| Abstract Scope |
The Basic Oxygen Furnace (BOF) is crucial in the steelmaking process, converting molten iron into stee by elevating temperature, reducing carbon content, and controlling composition. This stage generates extensive production data, including tabular data and time-series data such as off-gas data and oxygen blowing data. Using domain knowledge, the data was carefully filtered and preprocessed, and 6517 samples were used to train, validate and test. A neural network model was designed to integrate both tabular data and time-series data as input, creating predictive models for end-point temperature and end-point carbon content separately. Several advanced time-series algorithms, including LSTM, GRU, RNNs and CNNs, were tried and compared, with LSTM yielding the best results. After model optimization, the models achieved 93.94% (±0.02%) accuracy for carbon prediction and 89.03% (±15°C) accuracy for temperature prediction on testing data, underscoring the significant improvement achieved by incorporating time-series data. |