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A Smart Decision Modelling Support System For Industrial Cluster Decarbonisation


   Centre for Sustainable Engineering

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  Dr H Dawood, Dr Annalisa Occhipinti, Prof N Dawood  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Significant investment is required to deliver on wide scale industrial decarbonisation. Currently the challenges associated with a lack of coherent strategy, policy inertia, short term market forces and technological innovation are undermining the case for this investment. As we go forward to a net-zero economy, investment will be unlocked on the basis of having an evidence-based solution for decarbonisation together with a regional strategy for jobs and growth.

In recent decades, industrial clusters have grown (or reduced) largely driven by market forces and have not been planned strategically. Hence, a planned industrial cluster must also consider the wider implications of achieving growth, being economically viable and have corresponding Business Models (BM) which can deliver. This project will go beyond conventional engineering approaches and properly integrate economic and investment decisions, and thereby increase the probability of making smarter investments.

In this context, the proposed project will develop a Smart Digital Model (SDM) using digital technologies (such as AI and other machine learning) to support the decision-making process (DMP) for the identification of optimal solutions for regional growth, jobs and decarbonisation. State-of-the-art visualisation techniques will be utilised to present information to different audiences to support DMP and policymaking.

The project aims to develop a SDM based on AI & Machine Learning (ML) methods to support the selection of technology and develop a coherent strategy for regional decarbonisation and growth.

Its objectives are:

  • Develop a framework for the assessment of the decarbonisation strategies.
  •  Define the user and system requirements based around a user-case based   around the potential of a Teesside cluster hydrogen network.
  • Development of AI, ML and visualisation technologies (Game Engine) to underpin SDM analytical processes.
  • Validate the tool using Teesside cluster and innovation hub as a real-world case study.

Special Requisites: Apart from the entry requirements given below, a potential candidate should have a working knowledge of algorithms and programming.

Entry Requirements

Applicants should hold or expect to obtain a good honours degree (2:1 or above) in a relevant discipline. A masters level qualification in a relevant discipline is desirable, but not essential, as well as a demonstrable understanding of the research area. Further details of the expected background may appear in the specific project details. International students will be subject to the standard entry criteria relating to English language ability, ATAS clearance and, when relevant, UK visa requirements and procedures.

How to Apply

Applicants should apply online for this opportunity at: https://e-vision.tees.ac.uk/si_prod/userdocs/web/apply.html?CourseID=1191

Please use the Online Application (Funded PHD) application form. When asked to specify funding select “other” and enter ‘RDS’ and the title of the PhD project that you are applying for. You should ensure that you clearly indicate that you are applying for a Funded Studentship and the title of the topic or 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 for each application to a topic or project.

Applications for studentships that do not clearly indicate that the application is for a Funded Studentship and state the title of the project applied for on the proposal may mean that your application may not be considered for the appropriate funding.

For academic enquiries, please contact Dr Huda Dawood [Email Address Removed].

For administrative enquiries before or when making your application, contact [Email Address Removed].  


Funding Notes

The Fees-Paid PhD studentship will cover all tuition fees for the period of a full-time PhD Registration of up to four years. Successful applicants who are eligible will be able to access the UK Doctoral Loan scheme https://www.gov.uk/doctoral-loan to support with living costs. The Fully Funded PhD Studentship covers tuition fees for the period of a full-time PhD Registration of up to four years and provide an annual tax-free stipend of £15,000 for three years, subject to satisfactory progress. Applicants who are employed and their employer is interested in funding a PhD, can apply for a Collaborative Studentship
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