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Residence time distribution (RTD) revisited

Residence Time Distribution (RTD) theory is revisited and tracer technology discussed. The background of RTD following Danckwerts ideas is presented by introducing “distribution” functions for residence time, internal age and intensity function and how to experimentally obtain them with tracer techn...

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Autor principal: Rodrigues, Alírio E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7532993/
https://www.ncbi.nlm.nih.gov/pubmed/33041349
http://dx.doi.org/10.1016/j.ces.2020.116188
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author Rodrigues, Alírio E.
author_facet Rodrigues, Alírio E.
author_sort Rodrigues, Alírio E.
collection PubMed
description Residence Time Distribution (RTD) theory is revisited and tracer technology discussed. The background of RTD following Danckwerts ideas is presented by introducing “distribution” functions for residence time, internal age and intensity function and how to experimentally obtain them with tracer techniques (curves C and F of Danckwerts). Compartment models to describe fluid flow in real reactors are reviewed and progressive modeling of chromatographic processes discussed in some detail. The shortcomings of Standard Dispersion Model (SDM) are addressed, the Taylor-Aris model discussed and the Wave Model of Westerterp’s group introduced. The contribution of Computational Fluid Dynamics (CFD) is highlighted to calculate RTD from momentum and mass transport equations and to access spatial age distribution and degree of mixing. Finally smart RTD and future challenges are discussed.
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spelling pubmed-75329932020-10-05 Residence time distribution (RTD) revisited Rodrigues, Alírio E. Chem Eng Sci Review Residence Time Distribution (RTD) theory is revisited and tracer technology discussed. The background of RTD following Danckwerts ideas is presented by introducing “distribution” functions for residence time, internal age and intensity function and how to experimentally obtain them with tracer techniques (curves C and F of Danckwerts). Compartment models to describe fluid flow in real reactors are reviewed and progressive modeling of chromatographic processes discussed in some detail. The shortcomings of Standard Dispersion Model (SDM) are addressed, the Taylor-Aris model discussed and the Wave Model of Westerterp’s group introduced. The contribution of Computational Fluid Dynamics (CFD) is highlighted to calculate RTD from momentum and mass transport equations and to access spatial age distribution and degree of mixing. Finally smart RTD and future challenges are discussed. Elsevier Ltd. 2021-02-02 2020-10-04 /pmc/articles/PMC7532993/ /pubmed/33041349 http://dx.doi.org/10.1016/j.ces.2020.116188 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Review
Rodrigues, Alírio E.
Residence time distribution (RTD) revisited
title Residence time distribution (RTD) revisited
title_full Residence time distribution (RTD) revisited
title_fullStr Residence time distribution (RTD) revisited
title_full_unstemmed Residence time distribution (RTD) revisited
title_short Residence time distribution (RTD) revisited
title_sort residence time distribution (rtd) revisited
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7532993/
https://www.ncbi.nlm.nih.gov/pubmed/33041349
http://dx.doi.org/10.1016/j.ces.2020.116188
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