Faculty of Engineering and Environment
Department of Mechanical and Construction Engineering
Dr Reaz Hasan
Unlike conventional energy harnessing technologies such as thermal power stations, renewable energy technologies are highly variable and many different types and design continue to emerge with time. Even the more established technologies such as wind turbine and photovoltaic technologies are undergoing very significant changes. One of the common objectives for renewable technologies is that they are expected to reduce harm to environment. However, the full impact of a new technology can only be quantified by conducting a rigorous life cycle assessment using available input data from ‘various sources’. Literature suggests that there is a large variation in the quality of data which may give rise to significant uncertainty in the predicted impacts. Such a scenario poses difficulty to engineers and decision makers about the choice of technology as well as its design, manufacturing process and material selection. It is only recently that such uncertainty issues are being considered seriously.
In this project, the objective would be to undertake environmental impact assessments of the current and emerging renewable technologies with particular emphasis on ascertaining the associated uncertainties. Such analyses will allow the design stages to be optimised and also the decision making process for these technologies to be more precise. In an ongoing PhD work supervised by the proposers, a stochastic tool has been developed1 and validated against a wind turbine2 and the findings from the work indicate that the developed methodology can be further extended to analyse other emerging technologies. Hence the proposed study will clearly be novel being the first of its kind and will enable better understanding and interpretation of the environmental impacts of renewable technologies. The method will be used for exploring technology improvement opportunities and allow the policy makers make better decisions when information on uncertainty is needed.
The candidate we are looking for, should have:
• Knowledge in renewable technologies and sustainability
• Interest in Life Cycle Assessment. Prior knowledge on LCA tools is a plus
• Good mathematical skills including knowledge of statistics.
Funding: Must be self-funded. For further information on fees can be found:
Candidates are expected to pay an additional amount of £1,500 to cover bench fees.
Enquiries regarding this studentship should be made to: Dr Reaz Hasan [email protected]
How to Apply
Application forms should be requested from,
and returned to [email protected]
Deadline for applications: Open
Start Date: Open
Recent publications by supervisors relevant to this project
 Ozoemena, Matthew, Cheung, Wai Ming, Hasan, Reaz and Hackney, Philip (2014) A hybrid Data Quality Indicator and statistical method for improving uncertainty analysis in LCA of a small off-grid wind turbine. In: ARCOM Doctoral Workshop on Sustainable Urban Retrofit and Technologies , 19 June 2014, London South Bank University.
 Ozoemena, Matthew, Cheung, Wai Ming, Hasan, Reaz and Hackney, Philip (2013) A Review of Life Cycle Assessment of Renewable Energy Systems. In: 11th International Conference on Manufacturing Research, 19 - 20 September 2013, Cranfield University..