Cascade Power Generation Cycle Optimization

Version 1.0.0.0 (2,86 MB) von Mohammad Daneshian
Single-Objective Genetic Algorithm (GA) Multi-Objective Genetic Algorithm (NSGA II)
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Aktualisiert 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)

Zitieren als

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

Kompatibilität der MATLAB-Version
Erstellt mit R2019b
Kompatibel mit allen Versionen
Plattform-Kompatibilität
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MultiObjective

MultiObjective/+CoolProp

SingleObjective

SingleObjective/+CoolProp

Version Veröffentlicht Versionshinweise
1.0.0.0

Um Probleme in diesem GitHub Add-On anzuzeigen oder zu melden, besuchen Sie das GitHub Repository.
Um Probleme in diesem GitHub Add-On anzuzeigen oder zu melden, besuchen Sie das GitHub Repository.