A Hybrid Digital CO2 Emission-Control Technology for Maritime Transport: Physics-Informed Adaptive Speed Optimization on Fixed Routes

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Researchers can leverage the hybrid optimization framework combining exact methods and RL for further work in maritime operations and AI for sustainability.

🏢実務担当者:Shipping companies and operators can use this technology for pre-fixture voyage evaluation to optimize fuel costs and comply with CII, EU ETS, and FuelEU Maritime.

🏛政策担当者:Policymakers can note the effectiveness of AI-assisted solutions in achieving measurable emission reductions and consider supporting such technologies for maritime decarbonization.

This paper proposes a physics-informed hybrid digital CO2 emission-control technology for maritime transport, designed for adaptive ship speed optimization along a predefined geographical route between two ports, discretized into quasi-stationary segments and evaluated under forecasted metocean conditions, subject to economic and regulatory constraints associated with maritime decarbonization. The framework integrates two exact optimization methods, Backtracking (BT) and Dynamic Programming (DP), with a reinforcement learning approach based on Proximal Policy Optimization (PPO), operating on a unified physical, economic, and regulatory modeling core. By reducing propulsion fuel demand, the system acts as an upstream CO2 emission-control mechanism for ship propulsion. This operational stabilization of the engine load creates favourable boundary conditions for advanced combustion processes and reduces the volumetric flow of exhaust gas, thereby lowering the technical burden on potential post-combustion carbon capture systems. Segment-wise speed profiles are optimized subject to propulsion limits, Estimated Time of Arrival (ETA) feasibility, and regulatory constraints, including the Carbon Intensity Indicator (CII), the European Union Emissions Trading System (EU ETS) and FuelEU Maritime. The physics-based propulsion and energy model is validated using full-scale operational data from four real voyages of an oil/chemical tanker. A detailed case study on the Milazzo–Motril route demonstrates that adaptive speed optimization consistently outperforms conventional cruise operation. Exact optimization methods achieve voyage time reductions of approximately 10% and fuel and CO2 emission reductions of about 9–10%. The reinforcement learning approach provides the best overall performance, reducing voyage time by approximately 15% and achieving fuel savings and CO2 emission reductions of about 13%. At the route level, the Carbon Intensity Indicator is reduced by approximately 10% for the exact methods and by about 13% for PPO. Backtracking and Dynamic Programming converge to nearly identical globally optimal solutions within the discretized decision space, while PPO identifies solutions located on the most favourable region of the cost–time Pareto front. By benchmarking reinforcement learning against exact discrete solvers within a shared physics-informed structure, the proposed digital platform provides transparent validation of learning-based optimization and offers a scalable decision-support technology for pre-fixture evaluation of fixed-route voyages. The system enables quantitative assessment of CO2 emissions, ETA feasibility, and regulatory exposure (CII, EU ETS, FuelEU Maritime penalties) prior to transport contracting, thereby supporting economically and environmentally informed operational decisions.

• semanticscholar https://doi.org/10.3390/fire9030136first seen 2026-06-29 06:16:32