Fuel Oil Consumption Monitoring and Predicting Gas Emission Based on Ship Performance using Automatic Identification System (AISITS) Data

Identification System ◽

Oil Consumption ◽

Public Data ◽

Ship Operational Efficiency

Abstract: The Energy Efficiency Design Index (EEDI) is a necessary benchmark for all new ships to prevent pollution from ships. MARPOL has also applied the Ship Energy Efficiency Management Plan (SEEMP) to all existing ships. The Energy Efficiency Operational Indicator (EEOI) provided by SEEMP is used to measure a ship's operational efficiency. The shipowner or operator can make strategic plans, such as routing, hull cleaning, decommissioning, new construction, and so on, by monitoring the EEOI. Fuel Oil Consumption is the most important factor in calculating EEOI (FOC). It is possible to measure it when a ship is in operation. This means that the EEOI of a ship can only be calculated by the shipowner or operator. Other stakeholders, such as the shipbuilding firm and Class, or those who do not have the measured FOC, can assess how efficiently their ships are working relative to other ships if the EEOI can be determined without the real FOC. We present a method to estimate the EEOI without requiring the actual FOC in this paper. The EEOI is calculated using data from the Automatic Identification System (AIS), ship static data, and publicly available environmental data. Big data technologies, notably Hadoop and Spark, are used because the public data is huge. We test the suggested method with real data, and the results show that it can predict EEOI from public data without having to use actual FOC Keywords: Ship operational efficiency, Energy Efficiency Operational Indicator (EEOI), Fuel Oil Consumption (FOC), Automatic Identification System (AIS), Big data

ESTIMATION OF EXHAUST SHIP EMISSION FROM MARINE TRAFFIC IN THE STRAITS OF SINGAPORE AND BATAM WATERWAYS USING AUTOMATIC IDENTIFICATION SYSTEM (AIS) DATA

Jurnal Teknologi ◽

10.11113/jt.v77.6687 ◽

2015 ◽

Vol 77 (23) ◽

Cited By ~ 1

Author(s):

Hendra Saputra ◽

Mufti Fathonah Muvariz ◽

Sapto Wiratno Satoto ◽

Jaswar Koto

Keyword(s):

Exhaust Emission ◽

Identification System ◽

Exhaust Gas ◽

Emission Rates ◽

Gas Emission ◽

Marine Traffic ◽

Exhaust Gas Emission ◽

Ship Emission

This study focuses on the Strait of Singapore and Batam Waterways area because it is one of the world’s most congested straits used for international shipping. The study aims to estimate exhaust gas emission and the concentration of emission to several areas around the strait. This is accomplished by evaluating the density of shipping lanes in the strait by using the data which obtained by Automatic Identification System (AIS). MEET methodology is used to estimate emissions from ships. There were 1269 total number of ships through the strait on September 27, 2014 at 06.00 am-08.00 am produces total exhaust emission for NOx, CO, CO2, VOC, PM and SOx were about 12595.35 g/second (15.99%), 25725.19 g/second (32.66%), 11832.31 g/second (15.02%), 5973.23 g/second (7.58%), 443.71 g/second, (0.56%), 22185.57 g/second (28.17%), respectively. The ships under the Singapore flag contribute approximately 22.78% of total emissions in the Strait of Singapore and Batam Waterways followed by Panama, Indonesia and Malaysia 14.47%, 3.67%, 1.91%, respectively. Based on the total emission rates hips under Indonesia and Malaysia rank of seventh and eighth respectively.

Operational Analysis of Container Ships by Using Maritime Big Data

Journal of Marine Science and Engineering ◽

10.3390/jmse9040438 ◽

2021 ◽

Vol 9 (4) ◽

pp. 438

Author(s):

Min-Jae Oh ◽

Myung-Il Roh ◽

Sung-Woo Park ◽

Do-Hyun Chun ◽

Myeong-Jo Son ◽

...

Keyword(s):

Big Data ◽

Environmental Conditions ◽

Fuel Oil ◽

Identification System ◽

Oil Consumption ◽

Shipping Company ◽

Operational Analysis ◽

Operational Characteristics ◽

Ocean Environment

The shipping company or the operator determines the mode of operation of a ship. In the case of container ships, there may be various operating patterns employed to arrive at the destination within the stipulated time. In addition, depending on the influence of the ocean’s environmental conditions, the speed and the route can be changed. As the ship’s fuel oil consumption is closely related to its operational pattern, it is possible to identify the most economical operations by analyzing the operational patterns of the ships. The operational records of each shipping company are not usually disclosed, so it is necessary to estimate the operational characteristics from publicly available data such as the automatic identification system (AIS) data and ocean environment data. In this study, we developed a visualization program to analyze the AIS data and ocean environmental conditions together and propose two categories of applications for the operational analysis of container ships using maritime big data. The first category applications are the past operation analysis by tracking previous trajectories, and the second category applications are the speed pattern analysis by shipping companies and shipyards under harsh environmental conditions. Thus, the operational characteristics of container ships were evaluated using maritime big data.

Measuring Port Disruptions with Automatic Identification System Data

Ports 2019 ◽

10.1061/9780784482629.030 ◽

2019 ◽

Author(s):

Brandan M. Scully ◽

Katherine F. Chambers

Keyword(s):

Identification System ◽

System Data

Maritime navigation and radiocommunication equipment and systems. Class B shipborne equipment of the automatic identification system (AIS)

10.3403/30130838u ◽

2015 ◽

Keyword(s):

Identification System ◽

Class B ◽

Maritime Navigation

Vessel Detection and Automatic Identification System

SSRN Electronic Journal ◽

10.2139/ssrn.3696057 ◽

2020 ◽

Author(s):

Malika Pagare ◽

Hemlata Ahire ◽

Akshada Shitole

Keyword(s):

Identification System ◽

Vessel Detection

Automatic Identification System Receiver for Small Fishing Vessels

2020 IEEE REGION 10 CONFERENCE (TENCON) ◽

10.1109/tencon50793.2020.9293716 ◽

2020 ◽

Author(s):

Febus Reidj G. Cruz ◽

Jeremiah A. Ordiales ◽

Malvin Angelo C. Reyes ◽

Pinky T. Salvanera

Keyword(s):

Identification System ◽

Fishing Vessels

Automatic identification system data-driven model for analysis of ship domain near bridge-waters

Journal of Navigation ◽

10.1017/s0373463321000461 ◽

2021 ◽

pp. 1-22

Author(s):

Lei Jinyu ◽

Liu Lei ◽

Chu Xiumin ◽

He Wei ◽

Liu Xinglong ◽

...

Keyword(s):

Least Squares Method ◽

Identification System ◽

Collision Risk ◽

Domain Models ◽

System Data ◽

Quantification Model ◽

Highly Correlated

Abstract The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.