Çelik, VeliArcaklioğlu, Erol2020-06-252020-06-252005closedAccess0306-26191872-9118https://doi.org/10.1016/j.apenergy.2004.08.003https://hdl.handle.net/20.500.12587/3471ARCAKLIOGLU, Erol/0000-0001-8073-5207This paper suggests a mechanism for determining the constant specific-fuel consumption curves of a diesel engine using artificial neural-networks (ANNs). In addition, fuel-air equivalence ratio and exhaust temperature values have been predicted with the ANN. To train the ANN, experimental results have been used, performed for three cooling-water temperatures 70, 80, 90, and 100 C for the engine powers ranging from 1000 to 2300 - for six different powers of 75-450 kW with incremental steps of 75 kW. In the network, the back-propagation learning algorithm with two different variants, single hidden-layer, and logistic sigmoid transfer function have been used. Cooling water-temperature, engine speed and engine power have been used as the input layer, while the exhaust temperature, break specific-fuel consumption (BSFC, g/kWh) and fuel-air equivalence ratio (FAR) have also been used separately as the output layer. It is shown that R-2 values are about 0.99 for the training and test data; RMS values are smaller than 0.03; and mean errors are smaller than 5.5% for the test data. (c) 2004 Elsevier Ltd. All rights reserved.eninfo:eu-repo/semantics/closedAccessartificial neural-networkperformance mapsfuel-air equivalence ratiodiesel engineArticle81324725910.1016/j.apenergy.2004.08.0032-s2.0-14644428981Q1WOS:000229661300002Q2