## 1 Introduction  
When controlling basic oxygen steelmaking (BOS), it is essential to know the state of the melt, i.e., the carbon concentration in the melt and the melt temperature. Due to the high temperatures in the converter, it is problematic to measure these process variables with conventional devices (e.g., thermocouples). Although said process variables are measured at the beginning and end of the melting, it remains questionable to determine the dynamic course during the melt and the melting's endpoint. For this reason, indirect measuring techniques based on soft sensors are used. Information about the melt temperature and the carbon concentration in the melt will help the control personnel perform the appropriate control interventions, i.e., adjust the amount of blown oxygen, the nozzle height, or add sub-materials. These soft-sensors use software models based on various principles and approaches. Mathematical models enable real-time estimating process variables without a hardware sensor [1]. The problem with basic oxygen steelmaking (BOS) control is the absence of information about the melt temperature and the carbon concentration in the melt. These data are usually measured only at the beginning and end of the melt. Various mathematical models can estimate these variables during melting or at the endpoint. The main research objective of this study is to compare five machine learning methods for modeling target variables in the steelmaking process. Multivariate adaptive regression splines (MARS), support-vector regression (SVR), neural network (NN), \(k\)-nearest neighbors (\(k\)-NN), and random forests (RF) will be compared. Two approaches to data-driven converter process modeling will be explored, i.e., modeling from static data and from combined static and dynamic data of many melts. This study envisages finding the most powerful machine model for predicting the melt temperature and carbon concentration at the endpoint of the melt. This study also assesses which modeling approach is more powerful for a given target variable. This research also hypothesizes that changing the number of input observations when training models has some effect on the performance of machine models. In this case, the amount of the added lime and dolomitic lime were regarded as supplementary input observations. This paper verified this assumption by modeling and predicting from static data. This study also envisages the possibility of modeling the dynamic behavior of temperature and carbon concentration based on dynamic data of meltings. In this case, the added concentration of O\({}_{2}\) and H\({}_{2}\) were regarded as supplementary input observations. This paper verified this assumption by modeling and predicting from dynamic data. It is assumed that based on the continuous measurement of the waste gas composition, the temperature of the flue gases, and the volumetric amount of blown oxygen during melting, it is possible to determine the dynamic course of the temperature of the melt and the concentration of carbon. Then, a learned machine model could obtain an estimate of these target variables. It should be possible to evaluate the accuracy at the melting endpoint as well as the quality of the dynamic course from the simulation of the dynamic sleep of the target variables. In addition, the speed of machine learning methods in the training phase of individual models will be evaluated. The literature review and fundamentals of the BOS are explained in the following subsections. The second section presents the theoretical background of applied methods. Machine modeling was based on static and dynamic data from many meltings. This section describes the utilization of measurable static and dynamic data from BOS and proposes a joint of these data for prediction from dynamic data. The scheme of modeling and prediction in BOS is also proposed. The simulation results of prediction based on static data and dynamic data are provided in the third section. The results from five machine learning methods are compared.