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Computational method for multiphase flow characterization in the gas refinery

This paper presents a new computational method for the decentralized multiphase flow measurement based on the interconnections between the two subsystems to precisely estimate the states of the multiphase flow at the gas refinery. The states of the condensate and gas sub-systems were separately esti...

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Detalles Bibliográficos
Autores principales: Farahani, Abolfazl Varvani, Montazeri, Mohsen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6971397/
https://www.ncbi.nlm.nih.gov/pubmed/31993517
http://dx.doi.org/10.1016/j.heliyon.2020.e03193
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author Farahani, Abolfazl Varvani
Montazeri, Mohsen
author_facet Farahani, Abolfazl Varvani
Montazeri, Mohsen
author_sort Farahani, Abolfazl Varvani
collection PubMed
description This paper presents a new computational method for the decentralized multiphase flow measurement based on the interconnections between the two subsystems to precisely estimate the states of the multiphase flow at the gas refinery. The states of the condensate and gas sub-systems were separately estimated using the Differential Mean Value Theorem by considering the relationship between two subsystems, designing an observer and converting the conditions to linear matrix inequality. To check the stability and performance of the system against the changes, the Lyapunov theory has been used. The states behavior investigated with and without disturbance in the system output and dynamics. Additionally, the Unscented Kalman Filter based on the simplified drift flux model was used to estimate the states. It is found that both observers are capable to identify the states with some differences in performance and drift flux model is sufficient for estimation of parameters and states.
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spelling pubmed-69713972020-01-28 Computational method for multiphase flow characterization in the gas refinery Farahani, Abolfazl Varvani Montazeri, Mohsen Heliyon Article This paper presents a new computational method for the decentralized multiphase flow measurement based on the interconnections between the two subsystems to precisely estimate the states of the multiphase flow at the gas refinery. The states of the condensate and gas sub-systems were separately estimated using the Differential Mean Value Theorem by considering the relationship between two subsystems, designing an observer and converting the conditions to linear matrix inequality. To check the stability and performance of the system against the changes, the Lyapunov theory has been used. The states behavior investigated with and without disturbance in the system output and dynamics. Additionally, the Unscented Kalman Filter based on the simplified drift flux model was used to estimate the states. It is found that both observers are capable to identify the states with some differences in performance and drift flux model is sufficient for estimation of parameters and states. Elsevier 2020-01-18 /pmc/articles/PMC6971397/ /pubmed/31993517 http://dx.doi.org/10.1016/j.heliyon.2020.e03193 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Farahani, Abolfazl Varvani
Montazeri, Mohsen
Computational method for multiphase flow characterization in the gas refinery
title Computational method for multiphase flow characterization in the gas refinery
title_full Computational method for multiphase flow characterization in the gas refinery
title_fullStr Computational method for multiphase flow characterization in the gas refinery
title_full_unstemmed Computational method for multiphase flow characterization in the gas refinery
title_short Computational method for multiphase flow characterization in the gas refinery
title_sort computational method for multiphase flow characterization in the gas refinery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6971397/
https://www.ncbi.nlm.nih.gov/pubmed/31993517
http://dx.doi.org/10.1016/j.heliyon.2020.e03193
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