(352x) Accurate Prediction of Viscosity from Molecular Simulation: Perspective and Progress

Many physical properties are needed for chemical process engineering. Experimental measurement of these properties is ideal, but such is often not possible due to a variety of problems including: an inability to obtain samples with sufficient purity, thermal decomposition, safety issues, the phase of the compound at temperatures accessible to the apparatus, and cost. A variety of prediction methods and strategies are used in such cases to provide the needed property data. Many work well, especially those for thermodynamic properties, but the liquid viscosity of a compound is an important property for which prediction is notoriously difficult. Despite the approach taken, tests show that prediction methods commonly have upwards of 25-50% error outside of the set of compounds for which the method was trained, with some producing errors of more than 75%.

This work shows recent work on using molecular simulation to create a prediction formalism capable of reaching better than 10% accuracy. The emphasis is on showing how the prediction works for compounds outside of the training set. The presentation will begin with a short review on the weaknesses of current prediction methods. It will then explain the basics of calculating viscosity from molecular simulation and the issues that must be addressed surrounding both the method and the model. After this, results will be presented for prediction of long alkanes (normal and branched) and the alkylbenzene families. This will include a discussion on a systematic and automated way to select simulation method protocols and the correct development of model parameters. A comparison to other viscosity prediction methods will also be shown. Taken as a whole, the work will demonstrate that prediction of viscosity is far from a solved problem, that the molecular-level phenomena giving rise to viscosity are not intuitive, and that there is reason to hope for better predictions of the property in the near future.