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

D.B. Jani, M. Mishra, P.K. Sahoo

Research output: Contribution to journalArticle

19 Citations (Scopus)

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. © 2016 Elsevier Ltd.
Original languageEnglish
Pages (from-to)618-629
Number of pages12
JournalEnergy
Volume103
DOIs
Publication statusPublished - 2016

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Air conditioning
Compaction
Vapors
Neural networks
Stream flow
Cooling systems
Flow rate
Cooling
Testing
Air
Temperature

Cite this

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title = "Performance prediction of solid desiccant - Vapor compression hybrid air-conditioning system using artificial neural network",
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. {\circledC} 2016 Elsevier Ltd.",
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Performance prediction of solid desiccant - Vapor compression hybrid air-conditioning system using artificial neural network. / Jani, D.B.; Mishra, M.; Sahoo, P.K.

In: Energy, Vol. 103, 2016, p. 618-629.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Performance prediction of solid desiccant - Vapor compression hybrid air-conditioning system using artificial neural network

AU - Jani, D.B.

AU - Mishra, M.

AU - Sahoo, P.K.

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PY - 2016

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N2 - 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. © 2016 Elsevier Ltd.

AB - 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. © 2016 Elsevier Ltd.

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DO - 10.1016/j.energy.2016.03.014

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JO - Energy

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