Optimal COP prediction of a solar intermittent refrigeration system for ice production by means of direct and inverse artificial neural networks

Solar Energy

Volumn 86, Issue 4, 2012, Pages 1108-1117

  Hernandez J A ^a   Rivera, W ^b   Colorado, D ^a   Moreno Quintanar, G ^b  

^a UNIVERSIDAD AUTÓNOMA DEL ESTADO DE MORELOS   (Mexico)

^b INSTITUTO DE ENERGÍAS RENOVABLES   (Mexico)

Author keywords

Ammonia lithium nitrate; Artificial neural networks; CPC; Solar refrigeration

Indexed keywords

COEFFICIENT OF PERFORMANCE; COMPUTING TIME; COOLING WATER TEMPERATURE; COP PERFORMANCE; CPC; EXPERIMENTAL CONDITIONS; GENERATION TEMPERATURE; ICE PRODUCTION; INPUT PARAMETER; INPUT VARIABLES; INTERMITTENT REFRIGERATION; OUTPUT NEURONS; SOLUTION CONCENTRATION; UNKNOWN INPUTS; WORKING FLUID;

NEURAL NETWORKS; OPTIMIZATION; REFRIGERATORS; SOLAR REFRIGERATION; SUN;

FORECASTING;

ACCURACY ASSESSMENT; AMMONIA; ARTIFICIAL NEURAL NETWORK; CONCENTRATION (COMPOSITION); ICE CRYSTAL; LITHIUM; OPTIMIZATION; SENSITIVITY ANALYSIS; SOLAR RADIATION; WATER TEMPERATURE;

EID: 84858159681     PISSN: 0038092X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.solener.2011.12.021     Document Type: Article

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