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- In relation to this article, we declare that there is no conflict of interest.
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Received November 24, 2022
Revised February 28, 2023
Accepted March 2, 2023
- 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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Optimization analysis of the absorption-stabilization process for fluid catalytic cracking unit
Abstract
The absorption-stabilization process (ASP), an important part of petroleum refinery used in the end-use
products of petroleum (such as stable gasoline, liquid petroleum gas, and dry gas), is energy-intensive and has low
product quality. Aspen Plus process simulator was used for the development of the ASP process model. The developed
process model was validated with the actual plant data. The validated model was used to optimize to minimize the cost
of the ASP. This work shows that the optimization analysis of the ASP can further improve the product quality and
reduce thermal energy consumption. In the new process, changing feeding parameters of supplementary absorption oil,
stripping tower intermediate reboiler, and feeding position of stabilization tower reduced the C3 contents of dry gas
considerably and lowered the C2 and lighter contents of LPG. Additionally, the new process saved 1.32 MW of thermal
energy consumption compared with the existing process. The operating cost has been reduced from 10.921 million
USD annually to 9.830 million USD per year. Furthermore, the cost-saving effect of this optimization is about 9.99%
(1.091 million USD per year).
Keywords
References
2. R. Sadeghbeigi, Butterworth-Heinemann (2020).
3. Dr. P. McDonald, Survey, Oil Energy Trends, 10 (2017).
4. D. Xiang, W. Lidong and D. Yingsheng, J. Chem. Eng., 26, 46 (1998).
5. X. Xu, Oil Refining Des., 23, 14 (1993).
6. Z. Luhong, W. Lu and S. Jinsheng, Pet. Ref. Chem. Ind., 31, 44 (2000).
7. D. Xiang, W. Shaomin and L. Changgeng, Chem. Eng. Process, 30,16 (2002).
8. G. Soave and J. A. Feliu, Appl. Therm. Eng., 22, 889 (2002).
9. G. S. Soave, S. Gamba, L. A. Pellegrini and S. Bonomi, Ind. Eng.Chem. Res., 45, 5761 (2006).
10. S. Bandyopadhyay, M. Mishra and U. V. Shenoy, AIChE J., 50,1837 (2004).
11. S. H. Lee and M. J. Binkley, Hydrocarb. Process. (International ed.),90, 101 (2011).
12. P. Seferlis and A. N. Hrymak, Comput. Chem. Eng., 20, 1177 (1996).
13. G. Li, X. Yong, L. Yushu and H. Ben, IEEE, 825 (2011).
14. E. Lu, Q. Pan and H. Zhang, ESCAPE-15 (2005).
15. Aspen plus user guide[M], Aspen Tech. lnc. (2009).
16. I. D. G. Chaves, J. R. G. Lopez, J. L. G. Zapata, A. L. Robayo and G. R.
Nino, Case Studies. In: Process. Analy. and Sim. in Chem. Eng.Springer, Cham. (2016).
17. J. P. Gutierrez, L. A. Benítez, J. Martínez, L. A. Ruiz and E. Erdmann,Int. J. Eng. Res. (2014).
18. S. Lanyi, Chem. Ind. Press (2012).
19. S. K. Wasylkiewicz, L. C. Kobylka and F. J. L. Castillo, J. Chem. Eng.,92, 201 (2003).
20. X. Zhang, Y. Zou and S. Li, Info. Sci., 530, 95 (2020).
21. X. Zhang, Y. Zou and S. Li, Neurocomputing, 367, 64 (2019)