Download or read book Automated Reaction Mechanism Generation written by Gregory Russell Magoon and published by . This book was released on 2012 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chemical kinetic modeling plays an important role in the study of reactive chemical systems. Thus, an automated means of constructing chemical kinetic models forms a useful tool in the engineering and science surrounding such systems. This document describes work to further develop one such tool, known as RMG (Reaction Mechanism Generator). Focus is placed on improving the accuracy of parameter estimation in the mechanism generation process and expanding the scope of applicability of the tool. In particular, effort has targeted the generation and use of explicit three-dimensional molecular structures for chemical species considered during reaction mechanism generation. This work has resulted in the generation of a software system integrated with RMG that can automatically generate and use such structures with quantum chemistry or force field codes to obtain more reliable thermochemistry estimates for cyclic structures without human intervention. Ultimately, the result of these updates is improved usefulness and reliability of the software system as a predictive tool. An application of the tool to the high temperature oxidation of JP-10, a jet fuel often used in military applications, is described. Using the newly refined RMG system, a detailed chemical kinetic model was constructed for this system. The resulting model represents a significant improvement upon existing work for JP- 10 oxidation by capturing detailed chemistry for this system. Simulations with this model have been found to produce results for ignition delay and product distribution that compare favorably with experimental results. The successful application of the refined RMG software system to this system demonstrates the practical utility of these updates.

Download or read book RMG written by Jing Song and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Automated Reaction Mechanism Generation written by Michael Richard Harper (Jr.) and published by . This book was released on 2011 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nearly two-thirds of the United States' transportation fuels are derived from non-renewable fossil fuels. This demand of fossil fuels requires the United States to import ~ 60% of its total fuel consumption. Relying so heavily on foreign oil is a threat to national security, not to mention that burning all of these fossil fuels produces increased levels of CO2, a greenhouse gas that contributes to global warming. This is not a sustainable model. The United States government has recently passed legislation that requires greenhouse gas emissions to be reduced to 80% of the 2005 level by the year 2050. Furthermore, new legislation under the Energy Independence and Security Act (EISA) requires that 36 billion gallons of renewable fuel be blended into transportation fuel by 2022. Solving these types of problems will require the fuel industry to shift away from petroleum fuels to biomass-derived oxygenated hydrocarbon fuels. These fuels are generated through different biological pathways, using different "bugs." The question of which fuel molecules should we be burning, and thus, which bugs should we be engineering, arises. To answer that question, a detailed understanding of the fuel chemistry under a wide range of operating conditions, i.e. temperature, pressure, fuel equivalence ratio, and fuel percentage, must be known. Understanding any fuel chemistry fully requires significant collaboration: experimental datasets that span a range of temperatures, pressures, and equivalence ratios, high-level ab initio quantum chemistry calculations for single species and reactions, and a comprehensive reaction mechanism and reactor model that utilizes the theoretical calculations to make predictions. A shortcoming in any of these three fields limits the knowledge gained from the others. This thesis addresses the third field of the collaboration, namely constructing accurate reaction mechanisms for chemical systems. In this thesis, reaction mechanisms are constructed automatically using a software package Reaction Mechanism Generator (RMG) that has been developed in the Green Group over the last decade. The predictive capability of any mechanism depends on the parameters employed. For kinetic models, these parameters consist of species thermochemistry and reaction rate coefficients. Many parameters have been reported in the literature, and it would be beneficial if RMG would utilize these values instead of relying on estimation routines purely. To this end, the PrIMe Warehouse C/H/O chemistry has been validated and a means of incorporating said data in the RMG database has been implemented. Thus, all kinetic models built by RMG may utilize the community's reported thermochemical parameters.

Download or read book Building Robust Chemical Reaction Mechanisms written by Jing Song and published by . This book was released on 2004 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: (Cont.) The author applied object-oriented technology and unified modeling language in system analysis, architecture design, and implementation of RMG. Therefore it is designed and developed into a robust software with good architecture and detailed documentation, so that this software can be easily maintained, reused, and extended. RMG is successfully applied to generate a reaction mechanism for n-butane low temperature oxidation, which includes a complex autoignition process. The model generated by RMG caught the fundamental phenomena of autoignition, and the predicted ignition delay time and many major products' yields are in very good agreement with experimental data. This is the first time that model generation software automatically generated such a complicated reaction mechanism without human interference, and provided precise predictions on ignition delay and major products yields consistent with experimental data.

Download or read book Automated Discovery of Important Chemical Reactions written by Colin Andres Grambow and published by . This book was released on 2020 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Innovations in chemistry are often informed by decades of accumulated chemical knowledge encoded into manually constructed reaction templates and rules of reactivity. Examples include retrosynthetic analysis for organic synthesis planning; chemical reaction mechanism generation for complex combustion, pyrolysis, and low-temperature oxidation processes; and elucidation of low-energy catalytic pathways. Nonetheless, all known chemistry is dwarfed by the vastness of chemical space, most of which still lies unexplored. De novo reaction discovery is rare but presents an enormous potential to uncover novel synthetic routes and key pathways in reaction mechanisms. Automated potential energy surface exploration has become a promising method to search for new reaction pathways, albeit at the expense of costly quantum mechanical calculations. Therefore, this thesis develops methods to enable more computationally efficient discovery while also correctly determining thermochemistry and kinetics to allow for the construction of accurate reaction mechanisms. By utilizing automated transition state finding algorithms based on quantum chemistry, the thesis assesses which algorithm is most viable for the efficient discovery of new reactions, and it identifies key pathways of an important ketohydroperoxide system. It demonstrates that quantum chemical data can be used with emerging machine learning methods to estimate molecular thermochemistry. Leveraging a large data set of low-quality data in combination with a small data set of high-accuracy data in a transfer learning approach enables predictions that significantly improve upon group additivity methods, which are common in automated mechanism generation, and upon machine learning models that only use density functional theory data. Furthermore, an automated workflow is developed to further enhance high-level quantum chemistry calculations using bond additivity corrections. While quantum chemistry calculations are incredibly useful at providing highly accurate data, their high cost—especially when applied to thousands of reaction pathways—limits their utility for discovering new chemistry. Therefore, this thesis improves the throughput of automated discovery via a combination of quantum chemistry data generation and reactivity prediction using deep learning. It automatically generates a data set of tens of thousands of elementary chemical reactions that are used to train a novel activation energy prediction model, which can quickly assess the importance of new reactions.

Download or read book Enabling Automatic Generation of Accurate Kinetic Models for Complicated Chemical Systems written by Kehang Han and published by . This book was released on 2018 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decades have seen much progress in predictive kinetic modeling. Reaction mechanisms have shown increased predictive capability, providing key insights into chemical transformations under conditions of interest. Coupled and integrated in multiscale-multiphysics models, reaction mechanisms help elucidate physical phenomena that are driven by chemical kinetics and are recognized as a necessary tool for chemical selection, reactor design and process optimization. These past kinetic modeling achievements have opened new opportunities for novel scientific applications in chemical kinetics community and encouraged kinetic modelers to study even more complex chemical systems. As one can expect, the system complexity significantly increases modeling cost in both reaction mechanism construction and simulation. Over the years we have seen formulation of various lumping strategies. Despite simplicity, the lumping strategy introduces an intrinsic error where the lumps contain molecules with very different reactivities. Frequently, oversimplified models using the kinetic parameters fitted from a very limited set of pilot experiments, resulting in poor accuracy in extrapolation. This thesis focuses on automated detailed kinetic modeling strategy using Reaction Mechanism Generator (RMG). RMG-generated models more faithfully represent the chemistry so they have superior extrapolation potential. But as system complexity increases, several computational limitations prevent RMG from converging. This thesis has made several contributions: reducing memory usage, boosting algorithm scalability, improving thermochemistry estimation accuracy, which eventually expand RMG's modeling capability toward large complex systems. These contributions are available to the kinetics community through the RMG software package. To demonstrate the improved modeling capability of RMG, the thesis also includes a large chemical application: heavy oil thermal decomposition under geological conditions via a C18 model compound, phenyldodecane. As an extension of RMG, the thesis also explores a promising alternative to detailed kinetic modeling when dealing with extremely large chemical systems: fragment-based kinetic modeling, which generates a reaction network in fragment space rather than molecule space. The thesis shows via a case study that the new method creates a much smaller reaction network but with similar prediction accuracy on feedstock conversion and products' molecular weight distribution compared to its counterpart model generated by RMG.

Download or read book Automatic Reaction Mechanism Generation written by Connie Wu Gao and published by . This book was released on 2016 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Growing awareness of climate change and the risks associated with our society's dependence on fossil fuels has motivated global initiatives to develop economically viable, renewable energy sources. However, the transportation sector remains a major hurdle. Although electric vehicles are becoming more mainstream, the transportation sector is expected to continue relying heavily on combustion engines, particularly in the freight and airline industries. Therefore, research efforts to develop cleaner combustion must continue. This includes the development of more efficient combustion engines, identification of compatible alternative fuels, and the streamlining of existing petroleum resources. These dynamic systems have complex chemistry and are often difficult and expensive to probe experimentally, making detailed chemical kinetic modeling an attractive option for simulating and predicting macroscopic observables such as ignition delay or CO2 concentrations. This thesis presents several methods and applications towards high fidelity predictive modeling using Reaction Mechanism Generator (RMG), an open source software package which automatically constructs kinetic mechanisms. Several sources contribute to model error during automatic mechanism generation, including incomplete or incorrect handling of chemistry, poor estimation of thermodynamic and kinetics parameters, and uncertainty propagation. First, an overview of RMG is presented along with algorithmic changes for handling incomplete or incorrect chemistry. Completeness of chemistry is often limited by CPU speed and memory in the combinational problem of generating reactions for large molecules. A method for filtering reactions is presented for efficiently and accurately building models for larger systems. An extensible species representation was also implemented based on chemical graph theory, allowing chemistry to be extended to lone pairs, charges, and variable valencies. Several chemistries are explored in this thesis through modeling three combustion related processes. Ketone and cyclic ether chemistry are explored in the study of diisoproyl ketone and cineole, biofuel candidates produced by fungi in the decomposition of cellulosic biomass. Detailed kinetic modeling in conjunction with engine experiments and metabolic engineering form a collaborative feedback loop that efficiently screens biofuel candidates for use in novel engine technologies. Next, the challenge of modeling constrained cyclic geometries is tackled in generating a combustion model of JP-10, a synthetic jet fuel used in propulsion technologies. The model is validated against experimental and literature data and succeeds in capturing key product distributions, including aromatic compounds, which are precursors to polyaromatic hydrocarbons (PAHs) and soot. Finally, oil-to-gas cracking processes under geological conditions are studied through modeling the low temperature pyrolysis of the heavy oil analog phenyldodecane in the presence of diethyldisulfide. This system is used to gather mechanistic insight on the observation that sulfur-rich kerogens have accelerated oil-to-gas decomposition, a topic relevant to petroleum reservoir modeling. The model shows that free radical timescales matter in low temperature systems where alkylaromatics are relatively stable. Local and global uncertainty propagation methods are used to analyze error in automatically generated kinetic models. A framework for local uncertainty analysis was implemented using Cantera as a backend. Global uncertainty analysis was implemented using adaptive Smolyak pscudospcctral approximations to efficiently compute and construct polynomial chaos expansions (PCE) to approximate the dependence of outputs on a subset of uncertain inputs. Both local and global methods provide similar qualitative insights towards identifying the most influential input parameters in a model. The analysis shows that correlated uncertainties based on kinetics rate rules and group additivity estimates of thermochemistry drastically reduce a model's degrees of freedom and can have a large impact on model outputs. These results highlight the necessity of uncertainty analysis in the mechanism generation workflow. This thesis demonstrates that predictive chemical kinetics can aid in the mechanistic understanding of complex chemical processes and contributes new methods for refining and building high fidelity models in the automatic mechanism generation workflow. These contributions are available to the kinetics community through the RMG software package.

Download or read book Mathematical Modelling of Gas Phase Complex Reaction Systems Pyrolysis and Combustion written by and published by Elsevier. This book was released on 2019-06-06 with total page 1034 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical Modelling of Gas-Phase Complex Reaction Systems: Pyrolysis and Combustion, Volume 45, gives an overview of the different steps involved in the development and application of detailed kinetic mechanisms, mainly relating to pyrolysis and combustion processes. The book is divided into two parts that cover the chemistry and kinetic models and then the numerical and statistical methods. It offers a comprehensive coverage of the theory and tools needed, along with the steps necessary for practical and industrial applications. Details thermochemical properties and "ab initio" calculations of elementary reaction rates Details kinetic mechanisms of pyrolysis and combustion processes Explains experimental data for improving reaction models and for kinetic mechanisms assessment Describes surrogate fuels and molecular reconstruction of hydrocarbon liquid mixtures Describes pollutant formation in combustion systems Solves and validates the kinetic mechanisms using numerical and statistical methods Outlines optimal design of industrial burners and optimization and dynamic control of pyrolysis furnaces Outlines large eddy simulation of turbulent reacting flows

Download or read book Automatic Generation and Analysis of Chemical Kinetic Mechanisms written by Matthew Sean Johnson and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many important processes in the world are controlled by chemical kinetics, from the combustion of fuels in engines, the production of polymers, the electrochemistry of batteries to biological processes. However, many if not most overall chemical processes do not occur in a single step reaction between reactants and products and can involve hundreds of different elementary reactions and intermediates. In many cases how well we can resolve and parametrize these elementary reactions and intermediates control ability to predict the behavior of the associated process. These systems of species reactions and their associated parameters are usually referred to as detailed kinetic mechanisms. Creating detailed kinetic mechanisms, however, requires us to determine both what reactions can happen in a given system and how fast they occur. This can be incredibly tedious an challenging to do by hand so it is often more practical to use automatic mechanism generators such as the Reaction Mechanism Generator (RMG) software. RMG allows us to build a workflow for generating and refining these mechanisms where we run RMG to generate a mechanism analyze the mechanism to determine important parameters and improve those parameters based on quantum chemistry calculations, experiments and literature, integrate the new data into RMG's estimators and rerun RMG to generate a new mechanism. This thesis presents a number of improvements to different aspects of this workflow and applications of this workflow. New faster and more advanced techniques and software are presented for analyzing chemical kinetic mechanisms. Improvements are presented for RMG's algorithm for selecting species and reactions to include in the mechanism. Improved techniques for generating, refining and computing phenomenological rate coefficients for pressure dependent networks are also presented. Additionally presented, is the RMG-database that manages estimation with RMG and a new machine learning based algorithm for estimating the rate coefficients of reactions. Lastly, an application of this workflow to generate a mechanism for the combustion and pyrolysis of methyl propyl ether and the extension and application of RMG to model the solid electrolyte interphase in lithium batteries are presented.

Download or read book Predictive Chemical Kinetics written by Joshua William Allen and published by . This book was released on 2013 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of petroleum-based fuels for transportation accounted for more than 25% of the total energy consumed in 2012, both in the United States and throughout the world. The finite nature of world oil reserves and the effects of burning petroleum-based fuels on the world's climate have motivated efforts to develop alternative, renewable fuels. A major category of alternative fuels is biofuels, which potentially include a wide variety of hydrocarbons, alcohols, aldehydes, ketones, ethers, esters, etc. To select the best species for use as fuel, we need to know if it burns cleanly, controllably, and efficiently. This is especially important when considering novel engine technologies, which are often very sensitive to fuel chemistry. The large number of candidate fuels and the high expense of experimental engine tests motivates the use of predictive theoretical methods to help quickly identify the most promising candidates. This thesis presents several contributions in the areas of predictive chemical kinetics and automatic mechanism generation, particularly in the area of reaction kinetics. First, the accuracy of several methods of automatic, high-throughput estimation of reaction rates are evaluated by comparison to a test set obtained from the NIST Chemical Kinetics Database. The methods considered, including the classic Evans-Polanyi correlation, the "rate rules" method currently used in the RMG software, and a new method based on group contribution theory, are shown to not yet obtain the order-of-magnitude accuracy desired for automatic mechanism generation. Second, a method of very accurate computation of bimolecular reaction rates using ring polymer molecular dynamics (RPMD) is presented. RPMD rate theory enables the incorporation of quantum effects (zero-point energy and tunneling) in reaction kinetics using classical molecular dynamics trajectories in an extended phase space. A general-purpose software package named RPMD-rate was developed for conducting such calculations, and the accuracy of this method was demonstrated by investigating the kinetics and kinetic isotope effect of the reaction OH + CH4 --> CH3 + H2O. Third, a general framework for incorporating pressure dependence in thermal unimolecular reactions, which require an inert third body to provide or remove the energy needed for reaction via bimolecular collisions, was developed. Within this framework, several methods of reducing the full, master equation-based model to a set of phenomenological rate coefficients k(T, P) are compared using the chemically-activated reaction of acetyl radical with oxygen as a case study, and recommendations are made as to when each method should be used. This also resulted in a general-purpose code for calculating pressure-dependent kinetics, which was applied to developing an ab initio model of the reaction of the Criegee biradical CH 200 with small carbonyls that reproduces recent experimental results. Finally, the ideas and techniques of estimating reaction kinetics are brought together for the development of a detailed kinetics model of the oxidation of diisopropyl ketone (DIPK), a candidate biofuel representative of species produced from cellulosic biomass conversion using endophytic fungi. The model is evaluated against three experiments covering a range of temperatures, pressures, and oxygen concentrations to show its strengths and weaknesses. Our ability to automatically generate this model and systematically improve its parameters without fitting to the experimental results demonstrates the validity and usefulness of the predictive chemical kinetics paradigm. These contributions are available as part of the Reaction Mechanism Generator (RMG) software package.

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.

Download or read book Analysis of Kinetic Reaction Mechanisms written by Tamás Turányi and published by Springer. This book was released on 2014-12-29 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chemical processes in many fields of science and technology, including combustion, atmospheric chemistry, environmental modelling, process engineering, and systems biology, can be described by detailed reaction mechanisms consisting of numerous reaction steps. This book describes methods for the analysis of reaction mechanisms that are applicable in all these fields. Topics addressed include: how sensitivity and uncertainty analyses allow the calculation of the overall uncertainty of simulation results and the identification of the most important input parameters, the ways in which mechanisms can be reduced without losing important kinetic and dynamic detail, and the application of reduced models for more accurate engineering optimizations. This monograph is invaluable for researchers and engineers dealing with detailed reaction mechanisms, but is also useful for graduate students of related courses in chemistry, mechanical engineering, energy and environmental science and biology.

Download or read book Uncertainty Analysis in Automatic Reaction Mechanism Generation written by Sarah Victoria Petway and published by . This book was released on 2006 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: (Cont.) Comparison of the model with experimental data allowed identification of two rate constants. At 673 K and 60 Torr, kC5H11+O2-->OH+C5HI0O = 1.9x 10-14 ± 6x 10-15 cm3/molecule-s, and kOH+C5H1I-C5HOI+H20 = 3.1 x 10-12±1 .5 x 10-2 cm3/molecule-s. The computer-generated model is consistent with two prior literature studies.

Download or read book Mathematical Modelling of Gas Phase Complex Reaction Systems Pyrolysis and Combustion written by and published by Elsevier. This book was released on 2019-06-21 with total page 1034 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical Modelling of Gas-Phase Complex Reaction Systems: Pyrolysis and Combustion, Volume 45, gives an overview of the different steps involved in the development and application of detailed kinetic mechanisms, mainly relating to pyrolysis and combustion processes. The book is divided into two parts that cover the chemistry and kinetic models and then the numerical and statistical methods. It offers a comprehensive coverage of the theory and tools needed, along with the steps necessary for practical and industrial applications. Details thermochemical properties and "ab initio" calculations of elementary reaction rates Details kinetic mechanisms of pyrolysis and combustion processes Explains experimental data for improving reaction models and for kinetic mechanisms assessment Describes surrogate fuels and molecular reconstruction of hydrocarbon liquid mixtures Describes pollutant formation in combustion systems Solves and validates the kinetic mechanisms using numerical and statistical methods Outlines optimal design of industrial burners and optimization and dynamic control of pyrolysis furnaces Outlines large eddy simulation of turbulent reacting flows

Download or read book Predictive Chemical Kinetics for Auto Ignition of Fuel Blends written by Nathan Wa-Wai Yee and published by . This book was released on 2018 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive chemical kinetics plays an important part in the study of chemical systems by reducing the need for expensive experiments. The size and complexity of modem chemical mechanisms increasingly require the use of automated mechanism generators, such as the Reaction Mechanism Generator (RMG). Use of these automated generators for creating quality chemical mechanisms necessitates accurate reaction rates. Unfortunately, the vast majority of kinetic parameters governing rate constants are not known. The goals of this thesis are the accurate estimation of kinetic parameters and its application to the prediction of auto ignition in fuel blends. At the molecular scale, quantum chemical methods can give kinetic coefficients with accuracy nearing those of experiments. Even when specific kinetic parameters are unavailable, rates can be evaluated by analogy to similar molecules. RMG uses an averaging scheme based on arranging functional groups in a hierarchical tree structure. We have been able to continue expansion of the database to species with nitrogen and sulfur, improve methods for structural representation, and showcase validation for thermochemistry and kinetic parameter estimates. Studying kinetics at the mechanistic level allows insight into the interaction between chemical reactions. Specifically, we have been interested in finding and analyzing the reaction pathways relevant to auto ignition, simplifying well-studied fuel mechanisms for propane and methanol. We were able to define clear stages of ignition and report the controlling chemistry during each stage. Understanding of these base fuels provides the basis to analyzing ignition for larger and more novel fuels. Finally, from a macroscopic perspective we studied ignition for blends of phenolic additives in gasoline. Chemical mechanisms generated by RMG were modeled in a variable volume reactor that emulate end gas conditions of the CRF engine used to evaluate Research Octane Number (RON). We predicted the effect each additive has on the timing of ignition, which were later proven to be reasonably accurate by experimental validation. The chemical pathways that affect the ignition were analyzed and discussed. Finally, we developed a framework for predicting several different aspects of potential fuel additives, which could help eliminate costly experiments by identifying unsuitable candidates before they are even synthesized.

Download or read book Modeling of Chemical Reactions written by R.W. Carr and published by Elsevier. This book was released on 2007-09-04 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling of Chemical Reactions covers detailed chemical kinetics models for chemical reactions. Including a comprehensive treatment of pressure dependent reactions, which are frequently not incorporated into detailed chemical kinetic models, and the use of modern computational quantum chemistry, which has recently become an extraordinarily useful component of the reaction kinetics toolkit. It is intended both for those who need to model complex chemical reaction processes but have little background in the area, and those who are already have experience and would benefit from having a wide range of useful material gathered in one volume. The range of subject matter is wider than that found in many previous treatments of this subject. The technical level of the material is also quite wide, so that non-experts can gain a grasp of fundamentals, and experts also can find the book useful. A solid introduction to kinetics Material on computational quantum chemistry, an important new area for kinetics Contains a chapter on construction of mechanisms, an approach only found in this book

Download or read book Integrated Pressure dependence in Automated Mechanism Generation written by David Michael Matheu and published by . This book was released on 2003 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: (Cont.) The approach includes in the mechanism only those pressure-dependent pathways important for the conditions of interest, but can find any potentially important pressure-dependent reaction. It works by building partial pressure-dependent reaction networks, step-by-step, in harmony with a rate-based termination criteria which rationally controls overall mechanism size. It uses a fast, approximate method, the Quantum-Rice-Ramsperger-Kassel/Modified-Strong-Collision (QRRK/MSC), to predict rate constants k(T, P) employing only high-pressure-limit rate rules, pressure-dependent network structure, and heat capacity estimates. The error incurred by screening the pressure- dependent networks to include only important sections is small and bounded. Successful applications to various systems, including reactions through cycloalkyl intermediates, are presented. Application of this tool to methane pyrolysis revealed a new, unexpected mechanism. It explained the decades-old mystery of methane autocatalysis at low conversion, a phenomenon which had defied all "by-hand" attempts at mechanism development. Such work hints at the predictive power inherent in the next generation of automated mechanism builders.