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Rapid multistep kinetic model generation from transient flow data
Today, the generation of kinetic models is still seen as a resource intensive and specialised activity. We report an efficient method of generating reaction profiles from transient flows using a state-of-the-art continuous-flow platform. Experimental data for multistep aromatic nucleophilic substitu...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Royal Society of Chemistry
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5436494/ https://www.ncbi.nlm.nih.gov/pubmed/28580177 http://dx.doi.org/10.1039/c6re00109b |
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author | Hone, Christopher A. Holmes, Nicholas Akien, Geoffrey R. Bourne, Richard A. Muller, Frans L. |
author_facet | Hone, Christopher A. Holmes, Nicholas Akien, Geoffrey R. Bourne, Richard A. Muller, Frans L. |
author_sort | Hone, Christopher A. |
collection | PubMed |
description | Today, the generation of kinetic models is still seen as a resource intensive and specialised activity. We report an efficient method of generating reaction profiles from transient flows using a state-of-the-art continuous-flow platform. Experimental data for multistep aromatic nucleophilic substitution reactions are collected from an automated linear gradient flow ramp with online HPLC at the reactor outlet. Using this approach, we generated 16 profiles, at 3 different inlet concentrations and 4 temperatures, in less than 3 hours run time. The kinetic parameters, 4 rate constants and 4 activation energies were fitted with less than 4% uncertainty. We derived an expression for the error in the observed rate constants due to dispersion and showed that such error is 5% or lower. The large range of operational conditions prevented the need to isolate individual reaction steps. Our approach enables early identification of the sensitivity of product quality to parameter changes and early use of unit operation models to identify optimal process-equipment combinations in silico, greatly reducing scale up risks. |
format | Online Article Text |
id | pubmed-5436494 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-54364942017-06-02 Rapid multistep kinetic model generation from transient flow data Hone, Christopher A. Holmes, Nicholas Akien, Geoffrey R. Bourne, Richard A. Muller, Frans L. React Chem Eng Chemistry Today, the generation of kinetic models is still seen as a resource intensive and specialised activity. We report an efficient method of generating reaction profiles from transient flows using a state-of-the-art continuous-flow platform. Experimental data for multistep aromatic nucleophilic substitution reactions are collected from an automated linear gradient flow ramp with online HPLC at the reactor outlet. Using this approach, we generated 16 profiles, at 3 different inlet concentrations and 4 temperatures, in less than 3 hours run time. The kinetic parameters, 4 rate constants and 4 activation energies were fitted with less than 4% uncertainty. We derived an expression for the error in the observed rate constants due to dispersion and showed that such error is 5% or lower. The large range of operational conditions prevented the need to isolate individual reaction steps. Our approach enables early identification of the sensitivity of product quality to parameter changes and early use of unit operation models to identify optimal process-equipment combinations in silico, greatly reducing scale up risks. Royal Society of Chemistry 2017-04-01 2016-10-03 /pmc/articles/PMC5436494/ /pubmed/28580177 http://dx.doi.org/10.1039/c6re00109b Text en This journal is © The Royal Society of Chemistry 2016 http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License (http://creativecommons.org/licenses/by/3.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Chemistry Hone, Christopher A. Holmes, Nicholas Akien, Geoffrey R. Bourne, Richard A. Muller, Frans L. Rapid multistep kinetic model generation from transient flow data |
title | Rapid multistep kinetic model generation from transient flow data
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title_full | Rapid multistep kinetic model generation from transient flow data
|
title_fullStr | Rapid multistep kinetic model generation from transient flow data
|
title_full_unstemmed | Rapid multistep kinetic model generation from transient flow data
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title_short | Rapid multistep kinetic model generation from transient flow data
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title_sort | rapid multistep kinetic model generation from transient flow data |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5436494/ https://www.ncbi.nlm.nih.gov/pubmed/28580177 http://dx.doi.org/10.1039/c6re00109b |
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