Journal Article10.1016/J.OCEANENG.2018.08.050
Energy-efficient shipping: An application of big data analysis for optimizing engine speed of inland ships considering multiple environmental factors
86
TL;DR: The distributed parallel k-means clustering algorithm is adopted to achieve an elaborate route division by analyzing the corresponding environmental factors based on a self-developed big data analytics platform and shows that the proposed method can effectively reduce energy consumption and CO2 emissions of ships.
read more
About: This article is published in Ocean Engineering. The article was published on 01 Dec 2018. The article focuses on the topics: Efficient energy use & Energy consumption.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Optimization of Sailing Speed for Inland Electric Ships Based on an Improved Multi-Objective Particle Swarm Optimization (MOPSO) Algorithm
Kang Zhang ,Chenguang Liu ,Zhibo He ,Huimin Chen ,Qian Xiang ,Xiumin Chu +5 more
Review of Techniques and Challenges of Digital Twin Applications in Autonomous Waterborne Transportation
Bing Wu,Wentao Feng,Tengfei Wang,Tingting Cheng,Jiang Jinhui +4 more
- 16 Jul 2025
TL;DR: This paper reviews digital twin applications in autonomous waterborne transportation, proposing a three-layer technology architecture and analyzing key technologies and challenges for building autonomous waterborne transportation systems based on digital twin concepts.
An Energy Efftciency optimization Methodfor Inland Ship Fleet Considering Multiple Influencing Factors
Kai Wang,Jiayuan Li,Lianzhong Huang,Ranqi Ma,Xiaohao Qu,Yupeng Yuan +5 more
- 14 Jul 2019
TL;DR: The results indicate that the proposed method could determine the optimal sailing speeds for each ship in different sailing legs, thus to improve fleet energy efficiency and reduce CO2 emissions effectively.
Predicting main engine power and emissions for container, cargo, and tanker ships with artificial neural network analysis
TL;DR: In this article , an artificial neural network (ANN) model was used to predict the main engine power and pollutant emissions of container, cargo, and tanker ships over 14 parameters: maximum speed, average speed, breadth, year built, ship type, status, length overall (LOA), light displacement, summer displacement, fuel type, deadweight tonnage (DWT), gross tonnages, engine cylinder size, and engine stroke length.
References
•Book
Data Mining: Concepts and Techniques
Jiawei Han,Micheline Kamber,Jian Pei +2 more
- 08 Sep 2000
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
Trends in big data analytics
TL;DR: An overview of the state-of-the-art and focus on emerging trends to highlight the hardware, software, and application landscape of big-data analytics are provided.
878
An approximate power prediction method
J. Holtrop,G.G.J. Mennen +1 more
TL;DR: In this article, a statistical method was presented for the determination of the required propulsive power at the initial design stage of a ship, which was developed through a regression analysis of random model experiments and full-scale data, available at the Netherlands Ship Model Basin.
The effectiveness and costs of speed reductions on emissions from international shipping
TL;DR: In this paper, the authors evaluate whether vessel speed reduction can be a potentially cost-effective CO2 mitigation option for ships calling on US ports, by applying a profit-maximizing equation to estimate route-specific, economically-efficient speeds.
628