PPG - Engenharia Química
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- ItemSomente MetadadadosEstudo termodinâmico e modelagem matemática do equilíbrio líquido-vapor de misturas envolvendo Diesel e Biodiesel(Universidade Federal de São Paulo (UNIFESP), 2020-03-02) Melo, Edivaldo Bernardino De [UNIFESP]; Falleiro, Rafael Mauricio Matricarde [UNIFESP]; Universidade Federal de São PauloThe use of biodiesel has been growing in recent years. In Brazil there are laws that influence the addition of biodiesel to diesel oil. Knowing Liquid-Vapor Balance (ELV) data from these mixtures is essential to obtain improvements in separation, transportation and storage processes. Currently, ELV data from mixtures formed by components of the diesel/biodiesel mixture are scarce. Thus, the objective of this work was to experimentally determine ELV data involving hydrocarbons and ethyl esters at low pressures, and to model these data, via thermodynamics and Artificial Neural Networks. The data were obtained through Differential Exploratory Calorimetry (CSD), a technique that provides accurate results; in a short time and with small amounts of samples. Binary systems: hexadecane + ethyl myristato; dodecylbenzene + ethyl oleate; dodecylbenzene + ethyl stearate; ethyl laurato + dodecylbenzene; were obtained via CSD at a pressure of 2.67 kPa. Since these are unpublished systems, the UNIFAC predictive model was used to predict the equilibrium curve, the highest mean relative deviation found was 1.83%. The UNIQUAC and NRTL models were adjusted to the ELV data, and their binary parameters were calculated. Os valores dos coeficientes de atividade para o sistema: hexadecano + miristato de etila foram próximos de 1,0, indicando um comportamento próximo do ideal. The systems: dodecylbenzene + ethyl oleate, dodecylbenzene + ethyl stearate and ethyl laurate + dodecylbenzene, the values obtained were of the order of 3.0 for some regions, indicating a greater deviation from the ideality of the liquid phase. Artificial Neural Networks were used to calculate the ELV temperature from the molar fraction of the most volatile component in the mixture, critical temperature, critical pressure, acentric factor and mass connectivity index of each substance. For this, experimental data of binary systems formed by the compounds were used: fatty acids, ethyl esters and hydrocarbons. Two Neural Models were obtained and used in the prediction of the ELV of the boiling temperature of the mixture. Neural Model I presented for the myristic systems of ethyl + palmitic acid; ethyl palmitate + sauric acid a mean relative deviation respectively in relation to the UNIFAC model. Neural Model II presented for the hexadecane + ethyl myristato systems; dodecylbenzene + palmitic acid ethyl stearate, an average relative deviation of 5.02% and 2.29% respectively in relation to the data obtained via CSD.