This research develops a dual physics-based machine learning system to forecast fuel consumption and CO₂ emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship.

Ship recycling in Malaysia is an unknown business as no dedicated ship recycling yard is currently operated in this country. Most Malaysian shipowners send their end-of-life (EOL) ships to prominent ship recycling yards in Pakistan, India, and China, which are key players in the global ship recycling industry. Due to various factors, establishing a proper ship recycling facility for Malaysia is challenging. One issue that arises is the ability of Malaysian yards to offer competitive EOL ship prices, which can be achieved by increasing the revenue or lowering the ship recycling process costs. It will allow Malaysian recycling players to offer a high price to ship owners and attract them to recycle their ships locally. This paper studies the potential of an integrated ship recycling facility for a Malaysian shipyard. A medium size shipyard situated at Chukai, Terengganu, was selected as a case study. The facility analysis was performed to determine share resources, and a revised layout was proposed. The prospect of ship recycling facility integration in Malaysian shipyards was discussed and summarised. The result analysis signifies that the shipyard facility can be adequately shared for ship recycling activity, and no major change is required in the overall shipyard layout. Lastly, the findings suggest that an integrated ship recycling facility is feasible for a Malaysian shipyard.

This literature review critically examines the development and optimization of sustainable energy and exergy analysis software specifically designed for offshore wind farms, emphasizing the transformative role of machine learning (ML) in overcoming operational challenges. Offshore wind energy represents a cornerstone in the global transition to low-carbon economies due to its scalability and superior energy yields; however, its complex operational environment, characterized by harsh marine conditions and logistical constraints, necessitates innovative analytical tools. Traditional deterministic methods often fail to capture the dynamic interactions within wind farms, thereby underscoring the need for ML-integrated approaches that enhance precision in energy forecasting, fault detection, and exergy analysis. This PRISMA-ScR review synthesizes recent advancements in ML techniques, including Random Forest, Long Short-Term Memory networks, and hybrid models, demonstrating significant improvements in predictive accuracy and operational efficiency. In addition, it critically identifies current gaps in existing software tools, such as inadequate real-time data processing and limited user interface design, which hinder the practical implementation of ML solutions. By integrating theoretical insights with empirical evidence, this study proposes a unified framework that leverages ML algorithms to optimize turbine performance, reduce maintenance costs, and minimize environmental impacts. Emerging trends, such as incorporating digital twins and Internet of Things (IoT) technologies, further enhance the potential for real-time system monitoring and adaptive control. Overall, this review provides a comprehensive roadmap for the next generation of software tools to revolutionize offshore wind farm management, thereby aligning technological innovation with global renewable energy targets and sustainable development goals.