Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Estimation and Optimization of Ship Fuel Consumption in Maritime: Review, Challenges and Future Directions

Dusica Marijan, Hamza Haruna Mohammed, Bakht Zaman

Abstract

To reduce carbon emissions and minimize shipping costs, improving the fuel efficiency of ships is crucial. Various measures are taken to reduce the total fuel consumption of ships, including optimizing vessel parameters and selecting routes with the lowest fuel consumption. Different estimation methods are proposed for predicting fuel consumption, while various optimization methods are proposed to minimize fuel oil consumption. This paper provides a comprehensive review of methods for estimating and optimizing fuel oil consumption in maritime transport. Our novel contributions include categorizing fuel oil consumption \& estimation methods into physics-based, machine-learning, and hybrid models, exploring their strengths and limitations. Furthermore, we highlight the importance of data fusion techniques, which combine AIS, onboard sensors, and meteorological data to enhance accuracy. We make the first attempt to discuss the emerging role of Explainable AI in enhancing model transparency for decision-making. Uniquely, key challenges, including data quality, availability, and the need for real-time optimization, are identified, and future research directions are proposed to address these gaps, with a focus on hybrid models, real-time optimization, and the standardization of datasets.

Estimation and Optimization of Ship Fuel Consumption in Maritime: Review, Challenges and Future Directions

Abstract

To reduce carbon emissions and minimize shipping costs, improving the fuel efficiency of ships is crucial. Various measures are taken to reduce the total fuel consumption of ships, including optimizing vessel parameters and selecting routes with the lowest fuel consumption. Different estimation methods are proposed for predicting fuel consumption, while various optimization methods are proposed to minimize fuel oil consumption. This paper provides a comprehensive review of methods for estimating and optimizing fuel oil consumption in maritime transport. Our novel contributions include categorizing fuel oil consumption \& estimation methods into physics-based, machine-learning, and hybrid models, exploring their strengths and limitations. Furthermore, we highlight the importance of data fusion techniques, which combine AIS, onboard sensors, and meteorological data to enhance accuracy. We make the first attempt to discuss the emerging role of Explainable AI in enhancing model transparency for decision-making. Uniquely, key challenges, including data quality, availability, and the need for real-time optimization, are identified, and future research directions are proposed to address these gaps, with a focus on hybrid models, real-time optimization, and the standardization of datasets.
Paper Structure (47 sections, 4 figures, 9 tables)

This paper contains 47 sections, 4 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Motivation and Contribution
  3. Literature Search Protocol
  4. Data Required for Fuel Consumption Estimation
  5. Noon Reports
  6. Onboard Sensors Data
  7. AIS Data
  8. Meteorological Data
  9. Measurements, Reporting, and Verification (MRV)
  10. Models for Fuel Oil Consumption Estimation
  11. Physics-based Models
  12. Estimating Additional Resistance
  13. Estimating Speed Loss
  14. Statistical Approaches Fuel Curve
  15. Data-driven Models
  16. ...and 32 more sections

Figures (4)

  • Figure 1: FOC Estimation Data Sources: Overview of the key data types used in estimating FOC, incorporating navigational, engine, environmental, and regulatory inputs.
  • Figure 2: Taxonomy of FOC estimation models
  • Figure 3: Schematic representation of a resistance-based model for FOC estimation. The process begins with environmental and ship condition inputs (e.g., wind, waves, current, draft, and hull roughness) to compute the total resistance components (calm-water, wave, wind, fouling, and shallow-water resistance). These are then translated into effective engine power requirements through propulsion models. Finally, fuel oil consumption is estimated using SFOC and loading conditions.
  • Figure 4: XAI Methods in FOC Estimation