## 3 Results
### Prediction of Methane (CH4) Gas Levels  
CH4 is another energy-rich gas produced during the gasification process. As the primary component of natural gas, CH4 has the potential to provide energy during the combustion of biomass. The prediction of CH4 levels is essential for assessing the energy efficiency of the process and optimizing its use as a fuel. Specifically, the amount of methane used as an energy source in the biomass gasification process significantly affects the system's overall performance. The relationship between CH4 and the input variables in the gasification process is examined in the correlation matrix presented in Figure 7.  
As seen in Figure 7, the relationships between the input variables and the CH4 output are generally weak, with the input variables showing the highest correlation being SB (0.23), FC (0.20), S (\(-\)0.21), and Temp (\(-\)0.21). This indicates that predicting CH4 output with linear models may provide limited success. However, other noteworthy relationships between the variables may offer potential information for different analysis methods or modeling approaches. For example, a strong negative correlation (\(-\)0.77) is observed between Ash and VM, and an even stronger negative relationship (\(-\)0.93) is observed between C and O. This suggests strong negative relationships between these variables. Such relationships may help better understand the effects of the variables during modeling. In the analysis of nonlinear relationships, the variables MC (0.50), VM (0.51), and FC (0.56) show the highest mutual information values for the CH4 output. This indicates that these variables contribute significantly to CH4 production. In contrast, variables such as SB (0.19), ER (0.22), and Temp (0.22) show weaker effects, with lower values. Moreover, it is noteworthy that the O and N variables, which appear to have low impacts in the linear analysis, become more significant in the nonlinear analysis. This demonstrates that nonlinear approaches can reveal deeper relationships and should be considered in modeling processes. As a result, it appears difficult to strongly predict CH4 based on a linear model; thus, advanced modeling techniques, such as machine learning models, which can address  
Figure 7: Linear and nonlinear relationships between CH4 output and input variables. the interactions between different properties, are more appropriate. CH4 gas has been predicted using ML models, and the prediction performance results under optimal and suboptimal conditions are presented in Table 8.  
The optimal performance of the models for CH4 level prediction revealed that the XGBoost model achieved the lowest error rates (RMSE = 1.040, RAE = 10.547, MAPE = 14.338). XGBoost was followed by GBR and LightGBM models in terms of performance. Regarding execution time, XGBoost demonstrated a balanced performance between speed and accuracy, making predictions in only 52 s. The results presented in Table 8 highlight the effectiveness of XGBoost for CH4 level prediction, with its ability to learn complex data relationships leading to highly accurate predictions. Despite the weak correlations between CH4 output and input variables, as shown in Figure 7, XGBoost successfully navigated these challenges and delivered strong results. XGBoost facilitates the modeling of more complex data structures through tree-based architectures. Due to the learning capability of XGBoost, high prediction accuracy is achieved even in cases of nonlinear relationships or variables with low correlations. Suboptimal conditions also revealed XGBoost's superior performance (RMSE = 1.591, RAE = 13.750, MAPE = 16.411) compared to other models. In contrast, the Elastic Net and DT models produced predictions with the lowest accuracy, demonstrating weak performance. Even in the worst-case scenario, XGBoost achieved a correlation value of 0.837, highlighting the model's flexibility and robustness.