Biodiesel Density: Experimental Measurements and Prediction Models

Abstract

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%.

Correlation and prediction of biodiesel density for extended ranges of temperature and pressure

“…For the case of minority FAMEs and FAEEs the density could be predicted within a deviation of 1.5%, except for the linolenate esters at high temperatures, again due to the poor description of the polyunsaturation effect on densities. Pratas et al [36] also applied the original and the extended GCVOL models to 18 biodiesel samples of soy, rapeseed, palm, cottonseed, jatropha, and mixtures thereof at temperatures between 273.15 and 373.15 K and densities from 815 to 898 kg m À3 , and obtained overall ARDs of 0.6% and 2.7% for the original and the extended GCVOL, respectively. To solve the precision lack for the polyunsaturation ester effect, Pratas et al [36] found new parameter values A i , B i , and C i relative to the double bond (ACH@) contribution, based on density data measured for FAMEs [55,56].…”

“…Pratas et al [36] also applied the original and the extended GCVOL models to 18 biodiesel samples of soy, rapeseed, palm, cottonseed, jatropha, and mixtures thereof at temperatures between 273.15 and 373.15 K and densities from 815 to 898 kg m À3 , and obtained overall ARDs of 0.6% and 2.7% for the original and the extended GCVOL, respectively. To solve the precision lack for the polyunsaturation ester effect, Pratas et al [36] found new parameter values A i , B i , and C i relative to the double bond (ACH@) contribution, based on density data measured for FAMEs [55,56]. This revised variant of GCVOL was applied to the 18 biodiesels leading to a decrease in the overall ARDs to 0.25% in density, corresponding to %2 kg m À3 [36].…”

“…The composition of biodiesel is a crucial issue for suitable application of the thermodynamic property models. Pratas et al [36] have shown that discrepancies in density data reported by different authors are usually due to differences in the oil composition, and not caused by experimental errors during measurements. Thus, biodiesel detailed composition must be known for reliable prediction of their densities.…”

“…For FAMEs at atmospheric pressure, deviations in density were less than 0.5%, and for biodiesels studied by Pratas et al [1] predicted densities were all within 1% deviation. Pratas et al [36] extended a group contribution method developed for the prediction of molar volume (GCVOL) under high pressure. The prediction of biodiesel density with this method was made with relative deviations between 0.2% and 0.7%.…”

“…One of them was derived from the GMA EoS. The predictive GCVOL for high pressure used by Pratas et al [36] was also applied to the fuels that constituted the built database. The relative deviations of the predicted densities against experimental data were calculated for accurate evaluation of the different predictive methods.…”