461124 A Novel Analysis of Residence Time Distributions for Continuous Powder Mixing

Tuesday, November 15, 2016: 2:24 PM
Continental 4 (Hilton San Francisco Union Square)
Geng Tian1, Xiaochuan Yang1, Sau Lee1, Robert Fisher2, Shuaili Li2, Moo Sun Hong2, Zongyu Gu2 and Thomas O'Connor1, (1)Food and Drug Administration, Silver Spring, MD, (2)Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA

Continuous pharmaceutical manufacturing has attracted the attention of industry and academia for a wide variety of reasons. Because this type of manufacturing can utilize smaller manufacturing facilities, it can alleviate bottlenecks related to scale-up and improve control of product quality. With regards to materials, this type of process can eliminate the isolation and storage of intermediate products as well as decrease raw-material and final-product waste. By reducing the number of manufacturing steps, continuous pharmaceutical manufacturing can shorten manufacturing cycle times, provide greater response capacity, increase production yields, and lead to an overall product manufacturing efficiency (Lee et al., 2015).

The residence time distribution (RTD) is a probability distribution function that describes the length of time a material spends in the system. The RTD can be used to address the traceability of raw materials in continuous pharmaceutical manufacturing processes (Engisch & Muzzio, 2016), providing useful data for cases relating to out of specification product investigations, consumer complaints, product recalls or any other situations that may have public health impact. Knowledge of the RTD enables the prediction of when an affected material will reach a point downstream in the process, as well as how much of the adjacent material it may have affected through dispersion. The RTD of convective powder mixers has previously been experimentally studied and correlated to lumped parameters such as the Peclet number and mean residence time in the Taylor dispersion model (Gao, Vanarase, Muzzio, & Ireapetritou, 2011) . However, the RTD measured/fitted for one set of operating conditions may not predict the system RTD when the conditions are altered.

In this work, we conducted an analysis using previous experimental RTD data characterizing continuous powder mixing (Marikh, Berthiaux, Mizonov, Barantseva, & Ponomarev, 2006; Vanarase & Muzzio, 2011). Vanarase and Muzzio (2011) conducted experimental RTDs on a Gericke GCM 250 continuous mixer for eight different conditions (four different impeller rotation rates: 40, 100, 160, and 250 rpm at two throughput levels: 30 and 45 kg/h). Similarly, Marikh et al. (2006) measured eight experimental RTDs on a Gericke GCM 500 continuous mixer (four different throughputs: 40, 60, 80, 100 kg/h at two different impeller rotation rates: 15 and 60 rpm). The experimental RTDs were converted to RTDs in dimensionless time by scaling to each distribution with its respective mean residence time. It is observed that RTD in dimensionless time is invariant with throughput at different impeller rotation rates under certain conditions. In other words, the shape of RTD is the same but the RTD itself is just shrunk or stretched based on the mean residence time. Next, we introduced a novel concept of a time-dependent RTD to contrast with RTDs measured under steady-state conditions. This approach predicts transient changes in the RTD as throughput varies arbitrarily with time. It is anticipated that this effort will enhance process understanding to facilitate extending empirically measured RTDs for solid oral continuous drug product manufacturing processes in a straightforward manner.

References

Engisch, W., & Muzzio, F. (2016). Using Residence Time Distributions (RTDs) to Address the Traceability of Raw Materials in Continuous Pharmaceutical Manufacturing. J Pharm Innov, 11(1), 64-81.

Gao, Y., Vanarase, A. U., Muzzio, F., & Ireapetritou, M. (2011). Characterizing continuous powder mixing using residence time distribution. Chemical Engineering Science, 66, 417-425.

Lee, S. L., O’Connor, T. F., Yang, X., Cruz, C., Chatterjee, S., Madurawe, R., . . . Woodcock, J. (2015). Modernizing Pharmaceutical Manufacturing: from Batch to Continuous Production. J Pharm Innov, 10(3), 191-199.

Marikh, K., Berthiaux, H., Mizonov, V., Barantseva, E., & Ponomarev, D. (2006). Flow Analysis and Markov Chain Modelling to Quantify the Agitation Effect in a Continuous Powder Mixer. Chemical Engineering Research and Design, 84(11), 1059-1074.

Vanarase, A. U., & Muzzio, F. J. (2011). Effect of operating conditions and design parameters in a continuous powder mixer. Powder Technology, 208(1), 26-36.


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