Speed Optimization of Container Ship Considering Route Segmentation and Weather Data Loading: Turning Point-Time Segmentation Method

Abstract

As one of the ship energy efficiency optimization measures with the most energy saving and emission reduction potential, ship speed optimization has been recommended by the International Maritime Organization. In ship speed optimization, considering the influence of weather conditions, route segmentation and weather data loading methods significantly affect the reliability of speed optimization results. Therefore, taking the ocean-going container ship as the research object, on the basis of constructing the main engine fuel consumption prediction model and shaft speed prediction model based on machine learning methods, a route segmentation and weather loading-speed optimization iterative algorithm is proposed in this study. Single-objective speed optimization research is then conducted based on the algorithm. The research results show that the proposed algorithm effectively reduces the difference between optimized fuel consumption and actual fuel consumption, and can achieve a fuel-saving rate between 2.1% and 5.2%. This study achieves an accurate and reliable prediction of ship fuel consumption and shaft speed, and solves the strong coupling problem between route segmentation, weather loading, and speed optimization by iterative optimization of ship speed. The proposed algorithm provides strong technical support for ships to achieve the goal of energy saving and emission reduction.

A Prescriptive Model for Failure Analysis in Ship Machinery Monitoring Using Generative Adversarial Networks

“…In the maritime industry, black box models and data-driven algorithms are employed across a diverse range of applications. These applications include the prediction of fuel consumption by the main engine [17], voyage optimization via vessel speed optimization [18], and the prognostics and health management of ship machinery systems [19]. Each of these applications' predictive capabilities relies on data-driven models, which aim to enhance the ship's operational efficiency and decision-making processes within the maritime sector.…”

Navigating Energy Efficiency: A Multifaceted Interpretability of Fuel Oil Consumption Prediction in Cargo Container Vessel Considering the Operational and Environmental Factors

“…This technological prowess has given rise to a multitude of applications, including route planning [10], weather routing [11], speed optimization [12], trim optimization [13], ship routing and vessel scheduling [14], predictive maintenance [15], anomaly detection [16], shaft power prediction [17], safety management [18], etc. These diverse machine learning applications collectively hold the promise of bolstering operational efficiency, reducing fuel consumption, and curbing greenhouse gas emissions-a transformative shift within the shipping industry.…”

Vessel speed optimization study under emission control area region

“…Fuel consumption was calculated based on the payload on board and the speed of the vessel, based on which the optimal operating cost was achieved by optimizing the speed of each route and allocating the right type and number of vessels to the routes.SUN et al . [3] An iterative algorithm for route segmentation and weather loading speed optimization is proposed. Then a single-objective speed optimization study is carried out based on this algorithm.…”