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.
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 language | English |
|---|---|
| Pages | 22-22 |
| 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
- digital design
- pharmaceutical manufacturing
- many-objective optimisation algorithms