## 3 Results
### Prediction of Carbon Monoxide (CO) Gas Levels  
CO is a toxic gas produced in environments where combustion processes occur with insufficient oxygen. It is released during the decomposition of biomass in gasification and holds significance as a fuel due to its high energy content. Accurate prediction of CO levels is a critical step in optimizing the efficiency of the gasification process. Maintaining CO levels within a specific range is essential for efficient gasification, as CO can also indicate energy. The correlation matrix in Figure 4 illustrates the linear and nonlinear relationship between CO and input variables in the gasification process. This matrix is presented as a heatmap to visualize the relationships between variables, with color intensity representing the correlations' strength and direction. Insights derived from the relation help us understand the interactions between variables and serve as a starting point for gas level prediction using machine learning models. When the linear correlation matrix was examined (Figure 4), the variables most strongly related to CO output were found to be H (0.33), VM (0.32), and SB (0.30). These results indicate that these variables have a moderate positive impact on CO output. Increases in H, VM, and SB were observed to raise CO output; however, these effects were similar in magnitude, suggesting that other factors, which might provide stronger relationships, should be considered to fully explain CO output. On the other hand, the high negative correlation (\(-\)0.96) observed between carbon (C) and oxygen (O) highlights the importance of the chemical balance between these two elements. Similarly, the strong negative relationship (\(-\)0.80) between Ash and VM suggests that a reduction in volatile components during combustion leads to an increase in ash content. In the analysis of nonlinear relationships, the variables most strongly related to CO output were identified as C (0.34), SB (0.32), and FC (0.28). This analysis, in particular, shows that the C variable has the highest nonlinear impact on CO output, with differences in its value potentially causing significant changes in CO output. While SB and FC exhibited meaningful effects, with mutual information values of 0.32 and 0.28, respectively, their relationship with CO output was not as strong as that of C. These results indicate that both linear and nonlinear methods are necessary to accurately model the effects of variables on CO output. Table 5 presents a comparative analysis of the test performances of eight machine learning models in predicting CO under optimal and suboptimal conditions. When examining the best results obtained for CO level prediction by the models, it was observed that the XGBoost (RMSE = 3.109, RAE = 7.003, MAPE = 7.44) and GBR (RMSE = 3.174, RAE = 7.142, MAPE = 7.334) models exhibited the lowest errors. These results indicate that both models provide accurate predictions with low error rates; however, the XGBoost model outperforms the others. In contrast, the Elastic Net and KNN models demonstrate limited predictive performance with high error rates. When examining the models' prediction performances under suboptimal conditions, the XGBoost model provided CO level predictions with lower error (RMSE = 5.291, RAE = 12.804, MAPE = 14.286) compared to the other models. Furthermore, it was observed that XGBoost still offered high accuracy (R = 0.889) under these conditions. The Elastic Net and KNN models performed inadequately, exhibiting high error and low R  
Figure 4: Linear and nonlinear relationships between CO output and input variables.