Effluent ammonia nitrogen prediction using a phase space reconstruction method combining pipelined recurrent wavelet neural network

Abstract

In wastewater treatment processes (WWTPs), the effluent ammonia nitrogen (NH4-N) concentration is a significant index to measure effluent quality. Recently, the soft computing methods have been widely used to measure effluent NH4-N concentration. However, the performance of soft computing method is closely related with its input variables, which is difficult to choose. As an alternative, the time series prediction method PSR-PRWNN is proposed, which combines phase space reconstruction (PSR) technique and pipelined recurrent wavelet neural network (PRWNN). Different from soft computing methods, the time series prediction method is a method which predicts the effluent NH4-N concentration by using its history data rather than other variables data. Firstly, the chaotic characteristics of effluent NH4-N time series is proved by using the correlation dimension method. Based on chaotic characteristics, the phase space of effluent NH4-N concentration is reconstructed by PSR technique. Then, the relationship model between inputs and output of the reconstructed phase space is established by PRWNN. Thirdly, the parameters of PRWNN are trained by an online gradient algorithm with adaptive learning rates. Finally, the experimental results indicate that the PSR-PRWNN can obtain better training results and prediction accuracy than other algorithms.