Artificial neural network analysis of liquid desiccant regenerator performance in a solar hybrid air-conditioning system

Abdulrahman Th Mohammad, Sohif Mat, M. Y. Sulaiman, Kamaruzzaman Sopian, Abduljalil A. Al-abidi

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

In this paper, experimental tests are carried out to investigate the performance of a counter flow regenerator using lithium chloride (LiCl) solution as the desiccant. A single and multilayer artificial neural network (ANN) is used to predict the performance of the regenerator. Five parameters are used as inputs to the ANN, namely: air and desiccant flow rates, air inlet humidity ratio, and air and desiccant inlet temperatures. The outputs of the ANN are the temperature, humidity ratio, moisture removal rate (MRR), and the effectiveness. ANN predictions for these parameters are compared with the experimental values. The results show that the optimum testing model for MRR in the regenerator was the 5-5-5-1 structure with R2=0.93, whereas the optimum testing model for effectiveness was the 5-11-1 structure with R2=0.95. The maximum temperature and humidity ratio difference between the ANN model and experimental are 1.4°C and 2.1g/kg, respectively. The MRR and effectiveness of regenerator increase slowly as function of air inlet temperature. It was found that the MRR and effectiveness increased about 0.79% and 1.1%, respectively. The moisture removal rate decreased with increasing air inlet humidity ratio and increased with desiccant inlet temperature.

Original languageEnglish
Pages (from-to)11-19
Number of pages9
JournalSustainable Energy Technologies and Assessments
Volume4
DOIs
Publication statusPublished - Dec 2013

Fingerprint

Regenerators
Electric network analysis
Air conditioning
Moisture
Air intakes
Neural networks
Atmospheric humidity
Liquids
Temperature
Testing
Air
Multilayers
Lithium
Flow rate

Keywords

  • ANN
  • Desiccant
  • Effectiveness
  • Regenerator

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment

Cite this

Artificial neural network analysis of liquid desiccant regenerator performance in a solar hybrid air-conditioning system. / Mohammad, Abdulrahman Th; Mat, Sohif; Sulaiman, M. Y.; Sopian, Kamaruzzaman; Al-abidi, Abduljalil A.

In: Sustainable Energy Technologies and Assessments, Vol. 4, 12.2013, p. 11-19.

Research output: Contribution to journalArticle

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