Digital transformation of crystallization processes through model-based design of experiments and optimization

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. Crystallisation 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. The development of digital twins for crystallisation technologies is a complex, painstaking, and resource-intensive process, often relying on costly and experimentation-heavy approaches such as trial-and-error and factorial design of experiments. This 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. The 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, employing over 800 people who are actively supporting crop protection lead discovery, optimization, development and registration. 94% of Loughborough’s research impact is rated world-leading or internationally excellent. REF 2021

The standard University IELTS English language requirement is 6.5 overall with 6.0 in each individual element (reading, writing, listening and speaking).

October 2026

14 August 2026

• Loughborough, United Kingdom