Performance prediction of solid desiccant - Vapor compression hybrid air-conditioning system using artificial neural network

Journal article

Authors / Editors

Sahoo, Pradeep

Research Areas

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Publication Details

Author list: Jani DB, Mishra M, Sahoo PK

Publisher: Elsevier

Place: OXFORD

Publication year: 2016

Journal: Energy (0360-5442)

Journal acronym: ENERGY

Volume number: 103

Start page: 618

End page: 629

Number of pages: 12

ISSN: 0360-5442

Languages: English-Great Britain (EN-GB)

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Abstract

In the present study, ANN (artificial neural network) model for a solid desiccant vapor compression hybrid air-conditioning system is developed to predict the cooling capacity, power input and COP (coefficient of performance) of the system. This paper also describes the experimental test set up for collecting the required experimental test data. The experimental measurements are taken at steady state conditions while varying the input parameters like air stream flow rates and regeneration temperature. Most of the experimental test data (80%) are used for training the ANN model while remaining (20%) are used for the testing of ANN model. The outputs predicted from the ANN model have a high coefficient of correlation (R > 0.988) in predicting the system performance. The results show that the ANN model can be applied successfully and can provide high accuracy and reliability for predicting the performance of the hybrid desiccant cooling systems. (C) 2016 Elsevier Ltd. All rights reserved.

Keywords

ANN, COP, Desiccant cooling, Hybrid system, Regeneration

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