DEVELOPMENT OF MODEL-DRIVEN DECISION SUPPORT SYSTEM TO SCHEDULE UNDERWATER HULL CLEANING

Abstract

Maritime industries are constantly searching for a method to enhance ship efficiency, with increasing concern about the environmental impact and rising fuel prices. Marine biofouling is one of the factors that increase ship fuel consumption. However, removing the fouling of the ship requires effort for hull maintenance. Due to the trade-off between conducting maintenance and performance degradation, this study presents the development of a Model-Driven Decision Support System (MD-DSS) to predict the optimum time for underwater hull cleaning for biofouling management. Five stages (sub-models) are employed to develop a DSS, namely: ship resistance estimation, estimation of additional resistance due to biofouling, an iterative-based method for determining the best time to conduct the hull cleaning, and an analysis report. The implemented algorithm was validated by comparing its result with a manually scheduled maintenance date. The DSS is able to determine the best time (date) for maintenance in all given scenarios. By giving two scenarios of different maintenance costs and different fuel prices, the optimisation results produce the same number of maintenances. Within 60 months, four to five hull cleanings are required. It is also found that when the optimal number of maintenances is known, then increasing this number will not have any impact on reducing the hull cleaning costs because the reduction in fouling does not significantly reduce the costs incurred for maintenance. During several trials of the DSS, it is shown that the system can generate maintenance schedules for different time intervals of ship operation within an acceptable time. It takes approximately 52 minutes, 12 minutes, and 5 minutes consecutively to determine the maintenance schedules for ship operation intervals of 5 years, 2.5 years, and 1 year.