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Prediction of Influent Flow Rate and Influent Components using Artificial Neural Network (ANN)  

Moon, Taesup (Department of Civil and Environmental Engineering, Pusan National University)
Choi, Jaehoon (Department of Civil and Environmental Engineering, Pusan National University)
Kim, Sunghui (Department of Civil and Environmental Engineering, Pusan National University)
Cha, Jaehwan (Department of Civil and Environmental Engineering, Pusan National University)
Yoom, Hoonsik (Department of Civil and Environmental Engineering, Pusan National University)
Kim, Changwon (Department of Civil and Environmental Engineering, Pusan National University)
Publication Information
Journal of Korean Society on Water Environment / v.24, no.1, 2008 , pp. 91-98 More about this Journal
Abstract
This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.
Keywords
Artificial neural network; Influent prediction; Modeling; Wastewater treatment plant;
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