CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Artificial Neural Networks for Internal Combustion Engine Performance and Emission Analysis

by Anant Bhaskar Garg, Parag Diwan, Mukesh Saxena
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 6
Year of Publication: 2014
Authors: Anant Bhaskar Garg, Parag Diwan, Mukesh Saxena
10.5120/15212-3705

Anant Bhaskar Garg, Parag Diwan, Mukesh Saxena . Artificial Neural Networks for Internal Combustion Engine Performance and Emission Analysis. International Journal of Computer Applications. 87, 6 ( February 2014), 23-27. DOI=10.5120/15212-3705

@article{ 10.5120/15212-3705,
author = { Anant Bhaskar Garg, Parag Diwan, Mukesh Saxena },
title = { Artificial Neural Networks for Internal Combustion Engine Performance and Emission Analysis },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 6 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 23-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number6/15212-3705/ },
doi = { 10.5120/15212-3705 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:05:12.885094+05:30
%A Anant Bhaskar Garg
%A Parag Diwan
%A Mukesh Saxena
%T Artificial Neural Networks for Internal Combustion Engine Performance and Emission Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 6
%P 23-27
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents an analytical work for better design system that contributes to the reduction of fuel consumption and emission for vehicle performance. The main technological issue on engines today is to comply with emission standards with cost-effective measures in order to keep the engine price still attractive to customer. The experimental research of engine performance are time consuming and quite expensive. The purpose of this work is to optimize engine performance using artificial neural networks (ANN). Back propagation neural network was used to optimize prediction model performance. The paper analyzed data from various experimental tests in which different engine operating parameters are measured. The paper highlights the framework and suitable model of ANN to optimize several operating parameters of the engine. The optimization includes a range of standards engine-operating conditions, with specified limits in emissions.

References
  1. Rask, E. and Sellnau, M. (2004), Simulation-Based Engine Calibration: Tools, Techniques and Applications, SAE Technical Paper No. 2004-01-1264. Calibration of Aging Diesel Engine with Artificial Neural Networks 531.
  2. Vossoughi, G. R. and Rezazdeh, S. (2005), Opimization of the Calibration for an Internal Combustion Engine Management System using Multi-Objective Genetic Alogrithms, International Journal of Computational Intelligence,Vol. 2,No. 5, pp. 151-161
  3. Alonso, J. M. , Alvarruiz, F. , Desantes, J. M. , Hernández, L. , Hernández, V. and Moltó, G. (2007), Combining Neural Networks and Genetic Algorithms to Predict and Reduce Diesel Engine Emissions, IEEE Transactions on Evolutionary Computation, Vol. 11, No. 1, pp. 46-55.
  4. Desantes, J. M. ; Lopez, J. J. ; Garcia, J. M. and Hernandez, L. (2002), Application of Neural Networks for Prediction and Optimization of Exhaust Emissions in a H. D. Diesel Engine, SAE Technical Paper No. 01-1144.
  5. Zweiri, Y. H. (2006), Diesel Engine Indicated Torque Estimation Based on Artificial Neural Networks, International Journal of Intelligent Technology, Vol. 2. No. 2, pp. 233-239.
  6. Akcayoli M. A. , Can C. R. , H. Bulbul, A. Kilicarsalan (2004), Artificial Neural Network Based Modeling of Injection Pressure in Diesel Engines, www. wseas. us/elibrary/conferences/miami2004/papers/484-222. pdf
  7. Sekmen Y. , Gölcü M. , Erduranl? P. , Pancar Y. (2006), Prediction of Performance and Smoke Emission using Artificial Neural Network in a Diesel Engine , Mathematical and computational application, Association for scientific research, Vol. 11,3; 205-214
  8. Gholamhassan N. , Barat G. , Talal Y. , Hadi R. (2007), Combustion Analysis of a CI Engine Performance using Waste Cooking Biodiesel Fuel with an Artificial Neural Network Aid, American Journal of Applied Sciences 4 (10): 756-764.
  9. Ghobadian B. , Rahimi H. , Nikbakht A. M. , Najafi G. and Yusaf T. F. (2009), Diesel Engine Performance and Exhaust Emission Analysis using Waste Cooking Biodiesel Fuel with an Artificial Neural Network, Renewable Energy, 34 (4). Pp. 976-982.
  10. Tutuncu K. and Allahverdi N. (2009), Modeling the Performance and Emission Characteristics of Diesel Engine and Petrol- Driven Engine by ANN. International Conference on Computer Systems and Technologies, CompSysTech'09.
  11. Wu B. , Zoran F. , Denise M. K, Gregory L. Ohl, Michael J. Prucka and Eugene DiValentin (2004) Using Artificial Neural Networks for Representingthe Air Flow Rate through a 2. 4 Liter VVT Engine SAE international 2004-01-3054, Powertrain & Fluid Systems Conference and Exhibition Tampa, Florida USA October 25-28, 2004
  12. Gisca V, Mereacre A. and Pisarenco M. , (2004) Utilization of Neural Networks for Observing the Internal Combustion Engine's Function, 7th International cConference on Development and Application Systems, Suceava, Romania, May 27-29.
  13. Garg, A. B. , Diwan, P. , Saxena, M. , Artificial Neural Networks Based Methodologies For Optimization of Engine Operations (2012), International Journal of Scientific & Engineering Research, Volume 3, Issue 5, May
  14. He Y. , Rutland C. J. (2004), Application of artificial neural networks in engine modeling, International Journal of Engine Research, 5: 281
  15. Haykin, S. , (1998) Neural Networks - A comprehensive foundation, Prentice-Hall
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Networks Engine Operation ANN algorithms architecture

Powered by PhDFocusTM