Application of Genetic Algorithms Method to Optimize the Energy and Economic Aspects of Cogeneration System
Optimization is done to obtain a better condition. One of the commonly used optimization methods is genetic algorithms. The use of genetic algorithms is very broad, including in energy conversion. Genetic algorithms have been widely used to obtain the optimum conditions of energy conversion syst...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/43515 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Optimization is done to obtain a better condition. One of the commonly used
optimization methods is genetic algorithms. The use of genetic algorithms is very broad,
including in energy conversion. Genetic algorithms have been widely used to obtain the
optimum conditions of energy conversion system’s components.
In this study, genetic algorithms are used to optimize the cogeneration system.
Cogeneration produces electricity and useful heat simlutaneusly. Product of cogeneration
system is 30 MW electricity and 14 kg/s saturated steam. Objective of the optimization are
total efficiency and rate of expenditure. Single objective optimization is done to analyze
whether there is conflict between objectives. Multiple objective optimization is done to
optimize both of the objectives simultaneously. Optimization was carried out with the help
of MATLAB R2017b software. Sensitivity analysis was conducted to determine the effect of
parameter changes on the optimization results.
The result of a single objective optimization shows there is conflict between objectives,
where at maximum system efficiency the system expenditure rate is high while at the
minimum system expenditure rate the system efficiency is low. Simultaneous optimization
of the two objectives provides a pareto optimality, which is a graph of the relationship
between optimum total efficiency and optimum rate of expenditure of the cogeneration
system. The higher the total efficiency, the higher the rate of expenditure. Sensitivity analysis
shows that changes in air temperature entering the compressor, fuel prices, and interest rate
are sensitive to the results of optimization.
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