The effect of process parameters on audible acoustic emissions from high-shear granulation.

Product quality in high-shear granulation is easily compromised by minor changes in raw material properties or process conditions. It is desired to develop a process analytical technology (PAT) that can monitor the process in real-time and provide feedback for quality control. In this work, the application of audible acoustic emissions (AAEs) as a PAT tool was investigated. A condenser microphone was placed at the top of the air exhaust on a PMA-10 high-shear granulator to collect AAEs for a design of experiment (DOE) varying impeller speed, total binder volume and spray rate. The results showed the 10 Hz total power spectral densities (TPSDs) between 20 and 250 Hz were significantly affected by the changes in process conditions. Impeller speed and spray rate were shown to have statistically significant effects on granulation wetting, and impeller speed and total binder volume were significant in terms of process end-point. The DOE results were confirmed by a multivariate PLS model of the TPSDs. The scores plot showed separation based on impeller speed in the first component and spray rate in the second component. The findings support the use of AAEs to monitor changes in process conditions in real-time and achieve consistent product quality.

Audible acoustics in high-shear wet granulation: application of frequency filtering.

Previous work has shown analysis of audible acoustic emissions from high-shear wet granulation has potential as a technique for end-point detection. In this research, audible acoustic emissions (AEs) from three different formulations were studied to further develop this technique as a process analytical technology. Condenser microphones were attached to three different locations on a PMA-10 high-shear granulator (air exhaust, bowl and motor) to target different sound sources. Size, flowability and tablet break load data was collected to support formulator end-point ranges and interpretation of AE analysis. Each formulation had a unique total power spectral density (PSD) profile that was sensitive to granule formation and end-point. Analyzing total PSD in 10 Hz segments identified profiles with reduced run variability and distinct maxima and minima suitable for routine granulation monitoring and end-point control. A partial least squares discriminant analysis method was developed to automate selection of key 10 Hz frequency groups using variable importance to projection. The results support use of frequency refinement as a way forward in the development of acoustic emission analysis for granulation monitoring and end-point control.