Application of back propagation neural network in predicting nox emission from i. c. engines

Our eco-system is being adversely affected by emissions from internal combustion (I.C.) engines. One of the prominent emissions of I.C engines are the nitrogenous products commonly known as NOX. In the present work, an attempt has been made towards the application of Back Propagation Neural Network (BPNN) for predicting the NOX emission from a diesel engine so that better control of the engine parameters may be performed to minimize the level of emission. The data collected for training the Neural Network (NN) were compression ratio, injection timing, load, cylinder pressure, crank angle at peak pressure, temperature of cooling water, and temperature of exhaust gas and these inputs were strategically combined to predict NOX emission. It has been observed that by the right combination of input parameters to the NN may effectively predict the level of NOX emission with minimum Root Mean Squared (RMS) error of almost less than 7.5% for better control.