Prediction model of aluminum consumption with BP neural networks in IF steel production

To solve the high aluminum consumption problem in interstitial-free steel production in a steel plant, an aluminum consumption prediction model was established by mathematical statistics and BP neural networks. Compared with the multiple linear regression model, this model's result is more accurate. The influence of different smelting processes on aluminum consumption was analyzed, and the process parameters were optimized. The results show that the amount of aluminum consumption per ton of steel decreases 0.07 to 0.08 kg when the oxygen activity before RH or after decarbonization reduces by 0.005%. The effective utilization coefficient of aluminum-deoxidizing is from 70.31% to 80.35%; the aluminum consumption decreases about 0.1 kg when the temperature of steel before RH increases by 35 to 40℃. The heating utilization coefficient of aluminum thermal reaction is about 97.4%. When the blowing oxygen quantity is less than 100 m³ and greater than 100 m³, the ratio of oxygen reacting with aluminum is about 37.3% or about 74.6% respectively, and the aluminum consumption increases by 0.1 kg or 0.2 kg, respectively, with the blowing oxygen quantity increasing by 50 m³. After the process parameter optimization, the aluminum consumption decreases from 1.359 to 1.113 kg, which results in a decrease of 18.1%.