@inproceedings{scholars19257,
           title = {Gas turbine combustion profile modelling for predictive maintenance using am artificial neural network},
         journal = {Materials Research Proceedings},
           pages = {218--225},
          volume = {29},
            note = {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},
             doi = {10.21741/9781644902516-25},
            year = {2023},
        abstract = {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 {\^a}?? 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. {\^A}{\copyright} 2023, Association of American Publishers. All rights reserved.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161649939&doi=10.21741\%2f9781644902516-25&partnerID=40&md5=a4980477b7a36be12c3e622d26bf9540},
          author = {Farah, A. M. J. and Madiah, B. O. and Mochammad, F. and Rosdiazli, B. I.}
}