## 4 Control of Metal Melting Process
### Discussion of Results  
Comparing the results obtained by the two types of supervised machine learning algorithms, it can be concluded that the neural network model has given a better response and an excellent result in the testing phase (Figures 11-14). This is exactly what qualifies the NN model to be implemented into the control structure of the white cast iron production. However, it should also be noted that SVR models demonstrate a high level of success in the prediction of alloying additives.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Carburizing Agent (kg)** & **FeCr (kg)** & **FeMn (kg)** & **FeSi (kg)** \\ \hline Mean error (\%) & 3.39 & 2.05 & 4.81 & 6.65 \\ Max. error (\%) & 9.29 & 6.77 & 10.57 & 14.56 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum and mean error in the testing phase of SVR.  
Figure 10: Support vector regression (SVR) control strategy for the prediction of the amount of alloying elements.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline  & **Models** & \(C\) & \(\sigma\) & **Mean Squared Error (mse)** \\ \hline \multirow{4}{*}{Case 1} & Support Vector Regression 1 & 6 & 0.02 & 0.88 \\  & Support Vector Regression 2 & 6 & 0.02 & 0.98 \\  & Support Vector Regression 3 & 6 & 0.02 & 0.75 \\  & Support Vector Regression 4 & 6 & 0.02 & 0.65 \\ \hline \multirow{4}{*}{Case 2} & Support Vector Regression 1 & 10 & 0.005 & 0.45 \\  & Support Vector Regression 2 & 10 & 0.005 & 0.47 \\  & Support Vector Regression 3 & 10 & 0.005 & 0.42 \\  & Support Vector Regression 4 & 10 & 0.005 & 0.38 \\ \hline \multirow{4}{*}{Case 3} & Support Vector Regression 1 & 32 & 0.12 & 0.84 \\  & Support Vector Regression 2 & 32 & 0.12 & 1.13 \\ \cline{1-1}  & Support Vector Regression 3 & 32 & 0.12 & 0.95 \\ \cline{1-1}  & Support Vector Regression 4 & 32 & 0.12 & 0.48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean squared errors in the training phase—SVR models.  
Figure 11: Results of the prediction for alloying additive carburizing agent.  
Figure 12: Results of the prediction for alloying additive FeMn.  
Figure 13: Results of the prediction for alloying additive FeSi.  
Carburizing agent: In the testing phase, the NN model provides a smaller mean and maximum errors compared to the SVR model. The mean and maximum errors of the NN model is 0.47 and 1.97%, respectively. The mean and maximum errors of the SVR model is 3.39 and 9.29%, respectively.  
FeMn: In the testing phase, the NN model provides a smaller mean and maximum error compared to the SVR model. The mean and maximum errors of the NN model are 3.31% and 9.42%, respectively. The mean and maximum errors of the SVR model are 4.81% and 10.57%, respectively.  
FeSi: In the testing phase, the NN model provides smaller mean and maximum errors compared to the SVR model. The mean and maximum errors of the NN model are 1.88% and 8.18%, respectively. The mean and maximum errors of the SVR model are 6.65% and 14.56%, respectively.  
FeCr: In the testing phase, the NN model provides smaller mean and maximum errors compared to the SVR model. The mean and maximum errors of the NN model are 0.51% and 1.92%, respectively. The mean and maximum errors of the SVR model are 2.05% and 6.77%, respectively.  
If the maximum error of the SVR model is observed, which is the highest in the case of the FeSi additive (14.56%), the difference between the experimental value and the value obtained by the SVR algorithm is 2.91 kg. The maximum error of the NN model is in the case of the prediction of the FeMn additive (9.42%), and the difference between the experimental value and the value given by the NN model is only 400 g. Thus, both models provide small deviations from experimental value, which do not pose a threat for the final chemical composition of white cast iron.