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Special theme research article
Optimization of single mixed refrigerant natural gas liquefaction plant with nonlinear programming
Article first published online: 8 NOV 2011
DOI: 10.1002/apj.642
© 2011 Curtin University of Technology and John Wiley & Sons, Ltd.
Issue
Asia-Pacific Journal of Chemical Engineering
Supplement: Selected Papers from the 13th Asia-Pacific Confederation of Chemical Engineering (APCChE) Congress, 5–8 October 2010, Taipei, Taiwan
Volume 7, Issue Supplement S1, pages S62–S70, May 2012
Additional Information
How to Cite
Khan, M. S., Lee, S. and Lee, M. (2012), Optimization of single mixed refrigerant natural gas liquefaction plant with nonlinear programming. Asia-Pacific Jrnl of Chem. Eng, 7: S62–S70. doi: 10.1002/apj.642
Publication History
- Issue published online: 21 MAR 2012
- Article first published online: 8 NOV 2011
- Manuscript Accepted: 20 SEP 2011
- Manuscript Revised: 19 JUL 2011
- Manuscript Received: 27 JAN 2011
Funded by
- Ministry of Land, Transportation and Maritime Affairs (MLTM) of the Korean government
- Abstract
- Article
- References
- Cited By
Keywords:
- optimization;
- liquefaction;
- simulation;
- refrigerant;
- sequential quadric programming
ABSTRACT
The liquefaction of natural gas (NG) in a mixed refrigerant (MR) system is an energy-demanding process. Much energy is wasted because of its irreversibilities and its nonoptimal execution. The most important factors affecting this process's performance are the refrigerant's composition and flow rate, the suction and evaporation pressures, and the extent of refrigerant vaporization. They should be adjusted to optimize the overall operation. The adjustment of one of these variables will affect the other because of their highly nonlinear interactions. This work reports the optimization of a single MR (SMR) process of NG liquefaction. The SMR process was modeled in the UniSim Design commercial process plant simulator, and the model was optimized for compression energy with nonlinear programming (NLP) while satisfying constraints. The base case for optimization was selected by mesh searching, and case study demonstrates that NLP can reduce energy use and improve the process's efficiency. © 2011 Curtin University of Technology and John Wiley & Sons, Ltd.