{"title":"船用柴油发电机燃料消耗和排放的回归模型估计","authors":"O. Yüksel, Burak Köseoğlu","doi":"10.7225/toms.v11.n01.w08","DOIUrl":null,"url":null,"abstract":"This study aims to estimate the fuel consumption of marine diesel generators onboard. Objective technical specifications and operational data on the ship's power generating plants and port calls were collected from an oceangoing oil/chemical tanker and used to develop the mathematical model of the plant in the Python and MATLAB environment. The model consists of alternators, prime movers and load distributions of the ship’s power generating plant and provides information on fuel consumption in metric tons calculated based on hours of operation and specific fuel consumption data. Regression models have helped predict future fuel consumption for the plant and the optimal model for the dataset was identified by comparing four different algorithms. As the results have shown the Ordinary Least Squares Regression to be optimum, it was used to make one, five, and ten-year predictions. The predictions for one-year, five-year, and ten-year periods are 4,322,436, 10,684,860, and 18,615,472 t respectively. The selected model predicts fuel consumption with R2 of 0.999, MAE of 3.932, and RMSE of 2.935. Fuel consumption predictions facilitated plant emission calculation.","PeriodicalId":42576,"journal":{"name":"Transactions on Maritime Science-ToMS","volume":"353 1","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Regression Modelling Estimation of Marine Diesel Generator Fuel Consumption and Emissions\",\"authors\":\"O. Yüksel, Burak Köseoğlu\",\"doi\":\"10.7225/toms.v11.n01.w08\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study aims to estimate the fuel consumption of marine diesel generators onboard. Objective technical specifications and operational data on the ship's power generating plants and port calls were collected from an oceangoing oil/chemical tanker and used to develop the mathematical model of the plant in the Python and MATLAB environment. The model consists of alternators, prime movers and load distributions of the ship’s power generating plant and provides information on fuel consumption in metric tons calculated based on hours of operation and specific fuel consumption data. Regression models have helped predict future fuel consumption for the plant and the optimal model for the dataset was identified by comparing four different algorithms. As the results have shown the Ordinary Least Squares Regression to be optimum, it was used to make one, five, and ten-year predictions. The predictions for one-year, five-year, and ten-year periods are 4,322,436, 10,684,860, and 18,615,472 t respectively. The selected model predicts fuel consumption with R2 of 0.999, MAE of 3.932, and RMSE of 2.935. Fuel consumption predictions facilitated plant emission calculation.\",\"PeriodicalId\":42576,\"journal\":{\"name\":\"Transactions on Maritime Science-ToMS\",\"volume\":\"353 1\",\"pages\":\"\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2022-04-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transactions on Maritime Science-ToMS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7225/toms.v11.n01.w08\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MARINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions on Maritime Science-ToMS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7225/toms.v11.n01.w08","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
Regression Modelling Estimation of Marine Diesel Generator Fuel Consumption and Emissions
This study aims to estimate the fuel consumption of marine diesel generators onboard. Objective technical specifications and operational data on the ship's power generating plants and port calls were collected from an oceangoing oil/chemical tanker and used to develop the mathematical model of the plant in the Python and MATLAB environment. The model consists of alternators, prime movers and load distributions of the ship’s power generating plant and provides information on fuel consumption in metric tons calculated based on hours of operation and specific fuel consumption data. Regression models have helped predict future fuel consumption for the plant and the optimal model for the dataset was identified by comparing four different algorithms. As the results have shown the Ordinary Least Squares Regression to be optimum, it was used to make one, five, and ten-year predictions. The predictions for one-year, five-year, and ten-year periods are 4,322,436, 10,684,860, and 18,615,472 t respectively. The selected model predicts fuel consumption with R2 of 0.999, MAE of 3.932, and RMSE of 2.935. Fuel consumption predictions facilitated plant emission calculation.
期刊介绍:
ToMS is a scientific journal with international peer review which publishes papers in the following areas: ~ Marine Engineering, ~ Navigation, ~ Safety Systems, ~ Marine Ecology, ~ Marine Fisheries, ~ Hydrography, ~ Marine Automation and Electronics, ~ Transportation and Modes of Transport, ~ Marine Information Systems, ~ Maritime Law, ~ Management of Marine Systems, ~ Marine Finance, ~ Bleeding-Edge Technologies, ~ Multimodal Transport, ~ Psycho-social and Legal Aspects of Long-term Working Aboard. The journal is published in English as an open access journal, and as a classic paper journal (in limited editions). ToMS aims to present best maritime research from South East Europe, particularly the Mediterranean area. Articles will be double-blind reviewed by three reviewers. With the intention of providing an international perspective at least one of the reviewers will be from abroad. ToMS also promotes scientific collaboration with students and has a section titled Students’ ToMS. These papers also undergo strict peer reviews. Furthermore, the Journal publishes short reviews on significant papers, books and workshops in the fields of maritime science.