Wastewater quality monitoring system using sensor fusion and machine learning techniques

Xusong Qin, Furong Gao, Guohua Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

58 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1133-1144
Number of pages12
JournalWater Research
Volume46
Issue number4
DOIs
Publication statusPublished - 15 Mar 2012
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Ecological Modelling
  • Water Science and Technology
  • Waste Management and Disposal
  • Pollution

Cite this