## 3 Structuring of the Proposed Forecast Framework
### Variable Filtering  
The method described in this paper uses Dataset 1 to filter the variables. CFB unit control quantities are used as input features for the model in this paper. The differential values of these variables are input as features of the model. The correlation coefficients between the input features and differential value were calculated using the method in Section 2.3. This paper sets \(d_{max}\), \(l\), and \(M_{re}\) to 100, 200, 4000, respectively. Table 1 shows the calculation results of the correlation coefficients. The correlation coefficients of the input variables are sorted in descending order, and the results are shown in Table 1.  
The screening of variables based on empirical thresholds is unreliable, so the comparison experiments are used to perform variable screening. The experiments first rank the importance of the variable features according to the correlation coefficient. Then the experimental groups were constructed by adding the features in order of importance. Finally, the accuracy performance of the prediction model on the validation set is used as the basis forvariable selection. Table 2 shows the prediction performance of each experimental group on the validation set. EG-i indicates that the top i most essential features among the features are used as the input to the model. The same model hyperparameter settings were used for all experimental groups, as shown in Table 3. Table 2 presents the mean and standard deviation of all experimental groups in three runs. The best performance is in boldface. As can be seen from Table 2, the best prediction accuracy of the validation set was achieved for the input features of EG-6. Therefore, the input features of EG-6 are considered the results of the variable filtering. The selected variables are fuel quantity, primary air quantity on the right side, secondary air quantity at upper left side, primary air quantity on the left side, secondary air quantity at lower right side, and secondary air quantity at upper right side. \begin{table}
\begin{tabular}{c c c} \hline
**Variable Name** & **Correlation Coefficient** & **Rank Value** \\ \hline Fuel quantity & 0.33070 & 1 \\ Rotation speed of left slag cooler & 0.16932 & 9 \\ Rotation speed of right slag cooler & 0.14856 & 11 \\ Valve opening of left return valve & 0.20790 & 8 \\ Valve opening of right return valve & 0.14490 & 12 \\ Secondary air quantity at lower left side & 0.27176 & 7 \\ Secondary air quantity at lower right side & 0.28662 & 5 \\ Secondary air quantity at upper left side & 0.29472 & 3 \\ Secondary air quantity at upper right side & 0.27559 & 6 \\ Primary air quantity on the left side & 0.28987 & 4 \\ Primary air quantity on the right side & 0.31606 & 2 \\ Left limestone flow & 0.15164 & 10 \\ Right limestone flow & 0.08738 & 13 \\ \hline \end{tabular}
\end{table} 
Table 1: The results table of correlation coefficient. \begin{table}
\begin{tabular}{c c c} \hline
**Group Name** & **MAE** & **MAPE/\%** \\ \hline EG-1 & 0.0570 \(\pm\) 0.0001 & 10.47 \(\pm\) 0.03 \\ EG-2 & 0.0491 \(\pm\) 0.0002 & 9.02 \(\pm\) 0.15 \\ EG-3 & 0.0461 \(\pm\) 0.0004 & 8.40 \(\pm\) 0.06 \\ EG-4 & 0.0433 \(\pm\) 0.0010 & 7.82 \(\pm\) 0.14 \\ EG-5 & 0.0425 \(\pm\) 0.0005 & 7.74 \(\pm\) 0.18 \\ EG-6 & **0.0404 \(\pm\) 0.0010** & **7.44 \(\pm\) 0.23** \\ EG-7 & 0.0446 \(\pm\) 0.0002 & 8.21 \(\pm\) 0.08 \\ EG-8 & 0.0445 \(\pm\) 0.0003 & 8.14 \(\pm\) 0.06 \\ EG-9 & 0.0448 \(\pm\) 0.0004 & 8.03 \(\pm\) 0.04 \\ EG-10 & 0.0456 \(\pm\) 0.0017 & 8.12 \(\pm\) 0.18 \\ EG-11 & 0.0489 \(\pm\) 0.0019 & 8.63 \(\pm\) 0.29 \\ EG-12 & 0.0461 \(\pm\) 0.0018 & 8.25 \(\pm\) 0.37 \\ EG-13 & 0.0466 \(\pm\) 0.0008 & 8.26 \(\pm\) 0.12 \\ \hline \end{tabular}
\end{table} 
Table 2: The experimental results table of variable selection. \begin{table}
\begin{tabular}{c c} \hline
**Hyper-Parameters** & **Title 3** \\ \hline Timestep & 64 \\ N & 1 \\ Batch size & 100 \\ Learning rate & 0.001 \\ Size of the convolutional kernel & 8 \\ \hline \end{tabular}
\end{table} 
Table 3: The hyperparameter table of the variable screening model.