# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 4 Statistical Overview  
In order to complement the overview of the state of the art presented in the previous sections, an analysis of the distribution of publications by type of document, type of fuel, calculation methods used, and research approaches was performed. The quantitative analysis was carried out separately for two periods: 2015-2019 and 2020-2024. In the first stage, publications were compiled by type of document. As shown in Figure 4, the number of journal articles increased from 15 in the first period to 72 in 2020-2024, an increase of 380% and indicating a rapid development of this topic in peer-reviewed literature. At the same time, the number of conference papers increased from three to six (i.e., by 100%). The \"Other\" category is marginal, with only one such case recorded in a more recent period. Another aspect was the analysis of the type of fuel taken into account in the research (Figure 5). The largest increase was recorded for hydrogen, which was not analyzed at all in 2015-2019, and in the following years it appeared in as many as 17 publications (17.5% of the total). The subject of gasoline developed equally dynamically  
Figure 4: Number of publications by document type (2015–2019 vs. 2020–2024). 24 publications (an increase of 300%). In the case of biomass and biodiesel, a jump from 0 to 10 and from 2 to 9, respectively, was observed--indicating an increase in interest in renewable fuels. However, it should be noted that petrol, coal, natural gas, and hydrogen still dominated as the most frequently studied fuels. With regard to the computational methods and algorithms used (Figure 6), neural networks were the most frequently used, with an increase from 6 to 36 publications, which means an increase of as much as 500% and a share of 37% in the total number of analyzed papers (42 out of 97). In second place were genetic algorithms, used in 39 publications (an increase from 14 to 25). The vast majority of techniques, such as SVM, PCA, random forest, or regression, showed an increase in occurrence in the analyzed time period. The expansion of SVM (from 2 to 16--an increase of 700%) and regression (from 0 to 13) was particularly noticeable. Such dynamic growth confirms the growing role of artificial intelligence methods in advanced analysis of combustion processes. Figure 5: Number of publications by fuel type (2015–2019 vs. 2020–2024). Figure 6: Application of computational methods in publications (2015–2019 vs. 2020–2024). From the point of view of research methodology (Figure 7), the most commonly used approach was experiments, the number of which increased from 17 to 72 (a total of 89 publications, i.e., 91.8% of the total). The second most common category was conceptual work, with an increase from 15 to 55, for a total of 70 publications. Literature analyses appeared less frequently (4 in the first period, 24 in the second), constituting a total of 28 cases. This data shows that while the importance of reviews and theoretical concepts is increasing, empirical research remains the foundation for the verification of AI and GA models. In order to assess the relationship between the type of fuel and the method used, a chi-square test was performed. For the \"Methods and Algorithms\" category, a value of \(p=0.06\) was obtained, which is close to the conventional significance level (\(\alpha=0.05\)), but does not allow for unambiguous confirmation of the statistical relationship. For the \"Research Methodology\" category, \(p=0.95\) was obtained, which indicates a lack of significant differences--in other words, the choice of research methodology did not depend significantly on the type of fuel analyzed. To sum up, the presented quantitative data confirms the dynamic development of AI/GAs in fuel combustion. The number of publications, especially scientific articles, is clearly increasing, and methods such as ANNs and GAs are becoming the dominant tools in the analysis, optimization, and prediction of combustion phenomena.