A systematic methodology for kinetic modeling of chemical reactions applied to n-hexane hydroisomerization

Kinetic modeling provides chemical engineers with a unique opportunity to better understand reaction kinetics in general and the underlying chemistry in particular. How to systematically approach a modeling assignment in chemical reaction kinetics is typically less clear, especially for novices in t...

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Veröffentlicht in:	AIChE journal Jg. 61; H. 3; S. 880 - 892
Hauptverfasser:	Toch, Kenneth, Thybaut, Joris W., Marin, Guy B.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Blackwell Publishing Ltd 01.03.2015 American Institute of Chemical Engineers
Schlagworte:	bifunctional zeolite Catalysts Chemical engineering Chemical engineers Chemical reactions Data analysis hydroisomerization kinetic modeling Kinetics Mathematical analysis Mathematical models Methodology n-hexane Optimization Reaction kinetics Regression systematic approach
ISSN:	0001-1541, 1547-5905
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Abstract	Kinetic modeling provides chemical engineers with a unique opportunity to better understand reaction kinetics in general and the underlying chemistry in particular. How to systematically approach a modeling assignment in chemical reaction kinetics is typically less clear, especially for novices in the field. The proposed modeling methodology pursues an adequate compromise between statistical significance and physical meaning of the kinetic model and the corresponding parameters and typically results in models of an appropriate complexity. It comprises the following activities: (1) data analysis, aiming at qualitative information on the reaction mechanism and corresponding rate equations, (2) model regression to quantify this information via optimal parameter values, and (3) validation of the statistical significance and physical meaning of the parameter estimates. This methodology is successfully applied to n‐hexane hydroisomerization on a bifunctional catalyst. © 2014 American Institute of Chemical Engineers AIChE J, 61: 880–892, 2015
AbstractList	Kinetic modeling provides chemical engineers with a unique opportunity to better understand reaction kinetics in general and the underlying chemistry in particular. How to systematically approach a modeling assignment in chemical reaction kinetics is typically less clear, especially for novices in the field. The proposed modeling methodology pursues an adequate compromise between statistical significance and physical meaning of the kinetic model and the corresponding parameters and typically results in models of an appropriate complexity. It comprises the following activities: (1) data analysis, aiming at qualitative information on the reaction mechanism and corresponding rate equations, (2) model regression to quantify this information via optimal parameter values, and (3) validation of the statistical significance and physical meaning of the parameter estimates. This methodology is successfully applied to n‐hexane hydroisomerization on a bifunctional catalyst. © 2014 American Institute of Chemical Engineers AIChE J , 61: 880–892, 2015 Kinetic modeling provides chemical engineers with a unique opportunity to better understand reaction kinetics in general and the underlying chemistry in particular. How to systematically approach a modeling assignment in chemical reaction kinetics is typically less clear, especially for novices in the field. The proposed modeling methodology pursues an adequate compromise between statistical significance and physical meaning of the kinetic model and the corresponding parameters and typically results in models of an appropriate complexity. It comprises the following activities: (1) data analysis, aiming at qualitative information on the reaction mechanism and corresponding rate equations, (2) model regression to quantify this information via optimal parameter values, and (3) validation of the statistical significance and physical meaning of the parameter estimates. This methodology is successfully applied to n-hexane hydroisomerization on a bifunctional catalyst. copyright 2014 American Institute of Chemical Engineers AIChE J, 61: 880-892, 2015 Kinetic modeling provides chemical engineers with a unique opportunity to better understand reaction kinetics in general and the underlying chemistry in particular. How to systematically approach a modeling assignment in chemical reaction kinetics is typically less clear, especially for novices in the field. The proposed modeling methodology pursues an adequate compromise between statistical significance and physical meaning of the kinetic model and the corresponding parameters and typically results in models of an appropriate complexity. It comprises the following activities: (1) data analysis, aiming at qualitative information on the reaction mechanism and corresponding rate equations, (2) model regression to quantify this information via optimal parameter values, and (3) validation of the statistical significance and physical meaning of the parameter estimates. This methodology is successfully applied to n-hexane hydroisomerization on a bifunctional catalyst.
Author	Toch, Kenneth Thybaut, Joris W. Marin, Guy B.
Author_xml	– sequence: 1 givenname: Kenneth surname: Toch fullname: Toch, Kenneth organization: Laboratory for Chemical Technology, Ghent University, B-9052, Ghent, Belgium – sequence: 2 givenname: Joris W. surname: Thybaut fullname: Thybaut, Joris W. email: Joris.Thybaut@UGent.be organization: Laboratory for Chemical Technology, Ghent University, B-9052, Ghent, Belgium – sequence: 3 givenname: Guy B. surname: Marin fullname: Marin, Guy B. organization: Laboratory for Chemical Technology, Ghent University, B-9052, Ghent, Belgium
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SubjectTerms	bifunctional zeolite Catalysts Chemical engineering Chemical engineers Chemical reactions Data analysis hydroisomerization kinetic modeling Kinetics Mathematical analysis Mathematical models Methodology n-hexane Optimization Reaction kinetics Regression systematic approach
Title	A systematic methodology for kinetic modeling of chemical reactions applied to n-hexane hydroisomerization
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