Theoretical and experimental investigation of a hot box-type solar cooker performance

A box-type solar cooker with three reflectors hinged at the top of the cooker was used for the experimental data and a mathematical model. Energy-balance equations were applied to components of the cooker such as absorber plate, cooking vessel, cooking fluid, enclosure air inside the cooker, and glass cover. Then, Cramer's rule was used to solve the equations and build the models. The actual temperature distribution in Karabuk, Turkey recorded on a typical summer day was also calculated by computer simulation using the suggested model. The predicted temperatures agreed with measurements under transient conditions within about 3.2 °C for absorber plate, until the boiling point of water was reached, to +1.8 °C for enclosure air, and within ±2.5 °C for cooking fluid. Good agreement between experimental and theoretical results was observed. © IMechE 2007

Thermo-economic optimization of superheating and sub-cooling heat exchangers in vapor-compressed refrigeration system

In this study, superheating and sub-cooling heat exchangers in vapor-compressed refrigeration system are analyzed from thermodynamics and economical (refrigeration system operation cost, investment cost) viewpoints. Using four different refrigerants (R22, R502, R134a and R404a), the temperature of condenser at the interval of (35-55°C) and temperature of evaporator at the interval of (- 10 to 10°C) have been obtained from the calculation process. The second law analysis (analysis of irreversibility) of a reffigeration system is carried out and then the whole system is optimized thermo-economically. As a result of calculations, optimum superheating and sub-cooling, temperatures of heat exchanger (superheating, sub-cooling) areas corresponding to these temperatures are obtained. Copyright © 2007 John Wiley & Sons, Ltd

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

Turkey's net energy consumption

The main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using an artificial neural-network (ANN) technique in order to determine the future level of energy consumption in Turkey. In this study, two different models were used in order to train the neural network. In one of them, population, gross generation, installed capacity and years are used in the input layer of the network (Model 1). Other energy sources are used in input layer of network (Model 2). The net energy consumption is in the output layer for two models. Data from 1975 to 2003 are used for the training. Three years (1981, 1994 and 2003) are used only as test data to confirm this method. The statistical coefficients of multiple determinations (R 2-value) for training data are equal to 0.99944 and 0.99913 for Models 1 and 2, respectively. Similarly, R2 values for testing data are equal to 0.997386 and 0.999558 for Models 1 and 2, respectively. According to the results, the net energy consumption using the ANN technique has been predicted with acceptable accuracy. Apart from reducing the whole time required, with the ANN approach, it is possible to find solutions that make energy applications more viable and thus more attractive to potential users. It is also expected that this study will be helpful in developing highly applicable energy policies. © 2004 Elsevier Ltd. All rights reserved

Thermal performance parameters estimation of hot box type solar cooker by using artificial neural network

Work to date has shown that Artificial Neural Network (ANN) has not been used for predicting thermal performance parameters of a solar cooker. The objective of this study is to predict thermal performance parameters such as absorber plate, enclosure air and pot water temperatures of the experimentally investigated box type solar cooker by using the ANN. Data set is obtained from the box type solar cooker which was tested under various experimental conditions. A feed-forward neural network based on back propagation algorithm was developed to predict the thermal performance of solar cooker with and without reflector. Mathematical formulations derived from the ANN model are presented for each predicting temperatures. The experimental data set consists of 126 values. These were divided into two groups, of which the 96 values were used for training/learning of the network and the rest of the data (30 values) for testing/validation of the network performance. The performance of the ANN predictions was evaluated by comparing the prediction results with the experimental results. The results showed a good regression analysis with the correlation coefficients in the range of 0.9950-0.9987 and mean relative errors (MREs) in the range of 3.92516-7.040% for the test data set. The regression coefficients indicated that the ANN model can successfully be used for the prediction of the thermal performance parameters of a box type solar cooker with a high degree of accuracy. © 2007 Elsevier Masson SAS. All rights reserved