Tayfun Menlik

Determination of efficiency of flat-plate solar collectors using neural network approach

In this study, a new formula based on artificial neural network (ANN) technique was developed to determine the efficiency of flat-plate solar collectors. In practice, the ANN model can be used for modeling the efficiency of solar collectors with complex structures when other models may have difficulties. Logistic sigmoid transfer function was used in the network. Meteorological data of summer session (from July to September) for Ankara were used as training data in order to train the neural network. Surface temperature in collector, date, time, solar radiation, declination angle, azimuth angle and tilt angle are used in the input layer of the network. The efficiency of flat-plate solar collector is in the output layer. The results show that the maximum and minimum deviations were found 2.558484 and 0.001969, respectively. The advantages of ANN model compared to the conventional testing methods are speed, simplicity, and the capacity of the ANN to learn from examples.

The Evaluation of Renewable Energy Technologies for Electricity Generation in Turkey Using Intuitionistic Fuzzy TOPSIS

Abstract Due to global warming and climate changes, the utilization of renewable energy not only plays a key role in reducing emissions of greenhouse gases in the world but also will supply global energy in the future. Renewable energy technologies are efficient and effective solutions for clean and sustainable energy development in Turkey due to the advantages of Turkeys geographical location for the extensive usage of most renewable energy resources. In this article, the evaluation of renewable energy technologies for electricity generation in Turkey has been accomplished using intuitionistic fuzzy TOPSIS. Photovoltaic, hydro, wind and geothermal energy have been evaluated for long-term renewable technologies for Turkey.

Determination of freeze-drying behaviors of apples by artificial neural network

Freeze drying is the best drying technology regarding quality of the end product but it is an expensive method and the high costs of process limit its application to industrial scale. At the same time, the freeze-drying process is based on different parameters, such as drying time, pressure, sample thicknesses, chamber temperature, sample temperatures and relative humidity. So, the determination of drying behaviors, such as moisture content (MC), moisture ratio (MR) and drying rate (DR), of the freeze-drying process are too complex. In this paper, to help the freeze dryer designer and simplify this complex process, the use of artificial neural networks has been proposed. An artificial neural networks (ANN) model has been developed for determination the prediction of drying behaviors, such as MC, MR and DR, of apples in the freeze-drying process. The back-propagation learning algorithm with variant which is Levenberg-Marquardt (LM) and Fermi transfer function have been used in the network. In addition, the statistical validity of the developed model has been determined by using the coefficient of determination (R^2), the root means square error (RMSE) and the mean absolute percentage error (MAPE). R^2, RMSE and MAPE have been determined for MC, MR and DR, as 0.999, 0.0078895, 0.2668459, and 0.999, 0.0001099, 0.2968427 and 0.999, 0.0000008, 0.2703797, respectively.

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.

An Experimental Study on Freeze-Drying Behavior of Strawberries

In this article, the freeze-drying behavior and the drying kinetics of strawberries were investigated. Drying experiments were performed in a freeze-drying experimental setup constructed in the Department of Mechanical Education, Faculty of Technical Education, Gazi University, Ankara, Turkey. In experiments, 5- and 7-mm-thick sliced strawberry samples have been used. Some models, which were given in the literature, have been used to predict the drying behavior of strawberries. Experimental data findings were fitted for determining the best model to represent the drying behavior of strawberries and the drying kinetics used in these models. It was seen from the correlation coefficient, reduced chi-square, and root mean square error (0.9984, 2.688 × 10−4, 0.015 and 0.9976, 4.669 × 10−6, 0.002, respectively) that the Page model could sufficiently describe the drying behavior of 5- and 7-mm strawberry samples.

Derivation of empirical equations for thermodynamic properties of a ozone safe refrigerant (R404a) using artificial neural network

This study, deals with the potential application of the artificial neural networks (ANNs) to represent PVTx (pressure-specific volume-temperature-vapor quality) data in the range of temperature of 173-498K and pressure of 10-3600kPa. Generally, numerical equations of thermodynamic properties are used in the computer simulation analysis instead of analytical differential equations. And also analytical computer codes usually require a large amount of computer power and need a considerable amount of time to give accurate predictions. Instead of complex rules and mathematical routines, this study proposes an alternative approach based on ANN to determine the thermodynamic properties of an environmentally friendly refrigerant (R404a) for both saturated liquid-vapor region (wet vapor) and superheated vapor region as numerical equations. Therefore, reducing the risk of experimental uncertainties and also removing the need for complex analytic equations requiring long computational time and effort. R^2 values - which are errors known as absolute fraction of variance - in wet vapor region are 0.999401, 0.999982 and 0.999993 for specific volume, enthalpy and entropy for training data, respectively. For testing data, these values are 0.998808, 0.999988, and 0.999993. Similarly, for superheated vapor region, they are: 0.999967, 0.999999 and 0.999999 for training data, 0.999978, 0.999997 and 0.999999 for testing data. As seen from the results of mathematical modeling, the calculated thermodynamic properties are obviously within acceptable uncertainties.

Multi-criteria Axiomatic Design Approach to Evaluate Sites for Grid-connected Photovoltaic Power Plants: A Case Study in Turkey

Abstract Global warming and climate change are the most serious problems for developing countries as well as the world. Therefore, the usage of renewable energy sources is gaining importance for sustainable energy development and environmental protection. Turkey is one of the developing countries whose demand of electricity is sharply increasing. In order to meet this demand by means of renewable sources, solar power is a suitable source due to the high solar energy potential of Turkey among the renewable sources. In this article, the multi-criteria axiomatic design (AD) approach is proposed for the evaluation of sites for a grid-connected photovoltaic power plant (GCPP) in Turkey. For this aim, four evaluation criteria, which have great influence on determination of a GCPP site are taken into account.

Utilization of Fly Ash Nanofluids in Two-phase Closed Thermosyphon for Enhancing Heat Transfer

This study investigates how fly ash nanofluids affect the thermal performance of a two-phase closed thermosyphon at various states of operation. The utilization of nanofluids obtained from X2O3-type oxides, such as Al2O3, Fe2O3, or CuO, on the improvement of two-phase closed thermosyphon performance was reported in a number of studies in the literature. The present study experimentally demonstrated the effect of using a nanofluid obtained from fly ash comprised of various types of metal oxides in varying ratios on improving the performance of a two-phase closed thermosyphon. The fly ash was obtained from the flue gas that was captured in the cyclones of the Yatagan thermal power plant (Turkey). Triton X-100 (Dow Chemical Company) dispersant was used in the study to produce the 0.2% (wt) fly ash/water nanofluid via direct synthesis. A straight copper tube with an inner diameter of 13 mm, outer diameter of 15 mm, and length of 1 m was used as the two-phase closed thermosyphon. The nanofluid filled 33.3% (44.2 ml) of the volume ofthe two-phase closed thermosyphon. Three heating power levels (200, 300, and 400 W) were used in the experiments with three different flow rates of cooling water (5, 7.5, and 10 g/s) used in the condenser for cooling the system. A increase of 26.39% was achieved in the efficiency of the two-phase closed thermosyphon when 4% (wt) fly ash containing nanofluid was used to replace deionized water at a heat load of 200 W and with a cooling water flow rate of 5 g/s.

The Forecasting of Net Electricity Consumption of the Consumer Groups in Turkey

Abstract The main goal of this study is to reveal the future projections of net electricity consumption (NEC) as the consumer groups in Turkey by using the artificial neural network (ANN) technique. In this study the equations based on energy and economic indicators were obtained to predict the net electricity consumption as the consumer groups with high confidence to plan correct investments in Turkey. In this study, three different models were used in order to train the ANN. In Model 1, energy indicators such as installed capacity, generation, energy import and energy export were used as the input layer of the network. In Model 2, the sectoral share of Gross National Product (GNP) per capita was used. In Model 3, the sectoral share of Gross Domestic Product (GDP) per capita was used. The NEC of 25 different consumer groups are in the output layer for all models. The aim of using different models is to demonstrate the effect of sectoral share of economic indicators (GNP and GDP) on the estimation of NEC. R2 values are obtained ~1 for all models as consumer groups. Based on the output of the study, the ANN model can be used to estimate the NEC as the consumer groups from the energy and economic indicators.

Energy and exergy analysis of R22 and its alternatives in a vapour compression refrigeration system

In this paper, energy and exergy analyses and performance comparison of R22 and its alternatives, R407C and R410A, in a vapour compression refrigeration system (VCRS) have been presented. For this aim, detailed first law (energy) and second law (exergy) analysis of these three refrigerants have been performed. Analyses have been performed for different evaporator (range of -40°C to 0°C with 5°C intervals), condenser (range of 40°C to 55°C with 5°C intervals), subcooling/superheating (range of 0°C to 10°C with 2°C intervals) and dead state temperatures (range of 20°C to 30°C with 5°C intervals). It is observed from results that R407C is a better alternate to R22 than R410A. The worst component of VCRS observed from the analyses is condenser.