Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model
Locally weighted partial least square (LW-PLS) model has been commonly used to develop adaptive soft sensors and process monitoring for numerous industries which include pharmaceutical, petrochemical, semiconductor, wastewater treatment system and biochemical. The advantages of LW-PLS model are its...
| Main Authors: | , , |
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| Format: | Journal Article |
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De Gruyter
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/60167 |
| _version_ | 1848760583564820480 |
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| author | Yeo, Wan Saptoro, Agus Perumal, K. |
| author_facet | Yeo, Wan Saptoro, Agus Perumal, K. |
| author_sort | Yeo, Wan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Locally weighted partial least square (LW-PLS) model has been commonly used to develop adaptive soft sensors and process monitoring for numerous industries which include pharmaceutical, petrochemical, semiconductor, wastewater treatment system and biochemical. The advantages of LW-PLS model are its ability to deal with a large number of input variables, collinearity among the variables and outliers. Nevertheless, since most industrial processes are highly nonlinear, a traditional LW-PLS which is based on a linear model becomes incapable of handling nonlinear processes. Hence, an improved LW-PLS model is required to enhance the adaptive soft sensors in dealing with data nonlinearity. In this work, Kernel function which has nonlinear features was incorporated into LW-PLS model and this proposed model is named locally weighted kernel partial least square (LW-KPLS). Comparisons between LW-PLS and LW-KPLS models in terms of predictive performance and their computational loads were carried out by evaluating both models using data generated from a simulated plant. From the results, it is apparent that in terms of predictive performance LW-KPLS is superior compared to LW-PLS. However, it is found that computational load of LW-KPLS is higher than LW-PLS. After adapting ensemble method with LW-KPLS, computational loads of both models were found to be comparable. These indicate that LW-KPLS performs better than LW-PLS in nonlinear process applications. In addition, evaluation on localization parameter in both LW-PLS and LW-KPLS is also carried out. |
| first_indexed | 2025-11-14T10:18:05Z |
| format | Journal Article |
| id | curtin-20.500.11937-60167 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:18:05Z |
| publishDate | 2017 |
| publisher | De Gruyter |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-601672018-07-10T01:52:01Z Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model Yeo, Wan Saptoro, Agus Perumal, K. Locally weighted partial least square (LW-PLS) model has been commonly used to develop adaptive soft sensors and process monitoring for numerous industries which include pharmaceutical, petrochemical, semiconductor, wastewater treatment system and biochemical. The advantages of LW-PLS model are its ability to deal with a large number of input variables, collinearity among the variables and outliers. Nevertheless, since most industrial processes are highly nonlinear, a traditional LW-PLS which is based on a linear model becomes incapable of handling nonlinear processes. Hence, an improved LW-PLS model is required to enhance the adaptive soft sensors in dealing with data nonlinearity. In this work, Kernel function which has nonlinear features was incorporated into LW-PLS model and this proposed model is named locally weighted kernel partial least square (LW-KPLS). Comparisons between LW-PLS and LW-KPLS models in terms of predictive performance and their computational loads were carried out by evaluating both models using data generated from a simulated plant. From the results, it is apparent that in terms of predictive performance LW-KPLS is superior compared to LW-PLS. However, it is found that computational load of LW-KPLS is higher than LW-PLS. After adapting ensemble method with LW-KPLS, computational loads of both models were found to be comparable. These indicate that LW-KPLS performs better than LW-PLS in nonlinear process applications. In addition, evaluation on localization parameter in both LW-PLS and LW-KPLS is also carried out. 2017 Journal Article http://hdl.handle.net/20.500.11937/60167 10.1515/cppm-2017-0022 De Gruyter fulltext |
| spellingShingle | Yeo, Wan Saptoro, Agus Perumal, K. Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model |
| title | Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model |
| title_full | Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model |
| title_fullStr | Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model |
| title_full_unstemmed | Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model |
| title_short | Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model |
| title_sort | development of adaptive soft sensor using locally weighted kernel partial least square model |
| url | http://hdl.handle.net/20.500.11937/60167 |