Tailoring the particle swarm optimization algorithm for the design of offshore oil production risers

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.

[1]  Amitava Chatterjee,et al.  Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization , 2006, Comput. Oper. Res..

[2]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[3]  Xiaohui Hu,et al.  Engineering optimization with particle swarm , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[4]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[5]  E. Biscaia,et al.  The use of particle swarm optimization for dynamical analysis in chemical processes , 2002 .

[6]  Shang He,et al.  An improved particle swarm optimizer for mechanical design optimization problems , 2004 .

[7]  M. A. Abido.  Optimal des'ign of Power System Stabilizers Using Particle Swarm Opt'imization , 2002, IEEE Power Engineering Review.

[8]  A. Cockshott,et al.  Improving the fermentation medium for Echinocandin B production part II: Particle swarm optimization , 2001 .

[9]  Liyan Zhang,et al.  Robust PID controller design using particle swarm optimizer , 2003, Proceedings of the 2003 IEEE International Symposium on Intelligent Control.

[10]  Russell C. Eberhart,et al.  Guest Editorial Special Issue on Particle Swarm Optimization , 2004, IEEE Trans. Evol. Comput..

[11]  Michael N. Vrahatis,et al.  On the computation of all global minimizers through particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[12]  Breno Pinheiro Jacob,et al.  Alternative Configurations For Steel Catenary Risers For Turret-Moored FPSOs , 1999 .

[13]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[14]  J R Saunders,et al.  A particle swarm optimizer with passive congregation. , 2004, Bio Systems.

[15]  Beatriz Souza Leite Pires de Lima,et al.  A hybrid fuzzy/genetic algorithm for the design of offshore oil production risers , 2005 .

[16]  José Boaventura Cunha,et al.  Design of PID controllers using the particle swarm algorithm , 2002 .

[17]  Yuhui Shi,et al.  Cooperative Particle Swarm Optimization for Robust Control System Design , 2003 .

[18]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[19]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[20]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[21]  Carlos Alberto Duarte de Lemos,et al.  Alternative Design of Flexible Jumpers for Deepwater Hybrid Riser Configurations , 2001 .

[22]  Alexandre G. Evsukoff,et al.  Application of Genetic Algorithms to the Synthesis of Riser Configurations , 2003 .

[23]  Carlos A. Coello Coello,et al.  Use of Particle Swarm Optimization to Design Combinational Logic Circuits , 2003, ICES.

[24]  O. Weck,et al.  A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM , 2005 .

[25]  N. P. Padhy,et al.  Application of particle swarm optimization technique and its variants to generation expansion planning problem , 2004 .

[26]  Yaobin Chen,et al.  Battery pack state of charge estimator design using computational intelligence approaches , 2000, Fifteenth Annual Battery Conference on Applications and Advances (Cat. No.00TH8490).

[27]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[28]  Jonathan A. Wright,et al.  A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization , 2004 .

[29]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[30]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[31]  E. F. Costa,et al.  On the numerical solution and optimization of styrene polymerization in tubular reactors , 2003, Comput. Chem. Eng..

[32]  Yutian Liu,et al.  An adaptive PSO algorithm for reactive power optimization , 2003 .