Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-26T22:46:34.726Z Has data issue: false hasContentIssue false
  • English
  • Français

Multi-Objective Optimization of Corrosion Rate Parameters in Refining Process Using Particle Swarm Optimization.

Published online by Cambridge University Press:  28 February 2012

B. González ,
L. Torres ,
F. A. Reyes ,
I. Escamilla ,
C. Vera  and
R. Colas
B. González
Affiliation:
Corporación Mexicana de Investigación en Materiales, S.A. de C.V., Ciencia y Tecnología # 790, Fracc. Saltillo 400, Saltillo, Coahuila, México, C.P. 25290. Facultad de Ingeniería Mecánica y Eléctrica., Universidad Autónoma de Nuevo León., Ave. Universidad s/n., San Nicolás de los Garza, N. L., México, C.P. 66450
L. Torres
Affiliation:
Facultad de Ingeniería Mecánica y Eléctrica., Universidad Autónoma de Nuevo León., Ave. Universidad s/n., San Nicolás de los Garza, N. L., México, C.P. 66450
F. A. Reyes
Affiliation:
Corporación Mexicana de Investigación en Materiales, S.A. de C.V., Ciencia y Tecnología # 790, Fracc. Saltillo 400, Saltillo, Coahuila, México, C.P. 25290.
I. Escamilla
Affiliation:
Facultad de Ingeniería Mecánica y Eléctrica., Universidad Autónoma de Nuevo León., Ave. Universidad s/n., San Nicolás de los Garza, N. L., México, C.P. 66450
C. Vera
Affiliation:
Corporación Mexicana de Investigación en Materiales, S.A. de C.V., Ciencia y Tecnología # 790, Fracc. Saltillo 400, Saltillo, Coahuila, México, C.P. 25290.
R. Colas
Affiliation:
Facultad de Ingeniería Mecánica y Eléctrica., Universidad Autónoma de Nuevo León., Ave. Universidad s/n., San Nicolás de los Garza, N. L., México, C.P. 66450
Get access

Abstract

In this paper, a Particle Swarm Optimization (PSO) algorithm is presented to find the optimal combination of corrosion rate parameters for a refining process in the oil industry. The experimental data in this paper are constituted by results obtained from field tests. Maintenance control is a very important aspect in order to prevent substantial damage to facilities, equipment and people. Other important factor to consider is the cost of maintenance which tends to reduce the required actions. The main parameters in corrosion control are flow, concentration of sulfur species, total acid number (TAN), temperature, and chromium content. However it is not easy to know the combined effect of different variables due to synergistic effects. Particle swarm optimization (PSO) is a population based stochastic optimization technique, inspired by social behavior of bird flocking or fish schooling. The system is initialized with a population of random solutions and searches for optima by updating generations. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles.

Type
Articles
Information
MRS Online Proceedings Library (OPL) , Volume 1373: Symposium S4 – Advanced Structural Materials—2011 , 2012 , imrc-1373-s4-27
Copyright
Copyright © Materials Research Society 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Jayaraman, A., Singh, H., and Lefebvre, Y., Rev. Inst. Fr. Pet. 41, (1986) 265.Google Scholar
2. Slavcheva, E., Shone, B., and Turnbull, A., Br. Corros. J. 34, (1999) 125.Google Scholar
3. Cooper, C. M., Hydrocarbon Process. 51, (1972) 75.Google Scholar
4. Gutzeit, J., Oil Gas J. 74, (1976)156.Google Scholar
5. Gutzeit, J., Mater. Perform. 16, (1977) 24.Google Scholar
6. Babaian-Kibala, E., Craig, H. L., Rusk, G. L., et al. ., Mater. Perform. 32, (1993) 50.Google Scholar
7. Babaian-Kibala, E., Craig, H. L., Rusk, G. L., et al. ., in Proceedings of the Conference “Corrosion’93”, New Orleans, 1993, p. 631.Google Scholar
8. Slavcheva, E., Shone, B., and Turnbull, A., Corrosion 54, (1994) 922.Google Scholar
9. Hajeeh, M. Estimating corrosion: a statistical approach. Materials and Design, 24: (2003) 509–518.Google Scholar
10. Trasatti, S.P., Gabetta, G.,. Study of naphtenic acid corrosion by neural network.Google Scholar
11. Reyes-Sierra, Margarita and Coello Coello, Carlos A.. (2006) Int. J. Computational Intelligence Research. Vol. 2, (2006) pp. 287–308.Google Scholar
12. Eberhart, R.C., Kennedy, J., , Proceedings of IEEE International Conference on Neural Networks , Piscataway, NJ, 1995 pp. 1942–1948.Google Scholar
13. Coello, C.A. and Lechuga, M.S., Proceedings of IEEE, World Congress on Computational Intelligence (2002) pp. 1051–1056.Google Scholar