Wastewater quality monitoring system using sensor fusion and machine learning techniques

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

96 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1133-1144
Journal / PublicationWater Research
Volume46
Issue number4
Publication statusPublished - 15 Mar 2012
Externally publishedYes

Abstract

A multi-sensor water quality monitoring system incorporating an UV/Vis spectrometer and a turbidimeter was used to monitor the Chemical Oxygen Demand (COD), Total Suspended Solids (TSS) and Oil & Grease (O&G) concentrations of the effluents from the Chinese restaurant on campus and an electrocoagulation-electroflotation (EC-EF) pilot plant. In order to handle the noise and information unbalance in the fused UV/Vis spectra and turbidity measurements during the calibration model building, an improved boosting method, Boosting-Iterative Predictor Weighting-Partial Least Squares (Boosting-IPW-PLS), was developed in the present study. The Boosting-IPW-PLS method incorporates IPW into boosting scheme to suppress the quality-irrelevant variables by assigning small weights, and builds up the models for the wastewater quality predictions based on the weighted variables. The monitoring system was tested in the field with satisfactory results, underlying the potential of this technique for the online monitoring of water quality. © 2011 Elsevier Ltd.

Research Area(s)

  • Boosting-IPW-PLS, Online monitoring, Turbidity, UV/Vis spectroscopy, Variable weighting, Wastewater treatment

Bibliographic Note

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Citation Format(s)

Wastewater quality monitoring system using sensor fusion and machine learning techniques. / Qin, Xusong; Gao, Furong; Chen, Guohua.
In: Water Research, Vol. 46, No. 4, 15.03.2012, p. 1133-1144.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review