Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality

• Published in: Applied mathematical modelling 2011-08, Vol.35 (8), p.3674-3684
• Main Authors: Pai, T.Y., Yang, P.Y., Wang, S.C., Lo, M.H., Chiang, C.F., Kuo, J.L., Chu, H.H., Su, H.C., Yu, L.F., Hu, H.C., Chang, Y.H.
• Format: Article
• Language: English
• Subjects: Adaptive neuro fuzzy inference system; Applied sciences; Artificial intelligence; Artificial neural network; Biological wastewater treatment plant; Computer science; control theory; systems; Connectionism. Neural networks; Conventional activated sludge process; Exact sciences and technology; Industrial park; Pollution; Wastewaters; Water treatment and pollution
• ISSN: 0307-904X
• DOI: 10.1016/j.apm.2011.01.019

In this study, three types of adaptive neuro fuzzy inference system (ANFIS) were employed to predict effluent suspended solids (SS eff), chemical oxygen demand (COD eff), and pH eff from a wastewater treatment plant in industrial park. For comparison, artificial neural network (ANN) was also used. The results indicated that ANFIS statistically outperformed ANN in terms of effluent prediction. The minimum mean absolute percentage errors of 2.67%, 2.80%, and 0.42% for SS eff, COD eff, and pH eff could be achieved using ANFIS. The maximum values of correlation coefficient for SS eff, COD eff, and pH eff were 0.96, 0.93, and 0.95, respectively. The minimum mean square errors of 0.19, 2.25, and 0.00, and the minimum root mean square errors of 0.43, 1.48, and 0.04 for SS eff, COD eff, and pH eff could also be achieved. ANFIS’s architecture can overcome the limitations of traditional neural network. It also revealed that the influent indices could be applied to the prediction of effluent quality.