Hot metal desulfurization serves as the main unit process for removing sulfur in blast‐furnace based steelmaking. The available body of literature on modeling hot metal desulfurization is reviewed to provide an in‐depth analysis of the approaches used and results obtained. The mathematical models for reaction kinetics have evolved from simplistic rate equations to more complex phenomenon‐based models that provide useful information on the effect of physico‐chemical properties and operating parameters on desulfurization efficiency. Data‐driven approaches with varying levels of phenomenological basis have also been proposed with the aim of achieving better predictive performance in industrial scale applications. Bath mixing has been studied using physical and numerical modeling to optimize mixing conditions in ladles and torpedo cars. The coupling of gas‐particle jets and their penetration into the liquid have been a focal point of physical and numerical modeling. In recent years, the fluid flow phenomena in mechanically stirred ladles has been studied extensively using physical and numerical modeling. These studies have focused on the fluid flow field, reagent dispersion, and bubble dispersion.
Sulfur is considered as one of the main impurities in hot metal and hot metal desulfurization is often carried out using injection of fine-grade desulfurization reagent. The selection of variables used for predicting the course of hot metal desulphurization requires expert knowledge. However, it is difficult to model the complex interactions in the process and to evaluate a high number of possible variable subsets with manual variable selection techniques. As the amount of data gathered from the process increases, manual variable selection becomes too time-consuming and might lead to a suboptimal prediction model. The objective of this work is to execute an automatic variable selection procedure for prediction of hot metal desulfurization based on an industrial scale data set. The variable selection problem is formulated as a constrained optimization problem, in which the objective function is formulated based on repeated leave-multiple-out cross-validation. The implemented solution strategy is a binary-coded genetic algorithm (GA). By making use of the developed model, the effect of the main production variables on the rate and efficiency of primary hot metal desulfurization is quantified. The variables related to properties of the reagent and the injection parameters were found to be of great importance.
Using hydrogen as a reducing agent for iron production has been the focus of several studies due to its environmental potential. The aim of this work is to study the influence of H 2-H 2 O content in the gas phase on the reduction of acid iron ore pellets under simulated blast furnace conditions. Temperature and gas compositions for the experiments were determined with multi-point vertical probes in an industrial blast furnace. The results of the reduction tests show that higher temperatures and H 2 content increase the rate and extent of reduction. For all the gas and temperature combinations, morphological, mineralogical, and microstructure changes were observed using different characterization techniques. Microscopy images reveal that H 2-H 2 O, in the gas phase, has a positive influence on reduction, with metallic iron forming at the pellet's periphery and core at lower temperatures compared to CO-CO 2-N 2 reducing gas. Porosity and surface area changes were determined using a gas pycnometer and the BET method. The results indicate that increasing the reduction temperatures and H 2 content results in greater porosity and a larger surface area. Moreover, carbon deposition did not take place, even at lower temperatures. A rate minimum was detected for pellets reduced at 800°C, probably due to metallic iron formation, hindering the diffusion of reducing gases through the product iron layer.
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.
Calcium aluminate (CaO-Al 2 O 3) phases play a critical role in the study of non-metallic inclusions in aluminium killed, and calcium treated steels. In this study, the Raman spectroscopy technique, a versatile and non-destructive approach, was used to characterise binary calcium aluminate phases qualitatively and quantitatively. Calcium aluminate samples with varying CaO/Al 2 O 3 ratios were synthesised to produce a binary phase samples mixture of C12A7-C3A and C12A7-CA. Quantitative estimation was based on plotting a linear regression calibration model between the ratio of Raman band intensities and the phase fraction in the samples. With the linear regression, the phase fraction of C12A7-C3A and C12A7-CA was estimated with average absolute errors of 2.97 and 2.55 percentage points. This work demonstrates the potential suitability of using Raman spectroscopy technique for evaluating whether calcium aluminate phases in oxide inclusions fall within the liquidus region at steelmaking temperatures.
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