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A model has been developed for predicting the metal temperature in the specified superheat range above the liquidus temperature inside the tundish of a continuous casting machine (CCM) depending on the last temperature measurement in the steel-pouring ladle (prior to casting), as well as ladle history and secondary steelmaking process under the conditions of the Casting and Rolling Complex (CRC) of the JSC “Vyksa Metallurgical Plant” (VMZ). It is suggested to predict the superheat temperature of steel at the CRC by using statistical models considering the specifics of technological process. During the first stage, the model itself has been developed by integrating the following two approaches: a machinelearning algorithm and probabilistic graphical model (PGM and Bayesian networks). The first approach provides the point estimate of the intermediate and final temperatures of the specific melting, while the second approach allows evaluating the probability distribution. The PGM-approach is very promising in the situations involving missing, incorrect, or undefined input data, which are quite common at the metallurgical plants. During the first stage, the achieved level of accuracy of tundish temperature prediction was 6.0°C. During the second stage, the model was integrated into the shop automated process control system, which allowed increasing the prediction accuracy and ensuring a real-time control of the process parameters. The model is used as a guidance (master) tool for the process personnel operating the ladlefurnace and vacuum degasser equipment.

The paper presents a prediction model for steel temperature in a specified time interval during its overheating over a liquidus point in the tundish of a continuous casting machine (CCM). This depends on the last temperature measurement in a ladle prior to casting with consideration to the ladle’s history and treatment during secondary metallurgical processes in an casting and rolling complex (CRC). The prediction of steel superheat temperature at CRC is based on two modeling stages: the first stage integrates two approaches—a machine-learning algorithm and a probabilistic-graphical model (PGM and Bayesian networks); and the second stage uses a method for evaluating probability distributions. The first approach gives a point estimate of the intermediate and final temperatures of specific heat. The PGM-approach is useful for scenarios with uncertain input data, which is often the case for metallurgical enterprises. The accuracy level in predicting the temperature of the metal in the tundish at the first stage reached 6.0°C. At the second stage, the model integration into the process control system improved prediction accuracy, as well as provided technological parameter control in real time. The model is used as an advisor (master) for the technical team of the ladle furnace and vacuum degasser.

The research results of the nitrogen and vanadium influence on the microstructure, fine structure formation and mechanical properties of low-carbon steel pipe are considered. It is shown that it is possible to obtain cold-resistant mill products with strength classes from K48 to K60 with high (26–29%) percentage of elongation by dispersion hardening due to the precipitation of vanadium particles together with hardening of a manganese solid solution in the conditions of the Vyksa Metallurgical Plant casting and rolling complex.