| Abstract: | A mathematical model for predicting the melt temperatures in the ladle and in the tundish during continuous casting has been developed. First of all, a chain of models was created for the following stages of the ladle cycle; the preheating of the empty ladle, filling of the ladle, period in the ladle furnace, waiting period prior to casting, the casting period, and, finally, the free cooling period of the empty ladle. Models, written in CFD code, were used in sequence so that each simulation continued from the results of the simulation of the previous stage. An intermediate model was constructed to estimate the outlet temperature of melt drained from the ladle. Then the work was continued by performing simulations in the tundish, using as input the temperature of the simulated melt feed from the ladle and, as an initial condition, the temperature field of the remaining melt in the tundish. The final model “TEMPARV3” was created and tested by means of measured tundish data received from a steel plant. By means of statistical analysis the coefficients of correlation between the test data and model data at the start, in the middle period, and at the end of casting were calculated to be 0.9, 0.92 and 0.87, respectively. So, the most effective predictive power of the model in the tundish by means of a sequential casting schedule is realized during the middle period of the casting process. The model is applied interactively by a user interface, which expresses the predicted melt temperatures numerically and with graphical curves. The predictive model can be used off‐line as a tool for scheduling the stage operations in advance. The program may be utilized on‐line to estimate the superheat needed and to control periods of the operation. In extreme cases, when the model alerts the operator about the danger of superheat loss having a critical effect on casting, the operator has a chance to take adjustment measures. In addition to production work, the model could be of benefit for studying changes in operating parameters, for training operators, and for use as a “low‐cost computational pilot plant” in process development in general. |
