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(2001)ThesisThe volatile aroma compounds in the green plant tissue and grain of five Australian rice varieties were studied in detail. Selected volatile aroma compounds, including 2-acetyl-1- pyrroline (AP), observed in three fragrant varieties, YRF 203, YRF 205 and Kyeema and two non-fragrant varieties, Pelde and Langi, were compared. The development of volatile aroma compounds in the rice plant was followed from early tillering to full maturity. The effect of nitrogen fertilisation level on the volatile aroma compound composition was evaluated. Volatile compounds were extracted by the Likens-Nickerson simultaneous distillation-extraction technique. GC-MS, GC-FID and GC-O were used to separate, identify and quantify the volatile aroma compounds in the rice grain and plant extracts. The 127 volatile compounds identified in rice plant tissue and grain included 28 aldehydes, 23 ketones, 20 alcohols, 8 phenolic compounds, 17 heterocyclic compounds, 7 hydrocarbons, 2 sulphur compounds, 5 terpenes, 11 acids and 6 esters. Most of the homologous series of aliphatic aldehydes, ketones and alcohols that were identified in cooked rice were also identified in plant tissue, but in different proportions. AP was the major volatile compound in fragrant rice plant and grain and was probably the major factor that contributed to the difference between fragrant and non-fragrant varieties. Relative levels of AP in rice plant correlated with the relative level of AP in the rice plant of the same variety. Therefore, AP concentration in rice plant tissue can be used as an early indicator of aroma in rice grain. The non-fragrant whole rice contained more pent-l-en-3- one, D-Limonene and hepta-2,4-dienal than fragrant rice, while fragrant rice contained more but-2-enal, hex-2-en-l-ol, pyridine and AP. Milled fragrant rice contained more but- 2-enal, pyridine, AP and pyrrole than non-fragrant rice. The increase in nitrogen fertilisation resulted in an increase of AP in rice plant tissue and grain in fragrant varieties. In addition, the increase in nitrogen fertilisation resulted in an increase in pyridine, hepta-2,4-dienal and 2-methoxyphenol in the mature rice plant and an increase of pentanal, hexanal, pyridine, heptan-2-one, pentan-l-ol, hexan-l-ol, oct-l-en-3- ol and furfural in milled rice. AP concentration was relatively high at the beginning of plant development and then decreased during plant development. Therefore, AP can be detected at the early stages of plant development, without having to wait until grain maturity.
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(2008)ThesisThe present study is focused on two studies. The kinetics 0: methane steam reforming over a Ni/MgO catalyst at high pressure is reported in the first study. The second study is focused on the steam iron process over promoted Fe-oxide based catalyst using four different reductants; H2, H2 /CO mixture, CH4 and CH4 /C02 mixture. A kinetic study of methane steam reforming over a Ni/MgO catalyst at high pressure was carried out. The kinetic orders of methane and steam at 40 bars and 600 QC were found to be 0.82 and 0.62 respectively. 1~he estimation of energy of activation of the process was found to be 106 KJ/mol. T11e reaction rate data was explained by a Langmuir - Hinshelwood - Hougen - Wastson model. Four differe11t reductants (H2, H2 /CO mixture, CI-4 and CH4 /C02 mixture) , were applied for the study of the steam iron process. A study of the steam iron process using H2 as reductant focused on the first reduction of 4%Cr203 - 96% Fe203 with H2. The first reduction was found to be composed of a two step reduction up to 550 QC. The estimation of energy of activation for the process was found to be 92.4 KJ/mol and 68.2 KJ/mol respectively. The study of the steam iron process using H2 ICO mixture as reductant over 4%Cr203 - 96% Fe2O3 found that FeO was an intermediate for the reduction of Fe203 with H2ICO mixture to Fe metal. The application of methane as reductant for the steam iron process gave the worst results. As a result, NiO was added to Cr203 -Fe203 to increase the activity. Carbon formation on NiO also was found to be a serious problem. In order to minimize carbon formation on NiO, CO2 was introduced in a mix with CI-4 for the oxidation of deposited carbon during the reduction step. Although the introduction of CO2 can suppress carbon formation, the strong oxidation of reduced iron oxide by water formed during the reduction process coupled to the l1igher favorable reaction of the water gas shift reaction adversely affects the complete reduction of iron oxide to iron metal.
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(2007)ThesisGreenhouse gases including carbon dioxide are formed by the consumption of carbon anodes during aluminium production, making it a major contributor to global emissions. This consumption necessitates replacement of the anodes in electrolysis cells every 2-3 weeks. A solution to the environmental and economic problems posed may be found in an inert anode which facilitates direct decomposition of alumina to aluminium and oxygen. Finding a material which is stable in the aggressive high temperature electrolyte poses a major materials engineering challenge. In this study, apparatus was designed and constructed to allow cermets to be manufactured in the laboratory, and a method of establishing electrical contact developed. Additionally, apparatus was designed to perform high temperature conductivity measurements on the cermets. Nickel ferrite-nickel oxide-copper-silver cermets were prepared and conductivity measured. No significant change in the activation energy of the conduction process was observed for cermets with 40wt% excess NiO compared to those with no excess. No significant difference in conductivity was observed between the compositions at cell operating temperatures. Voltammetric techniques were used to identify anode processes. High anodic currents associated with oxidation of anode constituents were observed repeatedly, the magnitude of which could not simply explained by oxidation of the metal phase. This suggested the formation of other reduced species during sintering (confirmed by thermodynamic analysis). Gaseous oxidation products were confirmed at the anode at potentials expected for oxygen evolution, and the application of high potentials (>4V vs Al/A13+) was found to passivate the cermets. Voltammetry and chemical microanalysis (using scanning electron microscopy (SEM) with energy dispersive x-ray spectrometry (EDS)) showed that copper in the cermets was depleted at the anode surface, apparently by oxidation then dissolution into the electrolyte. The inclusion of silver powder into the cermets was not found to improve the corrosion resistance of the cermets, existing almost entirely as a discrete phase. Preliminary SEM and EDS results highlighted several areas for further investigation regarding the compounds formed during sintering and electrolysis and the anode corrosion mechanisms. Of particular interest were a copper nickel oxide formed during sintering and complex oxyfluorides containing anode and bath constituents, formed during electrolysis.
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(2007)ThesisThis study set out to establish artificial neural networks (ANN) as an alternate to regression methods (multiple linear, principal components and partial least squares regression) to predict consumer liking from trained sensory panel data. The study has two parts viz., I) Flavour study - evaluation of ANNs to predict consumer flavour preferences from trained sensory panel data and 2) Fragrance study – evaluation of different ANN architectures to predict consumer fragrance liking from trained sensory panel data. In this study, a multi-layer feedforward neural network architecture with input, hidden and output layer(s) was designed. The back-propagation algorithm was utilised in training of neural networks. The network learning parameters such as learning rate and momentum rate were optimised by the grid experiments for a fixed number of learning cycles. In flavour study, ANNs were trained using the trained sensory panel raw data as well as transformed data. The networks trained with sensory panel raw data achieved 98% correct learning, whereas the testing was within the range of 28 -35%. A suitable transformation methods were applied to reduce the variations in trained sensory panel raw data. The networks trained with transformed sensory panel data achieved between 80-90% correct learning and 80-95% correct testing. In fragrance study, ANNs were trained using the trained sensory panel raw data as well as principal component data. The networks trained with sensory panel raw data achieved 100% correct learning, and testing was in a range of 70-94%. Principal component analysis was applied to reduce redundancy in the trained sensory panel data. The networks trained with principal component data achieved about 100% correct learning and 90% correct testing. It was shown that due to its excellent noise tolerance property and ability to predict more than one type of consumer liking using a single model, the ANN approach promises to be an effective modelling tool.
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