Advancing Blast Furnace Thermal State Prediction: A Data-Driven Approach Using Thermocouple Integration and Multimodal Modeling

Abstract

This study develops a data-driven framework to predict the thermal state of blast furnaces using feature fusion from thermocouple data and spatial temperature distribution. The article proposes a hybrid framework based on multimodal integration and clustering algorithms, utilizing data extracted from thermocouples and the temperature distribution features around the furnace hearth. Through these fused features, multiple ensemble models are constructed to predict the thermal state of the blast furnace, with a focus on the thermocouple readings at the hearth. This method enhances understanding of the thermal state of the blast furnace, aiming to improve prediction accuracy and operational reliability. By validating the model with actual industrial data, its effectiveness in thermal state monitoring is demonstrated. The integration of multimodal data sources allows for the extraction of rich information from the thermocouple data, significantly enhancing the model's predictive performance.