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The ever-increasing focus of policy-makers on environmental issues are pushing the combustion community towards making combustion cleaner by optimizing the combustion equipment in order to reduce emissions, improve efficiency and satisfy the increasing energy demand. A major part of this involves advancing modelling capabilities of these complex combustion systems, which is a combination of computational fluid dynamics with detailed chemical kinetic models. A chemical kinetic model comprises of a series of elementary reactions with corresponding kinetic rate parameters and species thermodynamic and transport data. The predictive capability of these models depends on the accuracy to which individual chemical reaction rates, thermodynamic and transport parameters are known. A minor fraction of the rate constants and thermodynamic properties in the widely used kinetic mechanisms are experimentally derived or theoretically calculated. The remaining are approximated using rate rules and group additivity methods respectively for rate constants and thermodynamic properties. Recent works have highlighted the need for error checking when preparing such models using the approximations, but a useful community tool to perform such analysis is missing. In the initial part of this work, we developed a simple online tool to screen chemical kinetic mechanisms for bimolecular reactions exceeding collision limits. Furthermore, issues related to unphysically fast time scales can remain an issue even if all bimolecular reactions are within collision limits. Therefore, we also presented a procedure to screen ultra-fast reaction time scales using computational singular perturbation (CSP). The screening of kinetic models is a necessary condition, however, not a sufficient one. Therefore, exploring new approaches for the simulation of complex chemically reacting systems are needed. This work focuses on developing new methods for estimating thermodynamic data efficiently and accurately, thereby increasing the compliance of forth-mentioned screening. Machine Learning (ML) has been increasingly becoming a tool of choice for regression, replacing traditional function fittings. Group additivity incorporates simple functions and derive constants with a certain existing data and use these functions to estimate the unknown values. ML algorithms does the same without fixing a specific function there by letting algorithm to learn the non-linearity from the training data itself. With the new data coming in with time, ML algorithms learn better and improves over time, whereas this need not necessarily happen with traditional methods. In the first part of the study, data for standard enthalpy is collected from the literature sources and ML models are built on these databases. Two different models were built and studied for a straight-chain species and cyclic species dataset. Molecular descriptors are used as the datasets collected from literature are small for using any sparse representations as input. As expected, we observed a good improvement above group additivity method for these ML models. The improvement is observed to be more significant for cyclic species. With the motivation of ML models showing benefit over the group additivity method, a step further was taken. A homogenous and accurate dataset is necessary for building a ML model that can be used for generating the thermodynamic data for kinetic models. With this in mind, an accurate database for thermodynamic data is built from ab-intio calculations. The species in the dataset are taken from a detailed and well established mechanism to cover all the species in a typical kinetic mechanism. The calculations are performed at a high level of accuracy, in comparison to other similar datasets in literature. In the later part of this work, the dataset developed using ab-inito calculations is used for developing ML models. Unlike the ML models built from the literature datasets, this database consists of all the thermodynamic data required for kinetic models viz. standard enthalpy and standard entropy and heat capacity at 300 K and higher temperatures. To numerically mimic real gasoline fuel reactivity, surrogates are proposed to facilitate advanced engine design and predict emissions by chemical kinetic modelling. However, chemical kinetic models could not always accurately predict non-regular emissions, e.g. aldehydes, ketones and unsaturated hydrocarbons, which are important air pollutants. Therefore, we propose to use machine-learning algorithms directly to achieve better predictions, circumventing the kinetic models. Combustion chemistry of fuels constituting of 10 neat fuels, 6 primary reference fuels (PRF) and 6 FGX surrogates were tested in a jet stirred reactor. Experimental data were collected in the same setup to maintain data uniformity and consistency. Measured species profiles of methane, ethylene, propylene, hydrogen, carbon monoxide and carbon dioxide are used for machine-learning model development. The model considers both chemical effects and physical conditions. Chemical effects are described as different functional groups, viz. primary, secondary, tertiary, and quaternary carbons in molecular structures, and physical conditions as temperature. Both the Machine-learning models used in this study showed a good prediction accuracy. By expanding the experimental database, machine-learning models can be further applied to many other hydrocarbons in future work, for the direct predictions.

The purpose of this investigation was to apply the techniques of chemical kinetics to the transfer of acetone between water and toluene phases, and thereby attempt to develop mechanisms and evaluate resistances to such transfer. An extraction cell was constructed which consisted of a 20-liter glass battery jar with independently driven impellers in each phase and a stationary phase divider at the interface. Extraction tests were conducted on the toluene-acetone-water system in the cell at 4, 15, and 30℃ at initial acetone concentrations ranging from 0.008 to 0.035 gram acetone per gram solvent. In all tests the stirrer bars were in contra-rotation at approximately 60 revolutions per minute. Initial unidirectional transfer rates were correlated with initial acetone concentrations and the correlations were used to predict the net rate of mass transfer at other than initial conditions. The results of this investigation led to the following conclusions: The flux of acetone from toluene solution to pure water, gm/min, sq cm, at 30℃ (F_t) may be represented by the equation F_t = 0.0403 C_t where C_t is gm acetone/gm toluene. The flux of acetone from an aqueous solution to pure toluene at 30℃ may be represented by the equation F_w = 10 (- 0.0333 + 43.34 C_w) / 10⁴ for aqueous acetone concentrations between 0.010 and 0.035 gram acetone per gram water, and by F_w = 0.0251 C_w for aqueous acetone concentrations below 0.010 gram acetone per gram water. For the range of concentration and driving force studied, the transfer of acetone between solutions in toluene and in water at 30℃ may be evaluated as the difference in the unidirectional fluxes. A mechanism for the transfer of acetone between toluene and water is proposed which involves, for transfer in each direction, (1) eddy diffusion of the acetone to the region of the interface, (2) transfer of a small amount of solvent from the opposite side of the interface, (3) a change in solvation of the acetone involving solvent from the opposite phase dissolved at the interface, (4) molecular diffusion into the opposite phase, and (5) eddy diffusion of the solvated acetone away from the region of the interface into the bulk of the receiving phase. Neither accepted theories, which predict that the coefficient of mass transfer will vary as a simple function of molecular diffusivity, nor correlations based on the rate of stirring and physical properties of the phases accounted for the large increase in the flux of acetone from water to toluene with increasing initial acetone concentrations in the aqueous phase. / Ph. D.

This paper proposes and compares some statistical methods for the analysis of chemical reaction rate data. / Master of Science

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The kinetics of the gas-phase reactions of chlorine atoms with fluoromethane (CH3F) and fluoromethane-d3(CD3F) were tested experimentally. The relative rate method was applied by using CH4 as the reference compound for fluoromethane (CH3F) and CH4 and CH3F as the reference compound for fluoromethane-d3(CD3F). The rate constants for H-abstraction from CH3F and D-abstraction from CD3F were measured at room temperature and a total pressure of 920 Torr using Ar as a diluent. The rate constants are described by the expressions: kH= (3.50±0.52) x 10-13 cm3 molecule-1 s-1 and kD=(5.0±0.51) x 10-14 cm3 molecule-1 s-1. The kinetic isotope effect, equal to the ratio kH/kD, was found to be 7.0±1.2 at room temperature.

This thesis is concerned with the study of electrode kinetics, which we shall examine via comparison between theory with experiment. As such the first two chapters outline the basic principles of electrochemical experiments and their simulation. First, we examine the properties of voltammetry at porous electrodes by means of both simulations and experiments. We then introduce the symmetric Marcus-Hush (SMH) model of electrode kinetics as an alternative to the empirical Butler-Volmer model. First, we examine different methods for modeling the voltammetry of kinetically inhomogeneous electroactive monolayers. Next, we perform a critical evaluation of the SMH model for solution-phase systems through extensive comparison to experiments under diffusion-only and convective mass transport conditions using both cyclic and square wave voltammetry. The model is compared with the Butler-Volmer model throughout and is ultimately found to be poorly suited to the parameterisation of electrode kinetics, despite its foundations in the microscopic Marcus theory. We then introduce the asymmetric Marcus-Hush model, which removes the assumption that the Gibbs energy curves for reactant and product have the same curvature. This modification results in an additional parameter which quantifies the asymmetry of the system. A similar evaluation of this model is then undertaken for both surface-bound and solution phase systems and the asymmetric model is found to be a great deal more successful than its symmetric predecessor. Finally we outline a novel technique for extracting kinetic information directly from experimental cyclic voltammetry. The method is simple to implement and is general to all electrode geometries with one-dimensional symmetry.

Asymmetric phase-transfer catalysis is a powerful technique that enables a wide range of transformations under mild conditions, often using inexpensive and environmentally benign reagents. By extending the applications of phase-transfer catalysis we have developed a highly diastereo- and enantioselective synthesis of functionalized indolines bearing two contiguous stereocentres, one of which is quaternary and all carbon, in a single synthetic step. The reaction proceeds with complete diastereoselectivity and with high levels of enantioselectivity (up to 99% ee). Despite the development of phase-transfer catalysis as a primary synthetic tool in organic synthesis, the mechanistic understanding of these reactions still remains a challenge, due mainly to the difficulty of studying the complex multi-phase systems. Therefore, a further aim of this project was to understand the reaction mechanism of our phase-transfer catalysed transformation. Investigations into the mechanism of our phase-transfer catalysed reaction have been carried out by studying the reaction kinetics. These have shown that the reaction follows a sigmoidal curve with an induction period present. A detailed kinetic investigation was carried out which demonstrated that an autocatalytic mechanism is operational.

Thesis (MIng) --Stellenbosch University, 1999. / ENGLISH ABSTRACT: A method was developed at the Atomic Energy Corporation of South Africa (AEC) for the plasma based production of tetrafluoroethylene (TFE). The process involves the feeding of carbon particles into a direct-current CF4 plasma. The resultant plasma gas is quenched rapidly to obtain TFE and other fluorocarbons. The mixing of the particles with the plasma gas is very important in order to achieve a high C:F-ratio in the gas phase, which promotes the desired reactions. The gas enthalpy in the reactor is a governing factor in the TFE yields that are obtained. In this study research was done on particle mixing and the enthalpy distribution in the laboratory scale reactor. An enthalpy probe was used as the main diagnostic tool. Results indicated that particle mixing is quite uniform throughout the reactor. A basic one-dimensional mechanistic model of the reactor was also expanded to assist in· the scale-up of the process. In its present form the model is adequate for predicting trends in the reactor. The model could still be expanded further to include reaction kinetics and internal heat transfer in the particles. Considering the restrictions of the model, satisfactory agreement was obtained between the model and experimental results. / AFRIKAANSE OPSOMMING: 'n Proses vir die plasmagebaseerde produksie van tetrafluoroetileen (TFE) is deur die Atoomenergiekorporasie van Suid-Afrika (AEK) ontwikkel. Koolstofpartikels word in 'n gelykstroomCF4- plasma gevoer en die resulterende plasmagas word vinnig geblus ten einde TFE en ander fluoor-koolstofverbindings as produkte te verkry. Goeie vermenging van die koolstofpartikels met die plasmagas is van uiterste belang ten einde 'n hoe C:F-verhouding, wat die gewenste reaksies bevorder, in die gasfase te verkry. Die entalpie van die plasmagas in die reaktor is 'n bepalende faktor in die opbrengs TFE wat verkry word. Vir die doel van hierdie werkstuk is navorsing op laboratoriumskaal gedoen oor partikelvermenging en die entalpie-verspreiding in die reaktor. Die hoof diagnostiese apparaat wat vir die doel aangewend is, is die entalpiesonde. Resultate toon dat partikelvermenging naastenby uniform deur die reaktor voorkom. Verder is 'n basiese een-dimensionele meganistiese model van die reaktor uitgebrei ten einde van nut te wees in die opskaling van die proses. In sy huidige vorm is die model voldoende om algemene neigings in die reaktor te voorspel. Die model kan nog verder uitgebrei word om reaksie-kinetika en interne hitte-oordrag in die partikels in te sluit. Die beperkings van die model in ag genome, is ooreenstemming tussen die model en eksperimentele resultate egter bevredigend.

published_or_final_version / Chemistry / Doctoral / Doctor of Philosophy