Badano kinetykę termicznego rozkładu NiSO4 6H2O w warunkach nieizotermicznych. Wyróżniono trzy etapy procesu rozkładu, dla których wyznaczono zależności stopnia przemiany od temperatury. Analizę wyników przeprowadzono metodą sieci neuronowych. Opracowano modele kinetyczne najlepiej zgodne z wynikami badań doświadczalnych i wyznaczono parametry równania Arrheniusa A i E.
EN
Kinetics of NiSO4 6H2O thermal decomposition under non-isothermal conditions was investigated. Three stages of the decomposition process where distinguished and for each of them the dependences of the conversion degree on temperature were determined. Data analysis was performed using artificial neural networks. Kinetic models best predicting the experimental data were established and the parameters A and E of the Arrhenius equation were evaluated.
2
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The results of the research on the oxidation of TiC0.6, TiC0.8 and TiC0.97 carbides in dry air and under non-isothermal conditions are presented. The measurements were carried out at the following heating rates: 2; 5 and 10 K/min. The mass of the used samples was within the range of 4.70÷5.41 mg. It was found that the investigated process occurred in three stages. The Coats and Redfern equation was applied. The g(alpha) functions (kinetic models) of the best accuracy for individual steps and the kinetic parameters of the Arrhenius equation were determined. The reactivity of TiCx in the oxidation process in dry air was assessed from the obtained results. The reaction rate was assumed as the reactivity measure.
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The results of the investigation on thermal decomposition of CuSO4 · 5H2O under the non-isothermal conditions are presented. The measurements were carried out at the following heating rates: 2, 4, 6, 8 and 10 [K/min]. The mass of the used samples was about 30mg. The process occurred in five steps. Three of them were associated with dehydration and the two of them with the decomposition of the anhydrous salt. The dependencies of the conversion degree on temperature were determined for every stage of the process. They were the basis of the presented quantitative description . The Coats and Redfern equation was applied. The g(alpha) function (kinetic model) of the best accuracy for the individual step and the kinetic parameters of the Arrhenius equation were determined. The identification of the kinetic models was carried out by means of the artificial neural networks. A and E coefficients were evaluated using statistical methods. The described conversions were assumed as stochastic processes.
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