Yazar "Kurtgoz, Yusuf" seçeneğine göre listele

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    Biogas engine performance estimation using ANN
    (Elsevier - Division Reed Elsevier India Pvt Ltd, 2017) Kurtgoz, Yusuf; Karagoz, Mustafa; Deniz, Emrah
    Artificial neural network (ANN) method was used to estimate the thermal efficiency (TE), brake specific fuel consumption (BSFC) and volumetric efficiency (VE) values of a biogas engine with spark ignition at different methane (CH4) ratios and engine load values. For this purpose, the biogas used in the biogas engine was produced by the anaerobic fermentation method from bovine manure and different CH4 contents (51%, 57%, 87%) were obtained by purification of CO2 and H2S. The data used in the ANN models were obtained experimentally from a 4-stroke four-cylinder, spark ignition engine, at constant speed for different load and CH4 ratios. Using some of the obtained experimental data, ANN models were developed, and the rest was used to test the developed models. In the ANN models, the CH4 ratio of the fuel, engine load, inlet air temperature (T-in), air fuel ratio and the maximum cylinder pressure are chosen as the input parameters. TE, BSFC and VE are used as the output parameters. Root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (R) performance indicators are used to compare measured and predicted values. It has been shown that ANN models give good results in spark ignition biogas engines with high correlation and low error rates for TE, BSFC and VE values. (C) 2017 Karabuk University. Publishing services by Elsevier B.V.
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    Global solar radiation estimation using artificial neural network by the addition of nearby meteorological stations' solar radiation data and exergy of solar radiation: a case study
    (Inderscience Enterprises Ltd, 2016) Kurtgoz, Yusuf; Deniz, Emrah
    The artificial neural networks (ANNs) can be used to accurately predict the global solar radiation (GSR). There are many geographical, meteorological and terrestrial parameters affecting GSR. In this study, the most relevant of six input parameters are selected to predict the GSR of Goksun Station in Turkey using Waikato environment for knowledge analysis (Weka) Software. The effect of using nearby meteorological stations' GSR data as input on GSR prediction is investigated. Different ANN models are developed to demonstrate the difference between the exclusion and inclusion of these parameters on the model. The results show that the exclusion of less influential parameters and the inclusion of three nearby stations' GSR data has improved performance criteria. Petela, Spanner and Jeter's approaches are used for exergy analysis of measured and estimated GSR values. The mean exergy-to-energy ratio for both Petela and Spanner's approaches is 0.934, while Jeter's approach showed 0.950.
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    Solar radiation exergy and enviroeconomic analysis for Turkey
    (Inderscience Enterprises Ltd, 2017) Kurtgoz, Yusuf; Deniz, Emrah; Turker, Ilker
    The knowledge of useful solar energy amount is important for designing solar energy systems. In this study, solar radiation exergy (SRE) values are calculated for the 81 cities in Turkey. The data are obtained from the General Directorate of Renewable Energy and the General Directorate of Meteorology. The approaches of Petela, Spanner and Jeter are used to calculate the exergy-to-energy ratios for determining the maximum utilisable solar radiation energy. The exergy-to-energy ratios, the SRE values, the locations having the highest solar exergy potential and the monthly, seasonal and annual total SRE maps are presented for Turkey. Finally, enviroeconomic analysis is performed and its results are presented. The available results are visualised via maps with colourbars.