ANN Based Prediction of Performance and Emission Characteristics of a Diesel Engine Fueled by Distilled Tyre Pyrolysis Oil

Abstract

Investigating engine performance and emissions under varying conditions and for different fuels is a costly and timeconsuming exercise. This may also require sophisticated equipment which may not be readily available. In this study, two Analytical Neural Network (ANN) models were developed to predict diesel engine performance and emissions respectively, when fuelled by Distilled Tyre Pyrolysis Oil (DTPO). The models were based on back propagation learning algorithm. The data used to train and test the ANN was experimentally collected from a single cylinder four stroke diesel engine operated at speed ranging from 800 to 3500 rpm. The fuel blends contained 0-80% Distilled Tyre Pyrolysis Oil (DTPO) in diesel fuel. The fuel blends and engine speed were the input variables for each network. The performance of the model was evaluated by comparing experimental and ANN predicted results. The coefficient of determination (R 2) was found to be 0.9831, 0.9977, 0.9852, 0.9836, 0.9961, 0.9921 and 0.997 for Torque, Power, Brake Specific Fuel Consumption, Peak pressure, HC, NOx and CO respectively. The Mean Square Error between the measured and simulated values was 0.00396 for the engine performance model and 0.000163 for the emissions model. It can be concluded that the engine performance and emissions of a Diesel Engine running on DTPO and its blends with diesel fuel can be reliably predicted using Artificial Neural Network.