RESUMEN
This work presents new experimental viscosity and density data for aromatic and polyaromatic compounds in binary and ternary pyrene, 1-methylnaphthalene, and dodecane mixtures. The lack of experimental viscosity data for these mixtures requires the development of a new database, which is vital for understanding the behavior of mixtures in more complex systems, such as asphaltenes and fuels. The mixtures proposed in this work have been measured over a temperature range of (293.15 to 343.15) K at atmospheric pressure. Several mixture compositions have been studied at these conditions: 1.0, 2.5, 5.0, 7.5, 10.0, 12.5, and 15.0% pyrene mass fraction. The concentration of pyrene correlates with an increase in the viscosity and density values. At the lowest temperature in binary mixtures, the corresponding values reach 4.4217 mPa·s for viscosity and 1.0447 × 103 kg·m-3 for density, respectively. In ternary mixtures, the introduction of dodecane leads to the lowest maximum values of 3.5555 mPa·s for viscosity and 1.0112 × 103 kg·m-3 for density at the same temperature. The experimental data have been employed for the specific modification of viscosity models. These modifications could facilitate the prediction of the viscosity of mixtures that are more complex than those presented in this work. Various viscosity models have been employed, such as Linear, Ratcliff and Khan, modified UNIFAC-Visco, and Krieger-Dougherty. The settings in the models used reliably reproduce the experiment reliably. However, the Ratcliff model agrees excellently with the experiment, having a low standard deviation (2.0%) compared to other models. Furthermore, a model based on the equation of state of Guo is proposed to predict the viscosity values by modifying the specific parameters and adjusting them to the mixtures proposed in this work. The results from this study are compared to previous work, where pyrene, toluene, and heptane mixtures were analyzed. In this case, we find that the decrease of aggregation grade in the present systems is predicted by the model fixed in this work.
