Wastewater quality monitoring system using sensor fusion and machine learning techniques.
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.
Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
Xusong
X
Qin
Furong
F
Gao
Guohua
G
Chen
D001185:Artificial Intelligence; D057919:Biological Oxygen Demand Analysis; D004564:Electrocoagulation; D016018:Least-Squares Analysis; D008956:Models, Chemical; D009821:Oils / Q000032:analysis; D013056:Spectrophotometry, Ultraviolet; D014865:Waste Disposal, Fluid / Q000295:instrumentation* / Q000379:methods*; D060753:Water Quality / Q000592:standards*
D009821:Oils