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

Tuoyuan Cheng*, Fouzi Harrou, Ying Sun, Torove Leiknes

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


Monitoring inflow measurements of water resource recovery facilities (WRRFs) are 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.

Original languageEnglish (US)
Article number8491359
Pages (from-to)342-352
Number of pages11
JournalIEEE Sensors Journal
Issue number1
StatePublished - Jan 1 2019

Bibliographical note

Publisher Copyright:
© 2001-2012 IEEE.


  • K-nearest neighbor
  • Water resource recovery facility
  • influent measurements
  • process monitoring
  • radial visualization

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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