Hybrid Modelling Framework for on-Line Data-Driven Modelling of Particle Processes

Particles play a key role in many industrial productions, where particle processes are frequently used for removal of insolubles, product isolation, purification and polishing. Because particle processes are quite complex and fundamental understanding of underlying phenomena is lacking, control and monitoring these processes are often challenging tasks.

To overcome these challenges, it has previously been examined whether hybrid modelling could capture particle dynamics and be used to optimize these processes. Shallow neural networks have here been used to estimate particle phenomena kinetics and combined with low-order population balance models to produce predictions of particle size evolution.

During the last two decades, hybrid models have been employed in various particle processes, including crystallization operations [1,2,3] and a pharmaceutical milling process [4]. While these models have shown good predictive capabilities, they have been rather case-specific and been trained either using indirect process variables or using fairly small data sets of particle size distributions due to historical limitations of particle analysis methods.

With recent developments in particle monitoring tools, such as dynamic image analysis and focused beam reflectance measurements, it is now possible to measure particle properties with a much higher frequency, allowing for better capture of the process dynamics.