Thermodynamics Analysis of a Forced Convection Solar Air Heater Using Artificial Neural Network

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1 International Journal of Applied Mathematics and Physics, 3(1), January-June 2011, pp Global Research Publications, India Thermodynamics Analysis of a Forced Convection Solar Air Heater Using Artificial Neural Network P. T. Saravanakumar * & K. Mayilsamy ** ABSTRACT In this paper, an analysis has been developed the thermal performance of the flat plate air heater with and without thermal storages experimentally and using artificial neural networks (ANN). In this, ambient temperature, solar intensity and air velocity were used as input layers, while the outputs are collector outlet temperature and efficiency of the solar air heater (SAH). The back propagation learning algorithm methods were used in training and testing the data. Comparisons between predicted and experimental results are used to indicate that the proposed ANN model can be used for estimating outlet temperature of the collector and efficiency of SAHs with reasonable accuracy. Keyword: Solar air heater, Thermal Storage, Artificial Neural Networks,, Temperature. 1. INTRODUCTION Solar energy collectors are a special kind of heat exchangers that transform solar radiation energy to internal energy of the transport medium. The major component of any solar heating system is the flat-plate solar collectors. This device absorbs the incoming solar radiation and converts it into heat and also transfers this heat to a fluid flowing through the collector [8]. These conversions depend on the absolute performance of different materials such as glazing materials, collector absorbing plates and flowing fluid. The basic parameter to consider is the solar collector thermal efficiency. This is defined as the ratio of the useful energy delivered and the energy incident on the collector aperture. The definitions of various relations that are required in order to determine the useful energy collected and the interaction of the various constructional parameters on the performance of a solar collector are fairly complex. Therefore, this paper proposes to study the use of ANN model to study the performance of SAH. Solar air heaters (SAH) have low thermal efficiency because of low convective heat transfer coefficient between the air and absorber plate which leads higher temperature to the absorber plate causing maximum thermal losses to ambient. Artificial roughness or various arrangements created in the flow duct created turbulence near the collector wall or broke the boundary layer. Thus, the various arrangement in flow duct can be operated for the enhancement of heat transfer coefficient between the absorber plate and air and thereby improving the thermal performance of SAH. Increasing the area of the absorber plate and varying the shape of the plate area will increase the heat transfer rate to the flowing air, ultimately on the other hand, will increase the pressure drop in the collector. This increases the required power consumption to pump the air flow crossing the collector. Several configurations of SAHs have been developed in literature for various designs [2, 4, 14] with different shapes and dimensions of the air flow passage in plate type solar air collector were tested. Suitable sizing of the component of a solar system is a complicated problem which includes both predictable and unpredictable components. For estimation of the flow of energy and the performance of system, analytic computer codes are often used. The softwares employed are commonly * Assistant Professor, Dept. of Mechanical Engineering. Dr. Mahalingam College of Engineering and Technology, Pollachi, ** Professor, Dept. of Mechanical Engineering, Institute of Road and Transport Technology, Erode.

2 International Journal of Applied Mathematics and Physics (IJAMP) complicated, involving the solution of complex differential equations. Instead of complex rules and mathematical routines, artificial intelligent methods are used to learn the key information pattern within a multidimensional information domain. From the last two decades, the use of artificial neural network methods in mechanical engineering has been increasing gradually. This is mainly because of the effectiveness of artificial intelligence modeling systems improved to a great extent in the engineering area. Forecasting of performance is important in many air-conditioning and solar applications [1, 3, 5, 7, 11, 12, 13, 15]. Some applications in the solar energy field can be found in [6, 9, 10]. This paper describes the applicability of ANN to predict the efficiency and temperature leaving the collector unit of a SAH system with different thermal storage materials. For this purpose, an experimental SAH system was set up and tested on bright as well as cloudy day conditions. The data used were measured on an hourly basis using temperature sensor, which was located in different location in the absorber plates, entry and exit of the SAH and are shown in the Fig.1. Figure 1: Location of Temperature Sensor in the Absorber Plate 2. ARTIFICIAL NEURAL NETWORKS AND APPLICATION ANN commonly referred to as neural networks (NNs), have been widely used in a broad range of applications. These applications include pattern recognition, function approximation optimization, simulation, and estimation, automatic among many other application areas and furthermore, research has produced a large number of network paradigms. Nowadays, ANNs have been trained to solve complex problems that are difficult to solve by conventional approaches [12]. ANNs overcome the limitations of the conventional approaches by extracting the desired information by using the input data. An ANN does not need such a specific equation form. Instead, it needs sufficient input-output data. Also, it can continuously be re-trained, so that it can conveniently adapt to new data. An ANN has been investigated to deal with the problems involving incomplete or imprecise input information [5]. An ANN is an information processing paradigm that is inspired by the way the biological nervous systems such as the brain process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly inter connected processing elements (neurons) working in unison to solve specific problem. The network usually consists of an input layer, one hidden layer and an output layer. ANNs like people, learn by example. Learning in biological systems involves adjustment to the synaptic connections that exist between the neurons. This is true of ANNs as well. ANNs operate much as a black box model, requiring no detailed information about the function. An artificial neuron is a device with many inputs and one output. The neuron has two modes of operation; the training mode and the testing mode. In the training mode, neuron can be trained to fire (or not),

3 Thermodynamics Analysis of a Forced Convection Solar Air Heater Using Artificial Neural Network 59 for particular input patterns. In the testing mode, when a taught input pattern is detected at the input, its associated output becomes the current output. The SAH system which is modeled by ANN has three inputs and two outputs as shown in the Fig. 2. The input variables of SAH systems are the air temperature entering the collector unit (T amb ), solar radiation (I) and wind velocity (v) and these are main parameters effecting collector performance for the study. The velocity ranges are 0 to 4 m/s due to forced convection. The temperature range vary from 23 C to C.The collector efficiency ( ) and air temperature leaving the collector unit (T out ) constitute the output variables of the model. All network calculations were performed using NN toolbox of MATLAB. The training technique for ANN. The back propagation learning rule in MATLAB was used in this study. In addition, all inputs and outputs were normalized between 0 and 1 using the normalization technique available in the software, to ease the training and log-sigmoid was chosen. The data set for the efficiency of the system available included data pattern for each and these data are sufficient for analyzing the SAH system [5]. Figure 2: Schematic View of Multi-Layer Feed-Forward Neural Network From these data 20 patterns were used for training the ANN. The remaining 5 patterns were used as the test data set. For ANN the back propagation learning algorithm has been used in a feed forward, single hidden layer neural network. The variants of the Algorithm were used in the study of the Levenberg- Marquardt (LM), Scaled Conjugate Gradient (SCG) and Pola-Ribiere Conjugate Gradient (CGP) algorithms. LM algorithm was used to study the pattern. 3. EXPERIMENTATION An experimental set up was installed at the Pollachi, Tamil Nadu, India for the study. 3.1 Experimental Set Up A pictorial view of the forced convection solar collector is shown in Fig. 3. The solar collector consists of flat plate solar air heater of area (2 1) m 2 connected with drying chamber. The solar air heater has copper absorber plate coated with black paint to absorb the incident solar radiation. The absorber plate is placed directly behind the transparent cover (glass) with a layer of air separating it from the cover. The air to be heated is passed between the transparent cover (glass) and the absorber plate. To increase the temperature of air by green house effect, a glass cover of 5 mm thickness was placed. The gap between the glass and the absorber surface was maintained at mm for air circulation. One side of the collector was connected to the blower with the help of reducer and the other side was attached with drier cabin. The 100-mm gap between the absorber and insulation was filled with heat storage materials such as iron scraps to store the heat absorbed during sunshine hours and to obtain hot air during off sunshine hours. The solar air heater

4 60 International Journal of Applied Mathematics and Physics (IJAMP) was tilted to an angle about o horizontally. The system is oriented to face south to maximize the solar radiation the incident on the solar collector. On the basis of measurements, Aliyar, Pollachi Taulk (latitude of o N, longitude of o E), where the experiment was conducted had about 11 hours min of sunshine, but potential sunshine duration was about 8 hours per day only. Six calibrated RTD temperature sensors with ± 0.5 o C accuracy were fixed at different locations (as shown in Fig. 1) of the solar drier and were connected to a digital temperature indicator (0.1 o C resolution) through a rotary switch to measure the temperature of air flowing through the SAH system. Energy consumption to the blower was measured with an energy meter having ± 0.5 kwh accuracy and the blower energy was also taken to calculate the efficiency of the SAH.The solar intensity was measured using solar intensity meter having accuracy of about ± 10 W/m 2.The velocity of air at the inlet of the duct was measured with the help of vane type anemometer having ±0.01 m/s accuracy. 4. RESULTS AND DISCUSSION Figure 3: Pictorial View of Experimental Setup The performance of the neural network model for the efficiency ( ) of solar collector is shown in Figs Figure 4 shows the variation of solar intensity during experimentation. Maximum solar intensity recorded was about 900 W/m 2 during peak sunshine hours. The efficiency of the SAH in experimentally was calculated based on the following formula. Q η = (1) Acl Solar Intensity (I) am 9.00am 10.00am 11.00am 12.00pm 1.00pm Solar Intensity 2.00pm 3.00pm 4.00pm 5.00pm 6.00pm Figure 4: Average Solar Intensity During Experimentation

5 Thermodynamics Analysis of a Forced Convection Solar Air Heater Using Artificial Neural Network 61 The readings were taken for the following different cases of flat plate solar air heater with, without storage materials and with fins. These graphs show that the ANN Predicted values for collector efficiency and air temperature of collector outlet and these values were closer to the actual readings. The cases are shown in the Figs T ime in hr T ime in hr Tout(E XP) Tout(ANN) Tout(E XP) Tout(ANN) E F F IC E FIE FNC IC IE Y(E NCXP) Y(E XP) Time in hrs E F F IC E FIE FNC IC IE Y(ANN) NC Y(ANN) Figure 5: Temperature of Outlet Air and of Solar Air Heater with Respect to Time for Experimental Work and ANN Prediction without Thermal Storage Materials T im e T im in e h rs in h rs Tout(E Tout(E XP) XP) Tout(A Tout(A NN) NN) Time in hrs EFFICIENCY(EXP) EFFICIENCY(ANN) Figure 6: Temperature of Outlet Air and of Solar Air Heater with Respect to Time for Experimental Work and ANN Prediction with Thermal Storage Materials as Sand Tempearature in C Tempearature in C Time in hrs Tout(E Tout(E XP) XP) Tout(ANN) Tout(ANN) Time in hrs EFFICIENCY(EXP) EFFICIENCY(ANN) Figure 7: Temperature of Outlet Air and of Solar Air Heater with Respect to Time for Experimental Work and ANN Prediction with Thermal Storage Materials as Gravel T im T ime e in in h rs h rs Time in in hrs hrs Tout(E XP) XP) Tout(A N N) N N) E FE FIC F IC IE IE NC NC Y(E Y(E XP) XP) E FE FIC F IC IE IE NC NC Y(ANN) Figure 8: Temperature of Outlet Air and of Solar Air Heater with Respect to Time for Experimental Work and ANN Prediction with Thermal Storage Materials as Sand with Iron Scraps

6 62 International Journal of Applied Mathematics and Physics (IJAMP) T ime Tin ime hrsin hrs 60 Time in hrs Tout(E Tout(E XP) XP) Tout(ANN) Tout(ANN) EFFICIENCY(EXP) EFFICIENCY(ANN) Figure 9: Temperature of Outlet Air and of Solar Air Heater with Respect to Time for Experimental Work and ANN Prediction with Thermal Storage Materials as Gravel with Iron Scraps E ffic ienc y.0 E ffic ienc y T ime in T ime hrs in hrs T im e in T im h rse in h rs Tout(E XP) Tout(E XP) Tout(A NN) Tout(A NN) E F F IC IEE NF CF IC Y(EIE XP) N C Y(E XP) E F F IC IEE NC F F IC Y(AIE NNC N) Y(A N N) Figure 10: Temperature of Outlet Air and of Solar air Heater with Respect to Time for Experimental Work and ANN Prediction with Thermal Storage Materials as Sand with Fins Tout(E XP) Tout(E XP) Tout(A NN) Tout(A NN) E FFIC IE NCY(E XP) E FFICIE NCY(ANN) E FFIC IE NCY(E XP) E FFICIE NCY(ANN) Figure 11: Temperature of Outlet Air and of Solar Air Heater with Respect to Time for Experimental Work and ANN Prediction with Thermal Storage Materials as Gravel with Fins 5. CONCLUSIONS The aim of this paper was to use the neural networks for the calculation of the performance of SAH using different thermal storage materials and fin arrangements. An ANN method was intended to adopt system for efficient modeling and this did not require a pre-knowledge about the system. The performance of the proposed methodology was evaluated by using several statistical validation parameters. Two output parameters (collector efficiency and air temperature leaving the collector unit) were used in models. System design parameters, working conditions, different thermal storage materials and fins, etc. have a great effect on the efficiency of flat-plate solar collector. The advantages of the ANN compared to classical methods are speed, simplicity and capacity to learn from examples. So, engineering effort can be reduced in these areas. This study demonstrates that an ANN can be used instead of the simulation of mathematical models in this type of SAH system. REFERENCES [1] Adnan Sozen, and Erol Arcaklioglu, (2007), Exergy Analysis of an Ejector-Absorption Heat Transformer Using Artificial Neural Network Approach, Applied Thermal Engineering, 27:

7 Thermodynamics Analysis of a Forced Convection Solar Air Heater Using Artificial Neural Network 63 [2] Ahmad A., et al., H. K. (1996), Thermo Hydraulic Performance of Packed Bed Solar Air Heaters, Energy Conversion and Management, 37(2): [3] Erol Arcaklioglu, et al., (2004), Artificial Neural Network Analysis of Heat Pumps Using Refrigerant Mixtures, Energy Conversion and Management, : [4] Hawlader, et al., (2003), Microencapsulated PCM Thermal Energy Storage System, Applied Energy, 74(I-2): [5] Himet Esen, et al., (2009), Artificial Neural Network and Wavelet Neural Network Approaches for Modeling of a Solar Air Heater, Expert systems with Application, 36: [6] Kalogirou S. A., (2000), Applications of Artificial Neural-Networks for Energy Systems, Applied Energy, 67(1-2): 17-. [7] Kalogirou S. A., (2001), Artificial Neural Networks in Renewable Energy Systems Applications, A review. Renewable and Sustainable Energy Reviews, 5(4), [8] Kalogirou S. A., (2004a), Solar Thermal Collectors and Applications, Progress in Energy and Combustion Science, (3): [9] Kalogirou S. A., (2004b), Optimization of Solar Systems Using Artificial Neural-Networks and Genetic Algorithms, Applied Energy, 77: 3-5. [10] Kalogirou S. A., (2006), Predication of Flat Plate Collector Performance Parameters Using Neural Network, Solar Energy, 80(3): 2-9. [11] Kaygusuz K., and Sar1 A., (2003), Renewable Energy Potential and Utilization in Turkey, Energy Conversion and Management, 44: [12] Mohandas, et al., (1998), Estimation of Global Solar Radiation Using Artificial Neural Network, Renewable Energy, 14(1-4): [13] Sezayi Yilmaz, and Kemal Atik, (2007), Modeling of a Mechanical Cooling System with Variable Cooling Capacity by Using Artificial Neural network, Applied Thermal Engineering, 27: [14] Shanmugam V., and Natarajan E. (2007), Experimental Study of Regenerative Desiccant Integrated Solar Dryer with and without Reflective Mirror, Applied Thermal Engineering, 27(8-9): [15] Soteris A. Kalogirou, et al., (1999), Artificial Neural Network Used for the Performance Prediction of a Thermosiphon Solar Water Heater, Renewable Energy, 18:

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