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

Sustainable Energy Technologies and Assessments

Volumn 4, Issue , 2013, Pages 11-19

  Mohammad, Abdulrahman Th ^a,b   Mat, Sohif Bin ^a   Sulaiman, M Y ^a   Sopian, K ^a   Al abidi, Abduljalil A ^a  

^a UNIVERSITI KEBANGSAAN MALAYSIA   (Malaysia)

^b FOUNDATION OF TECHNICAL EDUCATION   (Iraq)

Author keywords

ANN; Desiccant; Effectiveness; Regenerator

Indexed keywords

AIR INLET TEMPERATURE; ANN; DESICCANT; EFFECTIVENESS; EXPERIMENTAL VALUES; INLET HUMIDITY RATIO; MAXIMUM TEMPERATURE; MULTILAYER ARTIFICIAL NEURAL NETWORKS;

AIR INTAKES; CHLORINE COMPOUNDS; DRIERS (MATERIALS); MOISTURE; NEURAL NETWORKS;

REGENERATORS;

AIR CONDITIONING; ARTIFICIAL NEURAL NETWORK; DESICCATION; OPTIMIZATION; PERFORMANCE ASSESSMENT;

EID: 84884546152     PISSN: 22131388     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.seta.2013.08.001     Document Type: Article

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