Environmental pollution monitoring: a novel vectorial algorithm technique for oil detection in wastewater

This work presents a new technique based on converting IR absorbance of each element and that of a mixture into phasors. Projecting the mixture phasor vector on that of each component results in the concentration of this component in the mixture. This novel technique, when compared with existing pat...

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Main Authors: Shalaby A.R.M., AlMuhanna K.A., Shalaby M.
Other Authors: 57219433216
Format: Article
Published: Bellwether Publishing, Ltd. 2023
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spelling my.uniten.dspace-256602023-05-29T16:12:22Z Environmental pollution monitoring: a novel vectorial algorithm technique for oil detection in wastewater Shalaby A.R.M. AlMuhanna K.A. Shalaby M. 57219433216 53363267300 57189881220 This work presents a new technique based on converting IR absorbance of each element and that of a mixture into phasors. Projecting the mixture phasor vector on that of each component results in the concentration of this component in the mixture. This novel technique, when compared with existing pattern recognition techniques, makes it easy to analyze the constituents of a mixture with high accuracy in the presence of traces of unknown components. It is found that the new algorithm is capable of detecting the presence of oil in water samples containing many other unknown polluting elements up to 0.1 mg/L. This new algorithm is tested on laboratory contaminated samples and then used to monitor pollution by lubricating oil in an industrial zone. � 2020 Taylor & Francis Group, LLC. Final 2023-05-29T08:12:21Z 2023-05-29T08:12:21Z 2020 Article 10.1080/00387010.2020.1832529 2-s2.0-85092702921 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092702921&doi=10.1080%2f00387010.2020.1832529&partnerID=40&md5=a5069a6e72573917ad9eeb1bedae808f https://irepository.uniten.edu.my/handle/123456789/25660 53 10 737 744 Bellwether Publishing, Ltd. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description This work presents a new technique based on converting IR absorbance of each element and that of a mixture into phasors. Projecting the mixture phasor vector on that of each component results in the concentration of this component in the mixture. This novel technique, when compared with existing pattern recognition techniques, makes it easy to analyze the constituents of a mixture with high accuracy in the presence of traces of unknown components. It is found that the new algorithm is capable of detecting the presence of oil in water samples containing many other unknown polluting elements up to 0.1 mg/L. This new algorithm is tested on laboratory contaminated samples and then used to monitor pollution by lubricating oil in an industrial zone. � 2020 Taylor & Francis Group, LLC.
author2 57219433216
author_facet 57219433216
Shalaby A.R.M.
AlMuhanna K.A.
Shalaby M.
format Article
author Shalaby A.R.M.
AlMuhanna K.A.
Shalaby M.
spellingShingle Shalaby A.R.M.
AlMuhanna K.A.
Shalaby M.
Environmental pollution monitoring: a novel vectorial algorithm technique for oil detection in wastewater
author_sort Shalaby A.R.M.
title Environmental pollution monitoring: a novel vectorial algorithm technique for oil detection in wastewater
title_short Environmental pollution monitoring: a novel vectorial algorithm technique for oil detection in wastewater
title_full Environmental pollution monitoring: a novel vectorial algorithm technique for oil detection in wastewater
title_fullStr Environmental pollution monitoring: a novel vectorial algorithm technique for oil detection in wastewater
title_full_unstemmed Environmental pollution monitoring: a novel vectorial algorithm technique for oil detection in wastewater
title_sort environmental pollution monitoring: a novel vectorial algorithm technique for oil detection in wastewater
publisher Bellwether Publishing, Ltd.
publishDate 2023
_version_ 1806427677810229248
score 13.222552