Prediction of Diesel Engine Performance Using Biofuels with Artificial Neural Network
Yükleniyor...
Dosyalar
Tarih
2010
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Biodiesel, bioethanol and biogas are the most important alternative fuels produced by using biologic origin sources. Effect of biofuel on engine performance is one of the research subjects of today. The engine experiments to test the engines are many times are hard, time consuming and high cost. Additionally, it is impossible to perform the test outside of limiting values. In this study, an artificial neural network, an artificial intelligence technique, is developed to successfully apply on automotive sector as well as many different areas of technology aiming to overcome difficulties of the experiments, minimize the cost, time and workforce waste. Diesel fuel, biodiesel, B20 and bioethanol-diesel fuel having different percentages (5%, 10%, and 15%) and biodiesel were mixed together, to use in developed artificial neural network Mixtures were also controlled for their fuel properties and motor experiments were performed to collect the reference values. Power, moment, hourly fuel consumption and specific fuel consumption were estimated by using the artificial neural network developed by using the reference values. Estimated values and experiment results are compared. As a result, from the performed statistical analyses, it is seen that realized artificial intelligence model is an appropriate model to estimate the performance of the engine used in the experiments. Reliability value is calculated as 99.94% (p = 0.9994 and p > 0.05) by using statistical analyses.
Açıklama
Anahtar Kelimeler
Artificial neural network, Biodiesel, E-diesel, Bioethanol, Engine performance
Kaynak
Expert Systems with Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
37
Sayı
Künye
Oğuz, H., Sarıtaş, İ., Baydan, H. E., (2010). Prediction of Diesel Engine Performance Using Biofuels with Artificial Neural Network. Expert Systems with Applications, (37), 6579-6586. Doi: 10.1016/j.eswa.2010.02.128
