Book of Abstracts: Past, Present and Future of Particle Technology Conference 2026

Ever since the EPSRC Specially-Promoted Programme on Particle Technology, coordinated by the late Leslie J. Ford, the UK has maintained a globally leading position in the field. Over the past decades, tremendous progress has been achieved in the design of advanced processes, novel instrumentation for powder characterisation, and the development of robust sensors for online and at-line process monitoring and control. Recent years have seen the emergence of transformative technologies that are reshaping particle science and engineering. High-resolution imaging (e.g. X-ray computed tomography, focused ion beam SEM), dynamic powder rheometry, and image-based and laser diffraction systems for particle shape and size analyses have greatly enhanced our ability to quantify particle morphology, flowability, spreadability, dispersion, breakability and cohesion under process-relevant conditions. Parallel advances in multi-scale modelling, from discrete element methods (DEM) and computational fluid dynamics (CFD) to population balance modelling (PBM), have deepened our understanding of complex particulate systems across manufacturing scales. Continuous manufacturing in the pharmaceutical sector and powder-based additive manufacturing (AM) are prime examples of areas that have benefited from these developments. These technologies rely on a much-improved grasp of powder behaviour, flow dynamics, and interparticle interactions, leading to enhanced process robustness and product quality. Today, however, the field is undergoing a new paradigm shift driven by the exponential growth of Artificial Intelligence (AI) and Machine Learning (ML). Data-centric approaches are increasingly being used to complement traditional experimental and simulation techniques. Machine learning algorithms, ranging from physics-informed neural networks and Gaussian process regression to deep convolutional and graph neural networks, are being applied to predict powder flow, optimise mixing and granulation processes, and identify critical material attributes from high-dimensional datasets. The integration of ML with digital twins and real-time sensor data is enabling intelligent process control, adaptive manufacturing, and predictive maintenance, moving the industry closer to fully autonomous, data-driven particulate processing. This booklet presents the abstracts from the three-day conference, encompassing plenary and keynote lectures, as well as oral and poster presentations. In support of developing future leaders in the field, half a day is dedicated to early-career researchers (ECRs), offering them a valuable opportunity to present their work, exchange ideas, and engage with established experts. The ECR session is sponsored by the Particle Technology Special Interest Group of the Institution of Chemical Engineers. The Royal Society of Chemistry Particle Characterisation Interest Group and Formulation Science & Technology Group sponsor the poster presentation award and poster reception, while Malvern Panalytical and Verder Scientific are sponsoring and exhibiting at the conference.