The formation of nonmetallic inclusions in the solidification process can essentially influence the properties of steels. Computational simulation provides an effective and valuable method to study the process due to the difficulty of online investigation. This paper reviews the modeling work of inclusion formation during the solidification of steel. Microsegregation and inclusion formation thermodynamics and kinetics are first introduced, which are the fundamentals to simulate the phenomenon in the solidification process. Next, the thermodynamic and kinetic models coupled with microsegregation dedicated to inclusion formation are briefly described and summarized before the development and future expectations are discussed.

A comprehensive model was developed to simulate manganese sulfide formation during the solidification of steel. This model coupled the formation kinetics of manganese sulfide with a microsegregation model linked to thermodynamic databases. Classical nucleation theory and a diffusion-controlled growth model were applied to describe the formation process. Particle size distribution (PSD) and particle-size-grouping (PSG) methods were used to model the size evolution. An adjustable parameter was introduced to consider collisions and was calibrated using the experimental results. With the determined parameters, the influences of the sulfur content and cooling rate on manganese sulfide formation were well predicted and in line with the experimental results. Combining the calculated and experimental results, it was found that with a decreasing cooling rate, the size distribution shifted entirely to larger values and the total inclusion number clearly decreased; however, with increasing sulfur content, the inclusion size increased, while the total inclusion number remained relatively constant.

A microsegregation model with local partition coefficients and temperature‐dependent diffusion coefficients based on the model of Ohnaka (I. Ohnaka, Trans. Iron Steel Ins. Jpn. 1986, 26, 1045) is proposed. In this model, multicomponent alloy effects and precipitations are considered, and the peritectic reaction can be indicated using the thermodynamic library ChemApp. The proposed model is validated by comparing the results to other models from the literature and to measured data. Local partition coefficients are calculated, and their significant influence on microsegregation prediction is shown. The characteristic temperatures determined using the proposed model are in good agreement with measured values.

A ladle furnace (LF) refining process model is proposed considering both steel mixing and multiphase chemical reactions. In the model, the LF, together with the melts, is divided into a finite number of tanks according to the tank‐in‐series model. The tank number is determined by simulations of the model with varied tanks and compared with the empirical mixing time. The “effective equilibrium reaction zone (EERZ)” method is used to describe the kinetics of multiphase reactions. The thermodynamic library—ChemApp—is applied to link the model to thermodynamic database. Steel/slag reaction, lining dissolution, alloy addition, and air absorption are considered. Using the model, an industrial LF treatment process is simulated and the reasonability of the predicted compositions of steel, slag, and inclusions is illustrated by comparing with measurements. The inhomogeneity of the steel and inclusion concentrations in different tanks are shown.

Influence of slag viscosity and composition on the inclusion content in the steel is studied using laboratory experiments and modeling simulations. The steel samples are taken during the experimental process to record the inclusion content change. Afterwards the prepared samples are analyzed using automated scanning electron microscope and energy dispersive spectroscopy (SEM/EDS) method. A simple steel/slag reaction model is constructed based on the effective equilibrium reaction zone (EERZ) method. The inclusion content evolution process is discussed by combining the experimental and calculated results. It is found that the inclusion content evolution in the steel is determined by the inclusion generation and removal.