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
Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Purdue Center for Particulate Products and Processes, West Lafayette, IN 47907, USA.
ESCAPE. 2022;51:1081-1086. doi: 10.1016/b978-0-323-95879-0.50181-8.
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.
我们报告了一项正在进行的工作进展,即开发一种用于流动性的虚拟传感器,这是在制粒生产线中实现实时过程监测的关键工具。该传感器基于相机成像来测量湿法制粒产生的颗粒的尺寸和形状分布。然后,使用统计方法将它们与流动性测量值相关联,如环剪试验、排水休止角、动态休止角和振实密度。虚拟传感器解决了这些基于离线表征方法的流动性测量问题,这些方法可能需要数小时才能完成。借助基于实时测量方法的虚拟传感器,颗粒流动性的预测变得更快,从而能够就过程控制和供应链及时做出决策。