ANNs are able to learn the key information patterns within multidimensional information domain. Instead of solving complex differential equations and applying the limited number of experimental data, faster and simpler solutions can be obtained by using artificial neural network. The thermodynamic analyses of adsorption systems are complex because of the complex differential equations involved. For the improvement of the system, a detailed computational and thermodynamic analysis must be carried out.
The low COP and SCP values as compared to the conventional refrigeration systems are the barriers for the commercialization of the adsorption refrigeration systems. By use of ozone-friendly refrigerants and ability to utilize the renewable energy sources, the adsorption systems can be preferred as an alternative to the conventional refrigeration systems. Furthermore, solar-power-based refrigerator is simple and is adaptable for small, medium, or large systems. The consumption of low grade energy by the adsorption units does not possess any problems of emission of greenhouse gases. The major attraction of solar adsorption refrigeration is that its working fluids satisfy the Montreal protocol on ozone layer depletion and the Kyoto protocol on global warming. In comparison with the vapour compression refrigeration systems, adsorption refrigeration systems have the benefits of energy savings if powered by waste heat or solar energy, like simpler control, absence of vibration, and low operation cost. Research has proved that the adsorption refrigeration technology has a promising potential for competing with the conventional vapour compression refrigeration systems. From this context, the adsorption refrigeration system attains a considerable attention in 1970s due to the energy crisis and ecological problems related to the use of CFCs and HFCs.
The conventional refrigeration systems require mechanical energy as the driving source and are responsible for the emission of CO 2 and the other greenhouse gases such as CFCs and HFCs which are considered major cause for ozone layer depletion. The RMS and covariance values are also found to be within the acceptable limits. The ANN predictions of performance parameters agree well with experimental values with R 2 values close to 1 and maximum percentage of error less than 5%. After training, it was found that LM algorithm with 9 neurons is most suitable for modeling solar adsorption refrigeration system. The back propagation algorithm with three different variants namely Scaled conjugate gradient, Pola-Ribiere conjugate gradient, and Levenberg-Marquardt (LM) and logistic sigmoid transfer function were used, so that the best approach could be found. The ANN used in the performance prediction was made in MATLAB (version 7.8) environment using neural network tool box.In this study the temperature, pressure, and solar insolation are used in input layer. Use of artificial neural network has been proposed to determine the performance parameters of the system, namely, coefficient of performance, specific cooling power, adsorbent bed (thermal compressor) discharge temperature, and solar cooling coefficient of performance. This paper proposes a new approach for the performance analysis of a single-stage solar adsorption refrigeration system with activated carbon-R134a as working pair.
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