Development of robust models for the prediction of Reid vapor pressure (RVP) in fuel blends and their application to oxygenated biofuels using the SAFT-γ approach

Abstract The growing demand for inexpensive, environmentally benign fuels has sparked research into fuel properties that affect fuel production, handling and engine efficiency. Reid vapor pressure (RVP) has been shown to be one such important fuel property. Having models that can accurately predict the RVP of a fuel rapidly and robustly can aid in the design of de novo fuels and increase the value proposition to the refinery. Conventional fossil fuels typically consist of 100 s of highly non-ideal components, especially when oxygenates are involved, and current methods often fail to predict RVP correctly. The large number of components can render conventional modelling approaches burdensome and inaccurate. To overcome these barriers, a robust methodology for the rapid and accurate prediction of the RVP of complex base fuels blended with oxygenated biofuels via surrogate models was developed. The aim was to minimize the number of components necessary to accurately represent the complex mixture, whilst retaining the ability to accurately predict RVP of the base fuel. Three models were developed using the SAFT-γ approach, which varied in complexity and accuracy of prediction. The RVP for a suite of oxygenates, blended at 10, 20, and 30%, by weight into blendstocks for oxygentated blending (BOB) was studied in order to evaluate which oxygenate families have potential to reduce RVP. The majority of oxygenates evaluated decreased the RVP of the mixture, with the branched alcohols and the ketones showing the largest decrease. These results have promising implications for biofuels development and biorefinery economics.