Aiming at the characteristics of the practical steelmaking process, a hybrid model based on ladle heat sta- tus and artificial neural network has been proposed to predict molten steel temperature. The hybrid model cou...Aiming at the characteristics of the practical steelmaking process, a hybrid model based on ladle heat sta- tus and artificial neural network has been proposed to predict molten steel temperature. The hybrid model could over- come the difficulty of accurate prediction using a single mathematical model, and solve the problem of lacking the consideration of the influence of ladle heat status on the steel temperature in an intelligent model. By using the hybrid model method, forward and backward prediction models for molten steel temperature in steelmaking process are es- tablished and are used in a steelmaking plant. The forward model, starting from the end-point of BOF, predicts the temperature in argon-blowing station, starting temperature in LF, end temperature in LF and tundish temperature forwards, with the production process evolving. The backward model, starting from the required tundish tempera- ture, calculates target end temperature in LF, target starting temperature in LF, target temperature in argon-blo- wiag station and target BOF end-point temperature backwards. Actual application results show that the models have better prediction accuracy and are satisfying for the process of practical production.展开更多
The stirring of a molten steel ladle with argon injection through a top submerged lance and a bottom nozzle is numerically studied here through Computational Fluid Dynamics (CFD) simulations. Three lance submergence d...The stirring of a molten steel ladle with argon injection through a top submerged lance and a bottom nozzle is numerically studied here through Computational Fluid Dynamics (CFD) simulations. Three lance submergence depths and three injection velocities are considered in the CFD numerical experiments. The turbulent dissipation rate is employed to characterize the stirring phenomenon. The mixing times are determined from the gas flow rate, ladle geometry and bath depth using an empirical correlation.展开更多
基金Item Sponsored by Fundamental Research Funds for Central Universities of China(FRF-BR-10-027B)
文摘Aiming at the characteristics of the practical steelmaking process, a hybrid model based on ladle heat sta- tus and artificial neural network has been proposed to predict molten steel temperature. The hybrid model could over- come the difficulty of accurate prediction using a single mathematical model, and solve the problem of lacking the consideration of the influence of ladle heat status on the steel temperature in an intelligent model. By using the hybrid model method, forward and backward prediction models for molten steel temperature in steelmaking process are es- tablished and are used in a steelmaking plant. The forward model, starting from the end-point of BOF, predicts the temperature in argon-blowing station, starting temperature in LF, end temperature in LF and tundish temperature forwards, with the production process evolving. The backward model, starting from the required tundish tempera- ture, calculates target end temperature in LF, target starting temperature in LF, target temperature in argon-blo- wiag station and target BOF end-point temperature backwards. Actual application results show that the models have better prediction accuracy and are satisfying for the process of practical production.
文摘The stirring of a molten steel ladle with argon injection through a top submerged lance and a bottom nozzle is numerically studied here through Computational Fluid Dynamics (CFD) simulations. Three lance submergence depths and three injection velocities are considered in the CFD numerical experiments. The turbulent dissipation rate is employed to characterize the stirring phenomenon. The mixing times are determined from the gas flow rate, ladle geometry and bath depth using an empirical correlation.
