Volume 2, Number 4 (10-2012)                   IJAE 2012, 2(4): 206-215 | Back to browse issues page


XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Shojaeefard H, Etghani M, Tahani, Akbari. Artificial Neural Network Based Multi-Objective Evolutionary Optimization of a Heavy-Duty Diesel Engine. IJAE. 2012; 2 (4) :206-215
URL: http://www.iust.ac.ir/ijae/article-1-155-en.html

Professor
Abstract:   (4646 Views)

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 monoxide (CO), hydrocarbon (HC) and Soot emissions has been considered. The tests were performed at various injection timings, loads and speeds. It is used artificial neural network (ANN) for predicting and modeling the engine performance and emission. Multi-objective optimization with respect to engine emissions level and engine power was used in order to deter mine the optimum load, speed and injection timing. For this goal, a fast and elitist non-dominated sorting genetic algorithm II (NSGA II) was applied to obtain maximum engine power with minimum total exhaust emissions as a two objective functions.

Full-Text [PDF 3560 kb]   (2052 Downloads)    
Type of Study: Research | Subject: General
Received: 2012/10/3 | Accepted: 2017/03/3 | Published: 2017/03/3

Add your comments about this article : Your username or email:
Write the security code in the box

© 2015 All Rights Reserved | International Journal of Automotive Engineering

Designed & Developed by : Yektaweb