Multi-objective online optimization of a marine diesel engine using NSGA-II coupled with enhancing trained support vector machine. (5th June 2018)

• Record Type: Journal Article
• Authors: Niu, Xiaoxiao; Wang, Hechun; Hu, Song; Yang, Chuanlei; Wang, Yinyan
• Is Part Of: Applied thermal engineering. Volume 137(2018)
• Page Start: 218
• Page End: 227
• Publication Date: 2018-06-05
• Subjects: Multi-objective optimization -- Marine diesel engine -- NSGA-II -- SVM -- Pareto-optimal solution; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2018.03.080
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: A multi-objective online optimization approach for diesel engines is proposed. A hierarchical control structure is proposed for online auto-optimizing. A new enhancing training method for the SVM model is proposed. The proposed online optimization approach shows an excellent performance. Abstract: The multi-objective optimization problems of diesel engines are always challenging for the engineers, especially with the application of new technologies that aim at improving the engine performance and emissions. This paper proposed a novel online optimization approach using NSGA-II coupled with a machine learning method (SVM). The proposed online optimization approach was conducted based on an engine physical model, which was calibrated and validated carefully using experimental data. In the optimization process, the engine physical model is used as a substitute of real engine to generate training data for the SVM and validate the accuracy of the optimization results; SVM, with fast computing speed, undertakes the massive calculating workloads of fitness evaluation on searching the Pareto optimal solutions. Moreover, this paper proposed an enhancing training method to guarantee the accuracy of SVM model. When applying on a marine diesel engine, the proposed online optimization approach has demonstrated its reliability and high efficiency. In addition, with fast computing speed, the well trained SVM model can develop the engine responses maps rapidly. Eventually, based on the Pareto-optimal solutions obtained by the proposed optimization approach, combining with the maps, the solving of multi-objective optimization problems will be significantly facilitated.