Effect of Boiling Point and Density Prediction Methods on Stochastic Reconstruction

Abstract

Stochastic reconstruction (SR) methods are used to generate a series of molecules that mimic the properties of complex mixtures using partial analytical data. Determining a quantitative composition using these methods is limited by the property prediction methods used. This paper addresses the use of two key measurements in the characterization of petroleum fractions, namely density and boiling point distributions. It is known that the different methods used in estimating these two basic properties have varying error rates. Boiling point prediction performances of the various group contribution methods were tested via the molecular library established for molecules that can be found present in the petroleum fractions. It has been observed that the combined use of these methods results in close to a 50% reduction in sum of squared errors than any single method. The predictive performances of the density calculation methods were similarly tested. The best-calculated density results were found via the Yen–Woods method with support from the linear mixing rule based on molar fractions.

A Perspective on the Impact of Process Systems Engineering on Reaction Engineering

“…Over the years, the set of techniques has been applied in many other application areas. Chemoinformatic methods in the context of reaction engineering largely include: identification of molecular descriptors (or functional groups) from chemometric models, stochastic reconstruction of candidate molecules using these structural indicators, and molecular similarity measures used to query databases for structures whose fingerprints are most similar to the ones obtained by chemometric models through deconvolution of data from spectral analyzers. Therefore, chemoinformatics can be used to build on the results of chemometric investigations to build advanced models and master equations to aid in system automation and further discovery. , …”

Machine Learning for Physicochemical Property Prediction of Complex Hydrocarbon Mixtures

“…A whole spectrum of methods exists to reconstruct the composition of complex mixtures . Yet, with all methods, it remains difficult to accurately calculate the mixture properties, such as boiling points, density, and viscosity . The inaccurate calculation of mixture properties affects the molecular reconstruction, which, in turn, limits the accuracy of detailed kinetic models.…”

“…This assumption creates an initial error on the mixture predictions, as it does not hold in reality. In addition, the accuracy of the calculated pure compound properties also plays a role . When there are no experimental density values available, densities of pure compounds can be calculated using an equation of state, group contribution methods, or via correlations .…”

“…2 Yet, with all methods, it remains difficult to accurately calculate the mixture properties, such as boiling points, density, and viscosity. 3 The inaccurate calculation of mixture properties affects the molecular reconstruction, which, in turn, limits the accuracy of detailed kinetic models. An accurate prediction of mixture density and viscosity is, for example, essential for kinetic models and reactor design.…”

“…In addition, the accuracy of the calculated pure compound properties also plays a role. 3 When there are no experimental density values available, densities of pure compounds can be calculated using an equation of state, group contribution methods, or via correlations. 29 These methods are again limited because either data of similar molecules or accurate (pseudo)critical properties are required.…”