Vessel Engine Configuration Optimization via Predictive Fuel Models
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Solution Overview
Problem
Seafaring vessels with multiple thrust engines face challenges in optimizing engine configurations to minimize fuel consumption while ensuring timely voyages, as existing systems lack efficient methods to determine the optimal engine settings based on real-time operational and environmental data.
Innovation Solution
A method and system utilizing machine learning models to determine the optimal engine configuration by predicting required power and fuel consumption for various engine configurations, considering environmental and operational data, and adjusting power output levels to minimize fuel usage while maintaining voyage constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If multiple thrust engines are operated at high power output levels to ensure timely voyages, then voyage speed and reliability are improved, but fuel consumption increases
Solution Approach 1:
The system dynamically adjusts engine operating parameters (power output levels, number of engines running) based on real-time conditions including environmental data, vessel operational data, and voyage requirements. Machine learning models predict optimal parameter combinations that minimize fuel consumption while maintaining required voyage speed and meeting arrival time constraints.
Solution Approach 2:
The engine configuration is made dynamic rather than static. The system continuously monitors vessel operational data and environmental conditions, then adjusts the number of engines running and their power output levels in real-time. This allows the system to adapt to changing conditions and optimize fuel efficiency throughout the voyage while ensuring timely arrival.
2Reliability
If multiple thrust engines are operated to complete voyages within required time constraints, then voyage reliability is improved, but fuel consumption increases
Solution Approach 1:
The system performs preliminary calculations and predictions using machine learning models to determine optimal engine configurations before making decisions. By analyzing historical data and predicting future conditions, the system can pre-determine the most fuel-efficient engine setup that will still meet voyage time requirements, avoiding last-minute adjustments that consume excessive fuel.
Solution Approach 2:
The system implements continuous feedback loops where actual fuel consumption and voyage progress are monitored and compared against predictions. This feedback is used to refine machine learning models and adjust engine configurations in real-time, ensuring that voyage timeliness requirements are met while minimizing fuel consumption through data-driven optimization.
3Measurement precision
If engine-specific machine learning models are used to predict fuel consumption for each thrust engine, then fuel consumption prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system divides the vessel's propulsion system into individual engine components, creating separate machine learning models for each thrust engine. This segmentation allows each model to learn and predict the specific fuel consumption characteristics of individual engines, accounting for variations in engine condition, efficiency, and performance. The modular approach enables precise predictions while maintaining manageable system complexity through independent, specialized models.
Data Source
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AI summary
An optimum engine configuration is determined, based on a predicted required power, for a seafaring vessel having a plurality of thrust engines. The predicted required power is determined by inputting environmental data, and voyage data to a required power model. At least some of the environmental data is received from a plurality of sensors. The optimum engine configuration is selected from a plurality of candidate engine configurations. Each candidate engine configuration includes a specified number of thrust engines running and a specified power output level of each thrust engine. The optimum engine configuration is selected based on a candidate total predicted fuel consumption of each candidate engine configuration. The candidate total predicted fuel consumption amount is determined as a sum of the engine-specific predicted fuel consumptions determined for each running thrust engine of that candidate engine configuration.