首页|Calculating osmotic pressure of liquid mixtures by association theory for sustainable separating of solvents by membrane processes
Calculating osmotic pressure of liquid mixtures by association theory for sustainable separating of solvents by membrane processes
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
? 2022 The Korean Society of Industrial and Engineering ChemistryRecent years, the separation of organic liquid mixtures achieved by organic solvent nanofiltration (OSN), organic solvent reverse osmosis (OSRO), and organic solvent forward osmosis (OSFO) has gained a great momentum. In the methods, it is of particular importance to know the osmotic pressure of liquid mixtures for the correct process design. Although the Van't Hoff law is frequently employed to estimate the osmotic pressure of such mixtures, unprecedented serious errors might be added to the results. In this study, the osmotic pressure of numerous binary liquid mixtures is predicted using the original definition of osmotic pressure along with the cubic plus association (CPA) equation of state (EoS) and the Van't Hoff equation at various temperatures and compositions. CPA EoS considers hydrogen bonding effects in associating compounds (those with hydrogen bonds such as water and alcohols). The obtained results reveal that unlike the Van't Hoff equation (the average deviation = 19.95 bar), the original definition of osmotic pressure with the aid of CPA EoS (the average deviation = 3.86 bar) can properly predict the osmotic pressure of the studied mixtures. Our findings are beneficial for various techniques utilized in the separation of organic liquid mixtures.
Equation of stateMembrane processOrganic solvent reverse osmosisOrganic solvent separationOsmotic pressureVan't Hoff law
Jalaei Salmani H.、Karkhanechi H.、Jeon S.、Matsuyama H.
展开 >
Department of Chemical Engineering Faculty of Engineering Ferdowsi University of Mashhad
MEMBRARE Co. Ltd.
Research Center for Membrane and Film Technology Department of Chemical Science and Engineering Kobe University
NSTL
Elsevier
