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    Monitoring Influent Measurements at Water Resource Recovery Facility Using Data-Driven Soft Sensor Approach

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    Type
    Article
    Authors
    Cheng, Tuoyuan cc
    Harrou, Fouzi cc
    Sun, Ying cc
    Leiknes, TorOve cc
    KAUST Department
    Biological and Environmental Sciences and Engineering (BESE) Division
    Environmental Science and Engineering Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Water Desalination and Reuse Research Center (WDRC)
    KAUST Grant Number
    OSR-2015-CRG4-2582
    Date
    2018-10-16
    Online Publication Date
    2018-10-16
    Print Publication Date
    2019-01-01
    Permanent link to this record
    http://hdl.handle.net/10754/629961
    
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    Abstract
    Monitoring inflow measurements of water resource recovery facilities (WRRFs) is essential to promptly detect abnormalities and helpful in the decision making of the operators to better optimize, take corrective actions, and maintain downstream processes. In this paper, we introduced a flexible and reliable monitoring soft sensor approach to detect and identify abnormal influent measurements of WRRFs to enhance their efficiency and safety. The proposed data-driven soft sensor approach merges the desirable characteristics of principal component analysis (PCA) with k-nearest neighbor (KNN) scheme. PCA performed effective dimension reduction and revealed interrelationships between inflow measurements, while KNN distances demonstrated superior detection capacity, robustness to underlying data distribution, and efficiency in handling high-dimensional dataset. Furthermore, nonparametric thresholds derived from kernel density estimation further enhanced detection results of PCA-KNN approach when compared with parametric counterparts. Moreover, the radial visualization plot is innovatively employed for fault analysis and diagnosis in combination with PCA and delineated interpretable visualization of anomalies and detector performances. The effectiveness of these soft sensor schemes is evaluated by using real data from a coastal municipal WRRF located in Saudi Arabia. Also, we compared the proposed soft sensor scheme with the conventional PCA-based approaches, including standard prediction error, Hotelling’s T2, and joint univariate methods. Results demonstrate that this soft sensor-based monitoring approach outperforms conventional PCA-based methods.
    Citation
    Cheng T, Harrou F, Sun Y, Leiknes TO (2018) Monitoring Influent Measurements at Water Resource Recovery Facility Using Data-Driven Soft Sensor Approach. IEEE Sensors Journal: 1–1. Available: http://dx.doi.org/10.1109/JSEN.2018.2875954.
    Sponsors
    This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Sensors Journal
    DOI
    10.1109/JSEN.2018.2875954
    Additional Links
    https://ieeexplore.ieee.org/document/8491359
    ae974a485f413a2113503eed53cd6c53
    10.1109/JSEN.2018.2875954
    Scopus Count
    Collections
    Articles; Biological and Environmental Sciences and Engineering (BESE) Division; Environmental Science and Engineering Program; Water Desalination and Reuse Research Center (WDRC); Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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