## 3 Results and Discussion
### ML Model Validation
Higher fuel gas quantities available for energy exchange tend to sustain desired temperatures at the outlet zone, such as the depicted target values specified as 600 \({}^{\circ}\)C and 560 \({}^{\circ}\)C, respectively. From left to right, these parameters represent the fuel flow rate, air flow rate, air temperature, oxygen concentration, and catalyst porosity variables. The y-axis represents the predicted outlet temperature, while the x-axis displays different values of each parameter across the subplots. On the far left, the curve indicates a significant non-linear relationship between fuel flow and temperature. As the fuel flow increases, the outlet temperature initially remains constant, but a steep rise is observed  
Figure 7: Demonstration of the machine learning model. Two different target temperatures of the reformer outlet are predicted using appropriate parameter combinations. beyond a certain threshold. This suggests that at lower fuel flow rates, the system maintains a stable temperature, but at higher rates, the thermal load on the system increases, leading to a rise in temperature. The second segment shows how increasing the air flow rate initially results in a similar rapid temperature rise, followed by a levelling-off of the curve. This behavior is consistent with an increasing partial oxidation effect as more air is introduced, leading to higher temperatures initially, but eventually reaching an equilibrium point where additional air no longer has a significant impact on the temperature. In the middle of each plot, the curves for air temperature and oxygen content illustrate smoother, less pronounced changes in temperature. The shape of these curves implies that while these factors influence the outlet temperature, their effect is more moderated and less dramatic compared to fuel and air flow rates. This suggests that the system is more sensitive to flow rates than to gas composition changes. The rightmost segment shows the effect of the catalyst condition on the outlet temperature. This curve appears flatter, indicating that changes in the catalyst's state have a more limited effect on the overall temperature response. This could mean that, under normal operating conditions, the catalyst plays a secondary role in influencing the outlet temperature compared to fuel and air dynamics. The lower second plot also shows the response of the outlet temperature to variations in multiple parameters. Although similar trends are observed, a few key differences emerge. Some of the curves in this plot exhibit steeper gradients, particularly in the initial stages of the parameter variation. For example, the steep rise in temperature for low air flow rates suggests a more rapid system response in this scenario, possibly due to an interaction effect between parameters that was captured in the model. In certain regions of the lower plot, the temperature response remains more moderate across a broader range of input values. This suggests that the system reaches a stable operating point more quickly under some combinations of parameter settings, which is likely influenced by both the input air temperature and oxygen concentration. Recapping the demonstrated analysis, the process exhibits strong non-linear responses to changes in fuel and air flow rates, with a sharp increase in temperature observed at higher input levels. This highlights the importance of careful control over these parameters to prevent excessive temperatures, which could be thermomechanically damaging to the catalyst and require detailed assessment. The shape of the curves also suggests possible interaction effects between variables. For example, the impact of air temperature or oxygen concentration may be more pronounced when combined with high fuel or air flow rates, as indicated by the steeper gradients observed in certain regions of the plots. The sensitivity of the model's predictions is evident in how some parameters cause more significant shifts in temperature than others.