Numerical modelling and simulation of methane enrichment: a systematic review on pressure swing adsorption technology.
Saved in:
| Title: | Numerical modelling and simulation of methane enrichment: a systematic review on pressure swing adsorption technology. |
|---|---|
| Authors: | Bahrun, Mohd Hardyianto Vai1 (AUTHOR), Bono, Awang2 (AUTHOR), Othman, Norasikin1,3 (AUTHOR), Zaini, Muhammad Abbas Ahmad1,3 (AUTHOR) abbas@cheme.utm.my |
| Source: | International Journal of Modelling & Simulation. Aug2026, Vol. 46 Issue 4, p1285-1323. 39p. |
| Subjects: | Mathematical models, Pressure swing adsorption process, Separation of gases, Methane, Physisorption, Heat balance (Engineering), Computer simulation |
| Abstract: | Enriching methane from sources such as natural gas, biogas, and coalbed methane is a valuable gas separation endeavor, given methane as a cleaner and more sustainable fuel resource. Pressure swing adsorption (PSA) is widely regarded as a mature technology for this purpose. The essence of the mathematical model is to capture the fundamental aspects of the problem, enabling an accurate description of the system. This review summarizes recent trends in the mathematical modelling of PSA for methane enrichment. It provides a comprehensive compilation of numerical modelling and simulation studies, with a critical emphasis on methane enrichment applications. A rigorous mathematical model describing the adsorption phenomenology in a packed bed column is presented, incorporating mass, momentum, and energy balances, which serve as the fundamental framework for the cyclic adsorption process. Key analyses and performance metrics of PSA systems are detailed through examples from the literature. A case study based on a single column is presented to exemplify the mathematical modelling pathway and serve as a basis for model validation. The paper concludes by highlighting instructive avenues for future research directions and development in this area. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Modelling & Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
