A Method for Pre-Calibration of DI Diesel Engine Emissions and Performance Using Neural Network and Multi-Objective Genetic Algorithm
Authors
Abstract:
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.
similar resources
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 optimization methodology is a good approach inasmuch as several types of objective are minimized or maxi...
full textA comparative analysis of two neural network predictions for performance and emissions in a biodiesel fuelled diesel Engine
full text
Modeling and Optimization of Energy Inputs and Greenhouse Gas Emissions for Eggplant Production Using Artificial Neural Network and Multi-Objective Genetic Algorithm
This paper studies the modeling and optimization of energy use and greenhouse gas emissions of eggplant production using artificial neural network and multi-objective genetic algorithm in Guilan province of Iran. Results showed that the highest share of energy consumption belongs to diesel fuel (49.24%); followed by nitrogen (33.30%). The results indicated that a total energy input of 13910.67 ...
full textModeling and Optimization of Energy Inputs and Greenhouse Gas Emissions for Eggplant Production Using Artificial Neural Network and Multi-Objective Genetic Algorithm
This paper studies the modeling and optimization of energy use and greenhouse gas emissions of eggplant production using artificial neural network and multi-objective genetic algorithm in Guilan province of Iran. Results showed that the highest share of energy consumption belongs to diesel fuel (49.24%); followed by nitrogen (33.30%). The results indicated that a total energy input of 13910.67 ...
full textArtificial Neural Network Based Multi-Objective Evolutionary Optimization of a Heavy-Duty Diesel Engine
In this study the performance and emissions characteristics of a heavy-duty, direct injection, Compression ignition (CI) engine which is specialized in agriculture, have been investigated experimentally. For this aim, the influence of injection timing, load, engine speed on power, brake specific fuel consumption (BSFC), peak pressure (PP), nitrogen oxides (NOx), carbon dioxide (CO2), Carbon mon...
full textMy Resources
Journal title
volume 28 issue 4
pages 61- 70
publication date 2009-12-01
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023