Dr Rodolfo Da Silva Machado De Freitas
Other Research Projects
Decarbonising the transport sector is a top priority worldwide. The difficult-to-decarbonise transport applications (including mainly shipping, road freight and aviation) emit more than 50% CO2 of the entire transport sector. Among efforts on developing low-emission fuels, liquid synthetic fuels that can massively reduce pollutant emissions are drawing increasing attention, as they can be integrated into the current transportation system using existing infrastructure and combusted in existing engines (such as diesel engines for optimal fuel economy) with minor adjustments as drop-in fuels. Liquid synthetic fuels such as oxymethylene ethers (OMEx, which possess liquid properties similar to diesel when x=3-5) can be produced from a range of waste feedstocks and biomass, thereby avoiding new fossil carbon from entering the supply chain. OMEx can also be produced as an electrofuel (or e-fuel), thereby used as a sustainable energy carrier. However, due to the lack of complete knowledge of the physicochemical properties associated with the fuel composition variability, i.e. variation in the oligomer length (the x value of OMEx) and the composition variation of OMEx-diesel blends in real engine environment, there are challenges in utilising OMEx in practical engines, mainly in engine and its operation adjustments for optimal performance and minimal pollutant emissions. To address the technical issues of OMEx utilisation, accurate information on physicochemical properties and pollutant emissions of the synthetic fuels over the engine operational ranges is mandatory, but this is not readily available. This project is intended to obtain a thorough understanding on liquid synthetic fuel utilisation. The project will address the fundamental challenges in utilising renewable synthetic fuels, in particular OMEx and the associated OMEx-diesel fuel blends. The study will follow a combined modelling / simulation - experimentation approach, predicting the physicochemical properties including emission characteristics of the alternative fuels using molecular dynamics simulations, tailor-made experimentation for first-hand information on fuel utilisation, and establishing a database / mapping to guide the synthetic fuel utilisation in real engines over a wide range of conditions using machine learning.