The presence of non-metallic inclusions (NMI) such as sulphides and oxides may be detrimental to the control of the steel casting process and product quality. The need for their identification and characterization is, therefore, urgent. This study uses time-gated Raman spectroscopy for the characterization of synthetic duplex oxide-sulphide phases that contain CaS and the oxide phases of Al₂O₃, CA, C12A7, C3A, and MgO·Al₂O₃ (MA). Binary phase samples of CaS–MA, C3A–CaS, C12A7–CaS, Al₂O₃–CaS, and MA–CaS were prepared with varying phase contents. The relative intensities of the Raman peaks were used to estimate the samples’ phase content. For a quantitative estimation, linear regression calibration models were used to evaluate the change in phase content in the samples. The most suitable Raman peak ratios had mean absolute error (MAE) values ranging from 3 to 7 wt. % for the external validation error, and coefficients of determination (R²) values between 0.94 and 0.98. This study demonstrated the use of Raman spectroscopy for the characterization of the calcium sulphide, magnesium aluminate spinel, Al₂O₃, and calcium aluminate phases of CA, C3A, and C12A7 in a duplex oxide-sulphide system, and it offers potential for inclusion characterization in steel.

Hot metal desulfurization operates as an extraction process in steel production after the blast furnace. Mathematical modeling of the process provides the basis for control and optimization solutions. Owing to complex dynamics, an exhaustive physico-chemical description of the process is computationally infeasible. Thus, a model with a gray–box structure is considered. In the model, a neural network model for sulfide capacity of the slag is implemented in series with the reaction model. The network is trained using an independent data set. The parameters for the reaction model are identified using genetic algorithm with real-encoded population and hybrid selection and recombination operators. The gray-box model parameter distributions are estimated using Metropolis Hastings Algorithm. This study suggests that a sufficiently low mean absolute error can be achieved for the end sulfur content. The relative contribution of the permanent contact reaction to overall reaction rate under industrial conditions remains uncertain.

The resulfurization of hot metal has not been comprehensively studied in literature. This study presents an experimental and mathematical modeling study of resulfurization in thermodynamic and kinetic point of view. The rate, extent, and mechanisms of resulfurization were evaluated by analyzing concurrently the physical properties and sulfur-extracting ability of the slag. Experiments were conducted in a chamber furnace in an argon atmosphere, and the hot metal was sampled with pre-defined basis. The experiments were continued until the metal–slag system reached an apparent thermodynamic equilibrium. To obtain a quantitative measure on the effect of system properties on the rate and extent of resulfurization, the results of this study were combined with previous studies handling the sulfide capacities of Na₂O-SiO₂ and CaO-SiO₂-Na₂O slag systems. The sulfide capacities of the slag and corresponding metal–slag sulfur partition ratios were mathematically modeled with data-driven techniques such as multiple linear and non-linear regression and artificial neural networks. Finally, with the help of these, to study the kinetics of resulfurization, a simple mechanistic reaction model was derived. The results suggest that resulfurization of hot metal follows 1st-order kinetics and that the rate and extent can be regulated through the control of the associated thermodynamic driving force and by modifying the physical properties of the slag. The rate-limiting factor was found to be determined by the morphology of the slag phase.