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Title: Simulation and fitting of complex reaction network TPR: The key is the objective function

Abstract

In this research, a method has been developed for finding improved fits during simulation and fitting of data from complex reaction network temperature programmed reactions (CRN-TPR). It was found that simulation and fitting of CRN-TPR presents additional challenges relative to simulation and fitting of simpler TPR systems. The method used here can enable checking the plausibility of proposed chemical mechanisms and kinetic models. The most important finding was that when choosing an objective function, use of an objective function that is based on integrated production provides more utility in finding improved fits when compared to an objective function based on the rate of production. The response surface produced by using the integrated production is monotonic, suppresses effects from experimental noise, requires fewer points to capture the response behavior, and can be simulated numerically with smaller errors. For CRN-TPR, there is increased importance (relative to simple reaction network TPR) in resolving of peaks prior to fitting, as well as from weighting of experimental data points. Using an implicit ordinary differential equation solver was found to be inadequate for simulating CRN-TPR. Lastly, the method employed here was capable of attaining improved fits in simulation and fitting of CRN-TPR when starting with amore » postulated mechanism and physically realistic initial guesses for the kinetic parameters.« less

Authors:
 [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1271860
Alternate Identifier(s):
OSTI ID: 1397364
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Surface Science
Additional Journal Information:
Journal Volume: 653; Journal ID: ISSN 0039-6028
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Temperature programmed desorption; Temperature programmed reaction; Microkinetics; Transient kinetics; Parameter optimization

Citation Formats

Savara, Aditya Ashi. Simulation and fitting of complex reaction network TPR: The key is the objective function. United States: N. p., 2016. Web. doi:10.1016/j.susc.2016.07.001.
Savara, Aditya Ashi. Simulation and fitting of complex reaction network TPR: The key is the objective function. United States. doi:10.1016/j.susc.2016.07.001.
Savara, Aditya Ashi. Thu . "Simulation and fitting of complex reaction network TPR: The key is the objective function". United States. doi:10.1016/j.susc.2016.07.001. https://www.osti.gov/servlets/purl/1271860.
@article{osti_1271860,
title = {Simulation and fitting of complex reaction network TPR: The key is the objective function},
author = {Savara, Aditya Ashi},
abstractNote = {In this research, a method has been developed for finding improved fits during simulation and fitting of data from complex reaction network temperature programmed reactions (CRN-TPR). It was found that simulation and fitting of CRN-TPR presents additional challenges relative to simulation and fitting of simpler TPR systems. The method used here can enable checking the plausibility of proposed chemical mechanisms and kinetic models. The most important finding was that when choosing an objective function, use of an objective function that is based on integrated production provides more utility in finding improved fits when compared to an objective function based on the rate of production. The response surface produced by using the integrated production is monotonic, suppresses effects from experimental noise, requires fewer points to capture the response behavior, and can be simulated numerically with smaller errors. For CRN-TPR, there is increased importance (relative to simple reaction network TPR) in resolving of peaks prior to fitting, as well as from weighting of experimental data points. Using an implicit ordinary differential equation solver was found to be inadequate for simulating CRN-TPR. Lastly, the method employed here was capable of attaining improved fits in simulation and fitting of CRN-TPR when starting with a postulated mechanism and physically realistic initial guesses for the kinetic parameters.},
doi = {10.1016/j.susc.2016.07.001},
journal = {Surface Science},
issn = {0039-6028},
number = ,
volume = 653,
place = {United States},
year = {2016},
month = {7}
}

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Cited by: 4 works
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