Abstract
The rise in freight passenger transportation is responsible for air pollution, green house gas emissions (especially CO2) and high fuel demand. New engine technology and fuels are discovered and tested throughout the world. Biodiesel, an alternative for diesel, has been seen as a solution. However, the amount of emissions generated by a biodiesel fueled vehicle has not been understood well since most research studies of this kind reported in the literature were conducted in the laboratory. In the present study, emissions (NOx, HC, CO, CO2 and PM) were measured from biodiesel fueled transit buses using an on-road emissions measuring device known as the Portable Emissions Measurement System (PEMS). On-road study is important in terms of understanding the amount of emissions generated under the real traffic and environmental conditions. Emissions were measured on buses fueled with regular diesel (B0), B10 blend (10% biodiesel + 90% diesel) and B20 blend (20% biodiesel + 80% diesel). This paper demonstrates the use of hybrid soft-computing techniques such as the neuro-fuzzy technique for developing emissions prediction models from real-world data. Hybrid soft-computing techniques have been shown to work well in handling data prone to noise and uncertainty, which is characteristic of real-world scenario. Two neuro-fuzzy methodologies were considered in this study: the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS). A brief review of model development, recommended parametric settings, and statistical evaluation of prediction performance of both techniques are discussed. In general, the ANFIS showed better prediction accuracy for the individual emissions compared to DENFIS although the prediction accuracies are comparable.
Highlights
Vehicular emission is the most prominent contributor of air pollution
Evolving Connectionist System (ECOS) can be considered as open architecture Artificial Neural Networks (ANN) in which the neurons are added to their structures and the connection weights are modified as the system evolves based on a continuous input data stream in an adaptive, life-long, modular way (Watts 2004, 2009; Kasabov, Song 2002)
Quite a few studies have been conducted to model diesel exhaust engine emissions data using ANNs recently, not many of them focused on on-road real-time emissions data
Summary
ACC – acceleration (mph/s) of the bus every second; ANFIS – adaptive neuro-fuzzy inference system; ANNs – artificial neural networks; BP – backpropagation; BPER – percentage of biodiesel in fuel; CO – carbon monoxide; CO2 – carbon dioxide; DENFIS – dynamic evolving neuro-fuzzy inference system; FIS – fuzzy inference system; FLA – fuzzy logic approach; HC – hydrocarbons; LSE – least squares error; MAP – manifold absolute pressure; MLP – multi-layered perceptron; NOx – oxides of nitrogen; PC – passenger count; PEMS – portable emissions measurement system; PM – particulate matter; R – coefficient of correlation; R2 – coefficient of determination; RMSE – root mean squared error; RPM – engine speed (revolution per second); SP – speed (mph) of the transit buses; T – intake air temperature; VSP – vehicle specific power (Watt/kg)
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