Operational Profile Based Optimization Method for Maritime Diesel Engines
Hoang Nguyen Khac,
Kai Zenger,
Xiaoguo Storm and
Jari Hyvönen
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Hoang Nguyen Khac: Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, 02600 Espo, Finland
Kai Zenger: Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, 02600 Espo, Finland
Xiaoguo Storm: School of Technology and Innovation, University of Vaasa, 65200 Vaasa, Finland
Jari Hyvönen: Engine Research and Technology Development, Wärtsila Marine Solutions, 65200 Vaasa, Finland
Energies, 2020, vol. 13, issue 10, 1-20
Abstract:
This paper presents an approach to a new engine calibration method that takes the engine’s operational profile into account. This method has two main steps: modeling and optimization. The Design of Experiments method is first conducted to model the engine’s responses such as Brake Specific Fuel Consumption (BSFC) and Nitrogen Oxide ( N O x ) emissions as the functions of fuel injection timing, common rail pressure and charged air pressure. These response surface models are then used to minimize the fuel consumption during a year, according to a typical load profile of a ferry, and to fulfill the N O x limits set by International Maritime Organization (IMO) regulations, Tier II, test cycle E2. The Sequential Quadratic Programming algorithm is used to solve this minimization problem. The results showed that the fuel consumption can be effectively reduced with the flexibility to trade it off with the N O x emissions while still fulfilling the IMO regulations. In general, this method can decrease the manual calibration effort and improve the engine’s performance with a tailored setting for individual operational profiles.
Keywords: diesel engines; operational profile; optimization; fuel efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:10:p:2575-:d:360188
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