TY  - JOUR
JF  - AIUB Journal of Science and Engineering
VL  - 23
Y1  - 2024///
N1  - cited By 0
A1  - Albaghdadi, A.M.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193211381&doi=10.53799%2fajse.v23i1.904&partnerID=40&md5=f94dc81802ac7728ae272569bdcec087
AV  - none
SP  - 34
PB  - AIUB Office of Research and Publication
TI  - Performance Prediction of A Power Generation Gas Turbine Using An Optimized Artificial Neural Network Model
IS  - 1
SN  - 16083679
N2  - 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. © 2024 AIUB Office of Research and Publication. All rights reserved.
EP  - 41
ID  - scholars20005
ER  -