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    Biodiesel production from animal fat-palm oil blend and performance analysis of its effects on a single cylinder diesel engine
    (SILA SCIENCE, 2011) Sugozu, Ilker; Eryilmaz, Tanzer; Ors, Ilker; Solmaz, Ozgur
    Nowadays, the decrease in fossil based energy reserves and their negative impact on the environment have increased the interest in alternative energy sources. Vegetable oils and animal fats are used as alternatives of fuels that are used in diesel engines. High viscosity of vegetable oils and animal fats cause several problems in diesel engines. Methods such as dilution, pyrolysis, and transesterification are utilized to eliminate these problems. In this study, using transesterification method, biodiesel is produced from 30% animal fat 70% palm oil blend which has a substantial potential for being an alternative fuel for diesel engines. The impact of biodiesel on engine performance and exhaust emissions are investigated on a single cylinder, air cooled, pre-combustion chamber diesel engine. Engine performance values of biodiesel are obtained close to those values of diesel fuel. A decrease in CO emission and a slight increase in NO emission are observed. Following the experimental results, it is concluded that biodiesel produced from 30% animal fat and 70% palm oil could be used as an alternative fuel for diesel engine. Moreover, the positive impacts of biodiesel on environment in terms of exhaust emissions also increase its potential of being an alternative fuel.
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    Hourly cooling load prediction of a vehicle in the southern region of Turkey by Artificial Neural Network
    (PERGAMON-ELSEVIER SCIENCE LTD, 2014) Solmaz, Ozgur; Ozgoren, Muammer; Aksoy, Muharrem Hilmi
    In this study, Artificial Neural Networks (ANNs) method for prediction hourly cooling load of a vehicle was implemented. The cooling load of the vehicle was calculated along the cooling season (1 May-30 September) for Antalya, Konya, Mersin, Mugla and Sanliurfa provinces in Turkey. For ANN model, seven neurons determinated as input signals of latitude, longitude, altitude, day of the year, hour of the day, hourly mean ambient air temperature and hourly solar radiation were used for the input layer of the network. One neuron producing an output signal of the hourly cooling load was utilized in the output layer. All data were divided into two categories for training and testing of the ANN. The 80% of the data was reserved to training and the remaining was used for testing of the model. Neuron numbers in the hidden layer from 7 to 40 were tested step by step to find the best matching ANN structure. The obtained results for different numbers of neurons were compared in terms of root mean squared error (RMSE), coefficient of determination (R-2) and mean absolute error (MAE). The best matching results for the training and testing were obtained as 8 neurons for the minimum testing RMSE value for the prediction of cooling load by the ANN model on the 23rd day of each month along the cooling season. For the model with 8 neurons RMSE, R-2 and MAE (Training/Testing) were found to be 0.0128/0.0259, 0.9959/0.9818 and 78.81/174.71 W/m(2), respectively. It is shown that the cooling load of a vehicle can be successfully predicted by means of the ANNs from geographical characteristics and meteorological data. (C) 2014 Elsevier Ltd. All rights reserved.
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    Prediction of Hourly Solar Radiation in Six Provinces in Turkey by Artificial Neural Networks
    (ASCE-AMER SOC CIVIL ENGINEERS, 2012) Solmaz, Ozgur; Ozgoren, Muammer
    The purpose of this study is to apply the method of artificial neural networks (ANNs) to predict the hourly solar radiation of six selected provinces in Turkey. Six neurons-receiving input signals of latitude, longitude, altitude, day of the year, hour of the day, and mean hourly atmospheric air temperature-were used in the input layer of the network. One neuron producing a corresponding output signal of hourly solar radiation was utilized in the output layer of the network. Two different models have been analyzed in the ANNs for training and testing. The results obtained from both models were compared by using different neurons, mean squared error (MSE), coefficient of determination (R-2), and mean absolute error (MAE). According to the results, the MSE value of training data in Model II was better than Model I. DOI: 10.1061/(ASCE)EY.1943-7897.0000080. (C) 2012 American Society of Civil Engineers.
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    PREDICTION OF HOURLY SOLAR RADIATION USING AN ARTIFICIAL NEURAL NETWORK
    (BRNO UNIV TECHNOLOGY VUT PRESS, 2011) Solmaz, Ozgur; Ozgoren, Muammer
    The aim of the presented study is to apply artificial neural networks (ANNs) method for prediction hourly solar radiation of the selected six provinces of Turkey. Six neurons which receive input signals of latitude, longitude, altitude, day of the year, hour of the day and hourly mean atmospheric air temperature were used in the input layer of the network. One neuron producing corresponding output signal of the hourly solar radiation was utilized in the output layer of the network. The model for training and testing in the formed ANNs was analyzed. Neuron numbers in the hidden layer (from 6 to 30 neurons step by step) and epoch numbers for 100 epochs were tested for different values. The obtained results for this model was compared by using different neurons, mean squared error (MSE), coefficient of determination (R-2), mean absolute error (MAE). The best results for the training were obtained as 25 neurons in terms of minimum MSE value of 0.000607. The R-2 values of the ANN for training and testing data of the 25 neurons are determined as 0.9879 and 0.9891 while the MAE values of which are 18.33 W/m(2) and 18.94 W/m(2), respectively.