Cascade Power Generation Cycle Optimization

versión 1.0.0.0 (2.86 MB) por Mohammad Daneshian
Single-Objective Genetic Algorithm (GA) Multi-Objective Genetic Algorithm (NSGA II)

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Actualizada 13 Feb 2021

De GitHub

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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)

Citar como

Mohammad Daneshian (2022). Cascade Power Generation Cycle Optimization (https://github.com/thegreatmd4/Cascade_Power_Generation_Cycle_Optimization/releases/tag/1.0.0.0), GitHub. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2019b
Compatible con cualquier versión
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MultiObjective

MultiObjective/+CoolProp

SingleObjective

SingleObjective/+CoolProp

Para consultar o informar de algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.
Para consultar o informar de algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.