Software Tools For Molecule Based Kinetic Modeling Of Complex Systems

Author : Fariba Seyedzadeh Khanshan
Publisher :
ISBN 13 :
Total Pages : 174 pages
Book Rating : 4.:/5 (972 download)

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Download or read book Automatic Generation of Detailed Kinetic Models for Complex Chemical Systems written by Fariba Seyedzadeh Khanshan and published by . This book was released on 2016 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Detailed chemical kinetic mechanisms represent molecular interactions that occur when chemical bonds are broken and reformed into new chemical compounds. Many natural and industrial processes such as combustion of hydrocarbons, biomass conversion into re- newable fuels, and synthesis of halogenated-hydrocarbon through halogenation reactions, include reaction network with hundred of species and thousands of reactions. Recently, the potential of such processes is leading to rapid industrial expansion and facing some technical drawbacks. Among various tools, detailed kinetic modeling is a reliable way to improve the scientific understanding of such systems and therefore optimize process conditions for desired production plans. Detailed chemical kinetic modeling is sensitive to the system chemistry, and sometimes too complex to model by hand. For example, utilizing predictive theoretical models by hand for biomass thermal conversion, which in- clude a wide variety of heavy cyclic oxygenated molecules, alcohols, aldehydes, ketones, ethers, esters, etc., is tedious. It is preferable to teach our chemistry knowledge to computers, and generate detailed chemical models automatically. To generate comprehensive detailed models, an extensive set of reaction classes, which would define how species can react with each other, should be implemented in mechanism generators. In this thesis, Reaction Mechanism Genera- tor (RMG), an open-source software, has been used to build detailed kinetic models for complex chemical systems. This thesis presents several significant contributions in the area of predictive automatic kinetic mechanism generation for biofuels thermal conversion and reactions of many chlo- rinated hydrocarbons. The first section of this thesis describes significant contributions in detailed kinetic modeling of bio-oil gasification for syngas production using RMG. The major challenge in modeling bio-oil gasification is the presence of a wide range of cyclic oxygenated species and several progress has been made in RMG to improve the automated chemical modeling of this process. RMG-built models were evaluated by comparison to available published data and to improve the understanding of such detailed models, dif- ferent types of analysis such as sensitivity analysis were performed. The second section of this thesis presents a theoretical study of the gas-phase unimolec- ular thermal decomposition of heterocyclic compounds via single step exo and endo ring opening reaction classes. Quantum chemical calculations were performed for a smaller set of reactants belonging to the endo and exo reaction classes and data were used to inspect the 'rate calculation rules' method. To study the e↵ect of the direct ring open- ing reactions in the automated detailed kinetic model generation, the bio-oil gasification mechanism, from Chapter 1, was updated after updating RMGs kinetic database with these new single step ring opening reaction classes and associated rate rules. The third section of this thesis provides significant contributions toward facilitating the automatic generation of predictive detailed kinetic models for 1,1,2,3- tetrachloropropene (1230xa) production and other hydrocarbon chlorination processes. In order to enable RMG to model chlorinated hydrocarbon conversions, the chlorine (Cl) chemistry has been added into the the Python version of the software. A model has been generated in RMG for 1230xa production with known associated thermodynamic and kinetic parameters. For model evaluation, reaction flux analysis and sensitivity analysis were performed to reveal the important reaction channels in the RMG-built model and several improvements to thermodynamic estimates were discussed. The ability to automatically generate these models for such complex chemical systems demonstrates the predictive capability of detailed chemical modeling. The impact of such models significantly improves the scientific understanding of two industrial chemical processes, bio-oil gasification and chlorination.