Prediction of powder flow of pharmaceutical materials using machine learning

Laura Pereira-Diaz; Cameron J. Brown; Alastair J. Florence

Prediction of powder flow of pharmaceutical materials using machine learning

Laura Pereira-Diaz, Cameron J. Brown, Alastair J. Florence

Research output: Contribution to conference › Poster

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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 language	English
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
Country/Territory	United Kingdom
City	Glasgow
Period	16/05/22 → 18/05/22

Keywords

machine learning
powder flow
pharmaceuticals

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Diaz-etal-CMAC-2022-Prediction-of-powder-flow-of-pharmaceutical-materialsFinal published version, 484 KBLicence: Strath_1

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title = "Prediction of powder flow of pharmaceutical materials using machine learning",

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.",

keywords = "machine learning, powder flow, pharmaceuticals",

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AU - Florence, Alastair J.

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N2 - 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.

AB - 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.

KW - machine learning

KW - powder flow

KW - pharmaceuticals

M3 - Poster

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EP - 78

T2 - CMAC Annual Open Day 2022

Y2 - 16 May 2022 through 18 May 2022

ER -

Prediction of powder flow of pharmaceutical materials using machine learning

Abstract

Conference

Keywords

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