A Novel Big Data Collection System for Ship Energy Efficiency Monitoring and Analysis Based on BeiDou System

Xiangming Zeng,Mingzhi Chen,Antonio Comi

doi:10.1155/2021/9914720

Xiangming Zeng, Mingzhi Chen + Show 1 more

Open Access

https://doi.org/10.1155/2021/9914720

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Abstract

The call for green shipping is increasing, and the reduction of greenhouse gas emissions from ships becomes more and more important. Traditional ship energy efficiency monitoring is based on the noon reports, which are susceptible to human error and have a time delay. Many ship energy efficiency monitoring systems have been designed and developed, but they usually cannot send data to the shore in time. In order to identify abnormal fuel consumption in time, this paper realizes a big data collection system for ship energy efficiency monitoring based on the BeiDou System. The system installed on two sister container ships has already collected a lot of data. Big data analysis methods, such as principal component analysis (PCA) and correlation analysis, are applied in the system to realize data visualization and analysis. Using PCA, it turns out that the shaft power of the main engine is related to a certain ship speed, which is also affected by load and weather conditions, and is the biggest factor in determining fuel consumption. To realize the assessment of hull fouling and the optimization of ship trim, a useful physics-based analysis is proposed. The analysis shows that the fouling of ship body greatly increases its resistance. Our analysis method can also find the best trim under specific loading condition. All these points are important for reducing fuel consumption and improving ship efficiency.

Highlights

International shipping contributes to about 2.89% of the global man-made emissions of CO2 averagely in 2018 [1, 2]
Linear regression is applied to fit the data. e relationship between shaft power and revolution speed is close to power of 2.5 to 2.6. e result shows that there is no clear difference between two conditions. e main difference lies in the low load area, where the ship has a higher resistance under full load and requires more power. e points are overlapped and the trend lines are nearly the same under the ship’s two different loading conditions
The relationship between multidimensional data is analyzed first. rough the Principal component analysis (PCA), it is established that the shaft power is the main component of the data. e correlation analysis between the parameters is completed through the statistics of the number of data. en, based on the physics-based method, it is determined that the ship’s loading condition has little effect on the power, and the slip ratio of the propeller has a strong influence on the power consumption

Summary

Introduction

International shipping contributes to about 2.89% of the global man-made emissions of CO2 averagely in 2018 [1, 2]. Reducing shipping carbon dioxide emissions has become a top priority. In order to reduce CO2 emissions, the regulations regarding Energy Efficiency Design Index (EEDI) and Ship Energy Efficiency Management Plan (SEEMP) entered into force on January 1st 2013 [3, 4]. A new chapter—Energy efficiency management—was added to MARPOL Annex VI. A new mechanism of data collection system for fuel oil consumption of ships came into force in 2018 to improve ship energy efficiency. The European Union regulation on Monitoring, Reporting, and Verification (MRV) started to monitor fuel consumption of ships having more than 5000 gross tonnage (GT) in 2018 [5]

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