A Method for Pre-Calibration of DI Diesel Engine Emissions and Performance Using Neural Network and Multi-Objective Genetic Algorithm
Diesel engine emission standards are being more stringent as it gains more publicity in industry and transportation. Hence, designers have to suggest new controlling strategies which result in small amounts of emissions and a reasonable fuel economy. To achieve such a target, multi-objective optimiz...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Iranian Institute of Research and Development in Chemical Industries (IRDCI)-ACECR
2009-12-01
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| Series: | Iranian Journal of Chemistry & Chemical Engineering |
| Subjects: | |
| Online Access: | http://www.ijcce.ac.ir/article_6828_1f6ce7b49efe9c8d5d808827a90e4b0b.pdf |
| _version_ | 1811293822424973312 |
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| author | Ehsan Samadani Amir Hossein Shamekhi Mohammad Hassan Behroozi Reza Chini |
| author_facet | Ehsan Samadani Amir Hossein Shamekhi Mohammad Hassan Behroozi Reza Chini |
| author_sort | Ehsan Samadani |
| collection | DOAJ |
| description | Diesel engine emission standards are being more stringent as it gains more publicity in industry and transportation. Hence, designers have to suggest new controlling strategies which result in small amounts of emissions and a reasonable fuel economy. To achieve such a target, multi-objective optimization methodology is a good approach inasmuch as several types of objective are minimized or maximized simultaneously. In this paper, this technique is implemented on a closed cycle two-zone combustion model of a DI (direct injection) diesel engine. The main outputs of this model are the quantity of NOx, soot (which are the two main emissions in diesel engines) and engine performance. The optimization goal is to minimize NOx and soot while maximizing engine performance. Fuel injection parameters are selected as design variables. A neural network model of the engine is developed as an alternative for the complicated and time-consuming combustion model in a wide range of engine operation. Finally design variables are optimized using an evolutionary genetic algorithm, called NSGA-II. |
| first_indexed | 2024-04-13T05:08:16Z |
| format | Article |
| id | doaj.art-77f2f198dccf4db1975d2153d837fecf |
| institution | Directory Open Access Journal |
| issn | 1021-9986 1021-9986 |
| language | English |
| last_indexed | 2024-04-13T05:08:16Z |
| publishDate | 2009-12-01 |
| publisher | Iranian Institute of Research and Development in Chemical Industries (IRDCI)-ACECR |
| record_format | Article |
| series | Iranian Journal of Chemistry & Chemical Engineering |
| spelling | doaj.art-77f2f198dccf4db1975d2153d837fecf2022-12-22T03:01:07ZengIranian Institute of Research and Development in Chemical Industries (IRDCI)-ACECRIranian Journal of Chemistry & Chemical Engineering1021-99861021-99862009-12-0128461706828A Method for Pre-Calibration of DI Diesel Engine Emissions and Performance Using Neural Network and Multi-Objective Genetic AlgorithmEhsan Samadani0Amir Hossein Shamekhi1Mohammad Hassan Behroozi2Reza Chini3Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, I.R. IRANDepartment of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, I.R. IRANDepartment of Mechanical Engineering, Iran University of Science and Technology, Tehran, I.R. IRANDepartment of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, I.R. IRANDiesel engine emission standards are being more stringent as it gains more publicity in industry and transportation. Hence, designers have to suggest new controlling strategies which result in small amounts of emissions and a reasonable fuel economy. To achieve such a target, multi-objective optimization methodology is a good approach inasmuch as several types of objective are minimized or maximized simultaneously. In this paper, this technique is implemented on a closed cycle two-zone combustion model of a DI (direct injection) diesel engine. The main outputs of this model are the quantity of NOx, soot (which are the two main emissions in diesel engines) and engine performance. The optimization goal is to minimize NOx and soot while maximizing engine performance. Fuel injection parameters are selected as design variables. A neural network model of the engine is developed as an alternative for the complicated and time-consuming combustion model in a wide range of engine operation. Finally design variables are optimized using an evolutionary genetic algorithm, called NSGA-II.http://www.ijcce.ac.ir/article_6828_1f6ce7b49efe9c8d5d808827a90e4b0b.pdfdiesel engineemissionmulti-objectiveneural networknsga-iiperformance |
| spellingShingle | Ehsan Samadani Amir Hossein Shamekhi Mohammad Hassan Behroozi Reza Chini A Method for Pre-Calibration of DI Diesel Engine Emissions and Performance Using Neural Network and Multi-Objective Genetic Algorithm Iranian Journal of Chemistry & Chemical Engineering diesel engine emission multi-objective neural network nsga-ii performance |
| title | A Method for Pre-Calibration of DI Diesel Engine Emissions and Performance Using Neural Network and Multi-Objective Genetic Algorithm |
| title_full | A Method for Pre-Calibration of DI Diesel Engine Emissions and Performance Using Neural Network and Multi-Objective Genetic Algorithm |
| title_fullStr | A Method for Pre-Calibration of DI Diesel Engine Emissions and Performance Using Neural Network and Multi-Objective Genetic Algorithm |
| title_full_unstemmed | A Method for Pre-Calibration of DI Diesel Engine Emissions and Performance Using Neural Network and Multi-Objective Genetic Algorithm |
| title_short | A Method for Pre-Calibration of DI Diesel Engine Emissions and Performance Using Neural Network and Multi-Objective Genetic Algorithm |
| title_sort | method for pre calibration of di diesel engine emissions and performance using neural network and multi objective genetic algorithm |
| topic | diesel engine emission multi-objective neural network nsga-ii performance |
| url | http://www.ijcce.ac.ir/article_6828_1f6ce7b49efe9c8d5d808827a90e4b0b.pdf |
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