Intelligent Prediction and Real-time Monitoring System for Gas Flow Distribution at the Top of Blast Furnace

Abstract

Aiming at the problem of complex internal environment of blast furnace and the difficulty of gas flow distribution (GFD) detection and prediction, an intelligent prediction and real-time monitoring system of blast furnace roof gas flow distribution based on long short-term memory neural network (LSTM) and fuzzy C-mean clustering (FCM) is proposed, which solves the problems of poor stability of traditional GFD model, low accuracy of multi-step prediction and “black box”. The system consists of a GFD identification model and a multi-step prediction model. The system consists of GFD identification model and cross-beam temperature measurement (CBTM) multi-step prediction model. The GFD identification model first uses the blast furnace CBTM information to establish temperature field of blast furnace burden surface, calculates the center and edge gas flow development index, and adopts the FCM algorithm model to identify the blast furnace GFD pattern; the CBTM multi-step prediction model uses the LSTM model to predict the temperature of 29 CBTM points respectively; Finally, the system returns the predicted CBTM data to the GFD identification model to complete the prediction of the GFD model in the future moment. The experiment shows that the system can effectively predict the GFD development pattern within the next 7 hours, the model prediction accuracy reaches 99%, and the correct rate of all kinds of GFD pattern recognition is above 95%, which can achieve better intelligent prediction and real-time monitoring effect than other traditional GFD models, and provide effective help and support for blast furnace operators to analyze the furnace condition.