| Abstract: | Neural nets can be adapted to complex patterns of interrelated input and output variables in a process even if the data sets contain a lot of noise. In this work two specific examples for the application of adaptive neural nets (ANN) in steel Industry are described. First, the sulphur content of hot-metal, obtained at the end of calcium carbide powder injection into 4001 torpedo ladles is predicted as a function of hot-metal weight, treatment time, initial sulphur content, gas flow rate and powder injection rate. The values predicted by the trained ANN model for a completely new set of input test data compare well with the actual values obtained on the shop floor. In the second example, the sulphur content of steel, obtained at the end of blow is predicted as a function of liquid-metal weight, total amount of oxygen injected, amount of iron ore added, and the temperature, contents of carbon, manganese, phosphorus and sulphur determined by in-blow sampling in a 300 t converter. The ANN predicted values of sulphur content of steel at tap (without reblow) also agree well with the values obtained on the shop floor. |
