Prediction of powder flow of pharmaceutical materials using machine learning

Research output: Contribution to conferencePoster

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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 languageEnglish
Pages78-78
Number of pages1
Publication statusPublished - 16 May 2022
EventCMAC Annual Open Day 2022 - Glasgow, United Kingdom
Duration: 16 May 202218 May 2022

Conference

ConferenceCMAC Annual Open Day 2022
Country/TerritoryUnited Kingdom
CityGlasgow
Period16/05/2218/05/22

Keywords

  • machine learning
  • powder flow
  • pharmaceuticals

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