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- aggregation classification "A1".
- aggregation creator B873339.
- aggregation creator B873340.
- aggregation creator B873341.
- aggregation creator B873342.
- aggregation creator B873343.
- aggregation creator person.
- aggregation date "2014".
- aggregation format "application/pdf".
- aggregation hasFormat 4370761.bibtex.
- aggregation hasFormat 4370761.csv.
- aggregation hasFormat 4370761.dc.
- aggregation hasFormat 4370761.didl.
- aggregation hasFormat 4370761.doc.
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- aggregation hasFormat 4370761.yaml.
- aggregation isPartOf urn:issn:0273-1223.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Earth and Environmental Sciences".
- aggregation title "Granularity determination of activated sludge through on-line profiles by means of case-based reasoning".
- aggregation abstract "Aerobic granulation from floccular sludge is difficult to detect in first stages with the naked eye. This work proposes a combination of multi-way principal components and case-based reasoning to predict the granulation state of a sequencing batch reactor, based solely on the on-line registered profiles of common sensors (i.e. pH, dissolved oxygen and oxidation-reduction potential). The methodology is able to discriminate between two active sludge granularities (floccular and granular). Two different scenarios are presented: one in which both granularities are present, and another scenario for which the granular state is not initially available. Analysis reported pH as the key variable in the transition between both states according to its variation, and that, in general, the granularity of the process can be correctly predicted at the end of the anaerobic phase. This methodology improves process monitoring capabilities during granulation and is an on-line alternative to a microscope analysis before the batch release.".
- aggregation authorList BK1252111.
- aggregation endPage "767".
- aggregation issue "4".
- aggregation startPage "760".
- aggregation volume "69".
- aggregation aggregates 4370775.
- aggregation isDescribedBy 4370761.
- aggregation similarTo wst.2013.776.
- aggregation similarTo LU-4370761.