Prediction of emissions from biodiesel fueled transit buses using artificial neural networks

The growing demand of freight transportation and passenger cars has led to air pollution, green house gas emissions (especially CO2) and fuel supply concerns. Research has been carried out on biodiesel which is shown to generate lower emissions. However, the amount of emissions generated is not well understood which entails more vigorous data collection and development of emissions models. A comprehensive data collection plan was developed and emissions (NOx, HC, CO, CO2 and PM10) from biodiesel fueled transit buses were collected using a portable emissions measurement system (PEMS). Linear models were developed and tested for each emission. However, the models could not capture the emissions spikes well resulting in very low R2 values. Artificial neural networks (ANNs) based models were then employed on this data because of their ability to handle nonlinearity and not requiring assumptions on the input data as needed by statistical models. Sensitivity analysis was performed on the input parameters, number of hidden layers, learning rate and learning algorithm to arrive at an optimum ANN architecture. The optimal architecture for this study was found to be two hidden layers with 50 hidden nodes for each of NOx, HC, CO, and PM and one hidden layer for CO2. The emissions were predicted using best-performance ANN models for each emission. Scatter-plots of observed versus predicted values showed R2 of 0.96, 0.94, 0.82, 0.98 and 0.78 for NOx, HC, CO, CO2 and PM emissions, respectively. Histogram on prediction error showed low frequency for large errors.