Ballast systems and ballast water dynamic allocation between ballast tanks are very important for ensuring the offshore operation efficiency and safety of the revolving floating crane (RFC). Its modeling and solving have multiple difficulties such as modeling complexity, solving complexity and engineering practicability. Early studies showed that domain knowledge is of great significance for the optimization of the design quality and innovation of such complex engineering issues. By analyzing the coupled operation process characteristics among the floating crane, ship hull and ballast system, a ballast water allocation optimization model based on dynamic programming strategy is established. The domain knowledge of ship ballasting is extracted, and a domain knowledge base of expert experience rules for the ballast water allocation is established. A Fuzzy-Particle Swarm Optimization (FPSO) algorithm is given to obtain the optimal allocation scheme, which uses fuzzy logic inference to process domain knowledge and improve the solving quality. Three different cases are given to illustrate the validity of the proposed model and algorithm by comparing it with other algorithms. The analysis results show that the established optimization method can effectively improve the operation efficiency and reduce the calculation time and the number of ballast tanks involved in allocation, which makes the optimal scheme more suitable for engineering applications.

Ballasting with water tanks is essential for improving the efficiency, cost and safety of lifting operation with revolving floating cranes. An optimisation model of ballast water high-efficiency adjustment was presented for the tube tunnel type ballast system. The water level variation of each ballast tank during ballasting process, the ballast water filling mass from sea before lifting and discharging mass after lifting operation are taken as optimisation variables, the minimal ballasting time during lifting process as the optimisation objective, and keeping the hull balance and stability in the lifting process as the constraint conditions. The ballast water allocation optimisation can be characterised by a multi-step decision process, and a dynamic programming method is presented which divides the decision stages based on the crane revolving angles in the rotary lifting process. An example is given to illustrate and analyse the proposed model and approach.