Optimizing Low-Carbon Fuels Shipping in Global Energy Supply Chains

N0MES CDT

The EPSRC Centre for Doctoral Training in Net Zero Maritime Energy solutions (N0MES) has a 4-year funded PhD place available for an exceptional researcher. With the support of the University of Liverpool (UoL), Liverpool John Moores University (LJMU) and 33 maritime energy sector partners, N0MES PGRs will pursue new, engineering-centred, interdisciplinary research to address four vital net zero challenges currently facing the North West, the UK and beyond:

a) Energy generation using maritime-based renewable energy (e.g. offshore wind, tidal, wave, floating solar, hydrogen, CCS);

b) Distributing energy from offshore to onshore, including port- and hinterland-side impacts and opportunities;

c) Addressing the short- and long-term environmental impacts of offshore and maritime

environment renewable energy generation, distribution and storage; and

d) Decommissioning and lifetime extension of existing energy and facilities.

The PhD

Low-carbon fuels (e.g. LNG, biofuels, green/blue ammonia and methanol etc.) are important energy options in achieving GHG emissions reduction in transition to Net Zero. Low-carbon fuels are not however readily available everywhere. They need to be transported from areas (with abundant resources) where they can be produced to regions where they are needed, and the most effective way of transporting them is maritime shipping. Maritime shipping allows the establishment of a global clean energy network, facilitating the widespread adoption of these fuels.

The shipping of low-carbon fuels has to be planned and scheduled very carefully to match the forecasted supplies of production facilities with forecasted demand and/or contractual obligations. The scheduling may have to be done well in advance, e.g a year ahead, taking into account multiple constraints on forecasted supply/demand, contractual obligations, bunkering terminals, vessel specifications, fleet capacity, and so on.

This PhD project will carry out research on developing optimisation (and potentially machine learning) models and algorithms to optimise the scheduling and planning of low-carbon fuels shipping as described above. Successful candidates will work as a team with other PhD students in the same domain of optimisation and machine learning for maritime transport, under the supervision of a multidisciplinary supervision team comprising LJMU academics and industry experts from a global energy company. You will join the world-leading LOOM research-institute in an inclusive environment, collaborating with 40 other PhD/postdoc researchers. In addition, you will have the unique opportunity to undertake funded secondments to the industry partner to enhance your experience/network, access real-world applications, and gain career opportunities.

Click here for more information about the N0MES centre at LJMU, the LOOM research centre where the researcher will be hosted, and our research capability.

Applications

We want all of our staff and Students to feel that Liverpool is an inclusive and welcoming environment that actively celebrates and encourages diversity. We are committed to working with students to make all reasonable project adaptations including supporting those with caring responsibilities, disabilities or other personal circumstances. For example, if you have a disability you may be entitled to a Disabled Students Allowance on top of your studentship to help cover the costs of any additional support that a person studying for a doctorate might need as a result.

We believe everyone deserves an excellent education and encourage students from all backgrounds and personal circumstances to apply.

Important Application Process

Candidates wishing to apply should send your application to Dr Ben Matellini [email protected] and Prof. Trung Thanh Nguyen [email protected], with an email title “PhD scholarship in the N0MES CDT”. Please send the below: an up-to-date CV and a transcript,  and a supporting statement [maximum 300 words] detailing; what inspires you within this project, how your skill set matches the project, up to 3 examples showing your commitment to science, piece of science that excites you & anything else to support your application.