Monitoring of an industrial liquid–liquid extraction system with kernel-based methods
The behaviour of liquid–liquid extraction systems can be complex and as a result linear methods of process condition monitoring such as principal component analysis or partial least squares may not be able to detect and identify process faults when they occur. In contrast, kernel-based methods repre...
| Main Authors: | , |
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| Format: | Journal Article |
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Elsevier Science BV
2005
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| Online Access: | http://hdl.handle.net/20.500.11937/3863 |
| _version_ | 1848744348236120064 |
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| author | Jemwa, G. Aldrich, Chris |
| author_facet | Jemwa, G. Aldrich, Chris |
| author_sort | Jemwa, G. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The behaviour of liquid–liquid extraction systems can be complex and as a result linear methods of process condition monitoring such as principal component analysis or partial least squares may not be able to detect and identify process faults when they occur. In contrast, kernel-based methods represent a general framework that can be used where linear methods do not perform satisfactorily. This is demonstrated in a case study on an industrial liquid–liquid extraction system, where a linear discriminant analysis problem is recast as a support vector machine learning problem. The support vector machine is subsequently used to extract features from the plant data that can be used for considerably more accurate fault detection than is possible with its linear equivalent or with other nonlinear methods. Fault identification can be accomplished from an analysis of the residuals of models using the features to reconstruct the original plant data. |
| first_indexed | 2025-11-14T06:00:02Z |
| format | Journal Article |
| id | curtin-20.500.11937-3863 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:00:02Z |
| publishDate | 2005 |
| publisher | Elsevier Science BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-38632017-02-28T01:25:25Z Monitoring of an industrial liquid–liquid extraction system with kernel-based methods Jemwa, G. Aldrich, Chris Support vector machines Process monitoring Fault identification Fault detection Liquid–liquid extraction Kernel-based methods The behaviour of liquid–liquid extraction systems can be complex and as a result linear methods of process condition monitoring such as principal component analysis or partial least squares may not be able to detect and identify process faults when they occur. In contrast, kernel-based methods represent a general framework that can be used where linear methods do not perform satisfactorily. This is demonstrated in a case study on an industrial liquid–liquid extraction system, where a linear discriminant analysis problem is recast as a support vector machine learning problem. The support vector machine is subsequently used to extract features from the plant data that can be used for considerably more accurate fault detection than is possible with its linear equivalent or with other nonlinear methods. Fault identification can be accomplished from an analysis of the residuals of models using the features to reconstruct the original plant data. 2005 Journal Article http://hdl.handle.net/20.500.11937/3863 Elsevier Science BV restricted |
| spellingShingle | Support vector machines Process monitoring Fault identification Fault detection Liquid–liquid extraction Kernel-based methods Jemwa, G. Aldrich, Chris Monitoring of an industrial liquid–liquid extraction system with kernel-based methods |
| title | Monitoring of an industrial liquid–liquid extraction system with kernel-based methods |
| title_full | Monitoring of an industrial liquid–liquid extraction system with kernel-based methods |
| title_fullStr | Monitoring of an industrial liquid–liquid extraction system with kernel-based methods |
| title_full_unstemmed | Monitoring of an industrial liquid–liquid extraction system with kernel-based methods |
| title_short | Monitoring of an industrial liquid–liquid extraction system with kernel-based methods |
| title_sort | monitoring of an industrial liquid–liquid extraction system with kernel-based methods |
| topic | Support vector machines Process monitoring Fault identification Fault detection Liquid–liquid extraction Kernel-based methods |
| url | http://hdl.handle.net/20.500.11937/3863 |