# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 4 Statistical Overview
At the same time, despite differences in the type of fuels, experiments supported by simulations and numerical modeling remain the predominant form of research. The overall numerical distribution by fuel type and type of calculation method and research methodology is presented in Table 2. The list shows that gasoline was the most frequently tested fuel (30 papers), followed by hydrogen (17), coal, and natural gas (16 each). The fewest cases analyzed were for biomass (10) and biodiesel (11). Figure 7: Type of research approach in publications (2015–2019 vs. 2020–2024). In terms of methods, neural networks were used in 42 papers (43%) and genetic algorithms in 39 (40%). They were especially dominant in the case of gasoline, natural gas, and hydrogen. Methods such as SVM (18 papers) and regression (13) played an important, albeit ancillary, role. Less frequently used were random forest (8), PCA (6), and MSE (12). In terms of the type of research, experiments accounted for 89 out of 97 publications (92%), which confirms the high dependence of AI/GA applications on empirical data. Conceptual work (70 cases) was often an extension of experimental results, while literature analyses (28) were used to develop theoretical models or comparisons between different approaches. It is worth noting that the statistical analysis showed no significant relationship between the type of fuel and the choice of research methodology (\(p\) = 0.95), while for the selection of calculation methods, a trend close to significance was observed (\(p\) = 0.06), which may indicate an ambiguous preference for specific algorithms depending on the fuel. Another aspect analyzed in the literature review was the geographical distribution of the publication, which allows for the identification of leading research centers dealing with the application of artificial intelligence and genetic algorithms in combustion engineering. Figure 8 shows the number of publications per selected country in two time periods: 2015-2019 and 2020-2024. This data has also been aggregated in Table 2, which additionally presents the percentage share of individual countries in the 97 total analyzed publications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **2015–2019** & **2020–2024** & **All Years** & **Share [\%]** \\ \hline Total & 18 & 79 & 97 & 100.0 \\ \hline \multicolumn{5}{c}{Document Type} \\ \hline Conference paper & 3 & 6 & 9 & 9.28 \\ Journal article & 15 & 72 & 87 & 89.69 \\ Other & 0 & 1 & 1 & 1.03 \\ \hline \multicolumn{5}{c}{Fuel Types} \\ \hline Biodiesel & 2 & 9 & 11 & 11.34 \\ Biomass & 0 & 10 & 10 & 10.31 \\ Coal & 3 & 13 & 16 & 16.49 \\ Gasoline & 6 & 24 & 30 & 30.93 \\ Hydrogen & 0 & 17 & 17 & 17.53 \\ Natural gas & 7 & 9 & 16 & 16.49 \\ \hline \multicolumn{5}{c}{Methods and Algorithms} \\ \hline CFD & 9 & 14 & 23 & 23.71 \\ Decision trees & 2 & 15 & 17 & 17.53 \\ Genetic algorithms & 14 & 25 & 39 & 40.21 \\ Mean square error & 0 & 12 & 12 & 12.37 \\ Neural networks & 6 & 36 & 42 & 43.3 \\ PCA & 2 & 4 & 6 & 6.19 \\ Random forests & 1 & 7 & 8 & 8.25 \\ Regression analysis & 0 & 13 & 13 & 13.4 \\ SVM & 2 & 16 & 18 & 18.56 \\ \hline \multicolumn{5}{c}{Research Methodology} \\ \hline Experiment & 17 & 72 & 89 & 91.75 \\ Literature analysis & 4 & 24 & 28 & 28.87 \\ Conceptual & 15 & 55 & 70 & 72.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of publications by fuel type, calculation method, and research approach (2015–2024). Based on the list, it is clear that China was the dominant leader in this area--in 2020-2024, as many as 36 papers were published there, for a total of 43 publications in the entire analyzed period. This is 44.33% of all publications, i.e., almost every second work came from Chinese institutions.