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Sustainable shipping management is a holistic framework that assesses the importance of external policy pressure, the eco-design of shipping services, and cross-functional green management within shipping companies. The most crucial factor impacting sustainable shipping management is external policy pressure, with eco-design and cross-functional green management following in significance. With the introduction of new IMO guidelines and policy developments by both developed and developing economies, the shipping industry will need to adopt sustainable practices to sustain its growth in the future.
Shipping firms will have to implement an ever-increasing array of green shipping practices (GSPs) to reduce the environmental impact of their operations while improving their performance. GSPs encompass the incorporation of alternative fuels, conducting environmental impact assessments, leveraging the power of predictive models and a lot more. This has been proven to be the only method to ensure an enhanced environmental and productivity performance for shipping firms. In this article we shall attempt to share some of the sustainable and clean shipping practices which we believe any firm operating in the maritime sector will benefit from:
1.CSP-1: Monitor Efficiency
Studies have shown that marine biofouling attached to the underwater ship hull can cause problems for ship operators by increasing total drag, reducing ship speed, and disturbing vessel performance. The increase in effective power due to biofouling can range from 18.1% for light slime to even higher percentages for heavier fouling, highlighting the substantial impact on ship resistance and fuel consumption. Effectively countering this issue is usually extremely cost intensive, however automating the process by investing in deep learning models trained on identifying patterns of prospective biofouling on a hull-form can help firms to undertake predictive periodic cleaning of the hull, thereby enhancing fuel economy.
2. CSP-2: Know your Ship
Life Cycle Assessment (LCA), is a comprehensive framework that evaluates the environmental impact and cost of ships across their entire life cycle, including construction, operation, maintenance, and recycling. The integration of Machine Learning(ML) methods with LCA has shown promise in optimizing scenarios and streamlining LCA processes across various use cases. ML can efficiently optimize aspects of LCA and be integrated into existing inventory databases to enhance the accuracy and efficiency of life cycle assessments. Incorporating advances in LCA, such as the spatially explicit modeling, is the way forward for the shipping industry. This approach delivers predictive ecological information and spatial resolution to evaluate key impacts on climate, water, and biodiversity, providing a better understanding of environmental impacts in supply chain decisions.
3. CSP-3: Set Aims and Targets
The International Maritime Organization (IMO) aims to cut total GHG emissions from international shipping by at least 50% by 2050 compared to 2008 levels. Optimizing the use of alternative fuels is crucial to meet these targets and comply with upcoming regulations. Given the absence of a clear “winner” among alternative fuels and the underdeveloped infrastructure for many options, optimizing the use of multiple alternative fuels offers flexibility and future-proofs ships against uncertainties, minimizing the risk of stranded assets.
CSP-3 For the Geeks and Pundits: Bidirectional-Long Short-term Memory(Bi-LSTM) networks with attention mechanisms are well-suited for capturing long-term dependencies in data sequences, making them ideal for understanding the complex relationships between various operational conditions and fuel consumption in ships. Bi-LSTM networks with attention mechanisms excel at analyzing real-time operational conditions and optimizing fuel consumption in ships due to their superior performance with high-frequency data streams, making them essential for efficient fuel management and decision-making in dynamic maritime environments.
4. CSP-4: Work on Execution
Voyage optimization algorithms aim to provide optimized ship routes for expected time of arrival (ETA) with well-planned waypoints and sailing speeds to enhance ship safety and energy efficiency. Various optimization algorithms, such as Dijkstra’s algorithm, genetic algorithms, A* algorithm, and heuristic functions, are utilized to generate efficient ship routes based on different objectives like fuel consumption, risk reduction, and crack propagation reduction. By optimizing routes based on factors such as weather, currents, and traffic, these algorithms enhance operational efficiency, lower costs, and contribute to the overall reduction of the shipping industry’s environmental impact.
CSP-4 For the Geeks and Pundits: Various optimization algorithms, such as Dijkstra’s algorithm, genetic algorithms, A* algorithm, and heuristic functions, are utilized to generate efficient ship routes based on different objectives like fuel consumption, risk reduction, and crack propagation reduction.
5. CSP-5: Ensure Data Quality
Machine learning algorithms such as Linear Regression, Random Forest, Long Short-Term Memory Artificial Neural Network (LSTM ANN), and Neural Basis Expansion Analysis for Time Series (NBEA-TS) are proficient at handling missing data and drift in STW sensor measurements, a vital aspect for adherence to new emissions regulations such as the Carbon Intensity Indicator (CII) and the Energy Efficiency existing Ship Index (EEXI). Vessel sensor data, encompassing engine performance, fuel consumption, and cargo status metrics, plays a critical role in optimizing vessel performance. Correlational databases need to be developed for analyzing power output and efficiency for various sea-states in the operational routes of a vessel.
