Monitoring and Control of Hydrocyclones by Use of Convolutional Neural Networks and Deep Reinforcement Learning
The use of convolutional neural networks for monitoring hydrocyclones from underflow images was investigated. Proof-of-concept and applied industrial considerations for hydrocyclone state detection and underflow particle size inference sensors were demonstrated. The behaviour and practical considera...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
Curtin University
2022
|
| Online Access: | http://hdl.handle.net/20.500.11937/91828 |
| _version_ | 1848765595976204288 |
|---|---|
| author | Giglia, Keith Carmelo |
| author_facet | Giglia, Keith Carmelo |
| author_sort | Giglia, Keith Carmelo |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The use of convolutional neural networks for monitoring hydrocyclones from underflow images was investigated. Proof-of-concept and applied industrial considerations for hydrocyclone state detection and underflow particle size inference sensors were demonstrated. The behaviour and practical considerations of model-free reinforcement learning, incorporating the additional information provided by the sensors developed, was also discussed in a mineral processing context. |
| first_indexed | 2025-11-14T11:37:45Z |
| format | Thesis |
| id | curtin-20.500.11937-91828 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:37:45Z |
| publishDate | 2022 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-918282023-05-01T07:09:00Z Monitoring and Control of Hydrocyclones by Use of Convolutional Neural Networks and Deep Reinforcement Learning Giglia, Keith Carmelo The use of convolutional neural networks for monitoring hydrocyclones from underflow images was investigated. Proof-of-concept and applied industrial considerations for hydrocyclone state detection and underflow particle size inference sensors were demonstrated. The behaviour and practical considerations of model-free reinforcement learning, incorporating the additional information provided by the sensors developed, was also discussed in a mineral processing context. 2022 Thesis http://hdl.handle.net/20.500.11937/91828 Curtin University fulltext |
| spellingShingle | Giglia, Keith Carmelo Monitoring and Control of Hydrocyclones by Use of Convolutional Neural Networks and Deep Reinforcement Learning |
| title | Monitoring and Control of Hydrocyclones by Use of Convolutional Neural Networks and Deep Reinforcement Learning |
| title_full | Monitoring and Control of Hydrocyclones by Use of Convolutional Neural Networks and Deep Reinforcement Learning |
| title_fullStr | Monitoring and Control of Hydrocyclones by Use of Convolutional Neural Networks and Deep Reinforcement Learning |
| title_full_unstemmed | Monitoring and Control of Hydrocyclones by Use of Convolutional Neural Networks and Deep Reinforcement Learning |
| title_short | Monitoring and Control of Hydrocyclones by Use of Convolutional Neural Networks and Deep Reinforcement Learning |
| title_sort | monitoring and control of hydrocyclones by use of convolutional neural networks and deep reinforcement learning |
| url | http://hdl.handle.net/20.500.11937/91828 |