The extraction of phospholipids from biomass, in particular from waste sewage sludge, has the potential to provide an additional feedstock for biodiesel production. One challenge in developing a competitive, economical recovery process is the identification of a suitable solvent and the optimum temperature and pressure for the recovery operation. Use of predictive computations provides a means to narrow the scope of conditions (temperature and pressure) as well as solvent composition that must be examined experimentally. For most substances, vapor pressure data as well as critical point information are readily available in the literature. However, for many phospholipids, these data are not readily available and would be prohibitively expensive to obtain experimentally due to the cost and limited availability of purified phospholipids. What is readily available for each phospholipid is its chemical structure. The ultimate goal of this effort is to provide a basis for predictive screening of solvent mixtures and identification of operating conditions suitable for economical recovery of mixed phospholipids from a complex matrix.

The prediction of the solubility of select phospholipids in solvents of differing compositions will be examined, including supercritical solvent mixtures. This effort combines the use of chemical structure for physical property estimation with the use of COSMO-Rs for the prediction of pure component vapor pressure of phospholipids and phase equilibria of phospholipids in conventional solvents. Prediction of phospholipid solubility in supercritical solvents is accomplished using the Peng-Robinson equation of state, utilizing estimated vapor pressure estimates for the phospholipids. Validation efforts and predictions for three model phospholipids, distearoyl phosphatidylcholine, dipalmitoyl phosphatidylcholine and dipalmitoyl phosphatidylethanolamine, will be presented.