Effect of gas forming compounds on the vibration of a submerged lance in hot metal desulfurization

Abstract Hot metal desulfurization is the main process step for removing sulfur in blast furnace-based steelmaking. A desulfurization reagent is pneumatically injected into the hot metal through a submerged lance causing it to vibrate. The aim of this study is to develop a mechanical vibration measurement-based method that can detect changes in the gas-forming properties of the reagent. The detection is performed using Elastic Net regression and eXtreme Gradient Boosting-based classification models the classification performance of which is compared. The lance aging causes changes in its dynamic characteristics, and the disturbing effect of this is removed from the measured data of the lance vibration prior to classification by means of a developed cleaning algorithm. The best classification performance in detecting changes in the gas-forming properties, with an area under the receiver operating characteristic curve of 0.916 and Matthews correlation coefficient of 0.699, is achieved using an Elastic Net regression-based classification model. The results of this work serve as a basis for developing industrial applications in which the effective utilization of the excitation, such as vibrations generated by the gas formation can be utilized for process monitoring and as a soft sensor for predicting the reagent-induced process variance

Data-driven mathematical modeling of the effect of particle size distribution on the transitory reaction kinetics of hot metal desulfurization

Abstract The aim of this work was to develop a prediction model for hot metal desulfurization. More specifically, the study aimed at finding a set of explanatory variables that are mandatory in prediction of the kinetics of the lime-based transitory desulfurization reaction and evolution of the sulfur content in the hot metal. The prediction models were built through multivariable analysis of process data and phenomena-based simulations. The model parameters for the suggested model types are identified by solving multivariable least-squares cost functions with suitable solution strategies. One conclusion we arrived at was that in order to accurately predict the rate of desulfurization, it is necessary to know the particle size distribution of the desulfurization reagent. It was also observed that a genetic algorithm can be successfully applied in numerical parameter identification of the proposed model type. It was found that even a very simplistic parameterized expression for the first-order rate constant provides more accurate prediction for the end content of sulfur compared to more complex models, if the data set applied for the modeling contains the adequate information