Posted: November 3rd, 2023

Machine Learning for Decarbonization: Optimizing Marine Propulsion Systems for IMO Compliance

Assessment Task Assignment Brief: Advanced Marine Systems Optimization

Module: Sustainable Maritime Engineering Systems (ME6008)

Assessment: Research Report (Assessment 2 of 3)

Weighting: 50%

Word Count: 4,000 words (excluding figures, tables, appendices, and reference list)

Submission Deadline: 10th March 2026

Task Description

This assignment requires a critical research report on the integration of Machine Learning (ML) and Artificial Intelligence (AI) for the design, operation, and maintenance of modern maritime vessels. The report must select one specific area (e.g., hull form hydrodynamics, propulsion system optimization, structural health monitoring, or operational route planning) and deliver a comprehensive analysis of its current state-of-the-art, engineering challenges, and future potential.

The report must move beyond descriptive literature review to provide a critical evaluation of the practical engineering and regulatory implications.

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Report Structure and Criteria

The report must adhere to the following structure and address the criteria below:

Section	Target Weighting	Criteria
1. Introduction	10%	Define the scope and selected application area. State the research aims and the report’s structure. Clearly establish the knowledge gap the report will address.
2. State-of-the-Art and Literature Review	30%	Systematically review recent (2019-2025) peer-reviewed literature on the application of ML/AI within the chosen area. Identify predominant ML/AI algorithms (e.g., tree-based algorithms, neural networks) and their documented results (e.g., fuel use optimization, failure prediction) (Arish et al., 2025; Vizentin et al., 2020).
3. Critical Engineering Analysis	30%	Critically evaluate the specific engineering challenges of implementing the identified ML/AI technologies. Discuss data acquisition, model validation, and the impact on the traditional design spiral methodology. Address the conflict between ML-driven optimization and established regulatory frameworks (e.g., IMO, Classification Societies).
4. Regulatory and Operational Implications	20%	Analyze the impact of AI/ML integration on vessel certification, seakeeping criteria (Zu et al., 2024), and operational resilience. Specifically discuss the challenges of demonstrating safety and reliability for autonomous or assisted systems to regulatory bodies.
5. Conclusion and Future Work	10%	Summarize key findings concerning the viability and challenges of the selected application. Propose concrete future research directions necessary to achieve industry-wide adoption.

Required Submission Format

• Font: 12-point Times New Roman.
• Line Spacing: 1.5.
• Referencing: Harvard Format for in-text citations and the reference list.
• Figures and tables must be clearly titled, referenced in the text, and conform to professional engineering standards.

Potential Paper Titles

1. Machine Learning for Decarbonization: Optimizing Marine Propulsion Systems for IMO Compliance
2. AI and Ship Design: Revolutionizing Naval Architecture
3. Autonomous Systems and Marine Engineering: Reliability and Regulation
4. Computational Fluid Dynamics and Neural Networks in Hull Hydrodynamics

Relevant Keywords

Machine Learning, Naval Architecture, Marine Propulsion, Decarbonization, Ship Design Optimization

References (Harvard Format)

Arish, N., Kamper, M. J. & Wang, R. J. (2025). Advancements in electrical marine propulsion technologies: A comprehensive overview. SAIEE Africa Research Journal, 116(1), pp. 14–29.

Kim, K. S. & Roh, M. I. (2024). Review of ship arrangement design using optimization methods. Journal of Computational Design and Engineering, 12(1), pp. 100-121. https://doi.org/10.1093/jcde/qwae112

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Rui, S., Guo, Z., Zhou, Z., Wang, Z., Ye, G. & Ma, D. (2024). Editorial: Frontiers in marine sciences, social sciences and engineering research related to marine (renewable) energy development. Frontiers in Marine Science, 11. https://doi.org/10.3389/fmars.2024.1421628

Vizentin, G., Vukelic, G., Murawski, L., Recho, N. & Orovic, J. (2020). Marine propulsion system failures—A review. Journal of Marine Science and Engineering, 8(9), 662. https://doi.org/10.3390/jmse8090662

Zu, M., Garme, K. & Rosén, A. (2024). Seakeeping criteria revisited. Ocean Engineering, 297, 116785. https://doi.org/10.1016/j.oceaneng.2024.116785

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