(438e) Closed Loop Dynamics of Ribbon Density in a Dry Granulation Process

Authors: 
Ganesh, S., Purdue University
Moreno, M., Purdue University
Su, Q., Purdue University
Nagy, Z. K., Purdue University
Reklaitis, G. V., Purdue University
End to end continuous processing is currently a key focus area in pharmaceutical manufacturing. The increase in research and development is evident from recent FDA approvals of drug products manufactured using continuous downstream processes [1]. Dry granulation is one of the unit operations added for improving material handling and blend uniformity in the continuous tableting line.

Dry granulation is an inherently continuous process. The particle size distribution and bulk density of the granules are critical quality attributes of the process, which depend primarily on the ribbon relative density. The ribbon density is hence a crucial intermediate quality attribute of the unit operation. It is thereby valuable to monitor and control the ribbon density for ensuring desired granule qualities.

Assurance of product quality in real-time requires satisfactory process models with a robust sensor network and control architecture. A dynamic, simplified process model along with real-time monitoring capability is important to the implementation of supervisory control of the process to achieve Quality by Control.

The Johanson model [2] describes the fundamental solid mechanics of roller compaction as a standalone steady state process. FEM analyses have suggested modifications to the model for improved prediction of ribbon density [3]. A process scale model, however, needs to consider the production rate and requires integration of previous unit operations that feed into the roller compactor, in addition to the solid mechanics of the roller compactor.

The process model by Reynolds et al [4] integrates the screw conveyor with the roller compaction system, describing the effect of screw speed on the ribbon density and production rate at steady state. A dynamic process model is, however, essential for the implementation of a control system, as proposed by Hsu et al [5]. The model did not include the flow rates through the system and lacked experimental validation.

This work focuses on a modified process model incorporating the solid mechanics, process dynamics and throughput. The model combines the dynamics of material flow from Hsu et al (2010a) and Reynolds et al (2010) and solid mechanics suggested by Liu and Wassgren (2016). Demonstration of real-time ribbon density monitoring using NIR and microwave sensors [6,7] enable the validation of such a dynamic model. Additionally, the model evaluates the integrated system of a hopper and screw conveyor feeding into the roller compactor.

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 solid mechanics material parameters are estimated using ribbons. The process variables and ribbon density are recorded and monitored using DeltaV DCS.

This work progresses to demonstrate the implementation of ribbon density as proposed by Hsu et al. The integrated process model and real time ribbon density monitoring enable the development of control strategies for the desired ribbon density and eventually the granule properties.

References:

  1. https://blogs.fda.gov/fdavoice/index.php/2016/04/continuous-manufacturin...
  2. Johanson JR. A Rolling Theory for Granular Solids. J Appl Mech. 1965;32(4):842-848
  3. Liu Y, Wassgren C. Modifications to Johanson’s roll compaction model for improved relative density predictions. Powder Technol. 2016; 297:294-302.
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  6. 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.
  7. McAuliffe MAP, O’Mahony GE, Blackshields CA, et al. The Use of PAT and Off-line Methods for Monitoring of Roller Compacted Ribbon and Granule Properties with a View to Continuous Processing. Org Process Res Dev. 2015;19(1):158-166.