Document Detail


Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters.
MedLine Citation:
PMID:  18196467     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids (SSeff) and chemical oxygen demand (CODeff) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for SSeff and CODeff could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well.
Authors:
T Y Pai; S H Chuang; T J Wan; H M Lo; Y P Tsai; H C Su; L F Yu; H C Hu; P J Sung
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, Non-U.S. Gov't     Date:  2008-01-15
Journal Detail:
Title:  Environmental monitoring and assessment     Volume:  146     ISSN:  0167-6369     ISO Abbreviation:  Environ Monit Assess     Publication Date:  2008 Nov 
Date Detail:
Created Date:  2008-10-07     Completed Date:  2009-02-17     Revised Date:  2009-05-11    
Medline Journal Info:
Nlm Unique ID:  8508350     Medline TA:  Environ Monit Assess     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  51-66     Citation Subset:  IM    
Affiliation:
Department of Environmental Engineering and Management, Chaoyang University of Technology, Wufeng, Taichung, 41349, Taiwan, Republic of China. bai@ms6.hinet.net
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MeSH Terms
Descriptor/Qualifier:
Environmental Monitoring / methods*
Humans
Industrial Waste / analysis*
Models, Statistical
Neural Networks (Computer)*
Sewage / analysis*
Taiwan
Waste Disposal, Fluid
Chemical
Reg. No./Substance:
0/Industrial Waste; 0/Sewage

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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