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Microkinetic Modeling of Complex Rea...

Gupta, Udit.

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Microkinetic Modeling of Complex Reaction Networks using Automated Network Generation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Microkinetic Modeling of Complex Reaction Networks using Automated Network Generation./
作者:	Gupta, Udit.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	211 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
標題:	Chemical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10809155
ISBN:	9780438031500

Microkinetic Modeling of Complex Reaction Networks using Automated Network Generation.
Gupta, Udit.

Microkinetic Modeling of Complex Reaction Networks using Automated Network Generation. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 211 p.

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

Thesis (Ph.D.)--University of Minnesota, 2018.

Complex reaction networks are found in a variety of engineered and natural chemical systems ranging from petroleum processing to atmospheric chemistry and including biomass conversion, materials synthesis, metabolism, and biological degradation of chemicals. These systems comprise of several thousands of reactions and species interrelated through a highly interconnected network. These complex reaction networks can be constructed automatically from a small set of initial reactants and chemical transformation rules. Detailed kinetic modeling of these complex reaction systems is becoming increasingly important in the development, analysis, design, and control of chemical reaction processes. The key challenges faced in the development of a kinetic model for complex reaction systems include (1) multi-time scale behavior due to the presence of fast and slow reactions which introduces stiffness in the system, (2) lack of lumping schemes that scale well with the large size of the network, and (3) unavailability of accurate reaction rate constants (activation energies and pre-exponential factors). Model simplication and order reduction methods involving lumping, sensitivity analysis and time-scale analysis address the challenges of size and stiffness of the system. Although there exist numerical methods for simulation of large-scale, stiff models, the use of such models in optimization-based tasks (e.g. parameter estimation, control) results in ill-conditioning of the corresponding optimization task.

ISBN: 9780438031500Subjects--Topical Terms:

560457
Chemical engineering.

Microkinetic Modeling of Complex Reaction Networks using Automated Network Generation.
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245 1 0 $a Microkinetic Modeling of Complex Reaction Networks using Automated Network Generation.
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300 $a 211 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 $a Advisers: Prodromos Daoutidis; Aditya Bhan.
502 $a Thesis (Ph.D.)--University of Minnesota, 2018.
520 $a Complex reaction networks are found in a variety of engineered and natural chemical systems ranging from petroleum processing to atmospheric chemistry and including biomass conversion, materials synthesis, metabolism, and biological degradation of chemicals. These systems comprise of several thousands of reactions and species interrelated through a highly interconnected network. These complex reaction networks can be constructed automatically from a small set of initial reactants and chemical transformation rules. Detailed kinetic modeling of these complex reaction systems is becoming increasingly important in the development, analysis, design, and control of chemical reaction processes. The key challenges faced in the development of a kinetic model for complex reaction systems include (1) multi-time scale behavior due to the presence of fast and slow reactions which introduces stiffness in the system, (2) lack of lumping schemes that scale well with the large size of the network, and (3) unavailability of accurate reaction rate constants (activation energies and pre-exponential factors). Model simplication and order reduction methods involving lumping, sensitivity analysis and time-scale analysis address the challenges of size and stiffness of the system. Although there exist numerical methods for simulation of large-scale, stiff models, the use of such models in optimization-based tasks (e.g. parameter estimation, control) results in ill-conditioning of the corresponding optimization task.
520 $a This research presents methods, computational tools, and applications to address the two challenges that emerge in the development of microkinetic models of complex reaction networks in the context of chemical and biochemical conversion---(a) identifying the different time scales within the reaction system irrespective of the chemistry, and (b) identifying lumping and parameterization schemes to address the computational challenge of parameter estimation. The first question arises due to the presence of both fast and slow reactions simultaneously within the system. The second challenge is directly related to the estimation of the reaction rate constants that are unknown for these chemical reaction networks. Addressing these questions is a key step towards modeling, design, operation, and control of reactors involving complex systems.
520 $a In this context, this thesis presents methods to address the computational challenges in developing microkinetic models for complex reaction networks. Rule Input Network Generator (RING), a network generation computational tool, is used for the network generation and analysis. First, the stiffness is addressed with the implementation of a graph-theoretic framework. Second, lumping and parameterization schemes are studied to address the size challenge of these reaction networks. A particular lumping and parameterization scheme is used to develop the microkinetic model for an olefin interconversion reaction system. Further, RING is extended for application of biochemical reaction network generation and analysis.
590 $a School code: 0130.
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650 4 $a Applied mathematics. $3 2122814
650 4 $a Bioengineering. $3 657580
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710 2 $a University of Minnesota. $b Chemical Engineering. $3 1021870
773 0 $t Dissertation Abstracts International $g 79-10B(E).
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793 $a English
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