Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/57360

Wastewater Quality Monitoring System Using Sensor Fusion and Machine Learning Techniques

Authors Qin, Xusong HKUST affiliated (currently or previously)
Gao, Furong View this author's profile
Chen, Guohua View this author's profile
Issue Date 2012
Source Water Research , v. 46, (4), 2012, p. 1133-1144
Summary 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.
Subjects
ISSN 0043-1354
Language English
Format Article
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