Table of Contents
ISRN Thermodynamics
Volume 2012, Article ID 102376, 8 pages
Research Article

Performance Prediction of Solar Adsorption Refrigeration System by Ann

Department of Mechanical Engineering, National Institute of Technology Calicut, Kerala 673601, India

Received 27 January 2012; Accepted 14 February 2012

Academic Editors: I. I. El-Sharkawy and P. Trens

Copyright © 2012 V. Baiju and C. Muraleedharan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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. 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. 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. 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. After training, it was found that LM algorithm with 9 neurons is most suitable for modeling solar adsorption refrigeration system. The ANN predictions of performance parameters agree well with experimental values with R2 values close to 1 and maximum percentage of error less than 5%. The RMS and covariance values are also found to be within the acceptable limits.