Prediction of oxygen-blowing volume in BOF steelmaking process based on BP neural network and incremental learning
Fei He, Chai Xianyi, Zhu Zhenghai
Abstract
Abstract In view of the characteristics of dynamic basic oxygen furnace (BOF) steelmaking process, prediction models based on backpropagation neural network and incremental learning (BPNN-IL) are proposed for total blow oxygen volume and second blow oxygen volume. The incremental learning is to adjust weights and thresholds of the BPNN according to the difference between the predicted value and actual value of each heat and to adapt to the change in furnace conditions. The combined BPNN-IL models are trained and tested by actual production data, and are further compared with multiple linear regression models and BPNN models. The results show that whether it is total blow oxygen volume or second blow oxygen volume, the BPNN-IL models could provide the most accurate prediction and the introduction of an incremental learning method could further improve the predictive accuracy. So the BPNN-IL method is effective in predicting the oxygen-blowing volume in the BOF steelmaking process.