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Models establishment for electrostatic meters through machine Learning (RDS19-SSEE)

  • Full or part time
  • Application Deadline
    Monday, April 29, 2019
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

About This PhD Project

Project Description

Fully funded PhD studentships for October 2019

As part of ongoing investment into areas of research strength, Teesside University is pleased to offer a number of fully-funded PhD studentships to exceptional doctoral candidates to commence in October 2019. The School of Science, Engineering and Design invites applications for fully-funded full-time PhD studentships in the following research projects:

Smart Systems and Energy Informatics Research Group
https://research.tees.ac.uk/en/organisations/smart-systems-and-energy-informatics-research-group

RDS19-SSEE Models establishment for electrostatic meters through machine Learning

Particulate measurement is a challenging task for its complexity in flow patterns and effect of multiple variables. However, this type of measurement becomes increasingly important in pharmaceutical, power generation, ore handling and transportation. Electrostatic meters are used for such application quite successfully, particularly in power generation, such as ultra-supercritical coal fired steam Plant, coal-biomass co-firing and integrated Gasification Combined Cycle (IGCC). The other areas of the applications includes iron and cement making and pharmaceuticals. For gas-solids flow metering and control, the flow rate measurement results depend on not only the flow rate, but also the velocity and concentration of particulates, particle size distribution and son so. The multiple variables are not linearly related from the past research findings. With the development of computing and artificial intelligence, modelling the meter through machine learning becomes practical. When conducting this project, various machine learning methods including linear regression and Neural Network methods under supervised machine learning will be studied. The most suitable method will be identified and data based on experiments and industrial application will be used for training and validation.

Supervisor: Dr Jianyong Zhang
https://research.tees.ac.uk/en/persons/jianyong-zhang

Applicants should use the link provided against the project area to the Research Centres and Research Groups identified and to the named supervisors for each project, to ensure that their application and proposal fits with the studentship offered.

Funding Eligibility

The studentship will cover tuition fees and provide an annual tax-free stipend of £15,000 for three years, subject to satisfactory progress. Applications are welcome from strong UK and International students.

Entry Requirements

Applicants should hold or expect to obtain a good honours degree (2:1 or above) and/or Masters level qualification in a relevant discipline, as well as a demonstrable understanding of the area; further details of the expected background may appear in the specific project details. International students would be subject to the standard entry criteria relating to English language ability, ATAS clearance and, when relevant, Tier 4 procedures.

How to Apply

Applicants should apply online for this opportunity. Please use the Full Time Funded PhD online application form. When asked to specify funding select “other” and enter the relevant studentship code given against the project that you applying for in the field required. You should also ensure that you add the studentship code as well as the title of the project on the proposal that you will need to upload when applying. If you would like to apply for more than one project, you will need to complete a further application form and specify the relevant title and code for each application to a topic or project. Please note that applications for funded studentships that do not quote the studentship title AND the Project ID on the proposal will be invalid and your application may not be considered for the appropriate funding.

For academic enquiries, please contact the relevant supervisor or staff in the research area directly. For administrative enquiries, contact .

Research at Teesside

As a Teesside University research student, you will join a growing and dynamic research community, allowing you to share your experiences, insight and inspiration with fellow researchers. You will benefit from our academic expertise, and be supported through a strong programme of research training. You will be offered opportunities and support at each stage of your research degree. Our research is designed to have impact, and to influence policy and practice within our region, the UK and beyond. We work with external organisations to anticipate and respond to research needs, and to put our research into practice in sectors as diverse as the arts, engineering, healthcare and computing. PhD students are encouraged to work with their supervisors to explore the potential impact of their work.

The successful candidate will be expected to participate fully in research group and centre activities, including training sessions and workshops, and will become a member of the University’s wider postgraduate research community. Mentoring and support will be provided for the development of a strong academic and professional CV during the PhD.

Key Dates

Closing date for applications is 5pm on Friday 29 April 2019

We envisage that interviews will take place in May 2019

Successful applicants will be expected to start on 7th October 2019

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