## 5 Conclusions  
From the point of view of automation and control of the BOS process, regulated process variables are essential, which we must know. However, some process variables are not long-term measurable by standard hardware (e.g., aggressive environments or high temperatures in the measuring area). This comparative study investigated two approaches to BOS process modeling to predict melt temperature and carbon concentration. These approaches have been investigated as a potential tool for software sensing process variables in BOS. The application of the MARS method to the BOS process showed interesting results. In addition, the results of MARS have not yet been compared in the literature with other machine learning methods to predict temperature and carbon in BOS. The study also proposed a modeling and prediction approach from combined static and dynamic BOS data, which has not yet been investigated in the literature. The prediction results from the dynamic observations of the BOS process using machine models showed an improvement in carbon prediction compared to the prediction from static data only. In addition, predictions from dynamic melting observations make it possible to simulate the entire dynamic course of the target quantity. The knowledge gained in this study can be summarized in several main points:  
* The speed of learning depends on the complexity of the algorithms of individual machine learning methods. * The \(k\)-NN method proved to be the fastest machine learning method for BOS modeling from static and combined static and dynamic data. * The RF method proved to be the slowest in training in all machine models. * The MARS method was shown to be the most powerful machine learning method for predicting endpoint temperature and carbon based on static data. This method best approximated nonlinearities between static variables. * In general, the prediction of melt carbon concentration from static data is less powerful than the prediction of melt temperature from static data. * It was found that changing the number of input observations affects the performance of machine models in the testing phase, so it is necessary to look for the optimal number of relevant observations so that the prediction performance from static data does not decrease. * In the case of observation number increases in lime and dolomitic lime, most models increased performance in training. * It was found that changing the number of input observations in the case of prediction from dynamic data can change the model's accuracy. The model's accuracy, in addition to the algorithm, depends on the user inputs and their significance. For example, adding some insignificant observations may reduce the accuracy of the prediction. * In case of temperature prediction from static data and observation number increase, only SVR with Gaussian kernel, NN, and piecewise-linear MARS model increased prediction performance in testing. In the case of carbon prediction from the static data and observation number increase, only SVR with a Gaussian kernel, NN, and the piecewise-linear MARS model improved prediction performance in testing. * The prediction results from the dynamic observations of the BOS process using machine models showed an improvement in carbon prediction compared to the prediction from static data only. * Predictions from dynamic melting observations make it possible to simulate the entire dynamic course of the target quantity. * In terms of quality, dynamic behavior was best simulated by SVR, MARS, and \(k\)-NN-based models. * The piecewise-linear MARS model proved to be the most accurate in predicting temperature, and the \(k\)-NN model was the most accurate in predicting carbon at the endpoint of melting from dynamic data. Conceptualization, J.K. and P.F. ; Data curation, J.K.; Formal analysis, J.K and P.F. ; Methodology, J.K. and M.D. ; Project administration, J.K.; Resources, M.D. and M.L. ; Supervision, M.D and M.L. ; Validation, J.K.; Writing--Original draft preparation, J.K. and P.F. ; Writing--review and editing M.D. and M.L.