Assessing the Accuracy of Predicting the Cloud Point of Biodiesel Using Ideal Solution Theory

Biodiesel is a promising replacement for petroleum based diesel in a number of different applications. Although biodiesel offers numerous advantages over petrodiesel, one of its major disadvantages is its inferior cold flow properties. Previous work has shown that the challenge with the cold flow properties is strongly correlated to the composition of the biodiesel, which primarily contains fatty acid methyl esters (FAME) and fatty acid ethyl esters (FAEE). Taking advantage of this fact, researchers have used a wide variety of thermodynamic models of varying complexities to conduct solid/liquid equilibrium analysis to estimate the cloud point of the biodiesel. The major issues that can cause variations in these models can include such parameters as degree of saturation, presence of non-FAMEs and co-crystallization. This work comprehensively looks at cloud point data from a wide variety of sources and analyzes them using the simplest model, the ideal solution theory. Eutectic points need to be evaluated for systems where the individual components have cloud points less than 10K apart. Sensitivity Analysis did not drastically alter the cloud point predicted by Ideal Solution Theory. Work has shown that with a few exceptions, ideal solution theory predicts the cloud point quite accurately, particularly when taking into account the variability of measurements such as cloud point and the heat of crystallization.