Predicting the different engine parameters of a rubber seed oil-ethanol dual fuel engine using artificial neural networks Online publication date: Tue, 06-Nov-2018
by J.S. Femilda Josephin; V. Edwin Geo; Ankit Sonthalia; C. Bharatiraja; Fethi Aloui
International Journal of Global Warming (IJGW), Vol. 16, No. 4, 2018
Abstract: The present study investigates the potential of artificial neural network for predicting the performance and emission characteristics of a compression ignition (CI) engine. A number of experiments are performed using diesel, rubber seed oil (RSO) and its methyl ester (RSOME) as the primary fuel, and ethanol as the secondary fuel in a dual fuel engine. The experimental data obtained is used for training and testing the neural network. The predictions are performed using feed forward-back propagation training algorithm. Engine load and ethanol energy share is used as network input parameters, and brake thermal efficiency (BTE), brake specific energy consumption (BSEC), NOx, HC, CO and smoke are the predicted parameters. The prediction performance of the network is measured by comparing it with experimental data. The measurement of statistical error shows that ANN can predict BTE, BSEC, NOx, HC, CO and smoke for a dual fuel engine with high accuracy.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Global Warming (IJGW):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook