Abstract
Particle processes, such as crystallization, flocculation and emulsification constitute a large fraction of the industrial processes for removal of insolubles, product isolation, purification and polishing. The outcome of these processes typically needs to comply with a given set of quality attributes related to particle size, shape and/or yield. With recent technological advances in commercially available on-line/at-line particle analysis sensors, it is now possible to directly measure the particle attributes in real-time. This allows for developing new direct control strategies. In this work, a model predictive control (MPC) strategy is presented based on a hybrid machine-learning assisted particle model. The hybrid model uses mechanistic models for mass and population balances and machine learning for predicting the process kinetics. In the presented approach, the hybrid model is trained in real-time, during process operation. Combined with MPC, this allows for continuous refinement of the process model. Thereby, the calculated control actions are provided robustly. This approach can be employed with limited prior process knowledge, and allows for directly specifying the target product properties to the controller. The presented control strategy is demonstrated on a theoretical case of crystallization to show the potential of the presented methodology.
Original language | English |
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Title of host publication | Proceedings of the 30th European Symposium on Computer Aided Process Engineering (ESCAPE30) |
Editors | Sauro Pierucci, Flavio Manenti, Giulia Bozzano, Davide Manca |
Publisher | Elsevier |
Publication date | 2020 |
Pages | 1177-1182 |
ISBN (Electronic) | 9780128233771 |
DOIs | |
Publication status | Published - 2020 |
Event | 30th European Symposium on Computer Aided Process Engineering (ESCAPE 30) - Virtual symposium, Milano, Italy Duration: 31 Aug 2020 → 2 Sept 2020 |
Conference
Conference | 30th European Symposium on Computer Aided Process Engineering (ESCAPE 30) |
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Location | Virtual symposium |
Country/Territory | Italy |
City | Milano |
Period | 31/08/2020 → 02/09/2020 |
Keywords
- Hybrid model
- Machine learning
- Soft-sensor
- On-line particle analysis
- Model predictive control (MPC)