In light of the Net-zero target by 2050 in the UK, this PhD project aims to tackle one of the fundamental challenges with net-zero transition: evaluating the cost and value of such transition within the industries. This project will focus on machine learning-based and ontology-based solutions to estimate the cost of Net-zero transition.
The potential outcome of this research will bring more insight and knowledge about the cost and value of net-zero transition for both SMEs and OEMs. The goal is to find an effective solution for calculating and predicting the cost of net-zero transition under multidimensional uncertainty.
This is a self-funded PhD position to work with Dr Maryam Farsi in the Centre for Digital Engineering and Manufacturing.
‘Net-zero’ is a sustainability development concept that refers to the balance between the amount of emissions produced and the amount removed from the atmosphere. In the UK, the Net-zero transition target by 2050, obligates industries to act agile and invest in Net-zero technologies and techniques to either reduce or capture the emissions from their processes. General speaking, there are three main investment strategies available to the industry: 1) to invest in greater energy infrastructures, 2) to invest in renewable energy and 3) to invest in Carbon Capture, Usage and Storage (CCUS) technologies, and also the combination of these options.
The approach industries may take and the level of investments vary from one sector to another sector and there is no single solution. However, there is no doubt that the industry strategy for the Net-zero transition should fulfil profitability, sustainability, and competitiveness together. In addition, their investment choice can be influenced by several risks and uncertainties in terms of the climate they are operating, the market value, feasibility of new technologies, etc.
As a result, this PhD project aims to answer the question of ‘How to estimate the cost and value of net-zero transition more effectively in the presence of multiple uncertainties and unknowns?’
Several challenges and limitations exist in the current cost and value estimation approaches that make the cost estimates inaccurate and untrustworthy. These barriers mainly arise from several uncertainties in cost data, lack of data and the complexities with operations and financial markets. Too little cost data makes the cost prediction inaccurate, and too much data raises several complications with data analytics that leads to many inevitable errors.
The focus of this PhD is two-folded; First, to develop a generic net-zero transition cost model that applies to businesses and industries in different sectors. Second, implement advanced ontology engineering and artificial intelligence techniques to improve cost estimation under multidimensional uncertainty. The PhD candidate is expected to develop and execute cutting-edge machine learning techniques together with system design approaches (e.g. ontology-based and agent-based modelling) to predict the cost of net-zero transition. This PhD research will focus on integrated strategies using machine learning and cost ontology methods to develop an intelligent and effective ontology system for estimating costs.
Aim
The PhD aims to develop an intelligent and effective cost model for net-zero transition using an artificial Intelligence–based approach. The potential cost model is expected to be used as a decision support toolkit within businesses and industries in different sectors such as aerospace, transport and marine to support net-zero strategies. This PhD will bring together several research themes in net-zero, machine learning, artificial intelligence, cost estimation, adaptive and predictive modelling.
PhD Objectives
- Conduct high-quality research and literature review on the relevant research area.
- Identify the through-life – from development to decommission – cost and value drivers for different net-zero strategies.
- Develop an integrated ontology-based and intelligent knowledge capture platform to collect and estimate the cost and value of net-zero transition.
- Quantify the uncertainty in the cost and value estimates and evaluate the level of confidence and accuracy.
At Cranfield, the candidate will be based at the Centre for Digital Engineering and Manufacturing which hosts cutting-edge digital engineering facilities. The student will have access to high-end computers and digital technologies in the Centre for ontology-based and knowledge-based systems development, Digital twin development, advanced dynamic modelling and simulations, AI, VR, AR developments. The candidate works on his/her research individually and collaborates with other researchers in the field at the Centre.
Entry requirements
Candidates should have a minimum of an upper second (2.1) honours degree (or equivalent) preferably in Statistics, Computer Science/ Industrial Engineering / Mathematics / Operations Research but candidates in other degrees related to Engineering or related quantitative fields would be considered. Candidates with an MSc degree in these disciplines will be desirable.
Cranfield Doctoral Network
Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.
How to apply
If you are eligible to apply for this research studentship please complete the online application form.
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