(742g) Real-Time Monitoring and Modeling of Milling in a Continuous Tableting Line Via Dry Granulation

Authors: 
Moreno, M., Purdue University
Su, Q., Purdue University
Nagy, Z. K., Purdue University
Reklaitis, G., Purdue University
In the Pharmaceutical Industry, batch operation is the traditional mode of manufacture. During batch production, the quality of the batch usually is subject to batch-to-batch variability and thus quality is controlled by statistical sampling and if necessary rejection of the entire batch. Conversion to the continuous processing mode offers the potential for quality improvement as well as potential cost savings. However, continuous processing requires on-line measurements and active process control and thus the development of Process Analytical Technology (PAT) tools is crucial [1].

In the continuous dry granulation tableting line there are a number of variables that relate to the final product quality. For instance, composition and tablet hardness are among the CQAs in this process. In the dry granulation line, the roller compactor serves as a granulation method. In this unit, the powders are fed into a hopper and then conveyed by the feed screw to two counter rotating rolls. The powder is gripped by the rolls and pulled into the nip area of the compacting rolls before being compacted into a ribbon. Then the ribbon is milled into granules by a hammer mill [2]; generating a granular feed for the tablet press, which has favorable flow and compaction properties.

The milling step is an important unit operation because the granules particle size distribution (PSD) will affect compaction properties and subsequently the tablet hardness. Therefore, the granules PSD is also a CQA. In the roller compaction process, an increase in roll pressure will translate to an increase in ribbon density. The ribbon density has a relation to granule PSD [3]. As a consequence, the ribbon density can be considered an intermediate CQA. The relationship between ribbon density and granule PSD is difficult to establish from first principles, but a correlation can be established by conducting a series of experiments. In this design of experiments, the roll pressure is varied, the ribbon is milled at different speeds and the PSD measured through either off-line means such as sieve analysis or else on-line using laser based methods.

In continuous operations, it is desirable to measure all of the CQAs online, so as to permit the implementation of appropriate process control logic. In the tableting line installed at Purdue University, the roller compacted (RC) ribbon properties such as density and composition can be measured online using either a microwave (MW) and/or a Near-Infrared (NIR) sensor [4]. The PAT tool for online PSD measurement that we have adopted in our work is a high speed imaging camera (Innopharma Eyecon Camera), with which our team has had some previously experience in wet granulation applications [5]. This equipment counts and images particles with a sampling time of 2s and has a scanning rate of 10m.s-1.

The milling unit operation is commonly modeled using a population balance model [6]. The key model component of the population balance model (PBM) is the breakage kernel. This component represents the process of creation of smaller particles, as larger particles are impacted by the mill hammers or pins which causes the fracture. A wide range of breakage kernels have been proposed, most of purely empirical or semi-empirical nature. Each of these models requires the estimation of model specific parameters which will depend on feed material properties as well as mill operating variables [7].

This project has three objectives. The first one is to calibrate the Innopharma Eyecon camera. The second objective is to establish the parameters of the breakage kernel and thus the PBM model and the third objective is to develop the empirical relation between the ribbon density and granules PSD both measured using the on-line PAT tools. The first objective is achieved by generating samples of granules prepared under different conditions then relating the Innopharma Eyecon Camera output to the sieve analysis results. Since the sieve analysis measures fractions mass while the camera measures particle size, there is a conversion required which depends on particle density. The data for meeting the needs of the second and third objectives is determined through a series of experiments in which the mill speed, the composition of the powder and the density of the ribbon in the roller compactor and PSD are measured on-line. This data is gathered in runs when the process reaches steady state.

The results of this three-part study enable the capability for on-line monitoring of the combined compactor-mill subsystem. The PBM model and ribbon density-PSD relationship are important components of a data reconciliation model for the entire line. In this paper we report the results of these studies and demonstrate their utility in data reconciliation.

References

[1] S. L. Lee, T. F. O. Connor, X. Yang, C. N. Cruz, L. X. Yu, and J. Woodcock, â??Modernizing Pharmaceutical Manufacturingâ?¯: from Batch to Continuous Production,â? no. 1, pp. 1â??25, 2015.

[2] R. W. Miller and P. J. Sheskey, â??Roller Compaction Technology for Pharmaceutical Industry.â? Marcel Dekker Inc., 2003.

[3] S. Yu, â??Roll Compaction Of Pharmaceutical Excipients,â? University of Birmingham, 2012.

[4] A. Gupta, J. Austin, S. Davis, M. Harris, and G. Reklaitis, â??A novel microwave sensor for real-time online monitoring of roll compacts of pharmaceutical powders online - A comparative case study with NIR,â? J. Pharm. Sci., vol. 104, no. 5, pp. 1787â??1794, 2015.

[5] A. S. El Hagrasy, P. Cruise, I. Jones, and J. D. Litster, â??In-line size monitoring of a twin screw granulation process using high-speed imaging,â? J. Pharm. Innov., vol. 8, no. 2, pp. 90â??98, 2013.

[6] E. B. Litster, J. D., The Science and Engineering of Granulation Processes, vol. 53, no. 9. 2013.

[7] J. K. Pettersen and K. L. Sandvik, â??Estimating the breakage and selection functions for a continuous mill,â? Int. J. Miner. Process., vol. 35, no. 3â??4, pp. 149â??158, Aug. 1992.