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The shipping company or the operator determines the mode of operation of a ship. In the case of container ships, there may be various operating patterns employed to arrive at the destination within the stipulated time. In addition, depending on the influence of the ocean’s environmental conditions, the speed and the route can be changed. As the ship’s fuel oil consumption is closely related to its operational pattern, it is possible to identify the most economical operations by analyzing the operational patterns of the ships. The operational records of each shipping company are not usually disclosed, so it is necessary to estimate the operational characteristics from publicly available data such as the automatic identification system (AIS) data and ocean environment data. In this study, we developed a visualization program to analyze the AIS data and ocean environmental conditions together and propose two categories of applications for the operational analysis of container ships using maritime big data. The first category applications are the past operation analysis by tracking previous trajectories, and the second category applications are the speed pattern analysis by shipping companies and shipyards under harsh environmental conditions. Thus, the operational characteristics of container ships were evaluated using maritime big data.

Thermal insulation panels are installed on the inner walls of liquefied natural gas (LNG) tanks of an LNG carrier to maintain the cryogenic temperature. Mastic ropes are used to attach thermal insulation panels to the inner walls and to fill the gap between the walls and panels. Because the inner walls of the LNG tanks can be corrugated owing to production errors, a large amount of mastic ropes are required to maintain the flatness of the thermal insulation panels. Therefore, in this study, an optimization method is proposed to minimize the total amount of mastic ropes for satisfying the flatness criterion of thermal insulation panels. For this purpose, an optimization problem is mathematically formulated. An objective function is used to minimize the total amount of mastic ropes considering constraints to flatten the thermal insulation panels. This function is applied to the design of membrane-type LNG tanks to verify the effectiveness and feasibility of the proposed method. Consequently, we confirm that the proposed method can provide a more effective arrangement design of mastic ropes compared with manual design.

In shipyards, blocks are controlled by connecting the crane and block with wires during block erection. During block lifting, if a block is not carefully controlled, it will cause damage. Block lifting using crane operation is performed by controlling the number of wires, hooks, and equalizers. Consequently, predicting stable block lifting is difficult. In this study, we proposed a control method to determine static equilibrium. Initially, an algorithm for finding the initial equilibrium block state (IES algorithm) was proposed, followed by deep reinforcement learning (DRL) based method for block lifting. The position, orientation, angular velocity of the block, and hoisting speed of the wires were applied as the DRL state. The control input of the crane was calculated by deriving the hoisting speed of the wires. To verify the proposed method, comparative studies on the application of the IES algorithm were carried out, and further block movement was compared. Conclusively, the proposed method effectively increased block lifting safety.

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