Linked Life Data

18196467

Source:http://linkedlifedata.com/resource/pubmed/id/18196467

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1-3
pubmed:dateCreated
2008-10-7
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
0167-6369
pubmed:author
pubmed:issnType
Print
pubmed:volume
146
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
51-66
pubmed:dateRevised
2009-5-11
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters.
pubmed:affiliation
Department of Environmental Engineering and Management, Chaoyang University of Technology, Wufeng, Taichung, 41349, Taiwan, Republic of China. bai@ms6.hinet.net
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't