TY - JOUR ID - 6828 TI - A Method for Pre-Calibration of DI Diesel Engine Emissions and Performance Using Neural Network and Multi-Objective Genetic Algorithm JO - Iranian Journal of Chemistry and Chemical Engineering JA - IJCCE LA - en SN - 1021-9986 AU - Samadani, Ehsan AU - Shamekhi, Amir Hossein AU - Behroozi, Mohammad Hassan AU - Chini, Reza AD - Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, I.R. IRAN AD - Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, I.R. IRAN Y1 - 2009 PY - 2009 VL - 28 IS - 4 SP - 61 EP - 70 KW - Diesel engine KW - Emission KW - Multi-Objective KW - Neural network KW - NSGA-II KW - Performance DO - 10.30492/ijcce.2009.6828 N2 - 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. UR - https://ijcce.ac.ir/article_6828.html L1 - https://ijcce.ac.ir/article_6828_1f6ce7b49efe9c8d5d808827a90e4b0b.pdf ER -