TY  - CONF
A1  - Farah, A.M.J.
A1  - Madiah, B.O.
A1  - Mochammad, F.
A1  - Rosdiazli, B.I.
SP  - 218
Y1  - 2023///
TI  - Gas turbine combustion profile modelling for predictive maintenance using am artificial neural network
AV  - none
N1  - cited By 0; Conference of International Conference on Sustainable Processes and Clean Energy Transition, ICSuPCET 2022 ; Conference Date: 1 December 2022 Through 2 December 2022; Conference Code:295119
ID  - scholars19257
N2  - Dry Low Emission (DLE) gas turbine has been developed as a solution to encounter the harmful high NOx emission from conventional gas turbine. However, it is prone to create a Lean Blowout (LBO) error that causes frequent shutdown due to its stringent condition that needs to be operate inside its desired operating condition that can be monitored through the temperature, NOx and CO emission concentration. This paper develops an Artificial Neural Network â?? Multilayer Perceptron (ANN-MLP) predictive maintenance model using actual DLE gas turbine data that predict trips from the gas exhaust emission and classification of warning stages on the LBO error. 94.12 of R2 for the regression model and 100 accuracy of the classification model using Python is obtained using four months period data. This proposed ANN-MLP model manage to predict the suitable maintenance time of DLE gas turbine using real time data which can help reduce cost lost from unscheduled shutdown. © 2023, Association of American Publishers. All rights reserved.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161649939&doi=10.21741%2f9781644902516-25&partnerID=40&md5=a4980477b7a36be12c3e622d26bf9540
VL  - 29
EP  - 225
ER  -