In ESCAPE. European Symposium on Computer Aided Process Engineering
We report progress of an ongoing work to develop a virtual sensor for flowability, which is a critical tool for enabling real time process monitoring in a granulation line. The sensor is based on camera imaging to measure the size and shape distribution of granules produced by wet granulation. Then, statistical methods were used to correlate them with flowability measurements such as ring shear tests, drained angle of repose, dynamic angle of repose, and tapped density. The virtual sensor addresses the issue with these flowability measurements, which are based on off-line characterization methods that can take hours to perform. With a virtual sensor based on real-time measurement methods, the prediction of granule flowability become faster, allowing for timely decisions regarding process control and the supply chain.
Lagare Rexonni B, da Conceicao Mariana Araujo, Camille Acevedo Rosario Ariana, Leigh Young Katherine, Huang Yan-Shu, Sheriff M Ziyan, Clementson Clairmont, Mort Paul, Nagy Zoltan, Reklaitis Gintaras V
2022
flowability, machine learning, monitoring, size and shape distribution, virtual sensor
