ISSN: 0256-1115 (print version) ISSN: 1975-7220 (electronic version)
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In relation to this article, we declare that there is no conflict of interest.
Publication history
Received June 17, 2025
Revised November 9, 2025
Accepted January 4, 2026
Available online April 25, 2026
articles This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Most Cited

Optimizing Pressure Swing Distillation for Di-n-Propyl Ether and n-Propyl Alcohol Separation Using Aspen HYSYS and Machine Learning Algorithms

Chemical Engineering Faculty , Sahand University of Technology 1Department of Earth Sciences , Utrecht University
s.abdoli1@uu.nl
Korean Journal of Chemical Engineering, April 2026, 43(5), 1507-1519(13)
https://doi.org/10.1007/s11814-026-00646-x

Abstract

This paper presents the design and simulation of a pressure-swing distillation (PSD) process for separating and purifying 

di-n-propyl ether (DnPE) from n-propyl alcohol (nPA) using Aspen HYSYS software. The minimum-boiling-point azeotrope

formed at atmospheric pressure makes conventional separation methods ineff ective. Three critical parameters, feed 

stage, feed temperature, and refl ux ratio, are systematically optimized to minimize energy consumption. The optimized 

process achieves product purities of 99.5% DnPE and 98.7% nPA while reducing energy consumption by 15% compared 

to conventional distillation. Additionally, an XGBoost regression model is developed to predict reboiler heat duty with 

95% accuracy, further enhancing process effi ciency. Particle swarm optimization is employed to identify optimal operating 

conditions based on the machine learning predictions. This integrated computational approach demonstrates signifi cant 

improvements in separation effi ciency, highlighting the industrial potential of the optimized PSD process for azeotropic 

mixtures. 

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