Scenario-Based Economic Analysis of Underwater Biofouling Using Artificial Intelligence

This study presents a novel framework to evaluate the impact of a certain cycle of underwater hull and propeller cleaning. The artificial neural network model was created to predict fuel consumption, and the coefficients for the six voyages were calculated. Three scenarios, in which the coefficients changed in different ways, were established, and the monthly fuel consumption values were calculated accordingly. The annual fuel cost saving when the cycle of underwater hull and propeller cleaning was four months was USD 10,402–26,685, and it was USD 9653–24,102 for a cycle of six months. We confirmed that using the novel framework we presented, the optimal hull cleaning timing could be determined for oceangoing vessels worldwide, considering economic impact based on data and machine learning models.