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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...

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Autores principales: Hone, Christopher A., Holmes, Nicholas, Akien, Geoffrey R., Bourne, Richard A., Muller, Frans L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Royal Society of Chemistry 2017
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.
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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
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
title_short Rapid multistep kinetic model generation from transient flow data
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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