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An artificial neural network model for gas turbine performance prediction / Anwr Mohammed Albaghdadi ; Supervised M. G. Khalafallah , Abdelnaby Mohamed

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Anwr Mohammed Albaghdadi , 2016Description: 91 P. : plans ; 30cmOther title:
  • التنبؤ باداء التوربينات الغازية باستخدام نموذج الشبكات العصبيه الاصطناعيه [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Mechanical Power Engineering Summary: The present thesis introduces a study for the application of ANN modeling to investigate the performance prediction of a gas-turbine power-generation unit. A wide range of operating data (over two years) of a small gas turbine (20 MW) is used for training, investigating and evaluating the ANN model. ANN shows good predict ability results that would add an interesting advantage to the maintenance team efforts. Moreover, the model could be used as a guide for operators to estimate different failure possibilities that could happened to various parts of the gas turbine engine
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.11.M.Sc.2016.An.A (Browse shelf(Opens below)) Not for loan 01010110071777000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.11.M.Sc.2016.An.A (Browse shelf(Opens below)) 71777.CD Not for loan 01020110071777000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Mechanical Power Engineering

The present thesis introduces a study for the application of ANN modeling to investigate the performance prediction of a gas-turbine power-generation unit. A wide range of operating data (over two years) of a small gas turbine (20 MW) is used for training, investigating and evaluating the ANN model. ANN shows good predict ability results that would add an interesting advantage to the maintenance team efforts. Moreover, the model could be used as a guide for operators to estimate different failure possibilities that could happened to various parts of the gas turbine engine

Issued also as CD

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