Estimation of ship flue gas emissions in dynamic operational conditions with ANN
Autor: | Uğur Buğra Çelebi, Levent Bilgili |
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Rok vydání: | 2020 |
Předmět: |
Estimation
Flue gas education.field_of_study Weather routing 010504 meteorology & atmospheric sciences Meteorology Artificial neural network 020209 energy Mechanical Engineering Population Ocean Engineering 02 engineering and technology 01 natural sciences 0202 electrical engineering electronic engineering information engineering Environmental science MATLAB education computer 0105 earth and related environmental sciences computer.programming_language |
Zdroj: | Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment. 235:432-447 |
ISSN: | 2041-3084 1475-0902 |
DOI: | 10.1177/1475090220979457 |
Popis: | While ship emissions are incomparably less in comparison to land units, the threat of the world’s population and certain regions are in a serious danger due to 70% of these emissions occur in areas up to 400 km from the coast and on certain maritime routes. The formation of ship emissions strongly depends on the dynamically varying travel conditions and it is important to identify the emissions in order to guide the rule-makers to take the necessary measures correctly. In this study, an emission estimation modelling based on dynamic variables, such as voyage duration, engine revolutions per minute, speed, displacement, weather condition, sea conditions and average draught, has been realized by using artificial neural networks (ANN). The difference between the results of ANN and traditional estimation methods was found to be 1.57%. Then, in order to determine the optimum route, the ANN model was implemented for June and January in the Northern and Southern routes of the Atlantic and Pacific Oceans and it was concluded that harder sea and weather conditions produced more emissions. Finally, fuel consumptions, fuel costs and social costs of the emissions for different routes were calculated. |
Databáze: | OpenAIRE |
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