## 4 Discussion  
Biomass gasification is critical for renewable energy production and reducing greenhouse gas emissions. Accurate prediction of gas composition during this process supports environmental sustainability by improving energy efficiency. The prediction of CO, CO2, H2, and CH4 gas levels in the biomass gasification process using machine learning methods facilitates the management of complex processes, enabling rapid and accurate predictions. Specifically, in systems like biomass gasification processes that involve numerous variables, predicting the levels of these four major gases offers the following advantages:  
* Accurate predictions of gas levels through machine learning optimize the ratios of gases to be used as fuel, thereby enhancing energy efficiency. This ensures that the biomass gasification process operates in a more efficient and energy-saving manner. \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 (\%)** & **Exc. (s)** & **RMSE** & **RAE (\%)** & **MAPE (\%)** & **Exc. (s)** \\ \hline RF & 1.248 & 11.954 & 19.119 & 104.16 & 2.311 & 23.016 & 45.116 & 66.62 \\ XGBoost & 1.040 & 10.547 & 14.338 & 52.11 & 1.503 & 14.018 & 16.786 & 52.36 \\ LightGBM & 1.126 & 11.612 & 15.363 & 32.42 & 2.861 & 29.946 & 56.48 & 16.01 \\ ElasticNet & 2.608 & 27.164 & 48.455 & 12.77 & 2.931 & 31.067 & 56.61 & 17.05 \\ AdaBoost & 1.186 & 12.338 & 17.431 & 59.97 & 2.994 & 31.411 & 60.675 & 36.05 \\ GBR & 1.116 & 10.746 & 15.56 & 40.93 & 1.957 & 15.745 & 16.371 & 40.17 \\ KNN & 1.318 & 11.443 & 12.254 & 15.85 & 2.833 & 29.771 & 45.336 & 24.92 \\ DT & 2.038 & 17.182 & 23.369 & 19.49 & 2.774 & 28.865 & 53.494 & 19.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Test performances of CH4 level prediction under optimal and suboptimal conditions. * The accurate prediction of CO, CO\({}_{2}\), H\({}_{2}\), and CH\({}_{4}\) levels allows for developing environmentally friendly strategies to reduce their emissions. This, in turn, enhances the environmental sustainability of biomass gasification processes. * Machine learning models allow for rapidly predicting gas levels in biomass gasification processes. This contributes to the process's time efficiency and enables operators to make faster decisions. * Instead of relying on prolonged experimental testing, machine learning allows for rapid and accurate predictions, thereby increasing the cost-effectiveness of the processes and providing significant time savings. This study conducted analyses to determine the most successful and robust model for predicting the levels of four gases: CO, CO\({}_{2}\), H\({}_{2}\), and CH\({}_{4}\). However, depending on specific needs, each gas can be predicted individually. For example, if researchers are focused on sustainable energy production, particular attention may be given to H\({}_{2}\), as it is often one of the most critical components. In studies related to biogas production, CH\({}_{4}\) output will emerge as an important parameter. If the focus is on environmental impacts, the carbon cycle, or carbon capture studies, the prediction of CO\({}_{2}\) levels may become a priority. While less emphasized than the other gases, CO remains an important component in combustion or reforming processes. The aim was to identify the most successful and robust ML models for predicting levels of the gases CO, CO\({}_{2}\), H\({}_{2}\), and CH\({}_{4}\) generated during biomass gasification. Bayesian optimization was used to determine the models' hyperparameters under optimal and suboptimal conditions. In the optimal case, parameter combinations leading to the highest R values were selected, while in the suboptimal case, those corresponding to the lowest R values were chosen. The goal was to predict gas levels under both optimal and suboptimal conditions accurately.