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Evaluating the ionic liquids, commercial solvents, and pressure-swing for efficient azeotropic separation

  • Ammar Abbas Ali Saif EMAIL logo , Aqeel Ahmad Taimoor , Saad Al-Shahrani , Usman Saeed , Sami-Ullah Rather and Majed A. Alamoudi
Published/Copyright: March 10, 2025
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Abstract

Techniques like extractive distillation and pressure swing distillation (PSD) have been commercially developed to separate azeotropic mixtures. Extractive distillation using conventional solvents offers advantages in achieving high purity levels, but challenges include difficulty in solvent recovery, increased energy consumption, higher costs, and potential negative environmental impact. Selecting appropriate solvents is a challenging task with potential compromises. PSD manipulates pressure to overcome azeotropic points without an entrainer, offering solvent-free operation. Ionic liquids may provide an alternative to conventional solvents in extractive distillation. Five imidazolium, ammonium and pyridinium based ILs have been chosen to investigate their impact on the separation of the selected azeotropic mixtures. The three techniques are employed for seven different (model) azeotropic mixtures to evaluate the commercial feasibility of each process by comparing energy requirements and Total Annual Cost (TAC). Aspen Plus software has been employed to perform a comparison simulation among the processes. This benchmark analysis and process simulation can aid in evaluating the efficacy and feasibility of the possible optimum commercial process in separation. There is no one-size-fits-all technique that provides the best cost-effective and energy-efficient process for every azeotropic separation.


Corresponding author: Ammar Abbas Ali Saif, Department of Chemical and Materials Engineering, King Abdulaziz University, 80204, Jeddah, Saudi Arabia, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interests: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: The raw data can be obtained on request from the corresponding author.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cppm-2024-0064).


Received: 2024-07-13
Accepted: 2025-02-17
Published Online: 2025-03-10

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