Cascade Power Generation Cycle Optimization

Version 1.0.0.0 (2,86 Mo) par Mohammad Daneshian
Single-Objective Genetic Algorithm (GA) Multi-Objective Genetic Algorithm (NSGA II)
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Mise à jour 13 fév. 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)

Citation pour cette source

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

Compatibilité avec les versions de MATLAB
Créé avec R2019b
Compatible avec toutes les versions
Plateformes compatibles
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MultiObjective

MultiObjective/+CoolProp

SingleObjective

SingleObjective/+CoolProp

Version Publié le Notes de version
1.0.0.0

Pour consulter ou signaler des problèmes liés à ce module complémentaire GitHub, accédez au dépôt GitHub.
Pour consulter ou signaler des problèmes liés à ce module complémentaire GitHub, accédez au dépôt GitHub.