Abstract
The emission results from the diesel engine have a damaging effect on both the environment and people. Therefore, how to reduce and control these harmful exhaust emissions is mandatory requires plenty of experiments that elevate the cost and efforts. This paper depicts an application of an intelligent technique for prognostic the internal combustion of diesel engine performance, emission, and combustion characteristics. Zinc oxide (ZnO), aluminium oxide (Al2O3), and titanium oxide (TiO2) nanoparticles were added to diesel fuel. Experimental investigations were conducted on direct injection, water-cooled four cylinders, in-line, natural aspirated Fiat diesel engine. The engine was run at a firm speed of 1500 rpm with a regular fuel injection pressure at 400 bars but varying operation loads. Thus, the diesel engine emission and combustion characteristics, such as brake specific fuel consumption, thermal efficiency, carbon monoxide, unburned hydrocarbons, nitrogen oxide, and smoke concentrations were considered. The engine performance and exhaust gas analysis were conducted for seven various fuel blends and five load conditions. Therefore, in this current work, optimized ANN is utilised to model the relationship among engine emissions and operating parameters of direct injection diesel engines. The measured data was recorded for each conducted experiment to develop a multi-input multi-output intelligent system. Results obtained from the developed model were found to be suitable for predicting the engine performance and emissions for a limited number of available data and it was found to match well with those from the experiments.
Published Version
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