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

Zhijie Liu (), Jianyu Jiang, Zhiqiang Gan and Chengxin Lin
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Zhijie Liu: Dalian Maritime University
Jianyu Jiang: Dalian Maritime University
Zhiqiang Gan: Tianjin Pumps & Machinery Group Co.
Chengxin Lin: Dalian Maritime University

Annals of Operations Research, 2022, vol. 311, issue 1, No 19, 279-294

Abstract: 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 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10479-019-03213-2

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