The Lean Blowout Prediction Techniques in Lean Premixed Gas Turbine: An Overview

Abdulrahman Abdullah Bahashwan, Rosdiazli Bin Ibrahim, Madiah Binti Omar and Mochammad Faqih
Additional contact information
Abdulrahman Abdullah Bahashwan: Department of Electrical and Electronics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Rosdiazli Bin Ibrahim: Department of Electrical and Electronics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Madiah Binti Omar: Department of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Mochammad Faqih: Department of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia

Energies, 2022, vol. 15, issue 22, 1-21

Abstract: The lean blowout is the most critical issue in lean premixed gas turbine combustion. Decades of research into LBO prediction methods have yielded promising results. Predictions can be classified into five categories based on methodology: semi-empirical model, numerical simulation, hybrid, experimental, and data-driven model. First is the semi-empirical model, which is the initial model used for LBO limit prediction at the design stages. An example is Lefebvre’s LBO model that could estimate the LBO limit for eight different gas turbine combustors with a ±30% uncertainty. To further develop the prediction of the LBO limit, a second method based on numerical simulation was proposed, which provided deeper information and improved the accuracy of the LBO limit. The numerical prediction method outperformed the semi-empirical model on a specific gas turbine with ±15% uncertainty, but more testing is required on other combustors. Then, scientists proposed a hybrid method to obtain the best out of the earlier models and managed to improve the prediction to ±10% uncertainty. Later, the laboratory-scale combustors were used to study LBO phenomena further and provide more information using the flame characteristics. Because the actual gas turbine is highly complex, all previous methods suffer from simplistic representation. On the other hand, the data-driven prediction methods showed better accuracy and replica using a real dataset from a gas turbine log file. This method has demonstrated 99% accuracy in predicting LBO using artificial intelligence techniques. It could provide critical information for LBO limits prediction at the design stages. However, more research is required on data-driven methods to achieve robust prediction accuracy on various lean premixed combustors.

Keywords: gas turbine; lean premixed combustor; lean blowout; prediction technique; data-driven (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/22/8343/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/22/8343/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:22:p:8343-:d:966693

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().