Abstract
The objective of this study was to conduct an empirical evaluation of the effectiveness of artificial intelligence systems in optimising the operation of commercial maritime vessels. The methodology involved collecting and processing telemetry from 58 synchronised onboard measurement channels, including temperatures, vibration metrics, gyroscopic data, trim and heel angles, and data from automatic identification systems, differential global positioning systems, and radar signals. Data were sampled at intervals of 1 s, filtered using the Hampel method, and aggregated into frames of 3 min. A hybrid deep learning model was developed to forecast vessel speed, fuel usage, and stability. Experiments were conducted on 16 vessels: six container carriers (3,000 20-foot equivalent units class) and 10 Handymax bulk carriers (40,000–55,000 deadweight tons). These vessels completed 97 voyages between March 2023 and February 2024, 45% of which took place in the Black Sea and 55% in the North Sea. A validation campaign comprising 9,230 h of simulator trials and real-world deployment was carried out to test the artificial intelligence model under variable sea states and in scenarios involving disruptions to automatic identification and differential global positioning systems. The results showed a 12.4% reduction in average fuel consumption and an 8.2% decrease in voyage duration. Ship stability improved, with a 22% reduction in roll amplitude. Predictive maintenance algorithms achieved 95% accuracy, enabling early fault detection and reducing unscheduled downtime. Only three manual interventions were recorded during deployment, and course deviations remained below 1.3°. An environmental analysis revealed a 4.2% improvement in carbon intensity, demonstrating compliance with the International Maritime Organization Carbon Intensity Indicator standards.