Prediction of powder flow of pharmaceutical materials using machine learning

Pereira-Diaz, Laura and Brown, Cameron J. and Florence, Alastair J. (2022) Prediction of powder flow of pharmaceutical materials using machine learning. In: CMAC Annual Open Day 2022, 2022-05-16 - 2022-05-18.

[thumbnail of Diaz-etal-CMAC-2022-Prediction-of-powder-flow-of-pharmaceutical-materials]
Preview
 Text. Filename: Diaz_etal_CMAC_2022_Prediction_of_powder_flow_of_pharmaceutical_materials.pdf
Final Published Version
License: Strathprints license 1.0
Download (495kB)| Preview

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.

CORE (COnnecting REpositories)