Numerical Analysis and ANN Modeling of the Intercooled, Reheat and Regenerative Gas Turbine Cycle

Abstract

AbstractContinuous rise in population is the cause for increase in demand factor of the power plant. However, running of thermal power plant results into the release of objectionable pollutants. One way to have a control on these pollutants is the use of renewable energy sources like solar and wind. However, these sources are intermittently available and hence it is important that the quick backup and balancing is needed when renewable source is not available. In these situations, use of gas turbine is a good option. This paper considers the numerical analysis of the combined intercooled, reheat and regenerative cycle. Objective of this work was to develop ANN model, understand the various parameters, there effect on gas turbine performance and optimize the cycle parameters for maximum efficiency. Analysis shows that for turbine inlet temperature 1200 K and compressor inlet temperature 293 K the optimum pressure ratio is 9 and thermal efficiency is 32%. Analysis of the gas turbine cycle was performed for different pressure ratio, turbine inlet temperature, compressor inlet temperature, and heat exchanger effectiveness. Data obtained from the analysis was then used for the development of the prediction model using artificial neural network. The developed model has root mean square error equal to 0.00018 and the regression coefficient of the trained network is 0.99. This way the developed mathematical model was validated, and it has a good predictability.KeywordsBrayton cycleReheatingRegenerationNeural network