Abstract
The lack of understanding of powder flow adds cost and time to the development of robust production routes and compromises manufacturing process performance in the pharmaceutical industry. In this work, implementing machine learning models enables rapid decision-making regarding manufacturing route selection, thus, minimizing the time and amount of material required. This work focuses on using ML models to predict powder flow behavior of pharmaceutical materials for routine, widely available materials.
Original language | English |
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Pages | 78-78 |
Number of pages | 1 |
Publication status | Published - 16 May 2022 |
Event | CMAC Annual Open Day 2022 - Glasgow, United Kingdom Duration: 16 May 2022 → 18 May 2022 |
Conference
Conference | CMAC Annual Open Day 2022 |
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Country/Territory | United Kingdom |
City | Glasgow |
Period | 16/05/22 → 18/05/22 |
Keywords
- machine learning
- powder flow
- pharmaceuticals