# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 2 Materials and Methods
### Classification Criteria  
All publications included in this review were classified according to five main analytical axes: the type of method applied, the type of fuel analyzed, the technical application area, the type of research, and the main function performed by machine learning (ML) methods and genetic algorithms (GAs). The classification was based on the analysis of titles, abstracts, keywords, and--when necessary--full texts. Due to the frequent occurrence of complex approaches involving more than one technique, the categorization was non-unique and multi-variant. The first criterion was the identification of the computational methods used. The most frequently applied was Computational Fluid Dynamics (CFD), which enables detailed modeling of flow, mixing, heat transfer, and chemical reactions in combustion systems. The second important area involved artificial neural networks (ANNs), used for emission prediction, engine parameter optimization, fuel property modeling, and replacing time-consuming simulations. Support vector machines (SVMs) were also popular, mainly used for ignition state classification, cyclic combustion stability, and emission pattern recognition. Many studies also applied decision trees (DTs) and random forests (RFs), which offer interpretability and effective analysis of multidimensional data. Genetic algorithms (GAs) played a key role in optimization tasks, such as piston geometry design, fuel composition optimization, and combustion condition adjustment. Principal component analysis (PCA) was also applied to analyze parameter relationships, enabling dimensionality reduction and identification of dominant factors. Regression analysis (RA) was used for building regression models, and mean square error (MSE) was applied to evaluate prediction accuracy. Many publications featured hybrid approaches, combining classical methods (e.g., SVM, RF) with deep learning (e.g., ANN) and CFD simulations. The second classification criterion was the type of fuel analyzed in the given study. Both conventional fuels, such as diesel and gasoline, and alternative fuels, such as biodiesel, bioethanol, biobutanol, waste biomass, hydrogen (H\({}_{2}\)), natural gas (NG), methanol, and various synthetic mixtures, including dual-fuel and ternary configurations, were considered. The fuels analyzed were often combined to achieve a compromise between efficiency and low emission of harmful substances. The third aspect of the classification concerned technical applications. The largest number of publications addressed combustion in piston engines, both conventional (e.g., compression ignition and spark ignition) and modern concepts such as HCCI (Homogeneous Charge Compression Ignition), GCI (Gasoline Compression Ignition), and RCCI (Reactivity Controlled Compression Ignition). The second important area involved biomass or coal gasification systems aimed, among others, at hydrogen production. Other studies focused on combustion in pulverized coal boilers, fluidized bed boilers, blast furnace systems, and industrial energy furnaces. In many cases, ML and GAs also supported the design of new fuel compositions, combustion condition optimization, and additive selection (e.g., nanoparticles). The fourth classification axis concerned the type of research. Four categories were included:  
1. Simulation studies--mainly based on CFD, supplemented with ANN or SVM models;
2. Laboratory and industrial experiments--involving real engines, thermogravimetric analysis (TGA), and emission and performance measurements;
3. Conceptual and theoretical analyses--developing new models, correlations, or control strategies;
4. Literature reviews--presenting the current state of knowledge and identifying areas requiring further research. The final classification criterion referred to the main function that ML or GA methods performed in the given study.