Improving neural network prediction of effluent from biological wastewater treatment plant of industrial park using fuzzy learning approach

被引：21

作者：

Pai, Tzu-Yi ^{[1
]}

Wang, S. C. ^{[1
]}

Chiang, C. F. ^{[2
,3
]}

Su, H. C. ^{[1
]}

Yu, L. F. ^{[1
]}

Sung, P. J. ^{[1
]}

Lin, C. Y. ^{[1
]}

Hu, H. C. ^{[1
]}

机构：

[1] Chaoyang Univ Technol, Dept Environm Engn & Management, Taichung 41349, Taiwan

[2] China Med Univ, Dept Publ Hlth, Taichung 40402, Taiwan

[3] China Med Univ, Inst Environm Hlth, Taichung 40402, Taiwan

来源：

BIOPROCESS AND BIOSYSTEMS ENGINEERING | 2009年 / 32卷 / 06期

关键词：

Adaptive network-based fuzzy inference system; Artificial neural network; Biological wastewater treatment plant; Conventional activated sludge process; Industrial park; ACTIVATED-SLUDGE PROCESS; INFERENCE SYSTEM; MIXED LIQUID; MODEL; RIVER; PERFORMANCE; ANFIS; GREY;

D O I：

10.1007/s00449-009-0304-2

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

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 (SSeff), chemical oxygen demand (CODeff), 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 SSeff, CODeff and pH(eff) could be achieved using ANFIS. The maximum values of correlation coefficient for SSeff, CODeff, 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 SSeff, CODeff, and pH(eff) could also be achieved.

引用

页码：781 / 790

页数：10

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