By Topic

Combining Neural Networks and Genetic Algorithms to Predict and Reduce Diesel Engine Emissions

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Alonso, J.M. ; High Performance Networking & Comput. Group, Univ. Politecnica de Valencia ; Alvarruiz, F. ; Desantes, J.M. ; Hernandez, L.
more authors

Diesel engines are fuel efficient which benefits the reduction of CO2 released to the atmosphere compared with gasoline engines, but still result in negative environmental impact related to their emissions. As new degrees of freedom are created, due to advances in technology, the complicated processes of emission formation are difficult to assess. This paper studies the feasibility of using artificial neural networks (ANNs) in combination with genetic algorithms (GAs) to optimize the diesel engine settings. The objective of the optimization was to find settings that complied with the increasingly stringent emission regulations while also maintaining, or even reducing the fuel consumption. A large database of stationary engine tests, covering a wide range of experimental conditions was used for this analysis. The ANNs were used as a simulation tool, receiving as inputs the engine operating parameters, and producing as outputs the resulting emission levels and fuel consumption. The ANN outputs were then used to evaluate the objective function of the optimization process, which was performed with a GA approach. The combination of ANN and GA for the optimization of two different engine operating conditions was analyzed and important reductions in emissions and fuel consumption were reached, while also keeping the computational times low

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:11 ,  Issue: 1 )