This paper presents a synergistic approach to stockline depth tracking within a blast furnace. Frequency modulated continuous wave (FMCW) radar can be used to measure the depth and surface profile of the burden surface; however, the radar signal is easily disturbed by radar anomalies during the process of continuous measurement. Data from the rotating chute and the charging signal provide information on the contextual relevance of these anomalies. An improved Kalman filter and anomaly detection model were developed to increase measurement accuracy by utilising contextual information. The approach was validated on production blast furnaces. The root mean squared (RMS) error in the measured depth was reduced by 17% when the proposed approach is used. The results suggest that this approach successfully adapts to changes in the pattern and characteristics of the burden surface.
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