Tailoring the particle swarm optimization algorithm for the design of offshore oil production risers
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In offshore oil production activities, risers are employed to connect the wellheads at the sea-bottom to a floating platform at the sea surface. The design of risers is a very important issue for the petroleum industry; many aspects are involved in the design of such structures, related to safety and cost savings, thus requiring the use of optimization tools.
In this context, this work presents studies on the application of the Particle Swarm Optimization method (PSO) to the design of steel catenary risers in a lazy-wave configuration. The PSO method has shown good efficiency for some applications, but its performance is dependent on the values selected for the parameters of the algorithm. Therefore, this work describes some variants of the method, and presents results of several experiments performed to analyze the behavior of its parameters, trying to improve the performance of the method and tailor it for the application to the design of riser systems.
The resulting method and its best set of parameters can then be taken as the default values in an implementation of the PSO method in the in-house OtimRiser computational tool, oriented to the design of risers, and also incorporating other optimization methods based on evolutionary concepts.
KeywordsOffshore systems Risers Particle swarm optimization method
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- Albrecht CH (2005) Evolutionary algorithms applied to mooring synthesis and optimization (in Portuguese). DSc Thesis, COPPE/UFRJ, Rio de Janeiro Google Scholar
- Coello C, Luna E (2003) Use of particle swarm optimization to design combinatorial logic circuits. In: Tyrell A, Haddow P, Torrensen J (eds) 5th international conference on envolvable systems: from biology to hardware, ICES, Trondheim, Norway, 2003. Lecture notes in computer science, vol 2606. Springer, Berlin, pp 398–409 CrossRefGoogle Scholar
- Eberhardt RC, Shi Y (1999) Empirical study of particle swarm optimization. In: IEEE Proc conference on evolutionary computation, Washington DC, pp 1945–1949 Google Scholar
- Eberhardt RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: IEEE international conference on evolutionary computation, San Diego, California, pp 84 –88 Google Scholar
- Hassan R, Cohanim B, de Weck O, Venter G (2005) A comparison of particle swarm optimization and the genetic algorithm. In: 1st AIAA multidisciplinary design optimization specialist conference, Austin, TX, No AIAA-2005-1897 Google Scholar
- Hu X, Eberhardt RC, Shi Y (2003) Engineering optimization with particle swarm. In: IEEE swarm intelligence symposium, Indianapolis, pp 53–57 Google Scholar
- Jacob BP, de Lima BSLP, Reyes MCT, Torres ALFL, Mourelle MM, Silva RMC (1999) Alternative configurations for steel catenary risers for turret-moored FPSO’s. In: Proceedings of the 9th international offshore and polar engineering conference, Brest, France, vol 2, pp 234–239 Google Scholar
- Kennedy J, Eberhardt R (1995a) Particle swarm optimization. In: Proc IEEE conference on neural networks, pp 1942–1948 Google Scholar
- Kennedy J, Eberhardt R (1995b) A new optimizer using particle swarm theory. In: Sixth international symposium on micro machine and human science. IEEE, New York, pp 39–43 Google Scholar
- Kennedy J, Eberhardt RC, Shi Y (2001) Swarm intelligence. The Morgan Kaufmann series in evolutionary computation. Academic Pres, San Francisco Google Scholar
- Krohling R, Coelho L, Shi Y (2002) Cooperative particle swarm optimization for robust control system design. In: 7th online world conference on soft computing in industrial applications Google Scholar
- Medeiros JACC (2005) Particle swarm as optimization tool in complex nuclear engineering problems (in Portuguese). DSc Thesis, COPPE—Federal University of Rio de Janeiro, Nuclear Eng Dept, Brazil Google Scholar
- Oliveira P, Cunha J, Coelho J (2002) Design of Pid controllers using the particle swarm algorithm. In: Twenty-first IASTED international conference: modelling, identification and control (MIC 2002), Innsbruck, Austria, pp 263–268 Google Scholar
- Peng J, Chen Y, Eberhart R (2000) Battery pack state of charge estimator design using computational intelligence approaches. In: Fifteenth annual battery conference on applications and advances, pp 173–177 Google Scholar
- Shi Y, Eberhardt RC (1998) A modified particle swarm optimizer. In: IEEE international conference on evolutionary computation, Anchorage, Alaska, pp 1945–1950 Google Scholar
- Siqueira NN (2006) The particle swarm optimization algorithm applied to nuclear systems surveillance test planning (in Portuguese). MSc Thesis, IEN—Nuclear Engineering Institute, Rio de Janeiro, Brazil Google Scholar
- Vieira LT (2008) Optimization of riser systems for offshore oil exploitation by parallel genetic algorithms (in Portuguese). DSc Thesis, COPPE-Federal University of Rio de Janeiro, Civil Eng Dept, Rio de Janeiro, Brazil Google Scholar
- Vieira LT, de Lima BSLP, Evsukoff AG, Jacob BP (2003) Application of genetic algorithms to the synthesis of riser configurations. In: Proceedings of the 22th international conference on offshore mechanics and arctic engineering, CD-ROM, paper OMAE2003-37231, Cancun, Mexico, pp 1–6 Google Scholar
- Zhang W, Liu M, Clerc Y (2003) An adaptive PSO algorithm for reactive power optimization. In: Sixth international conference on advances in power system control, operation and management (APSCOM), Hong Kong, China, pp 302–307 Google Scholar
- Zheng Y, Ma L, Qian J (2003) Robust Pid controller design using particle swarm optimizer. In: IEEE international symposium on intelligence control, pp 974–979 Google Scholar