Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines
Joint Authors
Jiang, Ming-Xin
Deng, Chao
Abdalla, Ahmed N.
Ibrahim, Thamir K.
Al-Sammarraie, Ahmed T.
Wu, Jun
Source
Advances in High Energy Physics
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-03
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract 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.
American Psychological Association (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
Modern Language Association (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
American Medical Association (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
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126623