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. (11th November 2022)

• Record Type: Journal Article
• Authors: Ashkezari, Abbas Zarenezhad; Askari, Elham
• Is Part Of: International journal of heavy vehicle systems. Volume 29:Number 3(2022)
• Page Start: 305
• Page End: 325
• Publication Date: 2022-11-11
• Subjects: ANN -- artificial neural network -- ant colony -- NOx -- bsfc -- diesel engines; Motor vehicles -- Design and construction -- Periodicals; Commercial vehicles -- Design and construction -- Periodicals; Motor vehicles -- Design and construction; Periodicals; 629.224
• Languages: English
• ISSNs: 1744-232X

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.