In this article, the adaptive neuro-fuzzy inference system (ANFIS) and multiconfiguration gas-turbines are used to predict the\noptimal gas-turbine operating parameters. The principle formulations of gas-turbine configurations with various operating\nconditions are introduced in detail. The effects of different parameters have been analyzed to select the optimum gas-turbine\nconfiguration. The adopted ANFIS model has five inputs, namely, isentropic turbine efficiency (Teff ), isentropic compressor\nefficiency (Ceff ), ambient temperature (T1), pressure ratio (rp), and turbine inlet temperature (TIT), as well as three outputs, fuel\nconsumption, power output, and thermal efficiency. Both actual reported information, from Baiji Gas-Turbines of Iraq, and\nsimulated data were utilized with the ANFIS model. The results show that, at an isentropic compressor efficiency of 100% and\nturbine inlet temperature of 1900 K, the peak thermal efficiency amounts to 63% and 375MW of power resulted, which was the\npeak value of the power output. Furthermore, at an isentropic compressor efficiency of 100% and a pressure ratio of 30, a peak\nspecific fuel consumption amount of 0.033 kg/kWh was obtained. The predicted results reveal that the proposed model\ndetermines the operating conditions that strongly influence the performance of the gas-turbine. In addition, the predicted\nresults of the simulated regenerative gas-turbine (RGT) and ANFIS model were satisfactory compared to that of the foregoing\nBaiji Gas-Turbines.
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