Optimization Of A Pumping Ship Trajectory To Clean Oil Contamination In The Open Sea

Thumbnail Image
Full text at PDC
Publication Date
Advisors (or tutors)
Journal Title
Journal ISSN
Volume Title
Pergamon-elsevier science
Google Scholar
Research Projects
Organizational Units
Journal Issue
Our aim is to find the optimal trajectory of a pumping 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.
Office of response and restoration of the U.S. National Ocean Service, Incident News Home,Website: E.K. Wilson, Oil Spill’s Size Swells, Chemical and Engineering News, American Chemical Society, 2010. United States Environmental Protection Agency, Oil Spill Response Techniques, Website: htm, (2009). A.P.G. Depollution, United States Coast Guards, Spilled Oil Recovery System, Website: sorsindex.asp, (2010). 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 (2010) 461–472. D. Di Serafino, S. Gomez, L. Milano, F. Riccio, G. Toraldo, A genetic algorithm for a global optimization problem arising in the detection of gravitational waves, Journal of Global Optimization 48 (1) (2010) 41–55. 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) 504–510. 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). B. Ivorra, B. Mohammadi, A.M. Ramos, Optimization strategies in credit portfolio management, Journal of Global Optimization 43 (3) (2009) 415–427. B. Ivorra, A.M. Ramos, B. Mohammadi, Semideterministic global optimization method: application to a control problem of the burgers equation,Journal of Optimization Theory and Applications 135 (3) (2007) 549–561. Office of response and restoration of the U.S. National Ocean Service, Website: W. Hundsdorfer, J.G. Verwer, Numerical Solution of Time-Dependent Advection-Diffusion-Reaction Equations, Springer Series in Comput. Math. 33 (2003). J.L. Lions, E. Magenes, Problemes Aux Limites Non Homogenes et Applications - Volume 2, Dunod, 1968. R. Eymard, T. Gallouet, R. Herbin, A. Michel, Convergence of a finite volume scheme for nonlinear degenerate parabolic equations, Numerische Mathematik 92 (1) (2002) 41–82. R. Glowinski, P. Neittaanmaki, Partial differential equations, in: Modelling and Numerical Simulation, in: Computational Methods in Applied Sciences,16, Springer, 2008. B. Mohammadi, J.-H. Saiac, Pratique de la Simulation Numérique, Dunod, 2002. C. Lanczos, Solution of systems of linear equations by minimized iterations, Journal of Research of the National Bureau of Standards 49 (1952) 33–53. H.A. Van der Vorst, Bi-CGSTAB : a fast and smoothly converging variant of Bi-CG for the solution of nonsymmetric linear systems, SIAM Journal on Scientific and Statistical Computing 13 (1992) 631–644. J. Holland, Adaptation in Natural and Artificial Systems, Univ. Michigan Press, 1975. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, 3rd ed., Springer, 1998. F. Herrera, M. Lozano, J.L. Verdegay, Tackling real-coded genetic algorithms: operators and tools for behavioural analysis, Artificial Intelligence Review 12 (4) (1998) 265–319. D.B. Fogel, An introduction to simulated evolutionary optimization IEEE Transactions on Neural Networks: Special Issue on Evolutionary Computation 5, (1994). D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, 1989. T. Back, D.B. Fogel, Z. Michalewicz (Eds.), Evolutionary Computation 1: Basic Algorithms and Operators, IOP Publishing, Bristol, 2000. K.A. De Jong, Evolutionary Computation: A Unified Approach, MIT Press, 2006. J. Yoshida, M. Miki, Y. Sakata, Best combinatorial crossover in genetic algorithms, Joho Shori Gakkai Kenkyu Hokoku 2000 (85) (2000) 41–44. H. Maaranen, K. Miettinen, A. Penttinen, On initial populations of a genetic algorithm for continuous optimization problems, Journal of Global Optimization 37 (3) (2007) 405–436. 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) (2006) 93–98. B. Ivorra, B. Mohammadi, D.E. Santiago, 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) (2006) 319–333. S.P. Murray, Turbulent diffusion of oil in the ocean, Journal of Limnology and Oceanography 17 (5) (1972) 651–660.