(392g) A Pilot Study: Detecting System Disturbances During a Jet Milling Process Using Online Acoustic Emission

A Pilot Study: Detecting system disturbances during a jet milling process using online acoustic emission

Hoong Yu Jia Adaline, Ng Wai Kiong, Reginald B. H. Tan. Institute of Chemical and Engineering Sciences 1, Pesek Road, Jurong Island, Singapore 627833 Tel: (65) 6796 3846 Fax: (65)6316 6183 Email: adaline_hoong@ices.a-star.edu.sg

Abstract

Air jet milling is an important unit operation in the pharmaceutical industry to achieve the desired particle size. Batch-to-batch variations in product quality often result from process upsets such as blockages at the feed inlet, air flow fluctuations, contamination by foreign object and changes in particle size of feed. Although the acoustic emission technology has been used to monitor granulation and compaction processes^1-4, little work has been reported on applying the technique to jet milling. The main aim of this study is to evaluate the feasibility of using this non-intrusive acoustic emission technique as a potential Process Analytical Technology (PAT) to monitor system changes during the jet milling process.

The acoustic emission setup comprises of two sensitive piezoelectric transducers (R6I and R15I) which are attached to the external surface of the jet mill. The R6I and R15I transducers differ by the operating frequency ranges, which are 40-100kHz and 70-200kHz respectively. These sensors monitor the stress waves emitted from the jet mill, which are caused by inter-particle collisions from deformation or fracture of particles and the impact of particles on the walls of the vessel⁵. These signals are sent through a pre-amplifier to attain a higher sensitivity in the signals. Different sieved fractions (60-180um, 355-500um, 750-1000um) of adipic acid (model compound) were used as feed. A bench-scale Hosokawa-Alpine spiral jet mill 50 AS was used to micronize the feed and the whole process was monitored using the transducers. The system changes or disturbances during the milling process were introduced by blocking the feed inlet, changing feed particle size, extending run time (operating at different flow rates) and contaminating the batch by the addition of a foreign object in the mill. The amplitude, counts, root mean square voltage, average signal level and energy signals were analyzed for each experiment. The root mean square voltage and energy signals appeared to be more responsive signals to such system changes. It is also apparent that there is a significant change in signals upon the addition and end of feeding. Our results have shown that the acoustic emissions may be able to give early warnings to process upsets for the monitoring of the jet milling process.

References:

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