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

被引：60

作者：

Pai, T. Y. ^{[1
,2
]}

Yang, P. Y. ^{[1
,3
]}

Wang, S. C. ^{[1
,3
]}

Lo, M. H. ^{[1
]}

Chiang, C. F. ^{[4
]}

Kuo, J. L. ^{[1
]}

Chu, H. H. ^{[1
]}

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

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

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

Chang, Y. H. ^{[1
]}

机构：

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

[2] Natl Taichung Univ Educ Taichung, Dept Sci Applicat & Disseminat, Taichung 40306, Taiwan

[3] Chaoyang Univ Technol, Grad Inst Biochem Sci & Technol, Taichung 41349, Taiwan

[4] China Med Univ, Dept Publ Hlth & Inst Environm Hlth, Taichung 40402, Taiwan

来源：

APPLIED MATHEMATICAL MODELLING | 2011年 / 35卷 / 08期

关键词：

Adaptive neuro fuzzy inference system; Artificial neural network; Biological wastewater treatment plant; Conventional activated sludge process; Industrial park; ONLINE MONITORING PARAMETERS; NEURAL-NETWORK; INFERENCE SYSTEM; ANAEROBIC TREATMENT; INPUT VARIABLES; UNSTEADY-STATE; MIXED LIQUID; SLUDGE; GREY; REMOVAL;

D O I：

10.1016/j.apm.2011.01.019

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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. (C) 2011 Elsevier Inc. All rights reserved.

引用

页码：3674 / 3684

页数：11

共 25 条

[21] Improving neural network prediction of effluent from biological wastewater treatment plant of industrial park using fuzzy learning approach [J].

Pai, Tzu-Yi ;

Wang, S. C. ;

Chiang, C. F. ;

Su, H. C. ;

Yu, L. F. ;

Sung, P. J. ;

Lin, C. Y. ;

Hu, H. C. .

BIOPROCESS AND BIOSYSTEMS ENGINEERING, 2009, 32 (06) :781-790

[22] Prediction of effluent quality of an anaerobic treatment plant under unsteady state through ANFIS modeling with on-line input variables [J].

Perendeci, Altunay ;

Arslan, Sever ;

Celebi, Serdar S. ;

Tanyolac, Abdurrahman .

CHEMICAL ENGINEERING JOURNAL, 2008, 145 (01) :78-85

[23] Evaluation of input variables in adaptive-network-based fuzzy inference system modeling for an anaerobic wastewater treatment plant under unsteady state [J].

Perendeci, Altunay ;

Arslan, Sever ;

Tanyolac, Abdurrahman ;

Celebi, Serdar S. .

JOURNAL OF ENVIRONMENTAL ENGINEERING-ASCE, 2007, 133 (07) :765-771

[24]

Steyer JP, 1997, WATER SCI TECHNOL, V36, P209, DOI 10.2166/wst.1997.0593

[25] Neural fuzzy modeling of anaerobic biological wastewater treatment systems [J].

Tay, JH ;

Zhang, XY .

JOURNAL OF ENVIRONMENTAL ENGINEERING-ASCE, 1999, 125 (12) :1149-1159

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