Application of Machine Learning for Fuel Consumption and Emission Prediction in a Marine Diesel Engine Using Diesel and Waste Cooking Oil
This study creates and tests a machine learning model that can predict fuel use and emissions (NO<sub>x</sub>, CO<sub>2</sub>, CO, HC, PN) from a marine internal combustion engine when it is running normally. The model learned from data collected from conventional diesel fuel...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/7/1328 |
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| author | Tadas Žvirblis Kristina Čižiūnienė Jonas Matijošius |
| author_facet | Tadas Žvirblis Kristina Čižiūnienė Jonas Matijošius |
| author_sort | Tadas Žvirblis |
| collection | DOAJ |
| description | This study creates and tests a machine learning model that can predict fuel use and emissions (NO<sub>x</sub>, CO<sub>2</sub>, CO, HC, PN) from a marine internal combustion engine when it is running normally. The model learned from data collected from conventional diesel fuel experiments. Subsequently, we evaluated its ability to transfer by employing the parameters associated with waste cooking oil (WCO) biodiesel and its 60/40 diesel mixture. The machine learning model demonstrated exceptional proficiency in forecasting diesel mode (R<sup>2</sup> > 0.95), effectively encapsulating both long-term trends and short-term fluctuations in fuel consumption and emissions across various load regimes. Upon the incorporation of WCO data, the model maintained its capacity to identify trends; however, it persistently overestimated emissions of CO, HC, and PN. This discrepancy arose primarily from the differing chemical composition of the fuel, particularly in terms of oxygen content and density. A significant correlation existed between indicators of incomplete combustion and the utilization of fuel. Nonetheless, NO<sub>x</sub> exhibited an inverse relationship with indicators of combustion efficiency. The findings indicate that the model possesses the capability to estimate emissions in real time, requiring only a modest amount of additional training to operate effectively with alternative fuels. This approach significantly diminishes the necessity for prolonged experimental endeavors, rendering it an invaluable asset for the formulation of fuel strategies and initiatives aimed at mitigating carbon emissions in maritime operations. |
| format | Article |
| id | doaj-art-bbb612dd408546da91a032300b95cddb |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-bbb612dd408546da91a032300b95cddb2025-08-20T03:36:14ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-07-01137132810.3390/jmse13071328Application of Machine Learning for Fuel Consumption and Emission Prediction in a Marine Diesel Engine Using Diesel and Waste Cooking OilTadas Žvirblis0Kristina Čižiūnienė1Jonas Matijošius2Mechanical Science Institute, Vilnius Gediminas Technical University, Plytinės Str. 25, 10105 Vilnius, LithuaniaDepartment of Logistics and Transport Management, Vilnius Gediminas Technical University, Plytinės Str. 27, 10105 Vilnius, LithuaniaMechanical Science Institute, Vilnius Gediminas Technical University, Plytinės Str. 25, 10105 Vilnius, LithuaniaThis study creates and tests a machine learning model that can predict fuel use and emissions (NO<sub>x</sub>, CO<sub>2</sub>, CO, HC, PN) from a marine internal combustion engine when it is running normally. The model learned from data collected from conventional diesel fuel experiments. Subsequently, we evaluated its ability to transfer by employing the parameters associated with waste cooking oil (WCO) biodiesel and its 60/40 diesel mixture. The machine learning model demonstrated exceptional proficiency in forecasting diesel mode (R<sup>2</sup> > 0.95), effectively encapsulating both long-term trends and short-term fluctuations in fuel consumption and emissions across various load regimes. Upon the incorporation of WCO data, the model maintained its capacity to identify trends; however, it persistently overestimated emissions of CO, HC, and PN. This discrepancy arose primarily from the differing chemical composition of the fuel, particularly in terms of oxygen content and density. A significant correlation existed between indicators of incomplete combustion and the utilization of fuel. Nonetheless, NO<sub>x</sub> exhibited an inverse relationship with indicators of combustion efficiency. The findings indicate that the model possesses the capability to estimate emissions in real time, requiring only a modest amount of additional training to operate effectively with alternative fuels. This approach significantly diminishes the necessity for prolonged experimental endeavors, rendering it an invaluable asset for the formulation of fuel strategies and initiatives aimed at mitigating carbon emissions in maritime operations.https://www.mdpi.com/2077-1312/13/7/1328machine learningmarine diesel engineemission predictionwaste cooking oil (WCO)model transferabilityemission modeling |
| spellingShingle | Tadas Žvirblis Kristina Čižiūnienė Jonas Matijošius Application of Machine Learning for Fuel Consumption and Emission Prediction in a Marine Diesel Engine Using Diesel and Waste Cooking Oil Journal of Marine Science and Engineering machine learning marine diesel engine emission prediction waste cooking oil (WCO) model transferability emission modeling |
| title | Application of Machine Learning for Fuel Consumption and Emission Prediction in a Marine Diesel Engine Using Diesel and Waste Cooking Oil |
| title_full | Application of Machine Learning for Fuel Consumption and Emission Prediction in a Marine Diesel Engine Using Diesel and Waste Cooking Oil |
| title_fullStr | Application of Machine Learning for Fuel Consumption and Emission Prediction in a Marine Diesel Engine Using Diesel and Waste Cooking Oil |
| title_full_unstemmed | Application of Machine Learning for Fuel Consumption and Emission Prediction in a Marine Diesel Engine Using Diesel and Waste Cooking Oil |
| title_short | Application of Machine Learning for Fuel Consumption and Emission Prediction in a Marine Diesel Engine Using Diesel and Waste Cooking Oil |
| title_sort | application of machine learning for fuel consumption and emission prediction in a marine diesel engine using diesel and waste cooking oil |
| topic | machine learning marine diesel engine emission prediction waste cooking oil (WCO) model transferability emission modeling |
| url | https://www.mdpi.com/2077-1312/13/7/1328 |
| work_keys_str_mv | AT tadaszvirblis applicationofmachinelearningforfuelconsumptionandemissionpredictioninamarinedieselengineusingdieselandwastecookingoil AT kristinaciziuniene applicationofmachinelearningforfuelconsumptionandemissionpredictioninamarinedieselengineusingdieselandwastecookingoil AT jonasmatijosius applicationofmachinelearningforfuelconsumptionandemissionpredictioninamarinedieselengineusingdieselandwastecookingoil |