Predicting performance of grey and neural network in industrial effluent using online monitoring parameters

被引：23

作者：

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

Chuang, S. H. ^{[1
]}

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

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

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

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

机构：

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

来源：

PROCESS BIOCHEMISTRY | 2008年 / 43卷 / 02期

关键词：

grey model; artificial neural network; industrial wastewater treatment plant; conventional activated sludge process; biological treatment; industrial park;

D O I：

10.1016/j.procbio.2007.10.003

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids (SSeff), chemical oxygen demand (CODeff) and pH(eff) in the effluent from conventional activated process of an industrial wand pH, astewater treatment plant using simple online monitoring parameters (pH in the equalization pond effluent; pH, temperature, and dissolved oxygen in the aeration tank). The results indicated that the minimum mean absolute percentage errors of 20.79, 6.09 and 0.71% for SSeff, CODeff and pH(eff), respectively, could be achieved using different types of GMs. GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN, According to the results, the online monitoring parameters could be applied on the prediction of effluent quality. It also revealed that GM could predict the industrial effluent variation as its effluent data was insufficient. (c) 2007 Elsevier Ltd. All rights reserved.

引用

页码：199 / 205

页数：7

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