№ files_lp_4_process_2_42985
Study on the predictive modeling of Loss-in-Weight feeder behavior in continuous and semi-continuous pharmaceutical manufacturing using Machine Learning, integrating experimental and literature data for multiple materials and feeder configurations.
Year: 2023
Region / City: UK, Germany
Subject: Pharmaceutical engineering, process modeling
Document type: Research article
Institution / Organization: Strathclyde University; AstraZeneca UK Limited; GSK Ware R&D; Pfizer Research and Development UK Ltd; DFE Pharma GmbH & Co. KG
Authors: Hikaru G. Jolliffe, Carlota Mendez Torrecillas, Gavin Reynolds, Richard Elkes, Hugh Verrier, Michael Devlin, Bastiaan Dickhoff, John Robertson
Target audience: Pharmaceutical engineers, process scientists, data scientists
Methodology: Machine Learning modeling of Loss-in-Weight feeder performance
Materials studied: 19 excipients, 2 generic APIs, 25 API grades/batches from literature
Equipment studied: GEA Compact Feeder, Gericke GZD200.22
Data type: Experimental and literature-derived datasets
Validation: Comparison of predicted feeder performance against industrial targets
Price: 8 / 10 USD
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