## 1 Introduction
### Presented Research Area in the Literature Review  
Various approaches to modeling and data prediction in steelmaking have been explored around the world. The latest innovations in converter process modeling and monitoring can be found in [2]. In recent years, many deterministic models of the steelmaking process have been developed and applied. Researchers have developed complex dynamical models to predict the chemical composition of the melt based on balance equations (e.g., [3]) or models to predict the temperature of steel based on thermodynamic reaction balance and gas analysis technology (e.g., [4; 5; 6; 7]). Gas analysis technology provides a way to predict the temperature of converters with long life continuously, and it can be applied to different tonnage converters. Interest has also increased in thermodynamic modeling of the BOS process. The literature reports thermodynamic models to predict the bath composition, temperature as well as decbarurization rate (e.g., [8; 9; 10]). Nowadays, significantly increased computational resources permit detailed mathematical and data-driven modeling of BOS (e.g., the decbarurization model proposed in [11]). On the other hand, the steady-state models are usually developed based on material and heat balance (e.g., [12; 13]). In the endpoint prediction of BOS, researchers also use different regression models. These are primarily linear or nonlinear models capable of predicting melt temperature and carbon concentration in the melt. The basic regression model is usually combined with another type of model (e.g., utilization of the Gray model in [14]) or adaptation level (e.g., a continual adaptation of the regression parameters by the least-squares method proposed in [15]). In addition, other researchers (e.g., [PERSON] et al. [16]) performed regression analysis of BOS data and proposed an oxygen and temperature regression model for target variable prediction. 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 produced steel. In addition, these endpoint process variables are problematic to measure online continuously over time. Applications of various machine learning methods aimed at predicting temperature and carbon in BOS can be found in the literature. Methods with kernel functions are trendy in machine learning and steelmaking. 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. Some applications of SVR on BOS can be found in [17; 18; 19], [20] or [21]. These are primarily data-driven models aimed at online or offline prediction of BOS process variables. The proposed black-box models improved the prediction of process variables compared to the analytical models currently used in steel production. Much research has focused on endpoint prediction in BOS using a neural network-based model. A neural network (NN) is capable of self-learning, approaching any nonlinear function, and processing data quickly. Some applications of NNs to predict process variables in BOS endpoints can be found in [22; 23; 24] or [25]. These applications were usually combined with various supporting optimization algorithms to improve or adapt the NN. There are also many NN applications and their improvements in the literature to predict stopping temperature and end blow oxygen in the LD converter (e.g., [26; 27; 28; 29]). One of the improvements of the NN applied to BOS is represented by the adaptive neural network fuzzy inference system (ANFIS) [30]. Recently, a data-mining method, case-based reasoning (CBR), has been widely applied in many areas. CBR solves new problems by using the solution of a previous similar case. [PERSON] and [PERSON] [31] proposed an improved CBR method to predict carbon content in the melt.