A fuzzy neural network model for monitoring A2/O process using on-line monitoring parameters

Abstract

An adaptive network based fuzzy inference system (ANFIS) model was employed to predict effluent chemical oxygen demand (CODeff) and ammonia nitrogen (NH4 + eff) from an anaerobic/anoxic/oxic (A2/O) process, and meanwhile a self-adapted fuzzy c-means clustering algorithm was used to identify the model's architecture and optimize fuzzy rules. When constructing the model or predicting, the on-line monitoring parameters, namely hydraulic retention time (HRT), influent pH (pH), dissolved oxygen in the aerobic reactor (DO) and mixed-liquid return ratio (r), were adopted as the input variables. Compared with the artificial neural network (ANN) model whose weight vector was optimized by a real-code genetic algorithm (GA), the ANFIS presented better estimate performance. When predicting, the mean absolute percentage errors (MAPEs) of 1.8458% and 2.8984% for CODeff and NH4 + eff could be achieved using ANFIS; the root mean square errors (RMSEs) for CODeff and NH4 + eff were 1.6317 and 0.1291, respectively; the correlation coefficient (R) values of 0.9928 and 0.9951 for CODeff and NH4 + eff could also be achieved. The results indicated that reasonable monitoring A2/O process performance, just using on-line monitoring parameters, has been achieved through the ANFIS.