Mehmet Özalp

Use of artificial neural networks for mapping of solar potential in Turkey

Turkey has sufficient solar radiation intensities and radiation durations for solar thermal applications since Turkey lies in a sunny belt, between 36° and 42° N latitudes. The yearly average solar-radiation is 3.6 kWh/m2day, and the total yearly radiation period is ~2610 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 R2values 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 posibilities 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.

A new approach to thermodynamic analysis of ejector–absorption cycle: artificial neural networks

Abstract Thermodynamic analysis of absorption thermal systems is too complex because of analytic functions calculating the thermodynamic properties of fluid couples involving the solution of complex differential equations. To simplify this complex process, the use of artificial neural networks (ANNs) has been proposed for the analysis of ejector–absorption refrigeration systems (EARSs). ANNs approach was used to determine the properties of liquid and two phase boiling and condensing of an alternative working fluid couple (methanol/LiBr), which does not cause ozone depletion for EARS. The back-propagation learning algorithm with three different variants and logistic sigmoid transfer function was used in the network. In addition, this paper presents a comparative performance study of the EARS using both analytic functions and prediction of ANN for properties of the fluid couple. After training, it was found that average error is less than 1.3% and R2 values are about 0.9999. Additionally, when the results of analytic equations obtained by using experimental data and by means of ANN were compared, deviations in coefficient of performance (COP), exergetic coefficient of performance (ECOP) and circulation ratio (F) for all working temperatures were found to be less than 1.8%, 4%, 0.2%, respectively. Deviations for COP, ECOP and F at a generator temperature of ∼90 °C for which the COP of the system is maximum are 1%, 2%, 0.1%, respectively, for other working temperatures. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable uncertainties.

Performance improvement of absorption refrigeration system using triple-pressure-level

Abstract In the absorption refrigeration system (ARS) working with aqua–ammonia, the ejector is commonly located at the condenser inlet. In this study, the ejector was located at the absorber inlet. Therefore, the absorber pressure becomes higher than the evaporator pressure and the system works with triple-pressure-level. The ejector has two main functions: (i) aiding pressure recovery from the evaporator, (ii) upgrading the mixing process and the pre-absorption by the weak solution of the ammonia coming from the evaporator. In addition to these functions, it can also act to lower the refrigeration and heat-source temperatures. Energy analyses show that the system’s coefficient of performance (COP) and exergetic coefficient of performance (ECOP) were improved by 49% and 56%, respectively and the circulation ratio (f) was reduced by 57% when ARS is initiated at lower generator temperatures. Due to the reduced circulation ratio, the system dimensions can be reduced; consequently, this decreases overall cost. The heat source and refrigeration temperatures decreased in the range of 5–15 °C and 1–3 °C, respectively. Exergy analyses show that the exergy loss of the absorber of ARS with ejector had a higher exergy loss than those of the other components. Therefore, a multiple compartment absorber can be proposed to reduce the exergy loss of the absorber of ARS with ejector.

Determination of thermodynamic properties of an alternative refrigerant (R407c) using artificial neural network

Thermodynamic analysis of the refrigeration systems is too complex because of thermodynamic properties equations of working fluids, involving the solution of complex differential equations. To simplify this complex process, this paper proposes a new approach (artificial neural network, ANN) to determine of thermodynamic properties of an environmentally friendly alternative refrigerant (R407c) for both saturated liquid-vapor region (wet vapor) and superheated vapor region. Instead of complex rules and mathematical routines, ANNs are able to learn the key information patterns within multidimensional information domain. Therefore, reducing the risk of experimental uncertainties and also removing the need for complex analytic equations requiring long computational time and efforts. R^2 values - which are errors known as absolute fraction of variance - in wet vapor region are 0.999706, 0.999949, 0.999909, 0.999988 and 0.999836 for specific volume, enthalpy, entropy, viscosity and thermal conductivity for training, respectively. Similarly, for superheated vapor, they are: 0.99992, 1, 0.99998, 0.99995 and 0.99996 for training data, respectively. Promising thermodynamics property results have been obtained for R407c within acceptable errors. PVTx properties predicted are in valid region for working conditions of the refrigeration systems in case of use to computer simulation programs.

Performance prediction of a solar driven ejector-absorption cycle using fuzzy logic

Theoretical performance analysis of the absorption systems is very complex because of analytic functions used for calculating the properties of fluid couples and simulation programs. To simplify this complex process, this paper proposes a new approach to performance analysis of solar driven ejector-absorption refrigeration system (EARS) operated aqua/ammonia. Performance of EARS was predicted using fuzzy logic controller at different working conditions instead of complex rules and mathematical routines. Fuzzy logic’s linguistic terms provide a feasible method for defining the operational characteristics of EARSs. Input data for fuzzy logic are experimental results performed in the climate condition of Ankara in Turkey. In the comparison of performance analysis results between analytic equations and by means of fuzzy logic controller, deviations coefficient of performance (COP), exergetic coefficient of performance (ECOP) and circulation ratio (F) for all working temperatures are less than 2, 5 and 0.2%, respectively. The statistical coefficient of multiple determinations (R2 value) equals to 1, 0.9996, 1 for the COP, ECOP and F, respectively. These accuracy degrees are acceptable in design of EARS. This study is considered to be helpful in predicting the performance of an EARS prior to its setting up in an environment where the temperatures are known. Also, this study provides a fast and accurate means of determining the performance under transient operating regimes without the need to resort to classical physical modeling.

A Study for Estimating Solar Resources in Turkey Using Artificial Neural Networks

Turkey has sufficient solar radiation and radiation period for solar thermal applications since it lies in a sunny belt between 36° and 42° N latitudes. The yearly average solar radiation is 3.6 kWh/m2day, and the total yearly radiation period is ∼2610 h. This study investigates the estimation of solar resources 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 transfer function were used in the network. In order to train the neural network, meteorological data for last three years (2000–2002) from 17 stations (namely cities) spread over Turkey were used as training (11 stations) and testing (6 stations) data. These cities selected can give a general idea about Turkey. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) is used in input layer of network. Solar radiation is in output layer. The maximum mean absolute percentage error was found to be less than 6.7% and R2 values to be about 99.8937% for the testing stations. However, these values were found to be 2.41% and 99.99658% for the training stations. The results indicate that the ANN model seems promising for evaluating solar resource posibilities at the places where there are no monitoring stations in Turkey. The results on the testing stations indicate a relatively good agreement between the observed and the predicted values.