Probabilistic technique for solving computational problem : application of Ant Colony Optimization (ACO) to find the best sCO2 Brayton cycle configuration
This papers studies the potential of a probabilistic technique to solve complex problems such as thermodynamic cycle layout optimization. The Ant Colony Optimization (ACO) algorithm has been used to find an optimal configuration of a supercritical CO2 Brayton Cycle (sCO2-BC) for a specified application (coal power plant). This optimization is done by coupling an existing process simulation software (ProSimPlus) and an existing optimization solver (MIDACO). In this study, more than a 1000 cycle configurations have been analyzed regarding performance, costs and the value of the Levelized Cost Of Electricity (LCOE). Main results show that the optimal sCO2-BC configuration depends on the optimization criteria (objective function).
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