Application of Artificial Neural Networks for Emission Modelling of Biodiesels for a C.I Engine under Varying Operating Conditions

R. Manjunatha, P. Badari Narayana, K. Hema Chandra Reddy

Abstract


The technical analysis conducted in this study deals with the modelling of diesel engine exhaust emissions using artificial neural networks. Objective of this study is to understand the effectiveness of various biodiesel fuel properties and engine operating conditions on diesel engine combustion towards the formation of exhaust emissions. The experimental investigations have been carried out on a single cylinder Direct Injection (DI) combustion ignition (CI) engine using blends of biodiesel methyl esters from Pongamia, Jatropha and Neem oils. The performance parameters such as brake power (BP), brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), volumetric efficiency, exhaust gas temperature (EGT) were measured along with regulated and unregulated exhaust emissions of CO, HC and NOx. An Artificial neural network (ANN) was developed based on the available experimental data. Multi layer perceptron neural network was used for nonlinear mapping between input and output parameters of ANN. Biodiesel blend percentage, calorific value, density, Cetane number of each biodiesel blend and operating load were used as inputs to train the neural network. The exhaust gas emissions - NOx, CO and HC are predicted for the new fuel and its blends. Different activation functions and several rules were used to train and validate the normalized data pattern and an acceptable percentage error was achieved by Levenberg-Marquardt design optimization algorithm. The results showed that training through back propagation was sufficient enough in predicting the engine emissions. It was found that R (Regression Coefficient) values were 0.99, 0.95 and 0.99 for NOx, CO and HC emissions, respectively. Therefore, the developed model can be used as a diagnostic tool for estimating the emissions of biodiesels and their blends under varying operating conditions.


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Modern Applied Science   ISSN 1913-1844 (Print)   ISSN 1913-1852 (Online)

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