuition fees at the Home rate and stipend of £19,668 @EPSRC rate + £5,000 top-up from industry per annum
Number of awards
Tuition fee status
Mode of study
Available to applicants in the following departments
- Electrical and Electronic Engineering
Eligibility criteriaApplicants should have a first-class Master's degree (or equivalent) in Engineering, Physics, Mathematics, or related areas. Suitable backgrounds for these PhD positions include, but are not limited to control theory and mathematical optimization. They should be highly motivated individuals with a keen interest in conducting interdisciplinary research. Students must also meet the eligibility requirements for Post-Graduate Studies at Imperial College London.
Applications are invited for a PhD studentship, to be undertaken at Imperial College London (Control and Power Group, Department of Electrical and Electronic Engineering). This studentship is funded by an EPSRC CASE award and industrial partner Andrew Moore & Associates. Andrew Moore and Associates are a leading maritime consultancy and are frequently involved in grain cargo surveys and claims disputes. As part of the project, the student will work closely with staff at Andrew Moore & Associates and benefit from mentoring and supervision of their staff.
The project will be supervised by Prof. Eric Kerrigan (Professor of Control and Optimization, Imperial College London) and Dr Bryn Jones (Consulting Scientist, Andrew Moore & Associates).
Please note: This scholarship is not available to continuing students.
Application processPlease submit application of admissions to Imperial College London. The application should also include a covering letter, and your CV. Queries regarding the application process should be directed to Ms Emma Rainbow firstname.lastname@example.org
Additional informationSummary of Project:
The international shipment of grain cargos is of vital importance to feeding the World's population. Grain cargos, such as soya bean, are prone to microbiological instability, meaning that they can spoil during transit, with the rate of spoil dependent upon factors such as initial condition, temperature and humidity. This often leads to disputes between the charterers of the cargo and the owners of the vessel, with the former, for example claiming that conditions at sea were the cause of damage. Apportioning blame is often difficult, owing to the lack of sensing on the state of the cargo during its voyage, the heterogeneity of the cargo state and the enormous volume of the cargo holds. This raises the question of what is the best sensing strategy to reliably determine the state of a grain cargo? Answering this question will ultimately provide greater transparency to all parties and could enable preventative action to reduce spoilage of these vital cargos.
The aim of this project is to formulate and solve the best sensing strategy to minimise the uncertainty in the state of a bulk grain cargo, with the state consisting of key parameters, such as temperature, humidity, CO2 concentration etc. Additionally, the performance trade-offs between different scenarios will be established, such as the relative benefits of fixed vs mobile (robotic) sensors, which sensors are the most important, how many are needed to achieve a certain performance level, etc. A successful outcome would be the provision of a â€˜digital twinâ€™ for a grain cargo.
As part of this project, the student will be expected to
1. Analytically model the dynamics of grain cargo spoilage within a representative cargo-hold domain, for a subset of typical grain types, and verify the model in simulation.
2. Formulate and solve an optimal model-based estimation problem. Key to this will be (i) defining a relevant metric to quantify the uncertainty in the state of the cargo and (ii) obtaining a suitable low/reduced-order model of the high-fidelity simulation model.
3. Quantify the relative performance benefits of single/multiple sensors of different types, along with the benefits of fixed vs moving (robotic) sensors. This will provide clear guidance regarding the practical implementation of the sensing strategy.
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