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We present a systematic and automatic approach for integrating tableting reduced-order models with upstream unit operations. The approach not only identifies the upstream critical material attributes and process parameters that describe the coupling to the first order and, possibly, the second order, but it also selects the mathematical form of such coupling and estimates its parameters. Specifically, we propose that the coupling can be generally described by normalized bivariate rational functions. We demonstrate this approach for dry granulation, a unit operation commonly used to enhance the flowability of pharmaceutical powders by increasing granule size distribution, which, inevitably, negatively impacts tabletability by reducing the particle porosity and imparting plastic work. Granules of different densities and size distributions are made with a 10% w/w acetaminophen and 90% w/w microcrystalline cellulose formulation, and tablets with a wide range of relative densities are fabricated. This approach is based on product and process understanding, and, in turn, it is not only essential to enabling the end-to-end integration, control, and optimization of dry granulation and tableting processes, but it also offers insight into the granule properties that have a dominant effect on each of the four stages of powder compaction, namely die filling, compaction, unloading, and ejection.
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A Machine Learning-assisted Hybrid Model to Predict Ribbon Solid Fraction, Granule Size Distribution and Throughput in a Dry Granulation Process
A quantitative model can play an essential role in controlling critical quality attributes of products and in designing the associated processes. One of the challenges in designing a dry granulation process is to find the optimal balance between improving powder flowability and sacrificing powder tabletability, both of which are highly affected by ribbon solid fraction and granule size distribution (GSD). This study is focused on developing a hybrid machine learning (ML)-assisted mechanistic model to predict ribbon solid fraction, GSD, and throughput for the purpose of implementing model predictive control of an integrated continuous dry granulation tableting process. It is found that the predictability of ribbon solid fraction and throughput are improved when modification is made to Johanson’s model by incorporating relationships between roll compaction parameters and ribbon elastic recovery. Such relationships typically are either not considered or assumed to be a constant in the models reported in the literature. To describe the nature of the bimodal size distribution of roller compactor granules instead of only using traditional 𝐷𝐷10, 𝐷𝐷50 and 𝐷𝐷90 values, the GSD is represented by a bimodal Weibull distribution with five fitting parameters. Furthermore, these five GSD parameters are predicted by ML models. The results indicate the ribbon solid fraction and screen size are the two most significant factors affecting GSD.
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- Award ID(s):
- 2140452
- PAR ID:
- 10447085
- Editor(s):
- Kokossis, A.; Georgiadis, M.C.; Pistikopoulos, E.N.
- Date Published:
- Journal Name:
- Computer aided chemical engineering
- Volume:
- 52
- ISSN:
- 2543-1331
- Page Range / eLocation ID:
- 813-818
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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