Sequential matching optimization for lifting trajectory and ballast water allocation of the intelligent revolving floating crane

Abstract

Revolving floating crane (RFC) is one of the core equipment applied to major offshore lifting operations due to its strong working ability. Its operation safety is directly determined by the collaborative matching between the lifting trajectory and the ballast water allocation. However, such matches are accomplished manually in the actual operations, leading to low efficiency, high energy consumption, etc. To address these problems, the unattended intelligent operation mode is an inevitable solution for the RFC. Therefore, a novel sequential matching optimization (SMO) method for the lifting trajectory and the ballast water allocation of the RFC is proposed in this study. The SMO method is proposed based on the point to point (PTP) method and the corresponding principles are firstly explained in great detail. Then, the SMO model is established for searching the optimal scheme with the lowest energy consumption of the lifting and ballast systems. Furthermore, to explore the corresponding superiority, the proposed SMO method is compared with the conventional method, finding that the proposed SMO method can perfectly achieve better lifting performances. Finally, numerical experiments regarding different lifting positions, crane loads, and quantities of the ballast tanks are performed to verify the feasibility of the SMO method.