Looking for an exciting opportunity to develop industrially-relevant skills, learn to apply state-of-the-art computer aided process engineering methods, and contribute towards solving global challenges, such as climate crisis? Join Cranfield Process Engineering team to study a PhD or MSc by Research and work with us to derive a framework that will revolutionise the process engineering of low-carbon processes via the application of machine learning. We integrate applied learning experience with world-leading research, professional development, professional mentoring and teamwork to transform you into the future engineering leader who will drive change through organisations in solving global challenges.
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Decarbonisation of the power, heat and industrial sectors is critical to meeting the Paris Agreement targets. Thus, achieving this ambitious emission reduction goal requires a wide deployment of novel clean technologies that will enable these sectors to remain competitive at near-zero or even negative CO2 emission technologies (NET). Carbon capture, utilisation and storage (CCUS), in addition to renewable energy sources, is regarded as the least cost-intensive option that could significantly contribute towards achieving the emission reduction targets by 2050.
Novel concepts based on CCUS/NET need to be developed and optimised to enable cost-effective decarbonisation of energy and industrial sectors. The world-leading research performed by Cranfield Process Engineering team led by Dr Hanak [Article 1, Article 2] confirmed that application of machine learning to assessment and optimisation of engineering processes can not only substantially reduce computational requirements, but primarily lead to new insights and discovery of synergies that make low-carbon technologies more economically feasible.
A large number of technologies and configurations for CCUS and NET have been developed over the past 20 years. However, these technologies are still reported to result in energy and economic penalties that hinder their commercial deployment. To accelerate the development of CCUS/NET, this project will develop a framework based on machine learning that will utilise the existing body of knowledge to develop and optimise novel environmentally-friendly processes for decarbonisation of energy and industrial sectors. Application of machine learning in process engineering will revolutionise the process design and optimisation process and will support an early deployment of low-carbon processes that will support fighting climate emergency.
You will join Cranfield Process Engineering team that integrates applied learning experience with world-leading research, professional development, professional mentoring and teamwork to transform you into the future engineering leader who will drive change through organisations in solving global challenges. Through our network of industrial partners and academic collaborators, you will be exposed to working across industrial and academic environments that will enable you to develop relevant employability skills, in addition to state-of-the-art computer aided process engineering methods. You will primarily co-create with Cranfield Process Engineering team to deliver impact in fighting the climate crisis and disseminate your work at international and national conferences, relevant research networks such as UKCCSRC, and through high-quality journal publications.
For an informal discussion regarding this opportunity, please contact Dr Hanak (Academic Lead in Process Engineering at Cranfield) at email@example.com.
 Hanak, D.P. and Manovic, V. (2017), Economic feasibility of calcium looping under uncertainty, Applied Energy, 208, 691–702. [Open Access copy
 Bailera, M., Hanak, D.P., Lisbona, P., and Romeo, L. (2018), Techno-economic feasibility of power to gas-oxyfuel boiler hybrid system under uncertainty, Journal of Hydrogen Energy, 44, 9505-9516 [Open Access copy]
At a glance
- Application deadlineOngoing
- Award type(s)PhD, MSc by Research
- Duration of awardPhd - 3 years. MSc by Research - 1 year
- EligibilityUK, EU, Rest of World
- Reference numberPhD - SWEE0089. MSc by Research - SWEE0090
Applicants should have a first or second class UK honours degree or equivalent in Chemical Engineering, Process Engineering, Energy Engineering or a related discipline.
This is a self-funded opportunity so the student would need to source their own funding.
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
For further information please contact:
Dr Dawid Hanak
T: (0) 1234 750111 Ext: 4121
If you are eligible to apply for the PhD, please complete the online PhD application form stating the reference No. SWEE0089
If you are eligible to apply for the MSc by Research, please complete the online MSc by Research application form stating the reference No. SWEE0090
For further information contact us today:
T: +44 (0)1234 758082