Mathematical modeling and genetic algorithm optimization of clove oil extraction with supercritical carbon dioxide

2010 ◽  
Vol 51 (3) ◽  
pp. 331-338 ◽  
Author(s):  
T. Hatami ◽  
M.A.A. Meireles ◽  
G. Zahedi
Keyword(s):  
Mathematical Modeling ◽  
Carbon Dioxide ◽  
Genetic Algorithm ◽  
Supercritical Carbon Dioxide ◽  
Oil Extraction ◽  
Clove Oil ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Supercritical Carbon

Oil Extraction from Rana catesbeiana by Supercritical Carbon Dioxide and Mechanical Pressing

2018 ◽  
Vol 95 (12) ◽  
pp. 1575-1585 ◽  
Author(s):  
Henrique Perina ◽  
Katia Andressa Santos ◽  
Mônica Lady Fiorese ◽  
Melissa Gurgel Adeodato Vieira ◽  
Edson Antonio da Silva ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Rana Catesbeiana ◽  
Oil Extraction ◽  
Mechanical Pressing ◽  
Supercritical Carbon

scholarly journals Design Optimization of Plate-Fin Heat Exchanger in a Gas Turbine and Supercritical Carbon Dioxide Combined Cycle with Thermal Oil Loop

2021 ◽  
Vol 12 (1) ◽  
pp. 42
Author(s):  
Yue Cao ◽  
Jun Zhan ◽  
Jianxin Zhou ◽  
Fengqi Si
Keyword(s):  
Heat Transfer ◽  
Carbon Dioxide ◽  
Genetic Algorithm ◽  
Heat Flux ◽  
Supercritical Carbon Dioxide ◽  
Pareto Frontier ◽  
Combined Cycle ◽  
Nsga Ii ◽  
Thermal Oil ◽  
Supercritical Carbon

This paper presents an investigation on the optimum design for a plate-fin heat exchanger (PFHE) of a gas and supercritical carbon dioxide combined cycle which uses thermal oil as intermediate heat-transfer fluid. This may promote the heat transfer from low heat-flux exhaust to a high heat-flux supercritical carbon dioxide stream. The number of fin layers, plate width and geometrical parameters of fins on both sides of PFHE are selected as variables to be optimized by a non-dominated sorting genetic algorithm-II (NSGA-II), which is a multi-objective genetic algorithm. For the confliction of heat transfer area and pressure drop on the exhaust side, which are the objective indexes, the result of NSGA-II is a Pareto frontier. The technique for order of preference by similarity to ideal solution (TOPSIS) approach is applied to choose the optimum solution from the Pareto frontier. Finally, further simulation is performed to analyze the effect of each parameter to objective indexes and confirm the rationality of optimization results.

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