Cascade Power Generation Cycle Optimization

Versione 1.0.0.0 (2,86 MB) da Mohammad Daneshian
Single-Objective Genetic Algorithm (GA) Multi-Objective Genetic Algorithm (NSGA II)
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Aggiornato 13 feb 2021
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The overall efficiency and fuel usage of the whole system (objectives) are affected by extractions pressures (opt.vars). The thermodynamic states had been extracted by CoolProp toolbox in MATLAB.

First we had to specify the pressures in the way that maximizes the efficiency and then minimizes the fuel usage. This process is a single-objective optimization. After that, we had to optimize both objectives at the same time, which is a multi-objective optimization. For this process, we used NSGA (II) in MATLAB. The obtained Pareto front has been reported as the result.

P.S.: NSGA (II) is Non-dominated Sorting Genetic Algorithm (version 2) which is an evolutionary method. (Meta Heuristic)

Cita come

Mohammad Daneshian (2024). Cascade Power Generation Cycle Optimization (https://github.com/thegreatmd4/Cascade_Power_Generation_Cycle_Optimization/releases/tag/1.0.0.0), GitHub. Recuperato .

Compatibilità della release di MATLAB
Creato con R2019b
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MultiObjective

MultiObjective/+CoolProp

SingleObjective

SingleObjective/+CoolProp

Versione Pubblicato Note della release
1.0.0.0

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