Kurumun Atıf Alan Makalesi
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Prediction of diesel engine performance using biofuels with artificial neural network
Scopus
Toplam 170 atıf DOI
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. © 2010 Elsevier Ltd. All rights reserved.
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Comparison of empirical equations and artificial neural network results in terms of kinematic viscosity prediction of fuels based on hazelnut oil methyl ester
Scopus
Havuzumuzda 11 atıf almış
This study investigates the prediction of kinematic viscosity values of hazelnut oil methyl ester (HOME) using empirical equations and artificial neural network (ANN) methods under varying temperature and blend ratio conditions with ultimate euro diesel (UED) fuel. Four different fuel blends (20, 40, 60, and 80% by volume mixing ratio) were studied along with UED fuel and pure biodiesel. Tests for kinematic viscosity were performed in the temperature range of 293.15–373.15 K at the intervals of 1 K for each fuel sample. Moreover, physicochemical properties of hazelnut crude oil (HCO), HOME and its blends, and also fatty acid composition of HCO and HOME were measured and discussed in light of ASTM and EN standards. Regression analyses were conducted using MATLAB software to determine the coefficient of determination (R2), root mean square error (RMSE), and correlation constants. The best R2 and RMSE values were obtained by Eq. 6 as 0.9999 and 0.0068, respectively. In the analyses conducted using ANN, R2, and RMSE were obtained as 0.999986 and 0.00149 respectively based on the overall HOME–UED fuel blends. Although R2 values obtained by these two methods were close to each other, RMSE obtained using ANN was smaller than that of the one obtained by Eq. 6. In conclusion, the ANN method captures the best accuracy for the prediction of biodiesel kinematic viscosity with the highest R2 of 0.999986 and the lowest RMSE of 0.00149, which is within ±1% error range of the experimental data. © 2016 American Institute of Chemical Engineers Environ Prog, 35: 1827–1841, 2016.
Atıf Yapan Makale Bilgileri
Kurumlar (2)
Ondokuz Mayis Üniversitesi
Samsun, Turkey
Yozgat Bozok University
Yozgat, Turkey