文献类型：期刊文献

英文题名：Molecular-Based Bayesian Regression Model of Petroleum Fractions

作者：Mei, Hua[1,2];Wang, Zhenlei[1];Huang, Biao[2]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 1H9, Canada

年份：2017

卷号：56

期号：50

起止页码：14865

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20175204573503);WOS:【SCI-EXPANDED(收录号:WOS:000418783500013)】；

基金：This work is supported by Natural Science Foundation of Shanghai (15ZR1408900) and China Scholar Council (201606745013).

语种：英文

外文关键词：Linear regression - Hydrocarbons

摘要：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.