## 4 Lean Blowout
### Prediction Techniques of Lean Blowout  
Lean blowout has various harmful effects that can lead to unplanned trips that increase costs because of unscheduled maintenance. Further, some following effects that occur before LBO, such as large pressure oscillations, can reduce the reliability and availability, decreasing the durability of the DLE gas turbine [83; 98]. Hence, the prevention of LBO is required to keep the operation of the DLE gas turbine safe. Various prediction techniques have been developed to prevent LBO events. The early detection of LBO is generally predicted using three methods, which are semi-empirical models, numerical simulation, and hybrid models [99]. In the early stage, semi-empirical techniques were adopted to study the LBO phenomenon using two different models, namely Characteristic Time (CT) and Perfect Stirred Reactor (PSR). The CT model considered the LBO would occur when the residence time is lesser than the total evaporating time and chemical reaction time as implemented by [PERSON] and [PERSON] in [100]. Meanwhile, the PSR model considered the LBO would occur when the heat release rate is less than the heat loss rate [99]. One of the most used PSR models is developed by [PERSON] [101; 102], improving [PERSON]'s PSR model for swirl-stabilized combustors. A semi-empirical model is also proposed by [103] according to flame volume; however, the study is limited to analysis only. The basis of the CT and PSR models lies in energy balance and time balance, respectively. [PERSON] later challenged the previous method in [104] by proposing the hybrid empirical method for prediction instead of just analysis. [PERSON] also proposed the other method for LBO prediction. Furthermore, [PERSON] [105] suggested utilizing the flame statistical characteristic. However, two major challenges in the study are the operating conditions of an engine that may gradually change and the chemiluminescent interferences from neighboring nozzles that may complicate LBO detection. [PERSON] in [106] uses symbolic time series analysis that is converted to a symbol string and computed based on the number of occurrences of each symbol over a given period, while [PERSON] in [98] proposed the prediction via Generalized D-Markov machine construction and using fuel iterative approximation in [107]. In numerical prediction methods, Unsteady Reynolds-averaged Navier-Stokes and Large Eddy Simulation are mainly implemented to visualize the flame behaviors and predict the LBO. [PERSON] in [108] implemented LES to experimentally predict the LBO of premixed flow past the V-gutter flame holder. While [PERSON] in [109] uses the technology of a Damkohler number extracted from RANS CFD results. The result shows a distinctive transition between stable and unstable flames by decreasing the fuel-air ratio or increasing the inlet velocity at atmospheric pressure and inlet temperature. Nevertheless, the listed approach is conducted on a laboratory scale using an ideal combustor with only the air and fuel flow. Thus, it exhibits limitations in associating the LBO error with the gas turbine operation. Moreover, the physical sensors and cameras in the study are also not suitable for the gas turbine's extreme temperature application in the field. Therefore, further improvements are needed to predict the LBO that will be able to capture the actual plant conditions. Nowadays, data-driven predictive analysis is more prevalent, where we can directly develop a predictive model through either simulated or actual plant data. Thus, this type of predictive approach has an excellent potential to predict the LBO that can represent the actual condition in the field. However, limited studies are available in the literature. A study by [110] implemented machine learning using a Support Vector Machine to early detect the LBO. The model successfully predicted the LBO approximately 20 ms before the event. [PERSON] in [111] further proposed a deep learning-based framework to predict LBO.