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Prediction of emission characteristics of a diesel engine using experimental and artificial neural networks
Prediction of emission characteristics of a diesel engine using experimental and artificial neural networks
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Date
2021
Authors
Tran Van Hung
Hussein H. Alkhamis
Abdulwahed F. Alrefaei
Yasin Sohret
Kathirvel Brindhadevi
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Abstract
The focus of the study is to investigate effects of corn blends on exhaust emissions using Artificial Neural Network (ANN)
approach. A series of experiments were conducted on the water-cooled multi-cylinder engine to calibrate the emissions of
CO, THC, and NOx. The biodiesel was prepared using the transesterification process. Furthermore, the MgO nanoparticles
of 10, 15, 20 and 30 ppm was added to the corn blends through ultrasonication. The ANN is developed to anticipate the
emission characteristics of the compression ignition engine. As engine load increases, the emission of carbon monoxide and
total hydrocarbons decreases significantly. On the contrary, the emission of NOx gases spiked at higher load. The ANN back
propagation algorithm is developed with four input network and one output network to predict the results. The blends C10,
C15, C20, and C30 were studied with the developed ANN by varying the engine load. Besides, the highest and lowest value
of mean square errors and correlation coefficient were found for CO, THC, and NOx. Meanwhile, the optimized regression
coefficients for the emission parameters ranged between 0.8875 and 0.9858. The predicted correlation coefficients for CO,
THC, and NOx were 0.9985, 0.9978 and 0.9986, respectively.
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Keywords
Diesel engine · ANN · Back propagation algorithm · Biodiesel · Emission · NOx