Monitoring Influent Measurements at Water Resource Recovery Facility Using Data-Driven Soft Sensor Approach
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Environmental Science and Engineering Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Water Desalination and Reuse Research Center (WDRC)
KAUST Grant NumberOSR-2015-CRG4-2582
Online Publication Date2018-10-16
Print Publication Date2019-01-01
Permanent link to this recordhttp://hdl.handle.net/10754/629961
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AbstractMonitoring 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.
CitationCheng 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.
SponsorsThis 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.
JournalIEEE Sensors Journal