Show simple item record

dc.contributor.authorYeo, Wan
dc.contributor.authorSaptoro, Agus
dc.contributor.authorPerumal, K.
dc.identifier.citationYeo, W. and Saptoro, A. and Perumal, K. 2017. Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model. Chemical Product and Process Modeling. 12 (4): Article ID 22.

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.

dc.publisherDe Gruyter
dc.titleDevelopment of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model
dc.typeJournal Article
dcterms.source.titleChemical Product and Process Modeling

The final publication is available at

curtin.departmentCurtin Malaysia
curtin.accessStatusOpen access

Files in this item


This item appears in the following Collection(s)

Show simple item record