The built environment research projects will be presented as a group in one single session. You will be able to download a PDF of the combined presentations after the conference.
Optimal modelling of distributed technologies in the built environment
Student: Bashayer Al-Muftah
Supervisor(s): Dr Salvador Acha (Department of Chemical Engineering), Professor Nilay Shah (Department of Chemical Engineering)
Poster: #1 Download PDF AVAILABLE AFTER CONFERENCE
This project aims to add a technology into a TSO model in order to evaluate its performance.
Heat exchanger influence on internal combustion engine combined heat and power coupled with organic rankine cycle system in commercial buildings
Student: Man Ken Michael Cheung
Supervisor(s): Dr Christos Markides (Department of Chemical Engineering), Dr Salvador Acha (Department of Chemical Engineering)
Poster: #2 Download PDF AVAILABLE AFTER CONFERENCE
This project will use MATLAB for thermodynamic modelling of internal combustion engine combined heat and power and Organic Rankine Cycles to get a more accurate representation of performance for economic analysis.
Co-benefits of housing stock retrofit
Student: Edward Cliffe
Supervisor(s): Dr Jeff Hardy (The Grantham Institute for Climate Change), Dr Christoph Mazur (Department of Chemical Engineering)
Poster: #3 Download PDF AVAILABLE AFTER CONFERENCE
In the UK, the housing stock accounts for 27% of final energy demand, and employing energy efficiency measures to this stock is regarded as on the most feasible ways for us to meet our decarbonisation targets. However, under the current traditional utility business model, which is reliant on quantity of energy sold, the only incentives to decarbonise are provided from largely unsuccessful government policy. Other benefits that accrue from insulating houses, using efficient lighting or installing other efficient services are largely ignored or unknown. Some of these benefits include consumer bill savings, improved health and well-being and increased social functioning. This project seeks to identify and monetise these co-benefits and explore non-traditional business models that can profit from them.
The future of cities: electricity, solar or hydrogen?
Student: Florian Deveza
Supervisor(s): Professor Nilay Shah (Department of Chemical Engineering), Mr. Gonzalo Bustos Turu (Department of Chemical Engineering)
Poster: #4 Download PDF AVAILABLE AFTER CONFERENCE
In 2014, 54% of the world’s population lived in cities, and it is projected that this number will reach 60% by 2030, accounting for 73% of direct energy use. This population shift increases urban energy consumption and carbon emissions thus contributing to climate change and rising levels of air pollution in urban areas. Hence, there is an increasing need to change the current energy systems by improving the efficiency of urban energy use and using renewable energy sources. This work aims to develop a methodological framework to assess different design options for future energy infrastructure in cities.
Modelling and Optimization of the district energy system of Olympic Park London
Student: Loukas Douvaras
Supervisor(s): Dr Christoph Mazur (Department of Chemical Engineering), Dr Edward O'Dwyer (Department of Chemical Engineering)
Poster: #5 Download PDF AVAILABLE AFTER CONFERENCE
The first step of the project is the modelling of the existing district energy system of the Queen Elizabeth Olympic Park in London. This is implemented in GAMS and Matlab software. The next step is to optimise the operation of the system in economic and environmental terms (carbon emissions, NPV) through the addition of sustainable energy technologies such as heat pumps and Li-on batteries. The project is conducted in collaboration with Engie, the system operator.
Predicting occupancy from ICT data for building performance
Student: Manyi Wang
Supervisor(s): Dr Bianca Howard (Department of Civil and Environmental Engineering), Dr Salvador Acha (Department of Chemical Engineering)
Poster: #6 Download PDF AVAILABLE AFTER CONFERENCE
This project will compare of building occupancy prediction methods in the context of lighting and HVAC energy saving from ICT data.