To develop reliable models for the densities and viscosities of biodiesel fuel, reliable data for the pure fatty acid esters are required. Densities and viscosities were measured for seven ethyl esters and eight methyl esters, at atmospheric pressure and temperatures from (273.15 to 363.15) K. A critical assessment of the measured data against the data previously available in the literature was carried out. It is shown that the data here reported presents deviations of less than 0.15 % for densities and less than 5 % for viscosities. Correlations for the densities and viscosities with temperature are proposed. The densities and viscosities of the pure ethyl and methyl esters here reported were used to evaluate three predictive models. The GCVOL group contribution method is shown to be able to predict densities for these compounds within 1 %. The methods of Ceriani and Meirelles (CM) and of Marreiro and Gani (MG) were applied to the viscosity data. It is shown that only the first of these methods is able to provide a fair description of the viscosities of fatty acid esters.

Density is an important biodiesel parameter, with impact on fuel quality. Predicting density is of high relevance for a correct formulation of an adequate blend of raw materials that optimize the cost of biodiesel fuel production while allowing the produced fuel to meet the required quality standards. The aim of this work is to present new density data for different biodiesels and use the reported data to evaluate the predictive capability of models previously proposed to predict biodiesel or fatty acid methyl ester densities. Densities were measured here for 10 biodiesel samples, for which detailed composition is reported, at atmospheric pressure and temperatures from 278.15 to 373.15 K. Density dependence with temperature correlations was proposed for the biodiesels, and isobaric expansivities are presented. The new experimental data presented here were used along with other literature data to evaluate predictive density models, such as those based on Kay’s mixing rules and the GCVOL group contribution method. It is shown that Kay’s mixing rules and a revised form of the GCVOL model are able to predict biodiesel densities with average deviations of only 0.3%. A comparison between biodiesel densities produced from similar vegetable oils, by different authors, highlights the importance of knowing the detailed composition of the samples. An extension of GCVOL for high pressures is also proposed here. It is shown that it can predict the densities of biodiesel fuels with average deviations less than 0.4%.

Biodiesels have several known components in their composition. The majority of components is well described in the literature, but a minority of components are poorly characterized. These are however required to develop reliable models to predict the biodiesel behavior. This work considers minor components of biodiesel: the polyunsaturated compounds (in C18), the monounsaturated (in C16, C20, and C22), and the long-chain saturated esters. In this work, densities and viscosities of pure fatty acid ester minor components of biodiesel fuel were measured (three ethyl esters and seven methyl esters), at atmospheric pressure and temperatures from (273.15 to 373.15) K. Correlations for the densities and viscosities with temperature are proposed. Three predictive models were evaluated in the prediction of densities and viscosities of the pure ethyl and methyl esters here reported. The GCVOL group contribution method is shown to be able to predict densities for these compounds within 1.5 %. The methods of Ceriani et al. (CM) and of Marrero et al. (MG) were applied to the viscosity data. The first show a better predictive capacity to provide a fair description of the viscosities of the minority esters here studied.

Viscosity is an important biodiesel parameter, subject to specifications and with an impact on the fuel quality. A model that could predict the value of viscosity of a biodiesel based on the knowledge of its composition would be useful in the optimization of biodiesel production processes and the planning of blending of raw materials and refined products. This work aims at evaluating the predictive capability of several models previously proposed in the literature for the description of the viscosities of biodiesels and their blend with other fuels. The models evaluated here are Ceriani's, Krisnangkura's, and Yuan's models, along with a revised version of Yuan's model proposed here. The results for several biodiesel systems show that revised Yuan's model proposed provides the best description of the experimental data with an average deviation of 4.65%, as compared to 5.34% for Yuan's model, 8.07% for Ceriani's model, and 7.25% for Krisnangkura's model. The same conclusions were obtained when applying these models to predict the viscosity of blends of biodiesel with petrodiesel.

Density is one of the most important biodiesel properties, because engine injection systems (pumps and injectors) must deliver an amount of fuel precisely adjusted to provide a proper combustion while minimizing greenhouse gas emissions. The pressure influence in fuel density has become particularly important with the increased use of modern common rail systems, where pressures can reach 250 MPa. Nevertheless, besides its importance, little attention has been given to high-pressure biodiesel densities. In fact, there are almost no reports in the literature about experimental high-pressure biodiesel density data. To overcome this lack of information, in this work, new experimental measurements, from 283 to 333 K and from atmospheric pressure to 45 MPa, were performed for methyl laurate, methyl myristate, and methyl oleate, for methyl biodiesels from palm, soybean, and rapeseed oils, and for three binary and one ternary mixture of these oils. Following previous works, where the cubic-plus-association equation of state (CPA EoS) was shown to be the most appropriate model to be applied to biodiesel production and purification processes, the new high-pressure experimental data reported here were also successfully predicted with the CPA EoS, with a maximum deviation of 2.5%. A discussion about the most appropriate CPA pure compound parameters for fatty acid methyl esters is also presented.

The solubilities of tetracycline hydrochloride, moxifloxacin hydrochloride, and ciprofloxacin hydrochloride were measured in several solvents, such as water, ethanol, 2-propanol, and acetone, in the temperature range of 293.15-323.15 K for ciprofloxacin.HCl and moxifloxacin.HCl and 288.15-310.15 K for tetracycline. All the antibiotics have the same solubility order; that is, they are more soluble in water than in ethanol, and more soluble in ethanol than in 2-propanol and acetone. The solubility in water is ∼3 orders of magnitude higher than that in acetone. The modeling of the experimental solid-liquid equilibria (SLE) data, using the NRTL and UNIQUAC models, proves that these models can correlate the solubility of studied antibiotics satisfactorily in the temperature range for which experimental data are available, with the UNIQUAC model generally being slightly superior to the NRTL model, when only two adjustable parameters are used for each binary system.

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in Wiley InterScience (www.interscience.wiley.com).Data for the mutual solubilities of fatty acid þ water mixtures are scarce and so measurements for seven fatty acid (C 5 -C 10 , C 12 ) þ water systems were carried out. This new experimental data was successfully modelled with the cubic plus association EoS. Using data from C 6 to C 10 and the Elliot's cross-associating combining rule a correlation for the k ij binary interaction parameter, as a function of the acid chain length, is proposed. The mutual solubilities of water and fatty acids can be adequately described with average deviations inferior to 6% for the water rich phase and 30% for the acid rich phase. Furthermore, satisfactory predictions of solid-liquid equilibria of seven fatty acids (C 12 -C 18 ) þ water systems were achieved based only on the k ij correlation obtained from liquid-liquid equilibria data.