Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities
Abstract
:1. Introduction
- Exhaust gases from main and auxiliary engines and incinerator—energy production can release to the atmosphere material like CO2, SO2, NOx, and hydrocarbons.
- 3.
- 4.
- Oily mixtures from the engine room (ER), which can pollute the ocean if pumped overboard. The only time when a vessel is allowed to pump engine room bilge water overboard is when pumping it through an oily water separator, which separates the oil from the water and measures the oil contents in the overboard water. If the oil content is more than 15 ppm (parts per million) it is not allowed to pump it overboard, and this water must retain onboard in dedicated tanks for delivery to an appropriate shore facility [22,23,24];
- 5.
- Paint, tin, and poor condition of the antifouling system, which is used to prevent the accumulation of marine organisms on the ship’s hull. These organisms, such as algae, crustaceans, and mollusks, can attach to the ship’s hull, leading to increased hydrodynamic resistance, fuel consumption, and operational costs as well as an increased risk of transferring invasive species between different ecosystems [25,26,27,28];
- 6.
- Discharge of ballast water and bilges, which can pose a threat to coastlines by introducing alien species into the local ecosystem and causing irreversible changes. Therefore, the Ballast Water Management Convention of 2004 obliges ships to apply one of the two ballast water control standards presented in the Section D Standards for Ballast Water Management. Ships carrying ballast water and using the D-1 method, i.e., ballast water exchange, in order to meet the requirements described in Regulation B-4 of the BWM Convention, should exchange ballast water at a distance of no less than 200 nautical miles from the nearest land and in water depths of no less than 200 m. If these requirements cannot be met, ballast water should be exchanged as far from land as possible, at a distance of no less than 50 nautical miles, maintaining a sea depth of 200 m. Ships performing ballast water exchange shall do so with an efficiency of 95 per cent volumetric exchange of ballast water (i.e., 95% of the volume of the tank must be exchanged).
- Electric pulse/pulse plasma systems;
- Filtration systems (physical);
- Chemical disinfection (oxidizing and non-oxidizing biocides), including ozone generators;
- Ultraviolet treatment;
- Deoxygenation treatment;
- Heat (thermal) treatment;
- Acoustic (cavitation) treatment;
- 7.
- Sewage and garbage—sewage should be collected in a dedicated tank and neutral-ized before being disposed of in the sea. Garbage should only be disposed of when permitted by the MARPOL convention and the implemented garbage management plan (GMP). Regarding garbage disposal, there are eight special areas defined in the Convention: the Baltic Sea area, the Mediterranean Sea, the Black Sea, the Red Sea, the North Sea, the Antarctic, the Gulfs, and the Wider Caribbean. Inside a special area, a vessel must be at least 12 nautical miles from land and en route, and only food waste ground to less than 25 mm can be discharged. Outside the special areas and while en route, food waste can be disposed at greater than 12 nautical miles from the nearest land. Between a distance of 3 and 12 nautical miles, only food waste that has been ground to less than 25 mm can be discharged from a vessel. The discharge overboard of all other garbage is strictly prohibited [32,33,34,35].
- Emission reduction: analyzing AI algorithms and predictive models that optimize fuel consumption and minimize emissions;
- Energy efficiency: investigating AI-driven solutions for route optimization and smart energy management on vessels;
- Operational optimization: evaluating the role of AI in autonomous shipping, navigation systems, and logistics management;
- Green ports: assessing AI applications in port operations, environmental monitoring, and smart port infrastructure.
2. Methodology
- Identification of Sources
- 2.
- Selection Criteria
- 3.
- Data Extraction
- 4.
- Synthesis
- 1.
- Thematic Analysis
- 2.
- Comparative Analysis
- 3.
- Gap Analysis
- 4.
- Evaluation of Impact
3. The Role of AI in Sustainable Shipping
3.1. Emission Reduction
3.2. Energy Efficiency
3.3. Operational Optimization
4. AI in Green Ports
4.1. Port Operations
4.2. Environmental Monitoring
4.3. Smart Port Infrastructure
5. Case Studies
5.1. Case Study 1: Successful AI Implementation in Shipping
5.1.1. Company Overview: Maersk Line
5.1.2. AI Implementation: Fuel Optimization and Predictive Maintenance
Fuel Optimization
Predictive Maintenance
Outcomes
- A substantial reduction in fuel consumption and CO2 emissions;
- Enhanced route planning, leading to more efficient and timely deliveries;
- Improved maintenance practices, resulting in reduced operational disruptions and costs;
- Overall, Maersk’s adoption of AI technologies positioned the company as a leader in sustainable shipping practices.
5.2. Case Study 2: AI in Maritime Environmental Monitoring
5.2.1. Project Overview: Port of Rotterdam
5.2.2. AI Implementation: Comprehensive Environmental Monitoring System
Air Quality Monitoring
Water Quality Monitoring
Outcomes
- Enhanced real-time monitoring and management of air and water quality;
- Rapid identification and response to pollution events, minimizing environmental damage;
- Improved compliance with environmental regulations and standards;
- Increased transparency and communication with the public regarding environmental performance.
6. Challenges and Opportunities
6.1. Challenges
6.2. Opportunities
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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AI Technology | Description | Application Examples |
---|---|---|
Machine Learning | Algorithms that learn from data to make predictions or decisions [68,69]. | Fuel optimization [70], predictive maintenance [57] |
Deep Learning | A subset of machine learning involving neural networks with many layers [71,72]. | Image recognition for cargo inspection [73], navigation systems [74]. |
Reinforcement Learning | Learning method where an agent learns by interacting with its environment [75,76]. | Autonomous shipping [77], route optimization [78]. |
Internet of Things (IoT) | Network of physical devices that collect and exchange data [79]. | Real-time monitoring, environmental sensors [80,81]. |
Edge Computing | Processing data near the source of data generation rather than in a centralized data-processing warehouse [82]. | Real-time data processing on ships, ports [83,84]. |
Predictive Analytics | Analyzing current and historical facts to make predictions about future events [44,85]. | Predictive maintenance, demand forecasting [78]. |
Benefit | Description | Impact |
---|---|---|
Emission Reduction | AI optimizes fuel usage and reduces unnecessary emissions [107,108]. | Lower greenhouse gas emissions [109]. |
Energy Efficiency | AI-driven route optimization and smart energy management systems improve efficiency [110,111]. | Reduced fuel consumption and operational costs [112,113] |
Operational Efficiency | AI enhances logistics and supply chain management through automation and predictive analytics [114]. | Faster, more reliable deliveries; reduced downtime [115,116]. |
Maintenance Savings | Predictive maintenance reduces unexpected failures and extends equipment lifespan [117,118]. | Lower maintenance costs, increased reliability [119,120]. |
Regulatory Compliance | AI helps monitor and ensure compliance with environmental regulations [121,122]. | Avoidance of fines, improved regulatory relations [123]. |
Case Study | Company/Project | AI Technology Used | Outcomes |
---|---|---|---|
Case Study 1: AI in Shipping [47,70,244] | Maersk Line | Fuel optimization, predictive maintenance | Reduced fuel consumption, lowered CO2 emissions, improved maintenance practices |
Case Study 2: AI in Environmental Monitoring [245,246] | Port of Rotterdam | Air and water quality monitoring, predictive models | Enhanced environmental monitoring, showed rapid response to pollution events, complied with regulations |
Challenge | Description | Examples |
---|---|---|
High Implementation Costs | Significant investment required for AI infrastructure and skilled personnel [261] | Cost of sensors, data storage, computing power |
Data Privacy and Security | Ensuring the protection of sensitive data and compliance with data regulations [199] | Handling of operational and proprietary data |
Shortage of Skilled Personnel | Lack of professionals with expertise in AI, data science, and machine learning [262] | Difficulty in hiring and retaining talent |
Technical Barriers | Integration with legacy systems and ensuring data quality and availability (Munim I in 2020). | Compatibility issues, fragmented data |
Connectivity Issues | Limited connectivity in remote maritime operations affecting real-time data transmission [45]. | Challenges in data transmission and system functionality |
Regulatory and Standardization | Variability in international regulations and lack of standardization in AI technologies [263] | Compliance with safety, security, and environmental regulations |
Opportunity | Description | Examples |
---|---|---|
Advanced Machine Learning Algorithms | Enhanced prediction and decision-making capabilities with advanced AI methods | Deep learning, reinforcement learning, neural networks for optimizing maritime operations |
IoT Integration | Development of sophisticated and interconnected systems | Real-time data from ships, ports, and cargo containers; optimization of operations and maintenance |
Edge Computing | Local data processing to address connectivity issues | Reduced latency, real-time decision making, enhanced reliability in remote maritime environments |
Green Technologies | Development and implementation of sustainable practices | Alternative fuels (hydrogen and ammonia), hybrid propulsion systems, optimization of fuel consumption |
Circular Economy Initiatives | Optimization of resource use and waste management | Enhanced recycling, reuse of materials, turning waste into valuable resources |
Long-term Cost Savings | Significant reduction in operational expenses | Improved fuel efficiency, reduced maintenance costs, optimized operations |
Competitive Advantage | Early adoption leading to market leadership | Offering more efficient, reliable, and sustainable services; increased market share and profitability |
Industry Collaboration | Shared knowledge and best practices to drive AI adoption | Public–private partnerships, joint research and development, standardization of AI technologies |
Government Support and Incentives | Financial support and policies promoting AI adoption | Tax incentives, grants, subsidies for AI implementation, support for research and development |
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Durlik, I.; Miller, T.; Kostecka, E.; Łobodzińska, A.; Kostecki, T. Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities. Appl. Sci. 2024, 14, 5994. https://doi.org/10.3390/app14145994
Durlik I, Miller T, Kostecka E, Łobodzińska A, Kostecki T. Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities. Applied Sciences. 2024; 14(14):5994. https://doi.org/10.3390/app14145994
Chicago/Turabian StyleDurlik, Irmina, Tymoteusz Miller, Ewelina Kostecka, Adrianna Łobodzińska, and Tomasz Kostecki. 2024. "Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities" Applied Sciences 14, no. 14: 5994. https://doi.org/10.3390/app14145994
APA StyleDurlik, I., Miller, T., Kostecka, E., Łobodzińska, A., & Kostecki, T. (2024). Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities. Applied Sciences, 14(14), 5994. https://doi.org/10.3390/app14145994