## 3 Results
### Prediction of Hydrogen (H\({}_{2}\)) Gas Levels  
H2 is a high-energy-content and environmentally friendly gas. Hydrogen produced during gasification is an important fuel, particularly among renewable energy sources. The prediction of H2 levels is critical for enhancing energy efficiency and obtaining sustainable fuel. Estimating hydrogen content is crucial in optimizing biomass gasification, as higher hydrogen content leads to higher energy production. The relationship between H2 and the input variables in the gasification process is examined in the correlation matrix presented in Figure 6.  
When Figure 6 is examined, a strong positive relationship (0.61) between H2 and SB is observed, while a strong negative relationship (\(-\)0.60) exists between ER and H2. Additionally, a very strong negative correlation (\(-\)0.96) is observed between C and O, and a significant negative relationship (\(-\)0.80) is seen between Ash and VM. These strong negative relationships provide important insights into how these variables may influence each other during the modeling process. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Optimal**} & \multicolumn{4}{c}{**Suboptimal**} \\ \cline{2-9}  & **RMSE** & **RAE (\%)** & **MAPE (\%)** & **Exe. (s)** & **RMSE** & **RAE (\%)** & **MAPE (\%)** & **Exe. (s)** \\ \hline RF & 4.711 & 12.454 & 16.895 & 107.02 & 8.637 & 24.828 & 38.337 & 48.79 \\ XGBoost & 3.590 & 8.667 & 11.616 & 33.34 & 6.432 & 17.752 & 28.253 & 37.76 \\ LightGBM & 4.048 & 10.605 & 12.669 & 35.27 & 9.611 & 26.294 & 41.801 & 16.96 \\ ElasticNet & 8.911 & 24.923 & 43.798 & 14.09 & 11.121 & 31.058 & 50.149 & 13.21 \\ AdaBoost & 4.370 & 11.624 & 13.462 & 53.87 & 9.576 & 27.028 & 43.340 & 40.37 \\ GBR & 3.599 & 9.282 & 11.606 & 47.20 & 8.546 & 24.057 & 39.029 & 43.80 \\ KNN & 4.175 & 10.662 & 12.429 & 12.95 & 11.156 & 32.576 & 47.798 & 16.56 \\ DT & 4.597 & 11.892 & 13.614 & 20.70 & 10.028 & 28.434 & 43.934 & 17.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Test performances of CO2 prediction under optimal and suboptimal conditions. In the nonlinear analysis, the highest mutual information (MI) values for H\({}_{2}\) output were observed in the MC (0.77), VM (0.78), FC (0.78), and Ash (0.78) variables. This indicates that these variables make significant contributions to H\({}_{2}\) production. Furthermore, many variables that showed low effects in the linear correlation were found to be more prominent in the nonlinear analysis. This suggests that nonlinear analyses provide a broader perspective compared to linear methods, offering a better evaluation of the relationships between variables. Table 7 presents the prediction performances for H\({}_{2}\) gas levels under optimal and suboptimal conditions. The H\({}_{2}\) level prediction results under optimal conditions demonstrate that the XGBoost model achieves excellent prediction accuracy (RMSE = 2.774, RAE = 6.471, MAPE = 7.542). Although the execution time of the XGBoost model is slightly higher than that of other models, it is approximately 37 s, which can be considered a negligible delay. When considering the models' lowest performances in H\({}_{2}\) level prediction, the XGBoost model (RMSE = 5.707, RAE = 13.280, MAPE = 16.290) stands out, with relatively low error rates compared to the others. This highlights XGBoost's flexibility in learning both linear and nonlinear relationships. Following XGBoost is the GBR model, while the poorest prediction performance is observed in the KNN model (RMSE = 13.323, RAE = 33.220, MAPE = 42.218). This suggests that linear models provide lower accuracy in H\({}_{2}\) level prediction.