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An automated computational approach to kinetic model discrimination and parameter estimation

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Abstract

We herein report experimental applications of a novel, automated computational approach to chemical reaction network (CRN) identification. This report shows the first chemical applications of an autonomous tool to identify the kinetic model and parameters of a process, when considering both catalytic species and various integer and non-integer orders in the model's rate laws. This kinetic analysis methodology requires only the input of the species within the chemical system (starting materials, intermediates, products, etc.) and corresponding time-series concentration data to determine the kinetic information of the chemistry of interest. This is performed with minimal human interaction and several case studies were performed to show the wide scope and applicability of this process development tool. The approach described herein can be employed using experimental data from any source and the code for this methodology is also provided open-source.

Graphical abstract: An automated computational approach to kinetic model discrimination and parameter estimation

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Article information


Submitted
08 Mar 2021
Accepted
07 May 2021
First published
07 May 2021

This article is Open Access

React. Chem. Eng., 2021, Advance Article
Article type
Paper

An automated computational approach to kinetic model discrimination and parameter estimation

C. J. Taylor, H. Seki, F. M. Dannheim, M. J. Willis, G. Clemens, B. A. Taylor, T. W. Chamberlain and R. A. Bourne, React. Chem. Eng., 2021, Advance Article , DOI: 10.1039/D1RE00098E

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