Performance comparison of CFCs with their substitutes using artificial neural network

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000224064200008In 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

A diesel engine's performance and exhaust emissions

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000225346000002This paper determines, using artificial neural-networks (ANNs), the performance of and exhaust emissions from a diesel engine with respect to injection pressure, engine speed and throttle position. The design injection-pressure of the diesel engine, for the turbocharger and pre-combustion chamber used, is 150 bar. Experiments have been performed for four pressures, namely 100, 150, 200 and 250 bar with throttle positions of 50, 75 and 100%. Engine torque, power, brake mean effective pressure, specific fuel consumption, fuel flow, and exhaust emissions such as SO2, CO2, NOx, and smoke level (%N) have been investigated. The back-propagation learning algorithm with three different variants, single and two hidden layers, and a logistic sigmoid transfer-function have been used in the network. In order to train the network, the results of these measurements have been used. Injection pressure, engine speed, and throttle position have been used as the input layer; performance values and exhaust emissions characteristics have also been used as the output layer. It is shown that the R-2 values are about 0.9999 for the training data, and 0.999 for the test data; RMS values are smaller than 0.01; and mean % errors are smaller than 8.5 for the test data. (C) 2004 Elsevier Ltd. All rights reserved

Prospects for future projections of the basic energy sources in Turkey

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000251419500006The main goal of this study is to develop the energy sources estimation equations in order to estimate the future projections and make correct investments in Turkey using artificial neural network (ANN) approach. It is also expected that this study will be helpful in demonstrating energy situation of Turkey in amount of EU countries. Basic energy indicators such as population, gross generation, installed capacity, net energy consumption, import, export are used in input layer of ANN. Basic energy sources such as coal, lignite, fuel-oil, natural gas and hydraulic are in output layer. Data from 1975 to 2003 are used to train. Three years (1981, 1994 and 2003) are only used as test data to confirm this method. Also, in this study, the best approach was investigated for each energy sources by using different learning algorithms (scaled conjugate gradient [SCG] and Levenberg-Marquardt [LM]) and a logistic sigmoid transfer function in the ANN with developed software. The statistical coefficients of multiple determinations (R-2-value) for training data are equal to 0.99802, 0.99918, 0.997134, 0.998831 and 0.995681 for natural gas, lignite, coal, hydraulic, and fuel-oil, respectively. Similarly, these values for testing data are equal to 0.995623, 0.999456, 0.998545, 0.999236, and 0.99002. The best approach was found for lignite by SCG algorithm with seven neurons so mean absolute percentage error (MAPE) is equal to 1.646753 for lignite. According to the results, the future projections of energy indicators using ANN technique have been obviously predicted within acceptable errors. Apart from reducing the whole time required, the importance of the ANN approach is possible to find solutions that make energy applications more viable and thus more attractive to potential users

Formulation based on artificial neural network of thermodynamic properties of ozone friendly refrigerant/absorbent couples

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000229277400018This paper presents a new approach based on artificial neural networks (ANNs) to determine the properties of liquid and two phase boiling and condensing of two alternative refrigerant/absorbent couples (methanol/LiBr and methanol/LiCl). These couples do not cause ozone depletion and use in the absorption thermal systems (ATSs). ANN's are able to learn the key information patterns within multidimensional information domain. ANNs operate such as a 'black box' model, requiring no detailed information about the system. On the other hand, they learn the relationship between the input and the output. In order to train the neural network, limited experimental measurements were used as training data and test data. In this study, in input layer, there are temperatures in the range of 298-498 K, pressures (0.1-40 MPa) and concentrations of 2%, 7%, 12% of the couples; specific volume is in output layer. The back-propagation learning algorithm with three different variants, namely scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM), and logistic sigmoid transfer function were used in the network so that the best approach can find. The most suitable algorithm and neuron number in the hidden layer are found as SCG with 8 neurons. For this number level, after the training, it is found that maximum error is less than 3%, average error is about 1% and R-2 value are 99.999%. As seen from the results obtained the thermodynamic equations for each pair by using the weights of network 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 Ltd. All rights reserved

Solar potential in Turkey

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000225346000004Most of the locations in Turkey receive abundant solar-energy, because Turkey lies in a sunny belt between 36degrees and 42 degreesN latitudes. Average annual temperature is 18 to 20 degreesC on the south coast, falls to 14-16 degreesC on the west coat, and fluctuates between 4 and 18 degreesC in the central parts. The yearly average solar-radiation is 3.6 kWh/m(2) day, and the total yearly radiation period is similar to2610 It. In this study, a new formulation based on meteorological and geographical data was developed 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 logistic sigmoid (logsig) transfer function were used in the networks. Meteorological data for last four years (2000-2003) from 12 cities (Canakkale, Kars, Hakkari, Sakarya, Erzurum, Zonguldak, Balikesir, Artvin, Corum, Konya, Siirt, and Tekirdag) spread over Turkey were used in order to train the neural-network. 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 3.832% and R-2 values to be about 99.9738% for the selected stations. The ANN models show greater accuracy for evaluating solar-resource posibilities in regions where a network of monitoring stations has not been established in Turkey. This study confirms the ability of the ANN to predict solar-radiation values accurately. (C) 2004 Elsevier Ltd. All rights reserved

Performance maps of a diesel engine

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000229661300002This paper suggests a mechanism for determining the constant specific-fuel consumption curves of a diesel engine using artificial neural-networks (ANNs). In addition, fuel-air equivalence ratio and exhaust temperature values have been predicted with the ANN. To train the ANN, experimental results have been used, performed for three cooling-water temperatures 70, 80, 90, and 100 C for the engine powers ranging from 1000 to 2300 - for six different powers of 75-450 kW with incremental steps of 75 kW. In the network, the back-propagation learning algorithm with two different variants, single hidden-layer, and logistic sigmoid transfer function have been used. Cooling water-temperature, engine speed and engine power have been used as the input layer, while the exhaust temperature, break specific-fuel consumption (BSFC, g/kWh) and fuel-air equivalence ratio (FAR) have also been used separately as the output layer. It is shown that R-2 values are about 0.99 for the training and test data; RMS values are smaller than 0.03; and mean errors are smaller than 5.5% for the test data. (c) 2004 Elsevier Ltd. All rights reserved

Effect of relative humidity on solar potential

ARCAKLIOGLU, Erol/0000-0001-8073-5207;WOS: 000232323500005In 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

Modelling of Turkey's net energy consumption using artificial neural network

The main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using artificial neural network (ANN) technique in order to determine the future level of the energy consumption in Turkey. Two different models were used in order to train the neural network: (i) Population, gross generation, installed capacity and years are used in input layer of network (Model 1). (ii) Energy sources are used in input layer of network (Model 2). The NEC is in output layer for two models. R2 values for training data are equal to 0.99944 and 0.99913, for Model 1 and Model 2, respectively. Similarly, R2 values for testing data are equal to 0.997386 and 0.999558 for Model 1 and Model 2, respectively. According to the results, the NEC prediction using ANN technique will be helpful in developing highly applicable and productive planning for energy policies. Copyright © 2005 Inderscience Enterprises Ltd

Performance analysis of ejector absorption heat pump using ozone safe fluid couple through artificial neural networks

ARCAKLIOGLU, Erol/0000-0001-8073-5207;WOS: 000221590600020Thermodynamic analysis of absorption thermal systems is too complex because the analytic functions calculating the thermodynamic properties of fluid couples involve the solution of complex differential equations and simulation programs. This study aims at easing this complex situation and consists of three cases: (1) A special ejector, located at the absorber inlet, instead of the common location at the condenser inlet, to increase overall performance was used in the ejector absorption beat pump (EAHP). The ejector has two functions: Firstly, it aids the pressure recovery from the evaporator and then upgrades the mixing process and pre-absorption by the weak solution of the methanol coming from the evaporator. (ii) Use of artificial neural networks (ANNs) has been proposed to determine the properties of the liquid and two phase boiling and condensing of an alternative working fluid couple (methanol/LiCl), which does not cause ozone depletion. (iii) A comparative performance study of the EAHP was performed between the analytic functions and the values predicted by the ANN for the properties of the couple. 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 the input layer, there are temperature, pressure and concentration of the couples. Specific volume is in the output layer. After training, it was found that the maximum error was less than 3%, the average error was less than 1.2% and the R-2 values were about 0.9999. Additionally, in comparison of the analysis results between analytic equations obtained by using experimental data and by means of the ANN, the deviations of the refrigeration effectiveness of the system for cooling (COPr), exergetic coefficient of performance of the system for cooling (ECOPr) and circulation ratio (F) for all working temperatures were found to be less than 1.7%, 5.1%, and 1.9%, respectively. Deviations for COPr, ECOPr and F at a generator temperature of similar to90 degreesC (cut off temperature) at which the coefficient of performance of the system is maximum are 0.9%, 1.8%, and 0.1%, respectively, for other working temperatures. When this system was used for heating, similar deviations were obtained. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable uncertainties. The results showed that the use of ANNs for determination of thermodynamic properties is acceptable in design of the EAHP. (C) 2003 Elsevier Ltd. All rights reserved

Estimation of GHG Emissions in Turkey Using Energy and Economic Indicators

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000265987900007The greenhouse gas emissions (total greenhouse gas, CO2, CO, SO2, NO2, E (emissions of non-methane volatile organic compounds)) covered by the Kyoto Protocol are weighted by their global warming potentials and aggregated to give total emissions in CO2 equivalents. The subject in this study is to obtain equations to predict the greenhouse gas emissions of Turkey using energy and economic indicators by the artificial neural network approach. In this study, three different models were used in order to train the artificial neural network. In the first of them sectoral energy consumption (Model 1), in the second of them gross domestic product (Model 2), and in the third of them gross national product (Model 3) are used input layer of the network. The greenhouse gas emissions are in the output layer for all models. The aim of using different models is to estimate the greenhouse gas emissions with high confidence to make correct investments in Turkey. The obtained equations are used to determine the future level of the greenhouse gas emissions and take measures to control the share of sectors in total emission. According to artificial neural network results, the maximum mean absolute percentage errors for Model 1 were found to be 0.147151, 0.066716, 0.181901, 0.105146, 0.124684, and 0.158157 for greenhouse gas, SO2, NO2, CO, E, and CO2, about training data with Levenberg-Marquardt algorithm by eight neurons, respectively. Similarly, for Model 2 these values were found to be 0.487212, 0.701938, 0.718754, 0.232667, 0.272346, and 0.575421, respectively. And finally, for Model 3, these values were found to be 0.126728, 0.115135, 0.069296, 0.214888, 0.080358, and 0.179481, respectively. R2 values are obtained very close to 1 for all models. The artificial neural network approach shows greater accuracy for estimating the greenhouse gas emissions