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

AbstractFor 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 e...

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