About
My name is Harry Kay and I finished my master’s degree in Chemical Engineering at the University of Manchester in 2023. Following my master’s degree, I started my PhD under the supervision of Dr Dongda Zhang. Throughout my undergraduate degree, I engaged in research within data analytics, machine learning, soft sensors, interpretable data driven modelling, and hybrid modelling in the fields of process systems engineering and (bio)chemical reaction engineering, providing essential skills for the development of my PhD. My PhD research is titled “Developing an adaptive digital twin framework for chemical process smart manufacturing“ and aims to develop an adaptive digital twin framework that seamlessly integrates machine learning with physical knowledge, enabling high-fidelity, predictive, and adaptive modelling of complex chemical and biochemical processes. These processes are central to sustainable manufacturing, particularly in advancing new catalysts, high-performance strains, and green formulations for agriculture and the chemical industry.
This PhD project addresses key industrial challenges by enabling faster, more data-efficient process development through hybrid modelling and adaptive digital twins – techniques widely recognised as transformative for the future of the chemical and biochemical industries. The project has already attracted strong industrial interest, leading to a consultancy with Eurokin, Europe’s largest industrial consortium in reaction engineering, to assess hybrid modelling for complex reaction networks. It also enabled a placement and collaboration with Johnson Matthey, where the framework demonstrated superior predictive performance over existing kinetic models and revealed new mechanistic insights into commercial catalysts. These results directly support industrial goals of reducing experimental burden, improving model reliability, and uncovering deeper reaction understanding. I have also engaged with a diverse range of academic collaborators including Imperial college London and a joint project with TU Berlin and the University of Wisconsin–Madison to develop a digital twin for a high throughput robotics lab for enzyme kinetics discovery.
Alongside my PhD, I was awarded the SCI Sydney Andrews scholarship award in 2025.
Recent publications
Advancing Catalytic Reaction Rate Model Development with Physics-Guided Hybrid Modeling
A Data-Efficient Symbolic Regression Framework for Automated Interpretable Bioprocess Modelling
A Data-Efficient Framework for Automated Identification of Reaction Networks and Interpretable Rate Models