PhD in Chemical Engineering

The development of accurate and reliable digital twins is a cornerstone of digital transformation. The availability of these high-fidelity models enables effective decision-making at all stages of development and operation, including early screening, process and product design, scale-up, plant-wide control, and quality assurance./nCrystallisation remains the mainstream purification technology in the manufacturing of high-value particulate products (such as agrochemicals and pharmaceuticals). The development of effective and scalable crystallisation processes is crucial for the precise control of the key quality attributes of crystal products, such as particle size distribution and purity, whilst considering economic factors such as productivity and yield./nThis PhD project aims to establish and experimentally validate systematic methodologies for crystallisation model development and optimal experimental design. Specifically, it will explore effective strategies for model discrimination and model-based design of experiments that enhance prediction robustness and accuracy while minimising experimental effort, cost, and environmental impact. Both batch and continuous crystallisation processes will be studied across multiple scales, including on a state-of-the-art high-throughput experimentation platform equipped with advanced analytical technologies./nThe project is co-funded by Syngenta and will be hosted in Professor Benyahia’s research laboratory within the Department of Chemical Engineering. The PhD candidate will be co-supervised by academic and industrial partners and will benefit from the support of a large, multidisciplinary research team. As part of this project, there is the opportunity to complete an industrial placement at Jealott’s Hill International Research Centre, Syngenta’s largest R&D site.