Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines
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...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in High Energy Physics |
| Online Access: | http://dx.doi.org/10.1155/2020/6590138 |
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| author | Chao Deng Ahmed N. Abdalla Thamir K. Ibrahim MingXin Jiang Ahmed T. Al-Sammarraie Jun Wu |
| author_facet | Chao Deng Ahmed N. Abdalla Thamir K. Ibrahim MingXin Jiang Ahmed T. Al-Sammarraie Jun Wu |
| author_sort | Chao Deng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-70c54d9380f749beaa85894c4cc8e244 |
| institution | Kabale University |
| issn | 1687-7357 1687-7365 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in High Energy Physics |
| spelling | doaj-art-70c54d9380f749beaa85894c4cc8e2442025-02-03T06:43:32ZengWileyAdvances in High Energy Physics1687-73571687-73652020-01-01202010.1155/2020/65901386590138Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-TurbinesChao Deng0Ahmed N. Abdalla1Thamir K. Ibrahim2MingXin Jiang3Ahmed T. Al-Sammarraie4Jun Wu5School of Physics and Electronic Information Eng., Henan Polytechnic University, Jiaozuo, ChinaFaculty of Electronic Information Eng., Huaiyin Institute of Technology, Huai’an, ChinaDepartment of Mechanical Engineering, Tikrit University, Tikrit, IraqFaculty of Electronic Information Eng., Huaiyin Institute of Technology, Huai’an, ChinaDepartment of Mechanical Engineering, University of California, Riverside, CA 92521, USASchool of Physics and Electronic Information Eng., Henan Polytechnic University, Jiaozuo, ChinaIn 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.http://dx.doi.org/10.1155/2020/6590138 |
| spellingShingle | Chao Deng Ahmed N. Abdalla Thamir K. Ibrahim MingXin Jiang Ahmed T. Al-Sammarraie Jun Wu Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines Advances in High Energy Physics |
| title | Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines |
| title_full | Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines |
| title_fullStr | Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines |
| title_full_unstemmed | Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines |
| title_short | Implementation of Adaptive Neuro-fuzzy Model to Optimize Operational Process of Multiconfiguration Gas-Turbines |
| title_sort | implementation of adaptive neuro fuzzy model to optimize operational process of multiconfiguration gas turbines |
| url | http://dx.doi.org/10.1155/2020/6590138 |
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