Combinatory method of ant colony optimisation: artificial neural network in intelligent control systems for diesel engines to reduce the emissions and improve the performance, using laboratory experiments Online publication date: Fri, 18-Nov-2022
by Abbas Zarenezhad Ashkezari; Elham Askari
International Journal of Heavy Vehicle Systems (IJHVS), Vol. 29, No. 3, 2022
Abstract: In this work, by using ant colony optimisation (ACO) algorithm, analysing and optimisation of the NOx emissions, and fuel consumption in a diesel engine are done by applying controllable variables of engine speed, inlet air temperature, and fuel mass rate. For this purpose, by using of experimental tests, the necessary requirements for modelling of the input variables and the output parameters were provided via artificial neural network (ANN), and the ACO algorithm was applied to reduce NOx and bsfc simultaneously. The results showed that, the application of ACO algorithm to the modelling led to subsequent 28% and 5% decrease in NOx and bsfc, respectively. Moreover, due to rapid convergence and significant optimisation of the output parameters, the combinatory method of ACO - ANN can be used as an effective method in intelligent control systems for diesel engines in order to reduce emissions as well as fuel consumption.
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 Heavy Vehicle Systems (IJHVS):
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