Cascade Power Generation Cycle Optimization

Version 1.0.0.0 (2.86 MB) by Mohammad Daneshian
Single-Objective Genetic Algorithm (GA) Multi-Objective Genetic Algorithm (NSGA II)
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Updated 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)

Cite As

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

MATLAB Release Compatibility
Created with R2019b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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MultiObjective

MultiObjective/+CoolProp

SingleObjective

SingleObjective/+CoolProp

Version Published Release Notes
1.0.0.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.