Prediction of CO emission from I. C. Engines using Back Propagation Neural Network

Environmental pollution is a great hazard to our eco-system which is also being adversely affected by emissions from internal combustion (I.C.) engines. In the present work, an attempt has been made towards the application of Back Propagation Neural Network (BPNN) for predicting the CO 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 peak pressure, crank angle at peak pressure, temperature of cooling water, and temperature of exhaust gas. It has been observed that by the selective combination of input parameters to the NN may effectively predict the level of CO emission with minimum RMS error for effective control of emission.