Past, Present and Future of Particle Technology

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.

Against this backdrop, we are delighted to announce a three-day conference and workshop organised by the Particle Technology Subject Interest Group (PTSIG) of the Institution of Chemical Engineers (IChemE). The event will feature four plenary and six keynote lectures by international leaders, along with a range of oral and poster presentations selected from submitted abstracts. In line with PTSIG’s commitment to nurturing the next generation of researchers, half a day will be dedicated to early-career scientists, providing a platform for them to showcase their work, exchange ideas, and engage with established experts.

We warmly invite you to participate in this exciting event, to review the remarkable progress in particle technology, explore the current frontiers of AI-enhanced powder science, and glimpse the future directions through the pioneering work of emerging researchers shaping the next era of particulate innovation.

Plenary Speakers

Keynote Speakers

Welcoming Abstract Submissions

Apart from the contributions from our distinguished plenary and keynote speakers, we are delighted to welcome abstract submissions from researchers, engineers, and scientists active in the field to submit their latest work for consideration as oral presentations and poster presentations. Please ensure you use the attached abstract template when preparing your submission, and indicate your preference in the email message.

Please direct your submission to Dr Wei Pin Goh at [email protected].