## 4 Discussion of Results
Models based on SVR also achieved interesting results in our study when predicting from static and dynamic data. Support-vector regression (SVR), based on a statistical learning algorithm, can be used as a soft sensor to model the complex relationship between input and output data. [PERSON] et al. [18] designed and amended a data-driven prediction model for BOF endpoint based on support-vector machines (SVM) and applied it to an information system for steelmaking. Their model based on off-gas analysis (i.e., CO, CO\({}_{2}\), O\({}_{2}\)) was implemented on an HMI monitoring device. When offline, temperature prediction achieved a standard deviation of \(\pm\) 18.8 \({}^{\circ}\)C, and online prediction achieved 16 \({}^{\circ}\)C. In offline carbon prediction, they reached a standard deviation of \(\pm\) 0.0061%, and in online prediction, they reached a standard deviation of 0.0040%. Online prediction has been successfully tested on 1400 meltings. Our SVR temperature model with the highest performance with the basic observations achieved RMSE = 16.60, and the average absolute error from those meltings was 11.90 \({}^{\circ}\)C. In the case of the best SVR carbon model, it achieved RMSE = 25.73, and the average absolute error was 0.004 vol.%. In our study, only the basic SVR algorithm was used, but in the literature, various improvements to SVR can be found. For example, [PERSON] et al. [20] proposed a twin support-vector regression (TSVR), a novel prediction model based on the fire work algorithm (FWA) optimizing the parameters of TSVR. They investigated 50 meltings. Their FWATSVR results in RMSE had a value of 18.494 in temperature prediction and 2.870 in carbon prediction. They improved the standard SVR algorithm, where the RMSE was 23.952 in temperature prediction and the RMSE was 3.072 in carbon prediction. They used nine static BOS variables as inputs to the training model. Another static control model based on wavelet transform weighted twin support-vector regression (WTWTSVR) was proposed in [21]. They used 9 input observations with 170 training samples and 50 for testing. The results showed that RMSE was 0.0023 with the carbon model and 4.0210 with the temperature model. The carbon model was learned in 0.4523 s, and the temperature model was learned in 0.0977 s.  
The best regression model created by the MARS method with the basic observations in our study achieved an MSE with the value of 209.09 and 14.46 when predicting temperature from static data. The indicators MSE with a value of 0.0001 and RMSE with a value of 24.81 were achieved in carbon prediction. This was a prediction on a test dataset where the average absolute error from ten melts was 11.34 \({}^{\circ}\)C in the case of temperature prediction and 0.0034 vol.% in the case of carbon prediction. Unfortunately, only one application of MARS on BOS was found in the literature. [PERSON] et al. [54] applied stochastic forecasting to hot metal temperature prediction based on MARS. They used a moving window approach for the training dataset selection to avoid the need for periodic re-tuning of the model. In temperature prediction on 2195 testing meltings, they achieved a mean absolute error of 11.2 \({}^{\circ}\)C, which is similar to our result. They used five static inputs to train the MARS model. In the literature, many NN applications and their improvements to predict BOS process variables can also be found. The best NN in our study with the basic observations achieved MSE = 537.45 and RMSE = 23.18 when predicting temperature from static data. On the other hand, MSE = 0.0004 and RMSE = 0.0196 were achieved in carbon prediction. This was a prediction on a test dataset where the average absolute error from ten melts was 13.34 \({}^{\circ}\)C in the case of temperature prediction and 0.0073 vol.% in the case of carbon prediction. In our study, we used only a simple NN with one hidden layer without further optimization of the network and its structure. For that reason, we can find better results by applying NNs to BOS in the literature.