Parametric optimization design for supercritical CO2 power cycle using genetic algorithm and artificial neural network

Applied Energy

Volumn 87, Issue 4, 2010, Pages 1317-1324

  Wang, Jiangfeng ^a   Sun, Zhixin ^a   Dai, Yiping ^a   Ma, Shaolin ^b  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b Dongfang Steam Turbine Works ^*  (China)

Author keywords

Artificial neural network; Genetic algorithm; Optimization; Power cycle; Waste heat recovery

Indexed keywords

BACKPROPAGATION; COMPUTER SYSTEM RECOVERY; DEEP NEURAL NETWORKS; EXERGY; GENETIC ALGORITHMS; NETWORK LAYERS; NEURAL NETWORKS; OPTIMIZATION; TEMPERATURE; THERMODYNAMICS; TURBINES; WASTE HEAT; WASTE HEAT UTILIZATION;

ENVIRONMENT TEMPERATURE; MULTI-LAYER FEED-FORWARD NETWORKS; PARAMETRIC -ANALYSIS; PARAMETRIC OPTIMIZATION; POWER CYCLE; THERMODYNAMIC PARAMETER; TURBINE INLET PRESSURE; TURBINE INLET TEMPERATURE;

CARBON DIOXIDE;

ARTIFICIAL NEURAL NETWORK; CARBON DIOXIDE; EXERGY; GENETIC ALGORITHM; OPTIMIZATION; THERMODYNAMICS; TURBINE;

EID: 73149124052     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2009.07.017     Document Type: Article

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