Vessel Fuel Optimization via Historical Data Analysis
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Solution Overview
Problem
Current methods for optimizing ship speed and power source configuration are based on static analysis and do not account for changes in a vessel's operational profile over time, leading to suboptimal fuel consumption and increased greenhouse gas emissions, with existing trim optimization methods being costly and limited in accuracy.
Innovation Solution
A method that collects and analyzes historic vessel data to determine optimal trim and draft conditions, using filtering and advanced mathematical models like Artificial Neural Networks to account for factors such as weather, hull fouling, and engine performance, providing real-time adjustments for improved fuel efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static analysis methods are used to determine optimal ship speed and power configuration, then the analysis can be performed at factory testing or initial sea trials, but the optimal operational parameters do not account for changes in vessel operational profile over time, leading to suboptimal fuel consumption
Solution Approach 1:
The patent transitions from static analysis to dynamic real-time optimization by continuously collecting operational data (speed, power, fuel consumption, trim, draft) and using machine learning models to adapt optimal parameters to changing vessel conditions over time, resolving the contradiction between accuracy and complexity
Solution Approach 2:
The system implements feedback loops where operational data is continuously monitored, analyzed, and used to adjust operational parameters in real-time, enabling the vessel to maintain optimal performance despite changes in operational profile, thereby improving accuracy without requiring excessive system complexity
2Loss of energy
If only trim optimization is performed using CFD computational modelling or sea trial measurements, then fuel consumption can be reduced, but the optimization is limited to a single parameter and covers only a limited number of sailing conditions, reducing overall accuracy
Solution Approach 1:
The patent expands optimization from single-parameter trim adjustment to multi-parameter optimization including speed, power configuration, trim, and draft, using machine learning models that analyze historical data across diverse sailing conditions to identify optimal combinations of parameters, thereby reducing fuel consumption while improving overall accuracy
Solution Approach 2:
The system creates a universal optimization framework that handles multiple sailing conditions and parameter combinations through a single integrated machine learning model, eliminating the need for separate CFD analyses or sea trials for each condition, thus improving both fuel efficiency and measurement precision across all operating scenarios
3Loss of energy
If conventional trim optimization systems are deployed, then some fuel savings can be achieved, but the systems rely on present moment trim, power and speed data, limiting accuracy and potential fuel savings
Solution Approach 1:
The system performs preliminary analysis of historical operational data to establish baseline performance and training datasets before real-time optimization, enabling the machine learning models to learn from past patterns and provide more accurate predictions and recommendations, thereby increasing both fuel savings and measurement precision
Solution Approach 2:
The patent implements continuous data collection and model training throughout the vessel's operation, allowing the system to continuously improve its accuracy by learning from ongoing operational data rather than relying solely on initial static analysis or present moment data, thus enhancing both fuel savings and precision over time
Data Source
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AI summary
A method for the reduction of ship fuel consumption through the optimisation of vessel draft, speed and trim using historical vessel data. Historical global, online data, is collected for multiple vessel operating parameters associated with its previous voyages. After initial filtering and cleaning of the gathered data, a process of analysing the data to determine the optimum draft, speed and trim for the vessels' given speed is described. The determined optimum draft, speed and trim values are then presented to the Captain or an automatic draft and trim optimisation system for the current draft and trim to be adjusted. This application therefore discloses a method for analysing historical vessel data to provide advice on optimum draft, trim and speed. A method for predicting the achievable fuel savings and recording the fuel savings achieved is also disclosed.