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Thermodynamic principle based hydrogen network synthesis with hydrorefining feed oil sulfur content variation for total exergy minimization.

Authors :
Zhang, Qiao
Li, Jing
Feng, Xiao
Source :
Journal of Cleaner Production. May2020, Vol. 256, pN.PAG-N.PAG. 1p.
Publication Year :
2020

Abstract

Process integration has been widely applied to hydrogen networks for resource conservation. Traditional methods almost focus on the component-less quantitative allocation between hydrogen streams and processing units but hardly component related properties, such as hydrogen partial pressure, to account for pressure energy and hydrogenation reactions. Sulfur is an important contaminant in crude oil to be removed and its content greatly influences on both fresh hydrogen demand and hydrogen stream properties. Therefore, this paper employs thermodynamic principles to correlate component related properties and combines it with component-less quantitative integration for the optimization of hydrogen networks with variational sulfur content. A mixed integer nonlinear programming model is formulated to minimize total exergy including fresh hydrogen and compression work. A practical refinery case is employed and results show the fresh hydrogen is from 17120.8 Nm3/h on 0.5 wt% sulfur to 18818.8 Nm3/h on 2.5 wt% sulfur and the error range of total pressure is from 4.5% to 5.8% at the above lower and upper sulfur content bounds. This approach is more scientific and reliable in both mass and energy conservation. Image 1 • Basic thermodynamic principles are employed to perform process integration. • A MINLP model with component-less and component related constraints is set up. • Sulfur content variation dominates both fresh hydrogen and compression work exergies. • The error range of total pressure is from 4.5% to 5.8% at lower and upper levels. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09596526
Volume :
256
Database :
Academic Search Index
Journal :
Journal of Cleaner Production
Publication Type :
Academic Journal
Accession number :
142598946
Full Text :
https://doi.org/10.1016/j.jclepro.2020.120230