Tools
Tools
Yuksel, O and Koseoglu, B (2022) Regression Modelling Estimation of Marine Diesel Generator Fuel Consumption and Emissions. Transactions on Maritime Science, 11 (1). pp. 79-94. ISSN 1848-3305
|
Text
Regression Modelling Estimation of Marine Diesel Generator Fuel Consumption and Emissions.pdf - Published Version Available under License Creative Commons Attribution. Download (5MB) | Preview |
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Marine diesel engines; Mathematical modelling; Linear regression; Support vector regression; Artificial neural networks; Time series analysis; Ship emissions |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Engineering |
| Publisher: | Faculty of Maritime Studies |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 28 Oct 2024 14:26 |
| Last Modified: | 28 Oct 2024 14:30 |
| DOI or ID number: | 10.7225/toms.v11.n01.w08 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/24596 |
 |
View Item |