Improving neural network prediction of effluent from biological wastewater treatment plant of industrial park using fuzzy learning approach.
Three types of adaptive network-based fuzzy inference system (ANFIS) in which the online monitoring parameters served as the input variable were employed to predict suspended solids (SS(eff)), chemical oxygen demand (COD(eff)), and pH(eff) in the effluent from a biological wastewater treatment plant in industrial park. Artificial neural network (ANN) was also used for comparison. The results indicated that ANFIS statistically outperforms ANN in terms of effluent prediction. When predicting, the minimum mean absolute percentage errors of 2.90, 2.54 and 0.36% 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 0.97, 0.95, and 0.98, respectively. The minimum mean square errors of 0.21, 1.41 and 0.00, and the minimum root mean square errors of 0.46, 1.19 and 0.04 for SS(eff), COD(eff), and pH(eff) could also be achieved.
Pai
Tzu-Yi
TY
Department of Environmental Engineering and Management, Chaoyang University of Technology, Wufeng, Taichung 41349, Taiwan. bai@ms6.hinet.net
Wang
S C
SC
Chiang
C F
CF
Su
H C
HC
Yu
L F
LF
Sung
P J
PJ
Lin
C Y
CY
Hu
H C
HC
D017143:Fuzzy Logic; D006863:Hydrogen-Ion Concentration; D007220:Industrial Waste / Q000706:statistics & numerical data; D017744:Medical Waste Disposal / Q000592:standards / Q000706:statistics & numerical data*; D016571:Neural Networks, Computer; D009862:Online Systems; D010100:Oxygen; D013624:Taiwan; D014865:Waste Disposal, Fluid / Q000592:standards / Q000706:statistics & numerical data*
D007220:Industrial Waste; D017744:Medical Waste Disposal; D010100:Oxygen