###A data-driven framework towards real-time robust optimisation of blue hydrogen production facilities
Project ID: 2228bd1194 (You will need this ID for your application)
Research Theme: Energy and Decarbonisation
UCL Lead department: Chemical Engineering
Lead Supervisor: Vasileios Charitopoulos
Project Summary:
Why this research is important? Achieving the Net-Zero ambition requires harnessing the full potential of alternative energy sources and thus the efficient production and utilisation of blue hydrogen is critical. Blue hydrogen can be produced by exploiting dedicated or excess of available renewable generation via water electrolysis. A key issue towards the successful deployment and operation of such production plants lies in the vast levels of uncertainty surrounding renewables generation. Conventional optimisation methods fail to address this issue due to the computational complexity which prohibits the timely deployment of any decisions and thus a novel modelling and optimisation paradigm is imperative.
What you will be doing? The aim of this project is to develop a novel machine learning-powered framework for the flexible operation and re-optimisation of blue hydrogen production plants. The motivation stems from the high level of volatility and uncertain generation of renewables which render state-of-the-art optimisation models impractical. The main goals of this project are: (i) the development of operational digital twins of blue hydrogen plants; (ii) the development of systematic methods for uncertainty quantification & sampling in renewables generation; and finally (iii) the development of a data-driven framework towards model-free decision making in the face of uncertainty.
Who you will be working with? The project is primarily supervised by Dr Vasileios Charitopoulos and the successful candidate will be part of the Sargent Centre for Process Systems Engineering at the UCL Chemical Engineering department.
Who we are looking for? The successful applicant must hold a minimum 2:1 MEng/MSc or equivalent degree in Chemical Engineering, Computer Science or other area pertinent to the project. Understanding of process systems engineering methods and tools is essential, along with a track-record in programming languages, e.g. Python, Julia and/or optimisation software, e.g. GAMS/gPROMS. Demonstrated experience in mathematical modelling/optimisation/machine learning is desirable.