Improved Property Predictions by Combination of Predictive Models

Property predictions are essential when dealing with molecules that have not been investigated experimentally yet. The accuracy of current predictive models like predictive perturbed-chain polar statistical associating fluid theory (PCP-SAFT) and conductor-like screening model for real solvents (COSMO-RS) is limited. We propose a combination of predictive models in order to yield a higher accuracy. Information from both predictive models are combined in PCP-SAFT parameter space using a log-likelihood function. Experimental vapor pressures, enthalpies of vaporization, and liquid densities over a wide temperature range are used to evaluate the predictions. The average error in the combined property prediction is lower than the error of the individual models. In addition, the maximum error is considerably lowered.