Investigation on the Effect Factors in Artificial Neural Networks for Predicting the Coagulant Dosage in Drinking Water Treatment Plants

• Main Authors: Guan-De Wu, 吳冠德
• Other Authors: 駱尚廉
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/24627485402784652604

博士 === 臺灣大學 === 環境工程學研究所 === 98 === In the past, artificial neural network (ANN) has been used successfully for the prediction of coagulant dosage. Recent developments of ANN with the addition of fuzzy theory have also been reported. In this study, research focus is placed on how to improve the predictability of ANN, considering the effect of fuzzy theory, inherent factor, data normalization, and time interval on predicting the coagulant dosage in drinking water treatment. Performance of ANN and Adaptive Network based Fuzzy Inference System (ANFIS) approaches is compared in order to understand the effect of fuzzy theory in ANN. Inherent factor is defined as the past time object value. The use of different transfer functions determines the necessity of data normalization in ANN. Experimental coagulant dosage data are collected from the drinking water treatment stations in Taipei County and Taipei City, Taiwan. With raw water quality data made available in the analysis, ANN is suitable for building the real-time optimal coagulant dosage. On the other hand, the inherent predicting approach is useful to decide the real time optimal coagulant dosage, and the predictability of ANFIS is better than ANN. The inherent factor can improve the predictability of ANN, but increasing the amount of inherent factor is not significant for increasing the predictability of ANN. The predictability of ANN can be improved by 1) using a hyperbolic-tangent transfer function in the hidden layer, 2) using a linear transfer function in the output layer, 3) using the input data without data normalization, and 4) using short time interval input data. When a heavy rain let the raw water have high turbidity, ANN provides operators to decide the optimal coagulant dosage immediately. The input variables of ANN can be selected by the Pearson correlation coefficient, and the transfer function in the hidden layer is a hyperbolic-tangent function, and the transfer function in the output layer is a linear function, and input data is without data normalization. The input variables of drinking water treatment in Taipei County are raw water turbidity and inherent factor (PAC(t-1)), and the input variables of drinking water treatment in Taipei City are raw water turbidity, distribution water turbidity, sedimentation water turbidity, and inherent factor ((D(t-1), D(t-2), D(t-3), D(t-4)).