https://orcid.org/0000-0003-4812-5278
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ABSTRACT
In order to improve the economic efficiency of the ship when sailing and reduce the engine optimization cost. We propose the Response Surface Methodology (RSM) combined with NSGA-II to optimize the engine parameters. First, a simulation model of a marine four-stroke dual-fuel engine is established in AVL-BOOST software. Then, control parameters such as engine speed, exhaust valve opening (EVO) and compression ratio (CR) are planned by design of experiments. The response surface model was established in Design-Expert software. The significant influence of control parameters on performance parameters was studied by analysis of variance (ANOVA). Finally, with the output power, indicated fuel consumption rate and nitrogen oxide emissions as the optimization objectives. Non-dominated Sequential Genetic Algorithm (NSGA-II) is used to optimize the parameters to improve engine performance and reduce emissions. The results show that the established response surface model has good prediction accuracy. The response surface model visualizes the mathematical relationship between the control parameters and the optimization targets. The ANOVA results show that engine speed, EVO and CR have significant effects on engine performance and emissions. The optimization results show that the engine speed is 793 rpm, the EVO is 145°CA, and the CR is 12.3. Compared to standard settings, the optimized data shows a 3.4% increase in power, a 0.3% reduction in ISFC, and a 6.2% reduction in nitrogen oxide (NOx) emissions. The combination of response surface analysis and NSGA-II algorithm to optimize engine performance and emissions is thereby a feasible method.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Author contributions
Hong Zeng, male, received Ph.D. degree in Marine Engineering from Dalian Maritime University, in 2012. Since 2013, he has been working as an Associate Professor with Marine Engineering College, Dalian Maritime University, China. From 2018 to 2019, he was a Visiting Researcher with the Department of Naval Architecture, Ocean and Marine Engineering at the University of Strathclyde, UK. He has published more than 30 journal and conference papers. His research interests include the application of the new generation of information technology in marine engineering, mainly focus on the modeling, simulation and control in marine engineering.
Data availability statement
The data used to support the findings of this study are available from the corresponding author upon request.
Nomenclature
| ANN | = | artificial neural networks |
| ANOVA | = | analysis of variance |
| ATDC | = | after top dead center |
| BMEP | = | exhaust valve opening |
| ISFC | = | indicated specific fuel consumption |
| NOx | = | nitrogen oxides |
| N2 | = | nitrogen |
| NSGA-II | = | Non-dominated Sequential Genetic Algorithm |
| O2 | = | oxygen |
| RSM | = | Response Surface Methodology |
Additional information
Funding
Notes on contributors
Hong Zeng
Hong Zeng, male, received Ph.D. degree in Marine Engineering from Dalian Maritime Univer-sity, in 2012. Since 2013, he has been working as an Associate Professor with Marine Engineering College, Dalian Maritime University, China. From 2018 to 2019, he was a Visiting Researcher with the Department of Naval Architecture, Ocean and Marine Engineering at the University of Strathclyde, UK. He has published more than 30 journal and conference papers. His research in-terests include the application of the new generation of information technology in marine engi-neering, mainly focus on the modelling, simulation and control in marine engineering.
Kuo Jiang
Kuo Jiang, male, received his Bachelor’s Degree in Marine Engineering from Ningbo University in 2021. He is currently pursuing his master’s degree in Marine Engineering at Dalian Maritime University, China. His research interests focus on dual-fuel engine simulation and optimization.
Zefan Wu
Zefan Wu graduated from Dalian Maritime University in 2021 with a major in Marine Engineering. And he is currently pursuing his master’s degree in Marine Engineering at Dalian Maritime University. His research topics are research on marine dual-fuel engine modeling and waste bypass valves.
Xinlong Liu
Xinlong Liu, male, received the Bachelor’s Degree in Marine Engineering from Dalian Maritime University in 2019. And the Master’s Degree in Marine Engineering from Dalian Maritime University in 2022. Currently, he is engaged in marine engine management and new energy innovation in Qingdao Port Barge Co. LTD.