DAML: A Method for Predicting Furnace Hearth Activity Based on Feature Measurement

Introduction: Monitoring the furnace hearth activity is of great significance for maintaining the production efficiency of steel. In order to solve the problem that it is difficult to collect sufficient blast furnace process parameters in actual production, resulting in the model not being fully trained, this research proposed a method based on distance-angle metric learning to predict hearth activity in scenarios with few samples. Methods: In this research, samples were randomly selected and grouped in pairs. The proposed method extracted features through two symmetrical back propagation neural networks to get multiple pairs of feature vectors, then predicted the state of furnace hearth activity by measuring the Manhattan distance and cosine similarity between each pair of feature vectors. Results: The experimental results showed that the proposed method can extract sample features stably, it still has a good analysis effect in scenarios with few samples. The optimal accuracy rate was 99.18%, which can meet the monitoring of hearth activity in actual production. Discussion: The measurement based on distance and angle can reflect the characteristics of each category more accurately. It also achieved good performance in scenarios with few samples and provided a theoretical basis for the reasonable regulation of the hearth activity. Conclusion: Compared with the related methods, the proposed method based on distance-angle metric learning in this research has better performance. It can meet the application requirements in actual production scenarios and has great research value.