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1. A Machine Learning-assisted Hybrid Model to Predict Ribbon Solid Fraction, Granule Size Distribution and Throughput in a Dry Granulation Process

   Yan-Shu Huang, David Sixon (July 2023, Computer aided chemical engineering)

   Kokossis, A.; Georgiadis, M.C.; Pistikopoulos, E.N. (Ed.)

   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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2. Pre-Nucleation in High-Shear Wet Granulation

   Kumar, D.S.; Oka, S.; Emady, H.N. (January 2023, Proceedings for 10th International Granulation Workshop)

   Wet granulation can be broken down into three rate processes: Wetting & Nucleation, Consolidation & Growth, and Breakage & Attrition. These stages are often inseparable from each other, which hinders the characterization of the relationship between operating conditions and product properties in wet granulation processes. Single-drop granulation is a form of drop-controlled nucleation intended to isolate the Wetting & Nucleation rate process by allowing a single droplet to form a single granule upon impacting a static powder bed. Single-dropped granules (pre-nucleated granules) were charged to a mixer granulator under varied conditions to observe how the individual rate processes and resulting granulated product are affected by various pre-nucleation conditions. Batches with larger fractions of pre-nucleated granules tended to produce a larger granule size than batches with few or no pre-nucleated granules. Pre-nucleated granules on their own failed to produce a reasonable granular product, but performed well under conditions with standard liquid addition. A difference in granular product can also be identified between the formation mechanisms utilized to produce the pre-nucleated granules. 

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3. Pre-nucleation in high-shear wet granulation

   https://doi.org/10.1016/j.powtec.2024.119522  

   Kumar, Diana S; Oka, Sarang; Emady, Heather N (March 2024, Powder Technology)

   Wet granulation is broken down into three rate processes that are often inseparable from each other, hindering characterization of the relationship between operating conditions and product properties. Single-drop granulation is a form of drop-controlled nucleation intended to isolate the Wetting & Nucleation rate process. In this work, single-dropped granules (pre-nucleated granules) were charged to a mixer granulator under varied conditions to observe how the individual rate processes and resulting granulated product were affected by various pre-nucleation conditions. Batches with larger fractions of pre-nucleated granules tended to produce a larger granule size than batches with few or no pre-nucleated granules. A clear and consistent trend of deviation from traditional granulation results is observed. Liquid to solid ratio is ruled out as a sole cause for the deviation, indicating that pre-nucleation may have applications in granular material production with respect to liquid dosing in granules, granule size control, and efficient material usage. 

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4. Binder Jetting Additive Manufacturing of Ceramics: Feedstock Powder Preparation by Spray Freeze Granulation

   Du, W. (June 2019, Proceedings of the ASME 2019 International Manufacturing Science and Engineering Conference (MSEC2019))

   Objective of this study is to prepare the binder jetting feedstock powder by spray freeze drying and study the effects of its parameters on the powder properties. Binder jetting additive manufacturing is a promising technology for fabricating ceramic parts with complex or customized geometries. However, this process is limited by the relatively low density of the fabricated parts even after sintering. The main cause comes from the contradicting requirements of the particle size of the feedstock powder: a large particle size (>5 μm) is required for a high flowability while a small particle size (<1 μm) for a high sinterability. For the first time, a novel technology for the feedstock material preparation, called spray freeze drying, is investigated to address this contradiction. Using raw alumina nanopowder (100 nm), a full factorial design at two levels for two factors (spraying pressure and slurry feed rate) was formed to study their effects on the properties (i.e., granule size, flowability, and sinterability) of the obtained granulated powder. Results show that high pressure and small feed rate lead to small granule size. Compared with the raw powder, the flowability of the granulated powders was significantly increased, and the high sinterability was also maintained. This study proves that spray freeze granulation is a promising technology for the feedstock powder preparation of binder jetting additive manufacturing. 

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5. Design Space Identification of the Rotary Tablet Press

   https://doi.org/10.69997/sct.156711  

   Shahab, Mohammad; Bachawala, Sunidhi; Gonzalez, Marcial; Reklaitis, Gintaras; Nagy, Zoltan (July 2024, Systems & Control Transactions)

   The determination of the design space (DS) in a pharmaceutical process is a crucial aspect of the quality-by-design (QbD) initiative which promotes quality built into the desired product. This is achieved through a deep understanding of how the critical quality attributes (CQAs) and process parameters (CPPs) interact that have been demonstrated to provide quality assurance. For computational inexpensive models, the original process model can be directly deployed to identify the design space. One such crucial process is the Tablet Press (TP), which directly compresses the powder blend into individual units of the final product or adds dry or wet granulation to meet specific formulation needs. In this work, we identify the design space of input variables in a TP such that there is a (probabilistic) guarantee that the tablets meet the quality constraints under a set of operating conditions. A reduced-order model of TP is assigned for this purpose where the effects of lubricants and glidants are used to characterize the design space to achieve the desired tablet CQAs. The probabilistic design space, which takes into account interactions between crucial process parameters and important quality characteristics including model uncertainty, is also approximated because of the high cost associated with the comprehensive experiments. 

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