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International Journal of Automotive Technology > Volume 6(2); 2005 > Article
International Journal of Automotive Technology 2005;6(2): 95-105.
PREDICTION OF EMISSIONS USING COMBUSTION PARAMETERS IN A DIESEL ENGINE FITTED WITH CERAMIC FOAM DIESEL PARTICULATE FILTER THROUGH ARTIFICIAL NEURAL NETWORK TECHNIQUES
N. BOSE, I. RAGHAVAN
Mepco Schlenk Enginnering College
ABSTRACT
Diesel engines have low specific fuel consumption, but high particulate emissions, mainly soot. Diesel soot is suspected to have significant effects on the health of living beings and might also affect global warming. Hence stringent measures have been put in place in a number of countries and will be even stronger in the near future. Diesel engines require either advanced integrated exhaust after treatment systems or modified engine models to meet the statutory norms. Experimental analysis to study the emission characteristics is a time consuming affair. In such situations, the real picture of engine control can be obtained by the modeling of trend prediction. In this article, an effort has been made to predict emissions smoke and NOx using cylinder combustion derived parameters and diesel particulate filter data, with artificial neural network techniques in MATLAB environment. The model is based on three layer neural network with a back propagation learning algorithm. The training and test data of emissions were collected from experimental set up in the laboratory for different loads. The network is trained to predict the values of emission with training values. Regression analysis between test and predicted value from neural network shows least error. This approach helps in the reduction of the experimentation required to determine the smoke and NOx for the catalyst coated filters.
Key Words: In-cylinder parameters, Diesel particulate filter (DPF), Catalyst coating, Artificial neural network techniques (ANN), Back propagation method, Emission prediction
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