A new approach based on computer vision and non-linear Kalman filtering to monitor the nebulization quality of oil flames

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dc.contributor.authorFLEURY, A. T.
dc.contributor.authorTRIGO, F. C.
dc.contributor.authorMARTINS, F. P. R.
dc.date.accessioned2022-01-12T22:01:59Z
dc.date.available2022-01-12T22:01:59Z
dc.date.issued2013-09-15
dc.description.abstractThe nebulization quality of oil flames, an important characteristic exhibited by combustion processes of petroleum refinery furnaces, is mostly affected by variations on the values ofthe vapor flow rate (VFR). Expressive visual changes in the flame patterns and decay of the combustion efficiency are observed when the process is tuned by diminishing the VFR. Such behavior is supported by experimental evidence showing that too low values of VFR and solid particulate material rate increase are strongly correlate d. Given the economical importance of keeping this parameter under control, a laborator ial vertical furnace was devised with the purpose of carrying out experiments to prototype acomputer vision system capable of estimati ng VFR values through the examination of test charact eristic vectors based on geometric properties of the grey level histogram of instantaneous flame images. Firstly, atraining set composed of feature vectors from all the images collected during experiments with a priori known VFR values are properly organized and analgorithm is applied to this data in order to generate a fuzzy measurement vector whose components represent membership degrees to the 'high nebulization quality'fuzzy set. Fuzzy classification vectors from images with unknown a priori VFR values are, then, assumed tobe state-vectors inarandom-walk model, and a non-linear Tikhonov regularized Kalman filter is applied to estimate the state and the corresponding nebulization quality. The successful validation of the output data, even based onsmall training data sets, indicates that the proposed approach could beapplied to synthesize a real-time algorithm for evaluating the nebulization quality of combustion processes in petroleum refinery furnaces that use oil flamesasthe heating source. © 2013 Elsevier Ltd. All rights reserved.
dc.description.firstpage4760
dc.description.issuenumber12
dc.description.lastpage4769
dc.description.volume40
dc.identifier.citationFLEURY, A. T.; TRIGO, F. C.; MARTINS, F. P. R. A new approach based on computer vision and non-linear Kalman filtering to monitor the nebulization quality of oil flames. Expert Systems with Applications, v. 40, n. 12, p. 4760-4769, Sept. 2013.
dc.identifier.doi10.1016/j.eswa.2013.02.008
dc.identifier.issn0957-4174
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/4104
dc.relation.ispartofExpert Systems with Applications
dc.rightsAcesso Restrito
dc.subject.otherlanguageComputer vision
dc.subject.otherlanguageEstimation of nebulization quality
dc.subject.otherlanguageFuzzy logics
dc.subject.otherlanguageNon-linear Kalman filter
dc.titleA new approach based on computer vision and non-linear Kalman filtering to monitor the nebulization quality of oil flames
dc.typeArtigo
fei.scopus.citations16
fei.scopus.eid2-s2.0-84885075809
fei.scopus.subjectCombustion efficiencies
fei.scopus.subjectExperimental evidence
fei.scopus.subjectFuzzy classification
fei.scopus.subjectGeometric properties
fei.scopus.subjectGrey level histogram
fei.scopus.subjectNon linear
fei.scopus.subjectReal time algorithms
fei.scopus.subjectSolid particulate materials
fei.scopus.updated2024-05-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84885075809&origin=inward
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