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    Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model

    259505.pdf (2.079Mb)
    Access Status
    Open access
    Authors
    Yeo, Wan
    Saptoro, Agus
    Perumal, K.
    Date
    2017
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Yeo, 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.
    Source Title
    Chemical Product and Process Modeling
    DOI
    10.1515/cppm-2017-0022
    ISSN
    1934-2659
    School
    Curtin Malaysia
    Remarks

    The final publication is available at www.degruyter.com

    URI
    http://hdl.handle.net/20.500.11937/60167
    Collection
    • Curtin Research Publications
    Abstract

    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.

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