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Öğe Artificial neural network analysis of heat pumps using refrigerant mixtures(Pergamon-Elsevier Science Ltd, 2004) Arcaklioglu, E; Erisen, A; Yilmaz, RIn this study, we have investigated the performance of a vapor compression heat pump with different ratios of R12/R22 refrigerant mixtures using artificial neural networks (ANN). Experimental studies were completed to obtain training and test data. Mixing ratio, evaporator inlet temperature and condenser pressure were used as input layer, while the outputs are coefficient of performance (COP) and rational efficiency (RE). The back propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. It is shown that the R-2 values are about 0.9999 and the RMS errors are smaller than 0.006. With these results, we believe that the ANN can be used for prediction of COP and RE as an accurate method in a heat pump. (C) 2003 Elsevier Ltd. All rights reserved.Öğe Effect of relative humidity on solar potential(Elsevier Sci Ltd, 2005) Sozen, A; Arcaklioglu, EIn this study, the effect of relative humidity on solar potential is investigated using artificial neural-networks. Two different models are used to train the neural networks. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine-duration, and mean temperature) are used in the input layer of the network (Model 1). But, relative humidity values are added to one network in model (Model 2). In other words, the only difference between the models is relative humidity. New formulae based on meteorological and geographical data, have been developed to determine the solar energy potential in Turkey using the networks' weights for both models. Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer-function were used in the network. The best approach was obtained by the SCG algorithm with nine neurons for both models. Meteorological data for the four years, 2000-2003, for 18 cities (Artvin, Cesme, Bozkurt, Malkara, Florya, Tosya, Kizilcahamam, Yenisehir, Edremit, Gediz, Kangal, Solhan, Ergani, Selquk, Milas, Seydisehir, Siverek and Kilis) spread over Turkey have been used as data in order to train the neural network. Solar radiation is in output layer. One month for each city was used as test data, and these months have not been used for training. The maximum mean absolute percentage errors (MAPEs) for Tosya are 2.770394% and 2.8597% for Models 1 and 2, respectively. The minimum MAPEs for Seydiehir are 1.055205% and 1.041% with R-2 (99.9862%, 99.9842%) for Models 1 and 2, respectively, in the SCG algorithm with nine neurons. The best value of R 2 for Models 1 and 2 are for Seydiehir. The minimum value of R-2 for Model 1 is 99.8855% for Tosya, and the value for Model 2 is 99.9001% for Yenisehir. Results show that the humidity has only a negligible effect upon the prediction of solar potential using artificial neural-networks. (c) 2004 Elsevier Ltd. All rights reserved.Öğe Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data(Pergamon-Elsevier Science Ltd, 2004) Sozen, A; Arcaklioglu, E; Ozalp, MTurkey is located at the Mediterranean at 36degrees and 42degrees N latitudes and has a typical Mediterranean climate. The solar energy potential is very high in Turkey. The yearly average solar radiation is 3.6 kW h/m(2) day, and the total yearly radiation period is similar to2610 h. This study consists of two cases. Firstly, the main focus of this study is to put forward the solar energy potential in Turkey using artificial neural networks (ANNs). Secondly, in this study, the best approach was investigated for each station by using different learning algorithms and a logistic sigmoid transfer function in the neural network with developed software. In order to train the neural network, meteorological data for last three years (2000-2002) from 17 stations (Ankara, Samsun, Edirne, Istanbul-Goztepe, Van, Izmir, Denizli, Sanliurfa, Mersin, Adana, Gaziantep, Aydin, Bursa, Diyarbakir, Yozgat, Antalya and Mugla) spread over Turkey were used as training (11 stations) and testing (6 stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration and mean temperature) are used in the input layer of the network. Solar radiation is in the output layer. The maximum mean absolute percentage error was found to be less than 6.735% and R-2 values were found to be about 99.893% for the testing stations. However, these values were found to be 4.398% and 99.965% for the training stations. The trained and tested ANN models show greater accuracy for evaluating the solar resource possibilities in regions where a network of monitoring stations has not been established in Turkey. The predicted solar potential values from the ANN are given in the form of monthly maps. These maps are of prime importance for different working disciplines, like scientists, architects, meteorologists and solar engineers, in Turkey. The predictions from the ANN models could enable scientists to locate and design solar energy systems in Turkey and determine the best solar technology. (C) 2004 Elsevier Ltd. All rights reserved.Öğe Investigation of thermodynamic properties of refrigerant/absorbent couples using artificial neural networks(Elsevier Science Sa, 2004) Sozen, A; Ozalp, M; Arcaklioglu, EThis paper presents a new approach to determine the properties of liquid and two phase boiling and condensing of two alternative refrigerant/absorbent couples (methanol-LiBr and methanol-LiCl), which do not cause ozone depletion for absorption thermal systems (ATSs) using artificial neural networks (ANNs). The back-propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. In input layer, there are temperatures in the range of 298-498 K (with 25 K increase), pressures (0.1-40 MPa) and concentrations of 2, 7, and 12% of the couples; specific volume is in output layer. After training, it is found that maximum error is less than 3%, average error is about 1% and R-2 values are 99.999%. As seen from the results obtained the thermodynamic properties have been obviously predicted within acceptable errors. This paper shows that values predicted with ANN can be used to define the thermodynamic properties instead of approximate and complex analytic equations. (C) 2004 Elsevier B.V. All rights reserved.Öğe Performance comparison of CFCs with their substitutes using artificial neural network(John Wiley & Sons Ltd, 2004) Arcaklioglu, EIn order to decrease global pollution due to chlorofluorocarbons (CFCs), the usage of HFC- and HC-based refrigerants and their mixtures are considered instead of CFCs (R12, R22, and R502). This was confirmed by an international consensus (i.e. Montreal Protocol signed in 1987). This paper offers to determine coefficient of performance (COP) and total irreversibility (TI) values of vapour-compression refrigeration system with different refrigerants and their mixtures mentioned above using artificial neural networks (ANN). In order to train the network, COPs and TIs of refrigerants and their some binary, ternary and quartet mixtures of different ratios have been calculated in a vapour-compression refrigeration system with liquid/suction line heat exchanger. In the calculations thermodynamic properties of refrigerants have been taken from REFPROP 6.01 which was prepared based on Helmholtz energy equation of state. To achieve this, a new software has been written in FORTRAN programming language using sub-programs of REFPROP, and all related calculations have been performed using this software using constant temperature method as reference. Scaled conjugate gradient, Pola-Ribiere conjugate gradient, and Levenberg-Marquardt learning algorithms and logistic sigmoid transfer function were used in the network. Mixing ratios of refrigerants, and evaporator temperature were used as input layer; COP and TI values were used as output layer. It is shown that R-2 values are about 0.9999, maximum errors for training and test data are smaller than 2 and 3%, respectively. It is concluded that, ANNs can be used for prediction of COP and TI as an accurate method in the systems. Copyright (C) 2004 John Wiley Sons, Ltd.Öğe Performance prediction of a vapour-compression heat-pump(Elsevier Sci Ltd, 2004) Sozen, A; Arcaklioglu, E; Erisen, A; Akcayol, MAThe performance of the heat pump was predicted using a fuzzy-logic controller under various working-conditions and mixing ratios of R12/R22 refrigerant mixtures, instead of requiring an expensive and time consuming experimental study [Int. J. Ref. 13 (1990) 163]. Fuzzy-logic's linguistic terms provide a feasible method for defining the performance of the heat pump. Input data for the fuzzy logic are mixing ratio, evaporator-inlet temperature and condenser pressure. In the comparison of performance, results obtained via analytic equations and by means of the fuzzy-logic controller, the coefficient of performance (COP), and rational efficiency (RE) for all working situations differ by less than 1.5% and 1%, respectively. The statistical coefficient of multiple determinations (R-2-value) equals to 0.9988 for both the COP and the RE. With these results, we believe that fuzzy logic can be used for the accurate prediction of the COP and the RE of the heat pump. (C) 2004 Elsevier Ltd. All rights reserved.Öğe Prospects for utilisation of solar driven ejector-absorption cooling system in Turkey(Pergamon-Elsevier Science Ltd, 2004) Sozen, A; Ozalp, M; Arcaklioglu, ESolar assisted refrigeration appears to be a promising alternative to the conventional electrical driven units. The main advantages of solar assisted refrigeration systems concern the reduction of peak loads for electricity utilities, the use of zero ozone depletion impact refrigerants, the decreased primary energy consumption and decreased global warming impact. The main focus of this study is to investigate usage possibility of ejector-absorption cooling system (EACS) in Turkey. This study determines whether or not required heat for generator of EACS can be obtained from solar energy in Turkey. There are two important reasons for the utilisation of EACSs in Turkey. One of them is that the production and use of the CFCs and HCFCs will be phased out in a few years according to Montreal Protocol, adopted in 1987. The other is that Turkey has high solar energy potential because of its location in the northern hemisphere with latitudes 3642 degreesN and longitudes 26-45 degreesE and the yearly average solar radiation is 3.6 kW h/m(2) day, and the total yearly radiation period is similar to2610 h. For analysis, 17 cities were selected in different regions of Turkey in which the radiation data and sunshine duration information have been collected since 2000. By using the meteorological data, it was aimed that required optimum collector surface area for maximum coefficient of performance (COPmax) conditions of EACSs operated with aqua-ammonia was defined. In addition, required minimum energy for auxiliary heater was calculated so that the system can be used throughout the year. It was found that the heat gain factor (HGF) varies in the range from 0.5 to 2.68 for the all the seasons in the selected cities. The maximum HGF of about 2.68 was obtained for Van in July. This study shows that there is a great potential for utilisation of solar cooling system for domestic heating/cooling applications in Turkey. (C) 2003 Elsevier Ltd. All rights reserved.Öğe Thermodynamic analyses of refrigerant mixtures using artificial neural networks(Elsevier Sci Ltd, 2004) Arcaklioglu, E; Cavusoglu, A; Erisen, AThe aim of this study is to make a contribution towards the efforts of reducing the use of CFCs by finding a drop-in replacement for pure refrigerants used in domestic and industrial appliances. The suggested solution is the use of HFC and HC based refrigerant mixtures. In this study, we investigate different possible ratios of these mixtures and their corresponding performances by using Artificial Neural-Networks (ANNs). We believe this dramatically reduces the times and efforts required to achieve these targets. Coefficients of Performances (COPs) and Total Irreversibilities (TIs) of refrigerants and their mixtures have been calculated for a vapor-compression refrigeration system with a liquid/suction line heat-exchanger. The constant cooling-load method is taken as a reference. The thermodynamic properties of refrigerants have been taken from REFPROP 6.01. To train the network, based on Scaled Conjugate Gradient (SCG), Pola-Ribiere Conjugate Gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function, we have used various ratios of 7 refrigerant mixtures of HFCs and HCs along with three CFCs (R12, R22, and R502). They were used as inputs while the COP and TI values, calculated,as above, were the outputs. The network has yielded R-2 values of 0.9999 and maximum errors for training and test data were found to be 2 and 3%, respectively. (C) 2003 Elsevier Ltd. All rights reserved.Öğe Use of artificial neural networks for mapping of solar potential in Turkey(Elsevier Sci Ltd, 2004) Sozen, A; Arcaklioglu, E; Ozalp, M; Kanit, EGTurkey has sufficient solar radiation intensities and radiation durations for solar thermal applications since Turkey lies in a sunny belt, between 36degrees and 42degrees N latitudes. The yearly average solar-radiation is 3.6 kWh/m(2) day, and the total yearly radiation period is similar to2610 h. The main focus of this study is to determine the solar-energy potential in Turkey using artificial neural-networks (ANNs). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function were used in the network. In order to train the neural network, meteorological data for the last 3 years (2000-2002) from 17 stations (namely cities) spread over Turkey were used as training (11 stations) and testing (6 stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) are used as inputs to the network. Solar radiation is in the output layer. The maximum mean absolute percentage error was found to be less than 6.7% and R-2 values to be about 99.8937% for the testing stations. However, the respective values were found to be 2.41 and 99.99658% for the training stations. The trained and tested ANN models show greater accuracies for evaluating solar resource possibilities in regions where a network of monitoring stations has not been established in Turkey. The predicted solar-potential values from the ANN were given in the form of monthly maps. These maps are of prime importance for different working disciplines, like those of scientists, architects, meteorologists, and solar engineers in Turkey. The predictions from ANN models could enable scientists to locate and design solar-energy systems in Turkey and determine the appropriate solar technology. (C) 2003 Elsevier Ltd. All rights reserved.
