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Research on the Application of Genetic Algorithm in Long-Distance Solid-Liquid Transportation Pumping Systems

[+] Author Affiliations
Yun Xu, Ping Chen, Wei Shi, Xianwu Luo

Tsinghua University, Beijing, China

Paper No. FEDSM2006-98195, pp. 587-590; 4 pages
  • ASME 2006 2nd Joint U.S.-European Fluids Engineering Summer Meeting Collocated With the 14th International Conference on Nuclear Engineering
  • Volume 1: Symposia, Parts A and B
  • Miami, Florida, USA, July 17–20, 2006
  • Conference Sponsors: Fluids Engineering Division
  • ISBN: 0-7918-4750-0 | eISBN: 0-7918-3783-1
  • Copyright © 2006 by ASME


This paper treats the operation optimization for the LQS two-phase flow pump dredging system including three parallel pumps and two serial pumps by using the genetic algorithm. The Yellow River is well known for its large concentration sand-laden-water. In order to stop the increasing river bed by the depositing sand, a bank-strengthening project has been put into action in Shangdong province since 1998. In the project, very large amount of the Yellow River sand at river bed is dredged up and pumped to several miles away to strengthen the back side of the river bank. The LQS two-phase flow pump dredging system, which is a recently developed apparatus for this project, is applied in the sand transportation system. The main target of the present work is to conduct the operation optimization computation for the LQS two-phase flow pump dredging system by using the genetic algorithm. The genetic algorithm introduces the mechanism of the heredity and selection by the nature into Mathematics, and presents a new optimization algorithm. Based on the operation characteristics of the LQS dredging system, the mathematic models with hydraulic constraints were established, and the numerical expression of genetic algorithm for operation optimization was given. Selecting the minimum power consumption for pumping the same amount of sand as the objective function and making additive multiple constraints, we developed a kind of new genetic algorithm, where the searching range is reduced gradually so as to strengthen the searching ability for the algorithm. The computation results indicate that by applying the modified genetic algorithm for the global optimal solution, the present operation saved much more power than the previous one. The developed method has been applied in the actual project, and verified to be very effective.

Copyright © 2006 by ASME



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