Show simple item record

dc.contributor.authorFogelman, S
dc.contributor.authorBlumenstein, M
dc.contributor.authorZhao, HJ
dc.date.accessioned2017-05-03T11:26:55Z
dc.date.available2017-05-03T11:26:55Z
dc.date.issued2006
dc.date.modified2010-08-26T07:35:47Z
dc.identifier.issn0941-0643
dc.identifier.doi10.1007/s00521-005-0015-9
dc.identifier.urihttp://hdl.handle.net/10072/13804
dc.description.abstractA simple method based on the mathematical treatment of spectral absorbance profiles in conjunction with artificial neural networks (ANNs) is demonstrated for rapidly estimating chemical oxygen demand (COD) values of wastewater samples. In order to improve spectroscopic analysis and ANN training time as well as to reduce the storage space of the trained ANN algorithm, it is necessary to decrease the ANN input vector size by extracting unique characteristics from the raw input pattern. Key features from the spectral absorbance pattern were therefore selected to obtain the spectral absorbance profile, reducing the ANN input vector from 160 to 10 selected inputs. The results indicate that the COD values obtained from the selected absorbance profiles agreed well with those obtained from the entire absorbance pattern. The spectral absorbance profile technique was also compared to COD values estimated by a multiple linear regression (MLR) model to validate whether ANNs were better and more robust models for rapid COD analysis. It was found that the ANN model predicted COD values closer to standard COD values than the MLR model.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofpagefrom197
dc.relation.ispartofpageto203
dc.relation.ispartofjournalNeural Computing And Applications
dc.relation.ispartofvolume15
dc.rights.retentionY
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchcode5204
dc.titleEstimation of Chemical Oxygen Demand by Ultraviolet Spectroscopic Profiling and Artificial Neural Networks
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, Griffith School of Environment
gro.date.issued2006
gro.hasfulltextNo Full Text
gro.griffith.authorZhao, Huijun


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record