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    Adaptive Soft Sensors for Non-Gaussian Chemical Process Plant Data Based on Locally Weighted Partial Least Square

    Yeo W 2019.pdf (3.798Mb)
    Access Status
    Open access
    Authors
    Yeo, Wan Sieng
    Date
    2019
    Supervisor
    Agus Saptoro
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Curtin Malaysia
    School
    Department of Chemical Engineering
    URI
    http://hdl.handle.net/20.500.11937/77028
    Collection
    • Curtin Theses
    Abstract

    This thesis proposes an improved algorithm attributed to its abilities to deal with non-Gaussian distributed and nonlinear data and missing measurements. It was formulated through a modification on locally weighted partial least square by incorporating an ensemble method, Kernel function and independent component analysis and expectation maximisation algorithms. The algorithm was then tested using process data generated from six simulated plants. Simulation results indicate superiority of this algorithm compared to the existing algorithms.

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