## 5 Conclusions  
Gas turbines must operate efficiently to achieve the target output as a prime-mover in energy production. Since the primary process should go through combustion, the emission becomes a new challenge that should be controlled. However, controlling the emission sometimes influences the combustion stability. Hence, the combustor technology is essential to improve combustion quality in the gas turbine.  
Improvement of clean combustion technology has been enhanced to minimize the emission produced by the gas turbine. Trapped vortex combustion (TVC) and flameless or mild combustion (MILD) were introduced to improve conventional combustion. Richburn, quench-mix, lean-burn (RQL) and continuous staged air (COSTAR) were applied for higher emissions reduction in the next generation. Lastly, the DLE and NanoSTAR were subsequently proposed to perform combustion with very low emissions. The comparison of the combustor technologies based on emission reduction and stability shows that the DLE gas turbine has the most profitable features against the others.  
In order to support the advancement of technology, various gas turbine models have been developed, which can be classified by the physical and black-box models. The physical model uses [PERSON]'s, IEEE, Aero-derivative, CIGRE, and frequency-dependent models. In contrast, the black-box model is developed by using an Artificial Neural Network. In dynamic gas turbine modeling, [PERSON]'s model has excellent suitability to represent the actual DLE gas turbine due to the functional derivation of the operating curves.  
Even though the DLE gas turbine has an excellent capability to reduce the emission, it is prone to frequent tripping due to some faults in a lean operation. According to a 4.4 MW DLE gas turbine case study, LBO reached the highest percentage of total trip causes, leading to high maintenance costs. Thus, this fault should be prevented to keep the DLE mode operating normally. On the other hand, other problems disturbing the DLE operation are auto-ignition, flashback, and instability.  
In order to prevent LBO, several aspects can be learned through various methods. The conventional one uses physical sensors and cameras, which are usually used on laboratory scales. In contrast, the predictive approach is usually performed statistically.  
The standard methods are semi-empirical models, numerical simulations, and hybrid models. Currently, the data-driven model caught the interest for early prediction of LBO. The advantage of the data-driven model, which predicts the event by learning from the real plant data, can capture the actual condition of such a case. Hence, the LBO can be accurately predicted based on related parameters from its actual data. However, a deeper analysis of the data's important features is also required to develop a good model with high accuracy.  
Conceptualization, M.B.O. and M.F.; methodology, M.B.O.; software, M.F.; validation, R.I. and B.A.A.O.; formal analysis, M.B.O.; investigation, M.F.; resources, R.I.; data curation, M.F.; writing--original draft preparation, M.F. and M.B.O.; writing--review and editing, M.F.; visualization, M.F.; supervision, M.B.O.; project administration, R.I.; funding acquisition, R.I. and M.B.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universiti Teknologi PETRONAS and Ministry of Higher Education Malaysia (MOHE) through grant YUTP (015 LC0-382) and PRGS (PRGS/1/2020/TK09/UTP/02/2).  
Not applicable.  
The authors are thankful to Universiti Teknologi PETRONAS and Ministry of Higher Education Malaysia (MOHE) for the support in carrying this research.  
The authors declare no conflict of interest.