Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.

General heat balance for oxygen steelmaking

Energy balances are a general fundamental approach for analyzing the heat requirements for metallurgical processes.The formulation of heat balance equations was involved by computing the various components of heat going in and coming out of the oxygen steelmaking furnace.The developed model was validated against the calculations of Healy and McBride.The overall heat losses that have not been analyzed in previous studies were quantified by back-calculating heat loss from 35 industrial data provided by Tata Steel.The results from the model infer that the heat losses range from 1.3%to 5.9%of the total heat input and it can be controlled by optimizing the silicon in hot metal,the amount of scrap added and the postcombustion ratio.The model prediction shows that sensible heat available from the hot metal accounts for around 66%of total heat input and the rest from the exothermic oxidation reactions.Out of 34%of the heat from exothermic reactions,between 20%and 25%of heat is evolved from the oxidation of carbon to carbon monoxide and carbon dioxide.This model can be applied to predict the heat balance of any top blown oxygen steelmaking technology but needs further validation for a range of oxygen steelmaking operations and conditions.

Numerical Analysis of Slag Splashing in a Steelmaking Converter

Some variables that influence the slag splashing phenomenon in an oxygen steelmaking converter are numerically analyzed in this work. The effect of lance height, jet velocity, jet exit angle and slag viscosity on the washing and ejection mechanisms of slag splashing is studied employing transient two-dimensional computational fluid dynamics simulations. A parameter here called average slag volume fraction is proposed for the quantitative evaluation of the slag splashing efficiency. Besides, a qualitative comparison is made between the computational fluid dynamics results and physical model results from literature.