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Performance and exhaust emissions of a biodiesel engine

Author

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  • Canakci, Mustafa
  • Erdil, Ahmet
  • Arcaklioglu, Erol

Abstract

In this study, the applicabilities of Artificial Neural Networks (ANNs) have been investigated for the performance and exhaust-emission values of a diesel engine fueled with biodiesels from different feedstocks and petroleum diesel fuels. The engine performance and emissions characteristics of two different petroleum diesel-fuels (No. 1 and No. 2), biodiesels (from soybean oil and yellow grease), and their 20% blends with No. 2 diesel fuel were used as experimental results. The fuels were tested at full load (100%) at 1400-rpm engine speed, where the engine torque was 257.6Â Nm. To train the network, the average molecular weight, net heat of combustion, specific gravity, kinematic viscosity, C/H ratio and cetane number of each fuel are used as the input layer, while outputs are the brake specific fuel-consumption, exhaust temperature, and exhaust emissions. The back-propagation learning algorithm with three different variants, single layer, and logistic sigmoid transfer function were used in the network. By using weights in the network, formulations have been given for each output. The network has yielded R2 values of 0.99 and the mean % errors are smaller than 4.2 for the training data, while the R2 values are about 0.99 and the mean % errors are smaller than 5.5 for the test data. The performance and exhaust emissions from a diesel engine, using biodiesel blends with No. 2 diesel fuel up to 20%, have been predicted using the ANN model.

Suggested Citation

  • Canakci, Mustafa & Erdil, Ahmet & Arcaklioglu, Erol, 2006. "Performance and exhaust emissions of a biodiesel engine," Applied Energy, Elsevier, vol. 83(6), pages 594-605, June.
    • Handle: RePEc:eee:appene:v:83:y:2006:i:6:p:594-605
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    References listed on IDEAS

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    1. ArcaklIoglu, Erol & Çavusoglu, Abdullah & Erisen, Ali, 2004. "Thermodynamic analyses of refrigerant mixtures using artificial neural networks," Applied Energy, Elsevier, vol. 78(2), pages 219-230, June.
    2. Gölcü, Mustafa & Sekmen, Yakup & ErduranlI, Perihan & Sahir Salman, M., 2005. "Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine," Applied Energy, Elsevier, vol. 81(2), pages 187-197, June.
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