The mixing state of fine powders measured by magnetic resonance imaging

Passive acoustic emissions from particulates in a V-blender

CONTEXT

Regulatory agencies are recommending the development of process analytical technologies (PAT) to improve the efficiency and product quality during pharmaceutical manufacturing.

OBJECTIVE

The objective of the research was to investigate the potential application of passive acoustic emission monitoring of a V-blender.

MATERIALS AND METHODS

Trials were conducted with sugar spheres, lactose or MCC in a V-blender. Vibrations from acoustic emissions were measured using PCB Piezotronics accelerometers with ICP signal conditioners.

RESULTS AND DISCUSSION

A wavelet filter was applied to the measured acoustic emissions to remove vibrations from the tumbling motion of the V-shell, allowing a focus on information about particle motion and interactions within the V-shell. The ideal sensor location was determined to be the lid of one of the V-shell arms due to the impact of the tumbling particles on the lid and transmission of the vibrations from other particle motion within the V-shell. The amplitude of vibrations increased with particle size due to larger particle momentum before a collision. The fill level and the V-shell scale also influenced the measured vibrations as particle motion was affected which in turn affected momentum. Changes in particle flowability could be detected through variations in the measured acoustic emissions.

CONCLUSION

The measured vibrations from passive acoustic emissions reflected particle motion and interactions within a V-blender demonstrating potential as a monitoring method.

Automated phase correction via maximization of the real signal

Due to improved quantification capabilities and enhanced signal-to-noise ratio (SNR), phase-corrected real reconstruction in magnetic resonance imaging is superior to the common magnitude reconstruction, especially at low SNR. This requires the development of an automated phase-correction algorithm. Existing methods are not well suited for multiple unconnected regions of very low SNR. For this situation, a method based on the real-signal maximization is implemented, in which the experimental image phase is approximated by a three-dimensional polynomial of up to third order. The presented implementation was successfully applied to data originating from different samples and pulse sequences.