Molecular-Based Bayesian Regression Model of Petroleum Fractions

Abstract

Molecular reconstruction of petroleum fractions is for determining the detailed molecular compositions in the mixture from a few measurable bulk properties, e.g., density, Reid vapor pressure (RVP), molecular weight, and ASTM boiling point curves etc., which is a great challenge because the number of hydrocarbon compounds is much larger than that of the bulk properties. In this paper, a novel molecular reconstruction method is developed which includes two Bayesian regression models for bulk properties’ prediction and molecular reconstruction. By defining a characteristic function of bulk property and then establishing its general mixing rule with respect to compositions, the bulk property is predicted from a linear regression model with sigmoidal basis functions whose parameters can be estimated by maximizing a posterior distribution from a well-determined database containing bulk properties and molecular information on petroleum fraction samples. Furthermore, by developing a prior distribution of the molecular information with an assumption that the compounds in the hydrocarbon mixture have an independently and identically distributed (iid) gamma distribution and combining the likelihood function used in bulk properties’ prediction, the molecular information is thus reconstructed by maximizing a new posterior distribution. Case studies of naphtha fractions demonstrate the effectiveness of the proposed method.