文献类型：期刊文献

英文题名：Molecular characterization of petroleum fractions using state space representation and its application for predicting naphtha pyrolysis product distributions

作者：Mei, Hua[1,2];Cheng, Hui[1];Wang, Zhenlei[1];Li, Jinlong[1]

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 1R1, Canada

年份：2017

卷号：164

起止页码：81

外文期刊名：CHEMICAL ENGINEERING SCIENCE

收录：;EI(收录号：20170703355597);WOS:【SCI-EXPANDED(收录号:WOS:000397696000008)】；

基金：This work is supported by Natural Science Foundation of Shanghai (16ZR1407300) and sponsored by the China Scholarship Council (201606745013).

语种：英文

外文关键词：Molecular characterization; State space representation; Basis fractions; Non-negative matrix factorization (NMF); Naphtha pyrolysis prediction

摘要：Molecular model of petroleum fractions plays an important role in the designing, simulation and optimization for petrochemical processes such as pyrolysis process, catalytic reforming and fluid catalytic cracking (FCC). However, it is very difficult to exactly characterize the composition distributions due to its internal complexity and containing numerous redundant information and measuring errors although many efforts have been made so far. As an improvement of the work in Mei et al. (2016), a molecular based representation method within a multi-dimensional state space is developed in this paper. In this method, each pure component in the petroleum mixtures is defined as a state variable and any petroleum fractions can be geometrically represented as a point in a multi-dimensional linear state space, in which a conception of basis fractions is further introduced by defining a group of linear independent vectors so that any petroleum fractions within the specified range (e.g. naphtha) can be obtained through a linear combination by such basis fractions. The redundant information and measuring errors in the predetermined petroleum fraction samples are eliminated through the procedure of calculating the basis fractions with non-negative matrix factorization (NMF) algorithm, meanwhile the scale of the feedstock database is highly decreased. As an application example of the basis fractions, a quick prediction approach on naphtha pyrolysis product distributions is developed by linearly combining the pyrolysis products of the basis fractions. In contrast to mechanistic models, this proposed method is more suitable for real-time control and optimization purpose with little loss of accuracy. (C) 2017 Elsevier Ltd. All rights reserved.