@article{scholars20005,
            note = {cited By 0},
       publisher = {AIUB Office of Research and Publication},
          number = {1},
         journal = {AIUB Journal of Science and Engineering},
          volume = {23},
           pages = {34--41},
             doi = {10.53799/ajse.v23i1.904},
           title = {Performance Prediction of A Power Generation Gas Turbine Using An Optimized Artificial Neural Network Model},
            year = {2024},
            issn = {16083679},
        abstract = {This paper introduces an innovative application of an Artificial Neural Network (ANN) based model for the performance prediction of a power generation gas turbine. this approach optimizes the ANN model by utilizing a comprehensive database to compare various ANN topologies. Based on optimization results, a two-layer Multi-Layer Perceptron (MLP) was constructed and used as the best-optimized topology for such applications. The training dataset comprises historical operational data from a Rolls-Royce (RB21-24G) gas turbine unit. Notably, this model shows substantial accuracy for different ambient conditions and variable power ratings. Furthermore, a sensitivity analysis using various methods was introduced to study the impact of each input on the model outputs. To validate the model's reliability and novelty, we introduce a degradation study, comparing one-year-later on-site operational data with predicted values generated by the ANN model. Remarkably, the results demonstrate strong consistency between measured data and model predictions. {\^A}{\copyright} 2024 AIUB Office of Research and Publication. All rights reserved.},
          author = {Albaghdadi, A. M.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193211381&doi=10.53799\%2fajse.v23i1.904&partnerID=40&md5=f94dc81802ac7728ae272569bdcec087}
}