## 3 Results and Discussion  
Based on the performed numerical CFD predictions, the collected data are used and assessed thoroughly for ML model development preparation. To understand the best combinations of predicted parameter data interactions, a three-dimensional scatter plot for initial data visualization is used. Three parameters are plotted in x, y, and z directions within a cube considering the fuel outlet temperature as the response variable. The evaluation considers the relationship between the parameters themselves and their collective effects on the pre-reformer fuel outlet temperature. Figure 4 depicts the results considering the oxygen content in the syngas, fuel, and air flow rates. Figure 4: Data screening examples using a multidimensional visualization approach with different perspectives for reliable machine learning model development data preparation and correlation assessment (**a–f**). Figure 3: Data implementation and systematic machine learning model development. There is a visible cluster of data points in the lower left region of the cube, with several points tightly packed together in Figure 4a indicating a strong correlation between the parameters in this region of the parameter space. The proximity of the points suggests that certain combinations of the variables tend to occur together frequently. This may imply a stable or common operational range for these parameters. Figure 4b shows that data points are somewhat more scattered compared to the first plot, but a subtle clustering can still be observed toward the center and lower sections of the cube. The wider distribution suggests weaker correlations between the parameters represented here. The system appears to explore a broader range of parameter values without strong dependencies, but the presence of mild clustering in the center may still indicate preferred combinations of values in the middle range of the variables. In the third orientation view of the first set of data, a denser cluster of points toward the center and bottom right of the cube are visible, indicating a region of higher proximity between data points (Figure 4c). The relatively dense clustering in the central and bottom right regions points to a notable correlation between the parameters in this region. Similar to the data in Figure 4a, this could suggest that certain parameter combinations are more likely to occur, potentially indicating a constrained or optimized range of operation for those variables. Considering the second data set where the oxygen content has been substituted with the parameter temperature, the data points depicted in Figure 4d are more evenly distributed, with only slight clustering toward the edges of the cube. The relatively even distribution suggests a lack of strong correlation between the parameters in this case, suggesting that the variables can vary more independently of each other, with no particular pattern of interaction that leads to clustering. There is a more distinct clustering of points toward the middle region of the cube, with fewer points appearing near the edges as shown in Figure 4e, where the clustering toward the center suggests that mid-range values for the three parameters are more commonly observed. This indicates that the component operates within a certain range of parameter combinations. The relatively tight clustering may also suggest a correlation in how the variables interact to achieve certain operational conditions. The last perspective illustrated in Figure 4f shows that the data points exhibit a wide distribution, with only minor clustering near the top section of the cube. The wide scattering of points implies weaker correlations between the variables in this plot, similar to the data in Figure 4d. The minor clustering near the top might indicate a slight preference for certain parameter combinations, but overall, the component appears to operate more flexibly across the parameter space.