Nonstationary Process Monitoring for Blast Furnaces Based on Consistent Trend Feature Analysis

Blast furnaces are the most crucial equipment in ironmaking processes. Stable operation of the blast furnace is a prerequisite for personnel safety and production efficiency. Therefore, early detection of abnormalities in blast furnaces is an important task for ironmaking processes. However, owing to the large fluctuations in the quality of raw materials, dynamic operating conditions, as well as the impact of the hot blast stoves switches, the measurements of blast furnace show severe nonstationary characteristics. All these factors make monitoring the blast furnace a challenging task. In this article, a nonstationary process monitoring method called consistent trend feature analysis (CTFA) is proposed, which can extract the trend-related features and discard perturbations in process data. The directions and amplitudes of the extracted trends are used for abnormality detection, and a local-learning-based method is proposed for determining a time-varying control limit. The detection performance of the proposed method is analyzed, with a sufficient condition and a necessary condition for the detectability given. The effectiveness of the proposed method is validated by the practical data collected from a large-scale blast furnace located in Liuzhou, China.