Feed factor profile prediction model for two-component mixed powder in the twin-screw feeder
In continuous pharmaceutical manufacturing processes, it is crucial to control the powder flow rate. The feeding process is characterized by the amount of powder delivered per screw rotation, referred to as the feed factor. This study aims to develop models for predicting the feed factor profiles (F...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | International Journal of Pharmaceutics: X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590156724000148 |
| _version_ | 1827221850919272448 |
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| author | Yuki Kobayashi Sanghong Kim Takuya Nagato Takuya Oishi Manabu Kano |
| author_facet | Yuki Kobayashi Sanghong Kim Takuya Nagato Takuya Oishi Manabu Kano |
| author_sort | Yuki Kobayashi |
| collection | DOAJ |
| description | In continuous pharmaceutical manufacturing processes, it is crucial to control the powder flow rate. The feeding process is characterized by the amount of powder delivered per screw rotation, referred to as the feed factor. This study aims to develop models for predicting the feed factor profiles (FFPs) of two-component mixed powders with various formulations, while most previous studies have focused on single-component powders. It further aims to identify the suitable model type and to determine the significance of material properties in enhancing prediction accuracy by using several FFP prediction models with different input variables. Four datasets from the experiment were generated with different ranges of the mass fraction of active pharmaceutical ingredients (API) and the powder weight in the hopper. The candidates for the model inputs are (a) the mass fraction of API, (b) process parameters, and (c) material properties. It is desirable to construct a high-performance prediction model without the material properties because their measurement is laborious. The results show that using (c) as input variables did not improve the prediction accuracy as much, thus there is no need to use them. |
| first_indexed | 2024-04-24T12:51:24Z |
| format | Article |
| id | doaj.art-b15f546fa69b4c8a8fc28175ee588722 |
| institution | Directory Open Access Journal |
| issn | 2590-1567 |
| language | English |
| last_indexed | 2025-03-21T16:29:09Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Pharmaceutics: X |
| spelling | doaj.art-b15f546fa69b4c8a8fc28175ee5887222024-06-17T05:56:40ZengElsevierInternational Journal of Pharmaceutics: X2590-15672024-06-017100242Feed factor profile prediction model for two-component mixed powder in the twin-screw feederYuki Kobayashi0Sanghong Kim1Takuya Nagato2Takuya Oishi3Manabu Kano4Department of Systems Science, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 6068501, Kyoto, JapanDepartment of Applied Physics and Chemical Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, 1840012 Tokyo, JapanResearch and Development Division, Powrex Corporation, 5-5-5 Kitagawara, Itami 6640837, Hyogo, JapanResearch and Development Division, Powrex Corporation, 5-5-5 Kitagawara, Itami 6640837, Hyogo, Japan; Department of Applied Chemistry, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, 1840012 Tokyo, JapanDepartment of Systems Science, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 6068501, Kyoto, Japan; Corresponding author.In continuous pharmaceutical manufacturing processes, it is crucial to control the powder flow rate. The feeding process is characterized by the amount of powder delivered per screw rotation, referred to as the feed factor. This study aims to develop models for predicting the feed factor profiles (FFPs) of two-component mixed powders with various formulations, while most previous studies have focused on single-component powders. It further aims to identify the suitable model type and to determine the significance of material properties in enhancing prediction accuracy by using several FFP prediction models with different input variables. Four datasets from the experiment were generated with different ranges of the mass fraction of active pharmaceutical ingredients (API) and the powder weight in the hopper. The candidates for the model inputs are (a) the mass fraction of API, (b) process parameters, and (c) material properties. It is desirable to construct a high-performance prediction model without the material properties because their measurement is laborious. The results show that using (c) as input variables did not improve the prediction accuracy as much, thus there is no need to use them.http://www.sciencedirect.com/science/article/pii/S2590156724000148Feed factor profileLoss-in-weight feederContinuous manufacturingMaterial propertiesMixed powderPrediction model |
| spellingShingle | Yuki Kobayashi Sanghong Kim Takuya Nagato Takuya Oishi Manabu Kano Feed factor profile prediction model for two-component mixed powder in the twin-screw feeder International Journal of Pharmaceutics: X Feed factor profile Loss-in-weight feeder Continuous manufacturing Material properties Mixed powder Prediction model |
| title | Feed factor profile prediction model for two-component mixed powder in the twin-screw feeder |
| title_full | Feed factor profile prediction model for two-component mixed powder in the twin-screw feeder |
| title_fullStr | Feed factor profile prediction model for two-component mixed powder in the twin-screw feeder |
| title_full_unstemmed | Feed factor profile prediction model for two-component mixed powder in the twin-screw feeder |
| title_short | Feed factor profile prediction model for two-component mixed powder in the twin-screw feeder |
| title_sort | feed factor profile prediction model for two component mixed powder in the twin screw feeder |
| topic | Feed factor profile Loss-in-weight feeder Continuous manufacturing Material properties Mixed powder Prediction model |
| url | http://www.sciencedirect.com/science/article/pii/S2590156724000148 |
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