# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 1 Introduction  
Over the past decade, there has been a dynamic rise in interest in applying computational methods to combustion science and engineering. Technological advancements, growing environmental demands, and the push for higher energy efficiency have led researchers and engineers to turn to advanced computational techniques--particularly artificial intelligence (AI), machine learning (ML), and genetic algorithms (GAs). While traditional approaches based on laboratory experiments and deterministic physico-chemical models remain important, they are often insufficient when faced with the complexity and variability of combustion processes over time and space [1,2]. The application of machine learning in the field of fuel combustion enables, among other things, automatic classification of combustion types, prediction of pollutant emissions, optimization of engine operating parameters, and the development of simplified surrogate models that significantly reduce computation time. Genetic algorithms, on the other hand, are used for multi-objective optimization of process parameters and for designing fuel mixtures. Hybrid methods are also of particular importance--combining classical Computational Fluid Dynamics (CFD) models with AI systems--which makes it possible to more accurately replicate real-world operating conditions and improve the precision of predictions [3,4]. However, it is worth noting that the scope and effectiveness of these applications can vary significantly depending on the type of fuel and the characteristics of the combustion system. For example, in the case of solid fuels such as biomass or coal, the classification of combustion types requires the analysis of additional variables related to the physical structure of the material (e.g., porosity, moisture), which can make it difficult to standardize the inputs for ML models. On the other hand, for gaseous fuels, such as hydrogen or natural gas, parameters related to mixing and ignition kinetics become important, which require a different approach to the construction of the predictive model. Therefore, the universal use of ML models requires taking into account the specifics of the energy medium and adapting both the input data and the model architecture to the analyzed context. An important area of development is the application of these methods to a wide range of fuels--both conventional (coal, gasoline, diesel) and alternative (biodiesel, biomass, hydrogen, natural gas). The diverse physico-chemical properties of these fuels require adaptive and flexible modelling approaches, which are difficult to achieve using classical methods alone. This is precisely why AI and genetic algorithms are gaining increasing recognition in processes such as high-pressure combustion, gasification, pyrolysis, and co-combustion [5,6]. Despite the growing number of scientific publications on the use of computational methods in combustion engineering, existing literature reviews often have a limited scope. They tend to focus on specific fuel types, individual methods (e.g., only neural networks or only CFD), or particular industrial applications (such as solely HCCI engines or power plant boilers). However, what is lacking is a comprehensive analysis that considers both the diversity of fuels and the range of computational approaches, as well as their practical integration [7]. This review addresses the existing gap by presenting a comprehensive analysis of the applications of machine learning and genetic algorithms in combustion science and engineering from 2015 to 2024. It is based on a systematic search of the Scopus database and includes 97 original scientific publications. The articles were classified according to fuel type (biodiesel, biomass, coal, gasoline, hydrogen, natural gas) and analytical method (CFD, ANN, SVM, regression, PCA, decision trees, GA, etc.). This structure allows readers to fully grasp current trends and effectively compare the approaches used across various sectors of combustion technology.