Comparative analysis and optimization of thermodynamic behavior of combined gas-steam power plant using grey-taguchi and artificial neural network

In the published studies, to best of authors’ understanding, grey Taguchi-based statistical technique has not been applied for optimization combined gas-steam power plants. view this, seven essential input parameters namely compressor inlet air temperature, pressure ratio, fuel volumetric flow rate fuel, gas turbine maximum efficiency, and efficiency are chosen with aim determining optimal combination design variables that maximize net generation, thermal exergetic effciency, minimize specific consumption. Also, impact weight each parameter on output indicators evaluated. While Taguchi approach helps create an orthogonal array L27 (3^7), ANOVA method determines contribution argument objective function. Unlike methodology, relational analysis is performed transform multi-objective function into a single by way estimating its grade. The most favorable determined as A1B1C1D1E3F3G3 under this state, optimum values consumption found be 259911 kW, 64.9 %, 66.27 0.1839 kg/kWh respectively. Moreover, ratio characteristic cycle (42.41 %) minimum temperature (0.59 %). effectiveness grey-Taguchi acknowledged validated using artificial neural network in MATLAB.