# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 3 Analysis of the State of the Art
### Methods and Algorithms
#### 3.1.3 Decision Trees, Random Forest, and Classification Analysis  
Decision trees have proven to be effective in classifying and analyzing combustion phenomena. In Ref. [35], a method for predicting the temperature in the rotary kiln was developed using random forest models optimized by a genetic algorithm, which significantly reduced the prediction error and shortened the simulation time from weeks to a few seconds. The method based on CFD data and industrial experiments can be used as a soft sensor in metallurgical process control systems. In Ref. [36], the random forest algorithm was used to model the coal flotation process, enabling the identification of the most important operational variables affecting yield and ash content. It is worth noting that many publications lack a systematic comparison of the discussed tree algorithms with classic statistical methods, such as logistic regression or discriminant analysis. Including such control analyses would allow for a better estimate of whether the accuracy improvements achieved by ML methods are due to the model's actual ability to capture complex nonlinear relationships, or merely to fitting into a specific dataset. For example, in the classification of ignition status or the evaluation of combustion cyclic variability, a comparison of RF results with logistic regression could indicate to what extent the interpretability and simplicity of classical methods can be maintained without significant loss of effectiveness. From the point of view of industrial implementations, such a combination of models is also important because classical methods are often better known and easier to validate in environments with limited computing power or stringent certification requirements. In Ref. [37], random forests were used to predict the cyclic variability of combustion (CCV) based on LES simulations in a gasoline engine. Accurate peak pressure predictions were obtained, although further tuning for other engine configurations was necessary. In Ref. [38], random forests were found to be more effective than SVM in predicting spontaneous combustion of coal under mining conditions. Their interpretative clarity and ability to capture complex relationships proved to be crucial, although the authors pointed to the need for further validation. In Ref. [39], a stacking model combining decision trees, SVMs and other algorithms was developed to predict octane losses in the refining process. The combination of the models significantly reduced the prediction error (MSE) and identified the most important features affecting RON. Ref. [40] compared several ML models (RF, RDT, GLM, BBD) in the sensitivity and uncertainty analysis of textile sludge co-incineration with incense sticks, indicating that random forests had the best predictive outcomes. The analysis showed that temperature and component ratio were key factors influencing the results of thermochemical processes (RM, DSC, DTG). In Ref. [41], a novel RF-WOA-LSSVM predictive model was developed for estimating the iodine number of biodiesel based on the FAME composition, achieving a coefficient of determination R\({}^{2}\) = 0.9989 and very low prediction errors.