8th World Congress on Particle Technology
(138a) Quality By Control of Dry Granulation Process for Continuous Tablet Manufacturing
Authors
Dry granulation is one of the intermediate unit operations added for improving material handling and blend uniformity. Since the particle size distribution and bulk density of the resulting granules affect the operation of the tableting process, Controlling the relative density of ribbons is essential for the desired properties of the granules. In this study, a process model of the roller compactor accounting for the compaction as well as the flow rate through the system is used to predict the ribbon density to enable model based control. The model combines the dynamics of material flow from Hsu et al [2] and Reynolds et al [3] and solid mechanics suggested by Liu and Wassgren [4]. In-line measurement of the ribbon density using a microwave sensor [5] enables the validation of the model and for controlling ribbon density at the set flow rate. The particle size distribution, blend uniformity of the granules, along with the mass flow rate of the materials entering the tablet press are measured using appropriate inline sensors.
Blends comprising of acetaminophen and microcrystalline cellulose at inlet flow rates of 8-12 kg/h and varying roll pressures are used for real-time monitoring of ribbon density. The process variables, ribbon density and mass flow rate are recorded and monitored using DeltaV DCS.
This work enables demonstration of the implementation of in-line ribbon density control, a key intermediate quality attribute for a dry granulation integrated continuous tableting line. The integrated process model along with real time monitoring of granule size distribution and the mass flow rate enable the development of real-time material tracking and control strategies for the integrated continuous tableting line.
References:
- https://blogs.fda.gov/fdavoice/index.php/2016/04/continuous-manufacturi…
- Liu Y, Wassgren C. Modifications to Johansonâs roll compaction model for improved relative density predictions. Powder Technol. 2016; 297:294-302.
- Reynolds G, Ingale R, Roberts R, Kothari S, Gururajan B. Practical application of roller compaction process modeling. Comput Chem Eng. 2010;34(7):1049-1057
- Hsu S-H, Reklaitis G V., Venkatasubramanian V. Modeling and Control of Roller Compaction for Pharmaceutical Manufacturing. Part I & II: Process Dynamics and Control Framework. J Pharm Innov. 2010;5(1-2):14-36
- Gupta A, Austin J, Davis S, Harris M, Reklaitis G. A Novel Microwave Sensor for Real-Time Online Monitoring of Roll Compacts of Pharmaceutical Powders Online-A Comparative Case Study with NIR. J Pharm Sci. 2015;104(5):1787-1794.