[School of Engineering PhD Scholarships] Machine Learning-Assisted Multi-Scale Modelling, Design and Optimisation of Flexible Green Ammonia Production Systems

About the project

About the Project Ammonia is essential for fertiliser production and is increasingly recognised as a potential carbon-free energy carrier and hydrogen vector. It could also play an important role in decarbonising hard-to-abate sectors where direct electrification is challenging. However, conventional ammonia production using the Haber-Bosch process is highly energy intensive and relies predominantly on fossil-derived hydrogen, resulting in substantial greenhouse gas emissions. This project will develop green ammonia production systems by combining renewable hydrogen with membrane-reactor technology, machine learning and advanced process optimisation. Membrane reactors integrate reaction and separation within a single unit. By continuously removing ammonia from the reaction zone, they can overcome thermodynamic limitations, potentially enabling lower operating pressures, higher conversion and reduced energy consumption. However, their performance depends on complex interactions between membrane properties, reaction kinetics, mass and heat transfer, reactor configuration and operating conditions. The project aims to develop an integrated multi-scale digital framework connecting membrane materials, membrane reactors and the wider ammonia production system. The research will involve four main activities: 1. Machine-learning-based membrane screening: Computational databases will be used to develop machine-learning models for predicting key membrane properties, including permeability, selectivity and stability, enabling rapid identification of promising membrane materials. 2. Membrane-reactor modelling: A rigorous model will be developed and validated to describe reaction kinetics, thermodynamics, membrane transport, mass transfer and heat transfer. Reduced-order models will be developed where appropriate to enable efficient system-level analysis. 3. Process modelling and optimisation: The membrane reactor will be integrated with renewable hydrogen and nitrogen production, ammonia separation, recycle, storage and heat recovery. Advanced optimisation techniques will determine suitable membrane technologies, reactor configurations, equipment capacities and operating conditions, including flexible operation under variable renewable energy. 4. Uncertainty-aware system design: A multi-period stochastic optimisation framework will be developed to account for uncertainties in renewable availability, membrane performance, reaction kinetics, operating conditions and economic parameters. The framework will identify robust designs and operating strategies while quantifying trade-offs between cost, energy consumption, flexibility, and environmental performance. The student will work closely with Dr Jie Li, whose expertise includes process systems engineering, machine learning and optimisation, Dr Shaojun Xu, who specialises in membrane/material design and green ammonia production, and EnerTherm, which will contribute expertise in heat integration and membrane-reactor modelling, together with industrial training and internship opportunities. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here. . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD Chemical Engineering, Process Engineering, Process Systems Engineering (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD Chemical Engineering, Process Engineering, Process Systems Engineering (or international equivalent). This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

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