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Ballast water dynamic allocation optimization model and analysis for safe and reliable operation of floating cranes

  • Zhijie LiuEmail author
  • Jianyu Jiang
  • Zhiqiang Gan
  • Chengxin Lin
S.I.: Reliability Modeling with Applications Based on Big Data
  • 22 Downloads

Abstract

Ballast water can adjust the heel and trim and its optimal allocation is very important for assuring safe operation of floating cranes. The combined ballast system based on pumps and water gravity self-flow can transfer a large amount of ballast water in a short time and is widely used for large floating cranes. Based on the hydrostatics of ships and optimal theories, the optimization model of ballast water allocation is built to minimize the ballasting time of floating cranes using the combined ballast system. In this model, the variations of water levels for all ballast tanks are taken as the optimization variables and the hull balance in the ballasting process as the constraint conditions. The ballasting process can be considered as the Markov process, so the dynamic programming solving model is established for the dynamic ballasting. The analysis of a numerical simulation case shows that the ballasting time of the combined ballast system obviously reduces compared with the ballast pump system. The tilted angles of the floating cranes are very small which assures the operation safety and reliability of floating cranes. The established optimization model and method can obtain the optimal ballasting process, effectively reduce the ballasting time, and provide decision model and solving algorithm support for improving the efficiency, and achieving the dynamic ballasting automatic or intelligent control of floating cranes based on computers.

Keywords

Offshore lifting safety Combined ballast system Ballast water optimal allocation Automatic ballasting 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Number 51879026); and the Fundamental Research Funds for the Central Universities of China (Grant Numbers 3132019120, 3132016354).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Naval Architecture and Ocean Engineering CollegeDalian Maritime UniversityDalianChina
  2. 2.Tianjin Pumps & Machinery Group Co.TianjinChina

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