Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
Water Res. 2012 Mar 15;46(4):1133-44. doi: 10.1016/j.watres.2011.12.005. Epub 2011 Dec 11.
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.
本研究采用一种多传感器水质监测系统，该系统结合了紫外/可见分光光度计和浊度计，用于监测校园中餐厅废水的化学需氧量（COD）、总悬浮固体（TSS）和油和油脂（O&G）浓度，并对电絮凝-电浮选（EC-EF）中试设备进行了监测。为了处理校准模型建立过程中融合的紫外/可见光谱和浊度测量中的噪声和信息不平衡问题，本研究开发了一种改进的提升方法，即提升迭代预测加权偏最小二乘（Boosting-IPW-PLS）。Boosting-IPW-PLS 方法将 IPW 纳入提升方案中，通过分配较小的权重来抑制与质量无关的变量，并基于加权变量构建废水质量预测模型。监测系统在现场进行了测试，结果令人满意，这证明了该技术在水质在线监测方面具有潜力。
