PREDICTION OF EMISSIONS FROM BIODIESEL FUELED TRANSIT BUSES USING ARTIFICIAL NEURAL NETWORKS
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Kasthurirangan Gopalakrishnan - Iowa State University, Department of Civil, Construction and Environmental Engineering, Ames, IA 50011, USA -
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.
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The authors would like to acknowledge CenSARA (Blueskyways Collaborative) and Iowa Department of Natural Resources (IDNR), the sponsors of the project. Special thanks are due to CyRide, the transit agency and Clean Air Technologies International, Inc. the provider of PEMS.
Ahn, K.; Rakha, H.; Trani, A.; Van Aerde, M. 2002. Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels, ASCE Journal of Transportation Engineering 128(2): 182-190.
Antony, R. 2008. Biodiesel Performance, Costs, and use. Available from internet: http://www.eia.doe.gov/oiaf/analysispaper/biodiesel.
Canakci, M.; Erdil, A.; Arcaklioglu, E. 2006. Performance and exhaust emissions of a biodiesel engine, Applied Energy 83(6): 594-605.
Clark, N.; Conley, J.; Jarrett, R.; Nennelli, A.; Toth-Nagy, C. 2001. Emissions modeling of heavy-duty conventional and hybrid electric vehicles. SAE paper 2001-01-3675.
Coskun, N.; Yildrim, T. 2003. The effects of training algorithms in MLP network on image classification. In Proceedings of the International Joint Conference on Neural Networks, 1223-1226.
De Lucas, A.; Duran, A.; Carmona, M.; Lapuerta, M. 2001. Modeling diesel particulate emissions with neural networks, Fuel 80(4): 539–548.
EPA. 2002. A Comprehensive Analysis of Biodiesel Impacts on Exhaust Emissions. US: Environmental Protection Agency.
Frey, H. 1997. Variability and Uncertainty in Highway Vehicle Emission Factors. In Proceedings of the Conference Emission Inventory: Planning for the Future, 208-219.
Frey, H.; Kim, K. 2005. Operational Evaluation of Emissions and Fuel Use of B20 Versus Diesel Fueled Dump Trucks. Federal Highway Administration FHWA/NC/2005-07. Center for Transportation and the Environment, North Carolina State University, Raleigh, NC.
Frey, H.; Rouphail, N.; Unal, A.; Colyar, J. 2001. Emissions Reduction Through Better Traffic Management: An Empirical Evaluation Based Upon On-Road Measurements. NC: North Carolina State University.
Frey, H.; Rouphail, N.; Zhai, H. 2008. Link-Based Emission Factors for Heavy-Duty Diesel Trucks Based on Real-World Data. Submitted to Transportation Research Board 2008 87th Annual Meeting.
Frey, H.; Rouphail, N.; Zhai, H.; Farias, T.; Goncalves, G. 2007. Comparing real-world fuel consumption for diesel and hydrogen-fueled transit buses and implication for emissions, Transportation Research Part D: Transport and Environment 12(4): 281-291.
Frey, H.; Unal, A.; Chen, J. 2002. Recommended Strategy for On-Board Emission Data Analysis and Collection for the New Generation Model. Raleigh, NC: North Carolina State University.
Gopalakrishnan, K.; Ceylan, H.; Attoh-Okine, N. 2010. Intelligent and Soft Computing in Infrastructure Systems Engineering: Recent Advances. Studies in Computational Intelligence (SCI) Series, Vol. 259, Springer-Verlag, Inc., Berlin.
Hagan, T.; Menhaj, M. 1994. Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks 5(6): 989-993.
Hashemi, N.; Clark, N. 2007. Artificial neural network as a predictive tool for emissions from heavy-duty diesel vehicles in Southern California, International Journal of Engine Research 8(4): 321-336.
Haykin, S. 1999. Neural networks: A comprehensive foundation. NJ, USA: Prentice-Hall, Inc. 823 p.
Jiménez-Palacios, J. 1999. Understanding and Quantifying Motor Vehicle Emissions with Vehicle. Cambridge: Massachusetts Institute of Technology.
Krijnsen, H.; Van Kooten, W.; Calis, H.; Verbeek, R.; Vanden Bleek, C. 2000. Evaluation of an artificial neural network for NO prediction from a transient diesel engine as a base for ANN would be well suited to inventory prediction from transient diesel engine as base for NOx control, Canadian Journal of Chemical Engineering 78(2): 408–417.
NBB (National Biodiesel Board). 2000. Accessed 4/28, 2008. Available from internet: http://www.biodieselgear.com/documentation/NBB_Biodiesel_brochure.pdf.
Rilett, L.; Zietsman, J.; Kim, S.; Tydlacka, J. 2004. Portable Emissions Measurement Systems: Lessons Learned. Washington DC: TRB.
Rouphail, N.; Frey, H.; Colyar, J.; Unal, A. 2001. Vehicle Emissions and Traffic Measures: Exploratory Analysis of Field Observations at Signalized Arterials. In Proceedings of the 80th Annual Meeting of the Transportation Research Board Conference.
Tsoukalas, L.; Uhrig, R. 1997. Fuzzy and neural approaches in engineering. New York: Wiley. 600 p.
Unal, A.; Frey, H.; Rouphail, N. 2004. Quantification of highway vehicle emissions hot spots based upon on-board measurements, Journal of the Air & Waste Management Association 54(2): 130-40.
Unal, A.; Rouphail, N.; Frey, H. 2003. Effect of arterial signalization and level of service on measured vehicle emissions. Washington DC:Transportation Research Record 1842, In: National Academy of Sciences. 47–56.
Vijayan, A.; Kumar, A.; Abraham, M. 2008. Experimental analysis of vehicle operation parameters affecting emission behavior of public transport buses operating on alternative diesel fuels, In Proceedings of the 87th Transportation Research Board Annual Meeting Conference.
Vojtisek-Lom, M.; Wilson, P.; 2003. Real-World, in-use Exhaust Emissions from Front-End Loaders Equipped with Continuously Regenerating Diesel Particulate Filters (DPF) at the World Trade Center Site. Clean Air Technologies Project Report to MJ Bradley and Associates.
Yuanwang, D.; Meilin, Z.; Dong, X.; Xiaobei, C. 2002. An analysis for effect of cetane number on exhaust different from the existing tests cycles, Fuel 81(15): 1963–1970.
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