Many-objective process optimisation with constraints for continuous tableting lines: a case study in lovastatin

Kai Eivind Wu, Cameron J. Brown, Murray N. Robertson, Blair F. Johnston, George Panoutsos

Research output: Contribution to conferencePoster

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Abstract

Background: Digital design, assisted by data science, experienced significant progress over recent years within the continuous manufacturing in the pharmaceutical sector.
Research Objective: The project focuses the fundamental research on robust numerical and visual performance indicators for assessing performance for many-objective optimisation algorithms under multiple constraints
Methods: A surrogate model-based machine learning algorithm is used, to train data-driven models that capture the manufacturing process behaviour. Then use optimisation algorithms to get optimal solutions.
Results: >75% dissolution release could be achieved in 45 minutes.
Original languageEnglish
Pages22-22
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

  • digital design
  • pharmaceutical manufacturing
  • many-objective optimisation algorithms

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