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2009 (Vol. 1, Issue: 3)
Article Information:

Evaluation of Artificial Neural Network Performance in Predicting Diesel Engine Nox Emissions

O. Obodeh and C. I. Ajuwa
Corresponding Author:  Obodeh, O. 

Key words:  Artificial neural networks, capabilities, multi-cylinder diesel engine, NOx emissions, , ,
Vol. 1 , (3): Page No: 125-131
Submitted Accepted Published
2009 June, 25 2009 July, 18

Diesel engine is becoming increasing popular due to its high efficiency and durability. Considering the most important greenhouse gas, carbondioxide (CO2), the diesel engine is superior to gasoline engine. Unfortunately, the diesel engine emits high level of oxides of nitrogen (NOx). Close control of combustion in the engine will be essential to achieve ever-increasing efficiency improvements while meeting increasingly stringent emissions standards. As new degrees of freedom are created, due to advances in technology, the complicated processes of emission formation are difficulty to assess. Artificial neural network (ANN)-based engine modelling offers the potential for a multidimensional, adaptive, learning control system which does not require knowledge of the governing equations for engine performance or the combustion kinetics of emissions formation that a conventional map-based engine model require. This paper evaluates the capabilities of ANN as a predictive tool for multi-cylinder diesel engine NOx emissions. The experiments were carried out with a stationary light-duty Nissan diesel engine test-rig designed and assembled to allow testing of the engine in a laboratory environment. Standard laboratory procedures were used to measure the engine operating param eters and its tailpipe emissions. ANNs were trained on experimental data and used to predict the oxides of nitrogen (NOx) emissions under various operating variables. Fraction of variance (R2) and mean absolute percentage error (x) were used for comparison in the sensitivity analysis. The Levenberg-Marquardt (LM) algorithm with 11 neurons produced the best results. Among the examined combinations of learning criteria in different architectures of backpropagation (BP) designs, a set of 0.05, 0.05 and 0.3 for learning rate, momentum and weight respectively, gave the best-averaged accuracy. For pre-specified engine speeds and loads with LM algorithm, x w ere found to be betw een 0.68 and 3.34%.
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  Cite this Reference:
O. Obodeh and C. I. Ajuwa, 2009. Evaluation of Artificial Neural Network Performance in Predicting Diesel Engine Nox Emissions.  Research Journal of Applied Sciences, Engineering and Technology, 1(3): Page No: 125-131.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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