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

Moumtez  Bensouici; Mohamed Walid Azizi; Fatima Zohra  Bensouici

doi:10.15282/ijame.20.3.2023.10.0824

Authors

M. Bensouici Department of mechanical and electromechanical, Institute of Sciences and Technology, University Center of Mila, Mila 43000, Algeria https://orcid.org/0000-0002-5855-1775
M.W. Azizi Department of mechanical and electromechanical, Institute of Sciences and Technology, University Center of Mila, Mila 43000, Algeria https://orcid.org/0000-0003-1066-740X
F. Z. Bensouici Faculty of Pharmaceutical Process Engineering, University of Constantine 3, UVN05, Ali Mendjeli N V, Khroub, Constantine 25000, Algeria https://orcid.org/0000-0002-3666-1391

DOI:

https://doi.org/10.15282/ijame.20.3.2023.10.0824

Keywords:

Thermodynamic analysis, Optimization, Regenerative gas turbine, Power plant, Performances, RSM

Abstract

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≤r_p≤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 (η_th) 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 η_th=50.61% and SFC=0.117 kg/kWh, corresponding to process parameters T₁=273.26K, T₃=1597.64K, r_p=6.95 and ε=84.89%. Lastly, verification simulations were conducted to validate the importance of the generated statistical models.