relation: https://khub.utp.edu.my/scholars/19618/
title: Supervised learning-based multi-site lean blowout prediction for dry low emission gas turbine
creator: Bahashwan, A.A.
creator: Ibrahim, R.
creator: Omar, M.
creator: Amosa, T.I.
description: Current dry low emission (DLE) gas turbines have extremely low nitrogen oxides (NOx) emissions, allowing them to comply with stringent environmental regulations. The ultra-low temperatures required to achieve such low emissions also increase the risk of a lean blowout (LBO). DLE gas turbines are particularly vulnerable to LBO, which can lead to system instability, higher carbon oxides (CO) emissions, damaged components, substantial financial losses and environmental damages. In this regard, this current study proposes a novel supervised learning-based prediction technique with efficient performance on out-of-distribution data from multiple DLE gas turbine plants (multi-site). The proposed prediction approach exploits the competitive advantages of both adaptive boosting (AdaBoost) and Linear support vector machine (LSVM) to improve the generalization capability as well as prediction accuracy. The proposed algorithm was trained and tested using a real-world DLE gas turbine dataset from six different sites. The result indicates that the proposed model consistently achieving LBO prediction accuracy rates above 99.9 and Mathew's correlation coefficient (MCC) score above 0.9 across all datasets. All lean-premixed gas turbines could benefit from the developed Ada-LSVM, as it is an accurate model with a high generalization performance that can be used across multiple sites. © 2023 Elsevier Ltd
date: 2024
type: Article
type: PeerReviewed
identifier:   Bahashwan, A.A. and Ibrahim, R. and Omar, M. and Amosa, T.I.  (2024) Supervised learning-based multi-site lean blowout prediction for dry low emission gas turbine.  Expert Systems with Applications, 244.       
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181681826&doi=10.1016%2fj.eswa.2023.123035&partnerID=40&md5=285b97fd49a128160150a91a99762ab3
relation: 10.1016/j.eswa.2023.123035
identifier: 10.1016/j.eswa.2023.123035