Linked Life Data

16433361

Source:http://linkedlifedata.com/resource/pubmed/id/16433361

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2006-1-25
pubmed:abstractText
A Portland cement process was taken into consideration and monitored for one month with respect to polluting emissions, fuel and raw material physical-chemical properties, and operative conditions. Soft models, based on linear (partial least-squares, PLS, and principal component regression, PCR) and nonlinear (artificial neural networks, ANNs) approaches, were employed to predict the polluting emissions. The predictive ability of the three regression methods was evaluated by means of the partition of the dataset by Kohonen self-associative maps into both a training and a test set. Then, a "leave-more-out" approach, based on the use of a training set, a test set, and a production set, was adopted. The training set was used to build the models, the test set was used to select the number of latent variables or the neural network training endpoint, and the production set was used to produce genuine predictions. ANNs proved to be much more effective in prediction with respect to PLS and PCR and, at least in the case of SO2 and dust, provided a predictive ability comparable with the experimental estimated uncertainty of the response. This showed that it is possible to satisfactorily predict the two responses. Such a prediction will result in the prevention of environmental and legal problems connected to the polluting emissions.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0013-936X
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
40
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
272-80
pubmed:dateRevised
2006-4-19
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Modeling of the polluting emissions from a cement production plant by partial least-squares, principal component regression, and artificial neural networks.
pubmed:affiliation
Department of Environmental Sciences and Life, University of Eastern Piedmont, Via Bellini 25/G, 15100 Alessandria, Italy. marengoe@tin.it
pubmed:publicationType
Journal Article