Controlling tangential flow filtration in biomanufacturing processes via machine learning: A literature review
With the rapid growth of the biopharmaceutical sector in recent years, in conjunction with many recent successful developments in machine learning and artificial intelligence, the demand for the sector to shift to Industry 4.0 has emerged. Process Analytical Technology (PAT) makes it possible to mon...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Digital Chemical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508124000735 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | With the rapid growth of the biopharmaceutical sector in recent years, in conjunction with many recent successful developments in machine learning and artificial intelligence, the demand for the sector to shift to Industry 4.0 has emerged. Process Analytical Technology (PAT) makes it possible to monitor and control the manufacturing processes of monoclonal antibodies (mAbs), both in upstream and downstream processing. Despite downstream processing being responsible for approximately 60% of the cost of biological drug production, most of the recent developments focus on its upstream counterpart. This paper investigates existing literature on the application of machine learning and/or process control in downstream processing, with an emphasis on ultrafiltration/diafiltration (UF/DF) via tangential flow filtration (TFF). Literature on the intersection between control systems and machine learning will also be explored. |
|---|---|
| ISSN: | 2772-5081 |