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

中文翻译：

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优化多配置燃气轮机运行过程的自适应神经模糊模型的实现

在本文中，自适应神经模糊推理系统（ANFIS）和多配置燃气轮机用于预测最佳燃气轮机运行参数。详细介绍了各种工况下的燃气轮机配置的原理公式。分析了不同参数的影响，以选择最佳的燃气轮机配置。采用的ANFIS模型有五个输入，即等熵涡轮效率（），等熵压缩机效率（），环境温度（），压力比（），涡轮进口温度（TIT）以及三个输出，即燃料消耗，功率输出和热效率。ANFIS模型利用了来自伊拉克Baiji燃气轮机的实际报告信息和模拟数据。结果表明，在等熵压缩机效率为100％且涡轮入口温度为1900 K时，峰值热效率达到63％，并且产生了375 MW的功率，这是功率输出的峰值。此外，在等熵压缩机效率为100％且压力比为30的情况下，获得峰值比燃料消耗量为0.033kg / kWh。预测结果表明，提出的模型确定了强烈影响燃气轮机性能的运行条件。此外，