Seasonal artificial neural network model for water quality prediction via a clustering analysis method in a wastewater treatment plant of China

Abstract

For recovering the water quality of a river, it is a key factor to improve purifying capacity of wastewater in wastewater treatment plants (WTPs). The relational model for some key parameters of WTP processes is important for it can reveal the current situation and handling ability of the WTP and offer managers more useful information to design the processes for the optimized operation. The seasonal artificial neural network (ANN) models were designed for improving purifying ability of wastewater in a WTP of Harbin, northeast of China. The ANN models revealed the relationship of raw water quality, energy consumption, and effluent water quality. The effluent water quality could be predicted by the models. The clustering analysis method, an important data mining method, was used to classify the WTP data for building seasonal models. Meanwhile, an annual model was built by the whole data. It indicates that the prediction accuracy of seasonal models is better than the annual model by contrasting the errors. Seasonal models should be a more effective tool to reveal the relationship of WTP data. So it can offer managers more precise information to control and design the processes of WTPs, which result in better purifying ability of wastewater.

Keywords:

• ANN model
• Clustering analysis
• Wastewater treatment plant
• Water quality prediction
• Optimization
• Seasonal model

Acknowledgment

This work was supported by China Postdoctoral Science Foundation (20110491056); supported by Postdoctoral Science Foundation of Heilongjiang Province of China (LBH-Z10172); Research and Innovation Fund project of Harbin Institute of Technology (HIT) in 2011; supported by Harbin Scientific and Technological Innovative Talents Research Special Fund Project of Harbin Municipal Science and Technology Bureau (2013RFQXJ121); Scientific and Technological Research General Program Project of Heilongjiang Provincial Department of Education (12531532).