Publication: Modeling, Simulation and Optimization of a Polluted Water Pumping Process in Open Sea
Loading...
Full text at PDC
Publication Date
2013-06
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Citations
Exportar
Abstract
The objective of this article is to find the optimal trajectory of a pumping (i.e., skimmer) ship, used to clean oil spots in the open sea, in order to pump the maximum quantity of pollutant on a fixed time period. We use a model previously developed to simulate the evolution of the oil spots concentration due to the coupling of diffusion, transport from the wind, sea currents and pumping process and reaction due to the extraction of oil. The trajectory of the ship is directly modeled by considering a finite number of interpolation points for cubic splines. The optimization problem is solved by using a global optimization algorithm based on the hybridization of a Genetic Algorithm with a Semi-Deterministic Secant Method, to improve the population. Finally, we check the efficiency of our approach by solving several numerical examples considering various shapes of oil spots based on real situations.
Description
UCM subjects
Unesco subjects
Keywords
Citation
C. Alavani, R.Glowinski, S.Gomez, B.Ivorra, P.Joshi,A.M..Ramos Modelling and simulation of a polluted water pumping process. Mathematical and Computer Modelling, 51:461-472, 2010.
A.P.G. Depollution. Website: http://apgdepollution.free.fr
[Back, 200] Back, T., Fogel, D.B., Michalewicz, Z. (eds.) Evolutionary computation 1: basic algorithms and operators.IOP Publishing, Bristol, 2000.
De Jong, K.A. Evolutionary Computation: a unified approach.MIT press, 2006.
D. Di Serafino, S. Gomez, L. Milano, F. Riccio and G. Toraldo. A genetic algorithm for a global optimization problem arising in the detection of gravitational waves, Journal of Global Optimization, 48(1): 41-55, 2010.
L. Debiane, B. Ivorra, B. Mohammadi, F. Nicoud, A. Ern, T. Poinsot, H.Pitsch. A low-complexity global optimization algorithm for temperature and pollution control in flames with complex chemistry. International Journal of Computational Fluid Dynamics, 20(2):93-98, 2006.
R. Eymard, T. Gallouet, R. Herbin and A. Michel, Convergence of a finite volume scheme for nonlinear degenerate parabolic equations. Numer. Math., 92(1): 41-82, 2002.
Fogel, D. B. An Introduction to Simulated Evolutionary Optimization. IEEE Trans.on Neural Networks: Special Issue on Evolutionary Computation, 5, 1994.
R. Glowinski, P. Neittaanmaki, Partial Differential Equations. Modelling and Numerical Simulation, Series: Computational Methods in Applied Sciences, Springer,16,2008.
D. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison Wesley, 1989.
S. Gomez, G. Severino, L. Randazzo, G. Toraldo, J.M. Otero. Identification of the hydraulic conductivity using a global optimization method, Agricultural Water Management, 93(3), 2009.
S. Gomez, G. Fuentes, R. Camacho, M. Vasquez, J. M. Otero, A. Mesejo, N. del Castillo. Application of an Evolutionary Algorithm in well test characterization of Naturally Fractured Vuggy Reservoirs. Society of Petroleum Engineering, SPE No.103931, 2006.
Holland, J. Adaptation in natural and artificial systems. Univ. Michigan Press, 1975.
Herrera, F., Lozano, M., Verdegay, J.L.: Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artif. Intell. Rev. 12(4): 265-319, 1998.
W. Hundsdorfer, J.G. Verwer, Numerical Solution of Time-Dependent Advection-Diffusion-Reaction Equations, Springer Series in Comput. Math. 33, 2003.
B.Ivorra, B.Mohammadi, and A.M. Ramos. Optimization strategies in credit portfolio management. Journal Of Global Optimization, 43(3):415-427, 2009.
B. Ivorra, B. Mohammadi, D.E. Santiago, and J.G. Hertzog. Semi-deterministic and genetic algorithms for global optimization of microfluidic protein folding devices. International Journal of Numerical Method in Engineering, 66(2):319-333, 2006.
B.Ivorra, A.M. Ramos, and B.Mohammadi. Semideterministic global optimization method: Application to a control problem of the burgers equation. Journal of Optimization Theory and Applications, 135(3):549-561, 2007.
C. Lanczos, Solution of Systems of Linear Equations by Minimized Iterations, J. Res. Natl. Bur. Stand. 49: 33-53, 1952.
Maaranen, H., Miettinen, K., Penttinen, A. On initial populations of a genetic algorithm for continuous optimization problems.J. Glob. Optim. 37(3): 405-436, 2007.
Michalewicz, Z. Genetic Algorithms + Data Structures = Evolution Programs, 3 edn. Springer, 1998.
B. Mohammadi and J-H. Saiac. Pratique de la simulation numérique. Dunod, 2002.
Office of response and restoration of the U.S. National Ocean Service. Website: http://response.restoration.noaa.gov.
Office of response and restoration of the U.S. National Ocean Service. Incident News Home. Website: http://www.incidentnews.gov/famous.
United States Coast Guards. Spilled Oil Recovery System. Website: http://www.uscg.mil/hq/nsfweb/nsfcc/ops/ResponseSupport/Equipment/sorsindex.asp, 2010.
United States Environmental Protection Agency. Oil Spill Response Techniques. Website: http://www.epa.gov/emergencies/content/learning/oiltech.htm, 2009.
H. A. Van der Vorst, Bi-CGSTAB : A Fast and Smoothly Converging Variant of Bi-CG for the Solution of Nonsymmetric Linear Systems, SIAM J. Sci. Stat. Comput. 13: 631-644,1992.
E. K. Wilson. Oil Spill’s Size Swells. Chemical Engineering News. American Chemical Society. ISSN 0009-2347, 2010.
J. Yoshida, M. Miki and Y. Sakata. Best Combinatorial Crossover in Genetic Algorithms. Joho Shori Gakkai Kenkyu Hokoku 2000(85): 41–44, 2000.