CDT-GIF: BU-3-12 AI-Enhanced Planning for Sustainable Hydrogen Supply Chain Integration

Supervisors: Dr Shuya Zhong (School of Management), Prof Mi Tian (Dept of Chemical Engineering).

This PhD is one of a number of projects hosted by the Centre for Doctoral Training in Green Industrial Futures (CDT-GIF). We are offering pioneering research projects that will enable PhD researchers to explore key technologies and solutions that will support UK industry to reach net zero.

Project: The UK targets 5GW green hydrogen by 2030, backed by £1bn investment. This requires new approaches to integrated infrastructure planning, as current fragmented methods create systemic inefficiencies.

The green hydrogen supply chain spans production, storage, transport, and application processes, forming a complex network where each process offers multiple technology pathways. Choosing one technology constrains options upstream and downstream, creating interdependent decisions that cannot be effectively managed through traditional sequential planning. Current methods optimise processes separately, with individual stakeholders acting on their own priorities without end-to-end visibility. This shifts costs and emissions between processes, degrading overall performance. For instance, a production facility might choose lower-cost technology that requires expensive downstream modifications, increasing total system costs despite appearing optimal locally. Integrated planning, jointly optimising all processes, is imperative for whole-system efficiency.

Hydrogen infrastructure must balance economic vs. environmental goals. Exploring millions of technology combinations while balancing these trade-offs exceeds traditional simulation-optimisation capabilities, necessitating AI enhancement.

Research Question: How can integrated planning across all hydrogen supply chain processes be achieved through AI-enhanced simulation-optimisation to deliver system-wide economic and environmental sustainability?

The research pursues three objectives

Developing simulation-optimisation models that capture the full complexity of green hydrogen supply chain networks. These models represent interdependencies and enable simultaneous decision-making across all processes. The models will incorporate strategic decisions including technology selection, capacity sizing, and facility location, accounting for how operational characteristics influence these strategic choices.
Designing machine learning (ML) algorithms learning from simulation-optimisation results, accelerating exploration of vast configuration spaces. ML models will be trained to recognise patterns in solutions and guide optimisation algorithms toward promising regions of the solution space. This hybrid approach maintains solution quality whilst dramatically reducing computation time.
Creating multi-objective optimisation capabilities navigating economic-environmental trade-offs under uncertainty. This involves generating solutions that reveal the relationship between competing objectives, enabling decision-makers to understand implications of different priorities.

The methodology develops a decision-making tool combining simulation, optimisation, and ML. Simulation assesses configurations; optimisation identifies best solutions; ML accelerates exploration. Industry case studies validate the tool's scalability across local to regional scales. This project provides templates for similar energy system challenges, advancing UK net-zero.

Supervisors: Dr Shuya Zhong and Professor Mi Tian

Industrial Partner: digiLab Solutions Ltd

CDT in Green Industrial Futures: The CDT is funded by the UK Engineering and Physical Sciences Research Council and is a partnership between Heriot Watt University, Imperial College London, and the Universities of Sheffield and Bath. The CDT is further supported by contributions from industrial partners. Bringing these leading universities together allows CDT-GIF students access to a wide range of academic expertise, resources and facilities.

The CDT-GIF has an exciting and challenging programme specifically designed for top performing junior researchers. Alongside the four-year research project, students will receive expert training and opportunities to contextualise their research within the wider net zero landscape, including:

Residential taught courses at each of the partner universities in Years 1 and 2 that provide training in the systematic considerations for industry including: Life Cycle Analysis (LCA), technoeconomics, business models, policy & regulation, public engagement, plant operation.
An international opportunity in Year 2 or 3 of the programme, including opportunities to visit a world-leading facility, conference or forum and explore the global context of industrial decarbonisation.
A work placement with one of our industrial partners.
A bespoke ‘net-zero leadership programme’, including regular exchanges with cohort members from the other universities, student-led activities, industry challenge sandpit, industrial site visits, and professional development opportunities.

Candidate requirements: As a minimum we require candidates to have a First-class or 2:1 Meng or MSc with merit (over 60%) in a relevant area, i.e., Operational Research, Systems Engineering, Manufacturing Engineering, Chemical Engineering, Process Engineering, Applied Mathematics, Computer Science, or other highly quantitative disciplines. Applicants who have a First-class BSc/BEng (Hons) and can demonstrate significant relevant industry/research experience may also be considered.

Essential skills include strong foundations in mathematical optimisation and simulation, proven programming proficiency in Python or similar languages, and a clear understanding of algorithmic problem solving. Knowledge of machine learning concepts is highly desirable. Familiarity with hydrogen/energy systems or supply chain management would be advantageous. Candidates should demonstrate analytical thinking, ability to handle complex systems, and enthusiasm for applying computational methods to sustainability challenges.

Non-native English speakers must ensure they meet the English language requirements.

Enquiries and applications.

For informal enquiries about this project contact Sophia Coe [email protected] or [email protected]

Applications will open soon. Apply via: https://greenindustrialfutures.site.hw.ac.uk/the-programme/how-to-apply/

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Project Title: AI-Enhanced Planning for Sustainable Hydrogen Supply Chain Integration
Project Number: BU-3-12

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Equality, Diversity and Inclusion. We warmly encourage applications from individuals of all backgrounds, experiences, and perspectives. Our programme values diversity as a cornerstone of innovation and collaboration, and we are committed to creating an inclusive environment where everyone feels respected, supported, and empowered to thrive. The CDT-GIF are committed to ensuring flexibility throughout our programme to support student’s needs and personal circumstances, for example those with medical conditions, caring responsibilities and other considerations. For example, we are open to exploring part-time options if appropriate for the nature of the research.