Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines
المؤلفون المشاركون
Jiang, Ming-Xin
Deng, Chao
Abdalla, Ahmed N.
Ibrahim, Thamir K.
Al-Sammarraie, Ahmed T.
Wu, Jun
المصدر
Advances in High Energy Physics
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-17، 17ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-03
دولة النشر
مصر
عدد الصفحات
17
التخصصات الرئيسية
الملخص EN
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 (Teff), isentropic compressor efficiency (Ceff), ambient temperature (T1), pressure ratio (rp), 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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Deng, Chao& Abdalla, Ahmed N.& Ibrahim, Thamir K.& Jiang, Ming-Xin& Al-Sammarraie, Ahmed T.& Wu, Jun. 2020. Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines. Advances in High Energy Physics،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1126623
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Deng, Chao…[et al.]. Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines. Advances in High Energy Physics No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1126623
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Deng, Chao& Abdalla, Ahmed N.& Ibrahim, Thamir K.& Jiang, Ming-Xin& Al-Sammarraie, Ahmed T.& Wu, Jun. Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines. Advances in High Energy Physics. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1126623
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1126623
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر