What if you could anticipate fouling in your polymer reactor — before it costs you throughput, energy and uptime? That’s the focus of CASAFA, a new project within DPI’s Enabling Tools & Technologies programme.
CASAFA stands for Deep-Learning-aided GPC-IR fingerprinting of complex polyolefin mixtures. But more importantly, it stands for smarter process control.
This project investigates one of industry’s most stubborn challenges: wall-sheeting in fluidised bed reactors. For R&D leaders, that means new tools to sharpen operational decisions, and reduce costly trial-and-error.
From complex mixtures to actionable insights
Fouling is a messy business, literally and scientifically. It’s caused by a mix of thermal softening, particle dynamics, and triboelectrification. CASAFA brings structure to that chaos.
The project explores how polymer fingerprints, extracted through GPC-IR and interpreted via deep learning, can be used to detect early-stage fouling risks. The aim: a predictive framework that helps industry choose the right anti-fouling strategies, tailored to specific materials and process conditions.
Blending experimentation with simulation
The strength of CASAFA lies in its hybrid approach. Researchers will combine advanced experimental techniques with high-resolution computational modelling. That includes tracking heat transfer, hydrodynamics and static charge behaviour in simulated reactor environments.
The result? A full-spectrum view of fouling mechanisms — and how to control them.
Part of DPI’s long-term innovation engine
CASAFA is embedded in DPI’s Enabling tools & technologies programme, the initiative that delivers the tools and insights needed to convert fundamental science into industrial advantage.
This isn’t research for the shelf. It’s research designed to help you optimise production, boost predictability and stay ahead of failure.
Want to learn how this research fits your innovation roadmap?
Explore the Enabling tools & technologies programme, or contact our programme manager using the contact form below.