Molecular Reconstruction of Crude Oil: Novel Structure-Oriented Homologous Series Lumping with a Cloud Model

Abstract

It is crucial to provide the detailed composition of crude oil for molecular management in refineries. Molecular reconstruction methods have been widely investigated for converting bulk properties to the detailed composition of feedstock by optimization calculations. In this work, a novel structure-oriented homologous series lumping with a cloud model (SOHSL-CM) is proposed for the molecular reconstruction of crude oil to achieve a molecular-level representation of crude oil. A two-dimensional homologous series distribution was applied to construct a molecular library with a clear structure, and a CM was designed to introduce fluctuations in the molecular distribution. Molecular reconstruction was demonstrated by industrial datasets containing 42 sets of crude oil data, with a mean absolute relative error <0.7%. In addition, the computational efficiency of the optimization was acceptable, with a time consumption of <1000 s. The accuracy, reliability, and efficiency of the proposed method indicate substantial potential for practical industrial applications.