Artificial Neural Network Prediction of NO_x Emissions from EGR and Non-EGR Engines Running on Soybean Biodiesel Fuel (B5) during Cold Idle Mode

摘要

Artificial neural network (ANN) prediction scheme was developed for nitrogen oxides (NO_x) emissions in cold idle mode based on the analysis of NO_x emissions from on-road transit buses operating on a blend of biodiesel. The input data necessary for training and testing the proposed ANN scheme was obtained from two different urban transit buses fueled with 5 vol % soybean methyl ester (SME) and 95% of ultra-low sulfur diesel (ULSD). One bus was equipped with exhaust gas recirculation (EGR) and the other one was not. The reduction of NO_x emissions was observed when EGR was implemented. A standard feed forward back-propagation algorithm was used in this analysis and in building the network structure, whereas Levenberge-Marquardt (LM) learning algorithm was used to predict the NO_x emissions. The study was carried out with 70% of total experimental data selected for training the neural network, 15% for the network's validation, and the remaining 15% data were used for testing the performance of the trained network. ANN results showed that the developed ANN model was capable of predicting the NO_x emissions of the tested engines with excellent agreement (correlation coefficients: 0.99＜R＜1), while root mean square errors (RMSEs) were 22.1 and 1.7 ppm for non-EGR and EGR-equipped engine series, respectively. ANN provided an accurate and simple approach to the analysis of this complex, multivariate problem, where idle NOx emissions from both non-EGR and EGR engines should be predicted.