## 4 Discussion of Results
Here, however, it must be noted that the shape of the curves may vary with each melting depending on the BOS inputs and control interventions. It was found that the SVR model with the polynomial kernel and the \(k\)-NN model can simulate the likeliest dynamic course of carbon in the melt. Comparing the two approaches to the prediction of BOS process variables, we can state that the temperature prediction was more efficient in the case of modeling from static melting data and the carbon prediction in the case of modeling from combined static and dynamic melting data. This was demonstrated by calculating the relative and absolute errors at the endpoints of the ten selected meltings. Unfortunately, both investigated approaches to BOS process modeling may have weaknesses in practical implementation. In the case of modeling and prediction from static data, some potential target variable was always part of the training, i.e., in the case of temperature modeling, the carbon concentration and the melting time were included in the training as input observations. In the case of carbon modeling, the training also included endpoint temperature and melting time as input observations. A similar approach was applied in [27]. On the other hand, in the case of modeling from the combined static and dynamic data database, only the measured data from the start and endpoint of the melting were used, which may be insufficient to estimate the dynamic course of the target process variables between these two points. The quality of the dynamic behavior of two target variables was only graphically compared with its standard waveforms. Therefore, to improve the prediction of the dynamic behavior of temperature and carbon, we propose to perform more checking measurements during multiple meltings to train the models better. Only the melting endpoint at which the relative and absolute errors were calculated was considered to assess the accuracy of the models. The accuracy of the models was verified on ten selected meltings. Although only two measurements from each melting were used to train the models, the prediction results from the dynamic data are more than promising. By online adaptation of the machine model, it can achieve an increase in prediction performance. In addition, the continuously adapted machine model can serve as a soft sensor in the steelmaking monitoring system. Recently, interest in machine learning algorithms has increased significantly in controlling the steel production process. The end parameters of the molten steel, such as steel temperature and carbon content, directly affect the quality of the production steel. In addition, these endpoint process variables are problematic to measure online continuously over time. A literature review shows that many studies have focused on developing predictions based on machine models. In the literature, we usually find only predictions from static data. Unfortunately, the authors often do not report relevant outputs, and model settings and results are presented differently. Works that solve the modeling of the dynamic course of the target variables of the BOS process are usually based on balance equations, thermodynamics, and various mathematical and physical laws. In our recent research (see [6], we have compared different approaches to the creation of a mathematical model of melt temperature in an LD converter. A proposed complex deterministic model for steelmaking has been compared with linear and non-linear regression models, SVR and ANFIS. The proposed deterministic model based on mathematical-physical laws is not as dependent on the parameters of previous meltings as it is with machine models. The SVR model achieved a lower average absolute deviation (i.e., 18.5 \({}^{\circ}\)C) than the deterministic model (i.e., 19.3 \({}^{\circ}\)C) but worse than the regression model (i.e., 11.9\({}^{\circ}\)C). The SVR model could better approximate the standard dynamic course of the melt temperature. Methods with kernel functions are very popular in machine learning and steelmaking.