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ISRN Mechanical Engineering
Volume 2012 (2012), Article ID 915154, 10 pages
Research Article

Exergy Assessment of Single Stage Solar Adsorption Refrigeration System Using ANN

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

Received 18 May 2012; Accepted 13 June 2012

Academic Editors: G. A. Britton and Y. He

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.


A new approach based on artificial intelligence is proposed here for the exergy assessment of solar adsorption refrigeration system working with activated carbon-methanol pair. Artificial neural network model is used for the prediction of exergy destruction and exergy efficiency of each component of the system. Pressure, temperature and solar insolation are used as input variables for developing the artificial neural network model. The back propagation algorithm with three different variants such as CGP, SCG and LM are used in the network A and network B. The most suitable algorithm and the number of neurons in hidden layer are found as LM with 9 for network A and SCG with 17 for the Network B. The artificial neural network predicted results are compared with the calculated values of exergy destruction and exergy efficiency. The values of the exergy destruction and exergy efficiency of components (condenser, expansion device, evaporator, adsorbent bed, solar concentrator and overall system) are found to be close to 1. The RMS and COV values are found to be very low in all cases. The comparison of the results suggests that the artificial neural network provided results are within the acceptable range.