Performance Analysis and Optimization of Regenerative Gas Turbine Power Plant using RSM: Optimization of regenerative gas turbine power plant

In the present study, a thermodynamic analysis of thermal performance is carried out in a regenerative GT power plant. The optimization procedure of design parameters is realized by the response surface methodology (RSM). The thermodynamic simulations were carried out using the EES code for numerous...

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Bibliographic Details
Published in:International journal of automotive and mechanical engineering 2023-10, Vol.20 (3), p.10671-10683
Main Authors: Bensouici, Moumtez, Azizi, Mohamed Walid, Bensouici, Fatima Zohra
Format: Article
Language:English
Subjects:
Compression ratio
Design optimization
Design parameters
Energy consumption
Gas turbines
Gas-fired power plants
Inlet temperature
Multiple objective analysis
Optimization
Parameter identification
Power plants
Process parameters
Regenerators
Regression models
Response surface methodology
Statistical analysis
Statistical models
Thermodynamic efficiency
Thermodynamics
Variance analysis
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Summary:In the present study, a thermodynamic analysis of thermal performance is carried out in a regenerative GT power plant. The optimization procedure of design parameters is realized by the response surface methodology (RSM). The thermodynamic simulations were carried out using the EES code for numerous variables such as compression ratio (2≤rp≤12), inlet temperature (273≤T1≤313K), turbine inlet temperature (1200≤T3≤1600K), and regenerator effectiveness (45≤ε≤85%). Analysis of variance (ANOVA) was carried out to identify the process parameters that influence thermal efficiency (ηth) and specific fuel consumption (SFC). Then, a second-order regression model was developed to correlate the process parameters with ηth and SFC. Consequently, numerical and graphical optimizations were performed to achieve multi-objective optimization for the desired criteria. According to the desirability function approach, it can be seen that the optimum objective functions are ηth=50.61% and SFC=0.117 kg/kWh, corresponding to process parameters T1=273.26K, T3=1597.64K, rp=6.95 and ε=84.89%. Lastly, verification simulations were conducted to validate the importance of the generated statistical models.
ISSN:2229-8649
2180-1606
DOI:10.15282/ijame.20.3.2023.10.0824