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Cristin-resultat-ID:
2022160
Sist endret:
15. august 2022, 08:46
NVI-rapporteringsår:
2022
Resultat
Vitenskapelig artikkel
2022
Advanced deep learning for dynamic emulsion stability measurement
Amit Patil
Bendik Sægrov-Sorte
og
Balram Panjwani
Tidsskrift
Tidsskrift
Computers and Chemical Engineering
ISSN 0098-1354
e-ISSN 1873-4375
NVI-nivå 2
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Om resultatet
Om resultatet
Vitenskapelig artikkel
Publiseringsår: 2022
Publisert online: 2021
Trykket: 2022
Volum: 157
Sider: 1 - 16
Artikkelnummer: 107614
Lenker
Lenker
original online (doi)
https://doi.org/10.1016/j.compchemeng.2021.107614
Importkilder
Importkilder
Scopus-ID: 2-s2.0-85120654987
Beskrivelse
Beskrivelse
Engelsk
Tittel
Advanced deep learning for dynamic emulsion stability measurement
Sammendrag
Microscopic probes are used to visualise dispersions in fluid-fluid systems to characterize dispersion behaviors. An advanced multi-stage filtered Hough method and a deep learning based neural networking method (Faster R-CNN) has been compared for droplet detection performance on an image set. The comparison was made with respect to an independent manual analysis. A test analysis for droplet detection performance analysis was prepared. The Faster R-CNN method performed better relative to advanced filtered Hough method. The Faster R-CNN had within 12 measurement error with respect to the visual or manual detection standard in terms droplet size measurement. This standardized method was utilized to analyze Exxsol D80 oil and water emulsions for evaluating droplet stability parameters. Accurate droplet stability coefficients in the inertial sub-range was evaluated alongside dependency on dispersion phase fraction. The droplet relaxation time scales for the Exxsol D80 oil and water system has been measured and reported where possible.
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Bidragsytere
Bidragsytere
Amit Vijay Patil
Bidragsyterens navn vises på dette resultatet som Amit Patil
Forfatter
ved Prosessteknologi ved SINTEF AS
Bendik Sægrov-Sorte
Forfatter
ved Metallproduksjon og prosessering ved SINTEF AS
Balram Panjwani
Forfatter
ved Metallproduksjon og prosessering ved SINTEF AS
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