Van Giao Nguyen, S. Rajamohan, K. Rudzki, Janusz Kozak, Prabhakar Sharma, Nguyen Dang Khoa Pham, Phuoc Quy Phong Nguyen, Phu Nguyen Xuan
{"title":"利用人工神经网络预测船舶燃油消耗量","authors":"Van Giao Nguyen, S. Rajamohan, K. Rudzki, Janusz Kozak, Prabhakar Sharma, Nguyen Dang Khoa Pham, Phuoc Quy Phong Nguyen, Phu Nguyen Xuan","doi":"10.2478/pomr-2023-0020","DOIUrl":null,"url":null,"abstract":"Abstract In marine vessel operations, fuel costs are major operating costs which affect the overall profitability of the maritime transport industry. The effective enhancement of using ship fuel will increase ship operation efficiency. Since ship fuel consumption depends on different factors, such as weather, cruising condition, cargo load, and engine condition, it is difficult to assess the fuel consumption pattern for various types of ships. Most traditional statistical methods do not consider these factors when predicting marine vessel fuel consumption. With technological development, different statistical models have been developed for estimating fuel consumption patterns based on ship data. Artificial Neural Networks (ANN) are some of the most effective artificial methods for modelling and validating marine vessel fuel consumption. The application of ANN in maritime transport improves the accuracy of the regression models developed for analysing interactive relationships between various factors. The present review sheds light on consolidating the works carried out in predicting ship fuel consumption using ANN, with an emphasis on topics such as ANN structure, application and prediction algorithms. Future research directions are also proposed and the present review can be a benchmark for mathematical modelling of ship fuel consumption using ANN.","PeriodicalId":49681,"journal":{"name":"Polish Maritime Research","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Using Artificial Neural Networks for Predicting Ship Fuel Consumption\",\"authors\":\"Van Giao Nguyen, S. Rajamohan, K. Rudzki, Janusz Kozak, Prabhakar Sharma, Nguyen Dang Khoa Pham, Phuoc Quy Phong Nguyen, Phu Nguyen Xuan\",\"doi\":\"10.2478/pomr-2023-0020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract In marine vessel operations, fuel costs are major operating costs which affect the overall profitability of the maritime transport industry. The effective enhancement of using ship fuel will increase ship operation efficiency. Since ship fuel consumption depends on different factors, such as weather, cruising condition, cargo load, and engine condition, it is difficult to assess the fuel consumption pattern for various types of ships. Most traditional statistical methods do not consider these factors when predicting marine vessel fuel consumption. With technological development, different statistical models have been developed for estimating fuel consumption patterns based on ship data. Artificial Neural Networks (ANN) are some of the most effective artificial methods for modelling and validating marine vessel fuel consumption. The application of ANN in maritime transport improves the accuracy of the regression models developed for analysing interactive relationships between various factors. The present review sheds light on consolidating the works carried out in predicting ship fuel consumption using ANN, with an emphasis on topics such as ANN structure, application and prediction algorithms. Future research directions are also proposed and the present review can be a benchmark for mathematical modelling of ship fuel consumption using ANN.\",\"PeriodicalId\":49681,\"journal\":{\"name\":\"Polish Maritime Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Polish Maritime Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.2478/pomr-2023-0020\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MARINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Polish Maritime Research","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.2478/pomr-2023-0020","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
Using Artificial Neural Networks for Predicting Ship Fuel Consumption
Abstract In marine vessel operations, fuel costs are major operating costs which affect the overall profitability of the maritime transport industry. The effective enhancement of using ship fuel will increase ship operation efficiency. Since ship fuel consumption depends on different factors, such as weather, cruising condition, cargo load, and engine condition, it is difficult to assess the fuel consumption pattern for various types of ships. Most traditional statistical methods do not consider these factors when predicting marine vessel fuel consumption. With technological development, different statistical models have been developed for estimating fuel consumption patterns based on ship data. Artificial Neural Networks (ANN) are some of the most effective artificial methods for modelling and validating marine vessel fuel consumption. The application of ANN in maritime transport improves the accuracy of the regression models developed for analysing interactive relationships between various factors. The present review sheds light on consolidating the works carried out in predicting ship fuel consumption using ANN, with an emphasis on topics such as ANN structure, application and prediction algorithms. Future research directions are also proposed and the present review can be a benchmark for mathematical modelling of ship fuel consumption using ANN.
期刊介绍:
The scope of the journal covers selected issues related to all phases of product lifecycle and corresponding technologies for offshore floating and fixed structures and their components.
All researchers are invited to submit their original papers for peer review and publications related to methods of the design; production and manufacturing; maintenance and operational processes of such technical items as:
all types of vessels and their equipment,
fixed and floating offshore units and their components,
autonomous underwater vehicle (AUV) and remotely operated vehicle (ROV).
We welcome submissions from these fields in the following technical topics:
ship hydrodynamics: buoyancy and stability; ship resistance and propulsion, etc.,
structural integrity of ship and offshore unit structures: materials; welding; fatigue and fracture, etc.,
marine equipment: ship and offshore unit power plants: overboarding equipment; etc.