Elimination of fluctuations and optimization of the sweetening loop of the Khangiran Gas Refinery

  • Ali Karsaz Khorasan Institute of Higher Education
  • Roghayeh Akbarian Khorasan Institute of Higher Education
  • Hassn Abadi Khorasan Institute of Higher Education
  • Mohammad Sanadgol Khorasan Institute of Higher Education
Keywords: PID controller, Governor, fuzzy and proportional controller.

Abstract

    In this article, it deals with the modeling of the absorption process of acid gases in the contact tower by the amine fluid in the Khangiran Gas Refinery. The performed simulations show that the concentration of acid gases in the amine fluid increases with the reduction of the contact surface in the contact tower. The obtained model is in good agreement with operational data. In this research, the identification of sources of noise and fluctuations in the mentioned unit, adding the modeling of sensors and actuators and control systems to the simulation, especially PID control, has also been investigated.

     By simulating and implementing PID control on the sour gas feed valve of the contact tower and making step changes in the input setting point, fluctuations were observed in all the variables of the absorption tower.

These fluctuations, along with other sensor uncertainties such as temperature sensors, flow transmitters, and pressure transmitters, indicate the presence of fluctuations throughout the tower. In order to eliminate these fluctuations, a controller based on fuzzy logic was used. The appropriate reduction in fluctuations and disturbances by this controller up to 80% shows the success of this controller in eliminating the chattering phenomenon in the gas purification unit.

References

[1] J. Song, X. Niu, L. Ling, B.Wang, A density functional theory study on the interaction mechanism
between H2S and the -Fe2O3(0001) surface, Fuel. Process. Technol. 115(2013) 26–33.
[2] S. Komati, A. K. Suresh, CO2 absorption into amine solutions a novel strategy for intensification
based on the addition of ferrofluids, J. Chem. Technol. Biot. 83(2008) 1094–1100.
[3] Zadeh, L.; “Fuzzy sets”, Information and Control, vol. 8, no. 3, pp. 338-353, 1965
[4] J.-S. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE transactions on systems, man, and cybernetics, 23(3) (1993) 665-685.
[5] M.A. Shoorehdeli, M. Teshnehlab, A.K. Sedigh, M.A. Khanesar, Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods, Applied Soft Computing, 9(2) (2009) 833-850
Published
2023-08-01
How to Cite
Karsaz, A., Akbarian, R., Abadi, H., & Sanadgol, M. (2023). Elimination of fluctuations and optimization of the sweetening loop of the Khangiran Gas Refinery. Majlesi Journal of Mechatronic Systems, 11(4), 32-43. Retrieved from https://ms.majlesi.info/index.php/ms/article/view/548
Section
Articles