An advanced, predictive technique has been developed using principal component analysis (PCA) together with computational fl uid dynamics (CFD) modeling to provide real-time monitoring and assessment of the performance and integrity of a water-cooled tapblock in a smelting furnace. A CFD model of a tapblock was built to simulate its thermal response to metal tapping for a range of furnace operating conditions and to assess the impact of process variations that are detrimental to furnace integrity. This information was then used to develop a PCA model that identifi es modes of acceptable and unacceptable tapping operation based on the realtime analysis of the temperature measurements coming from the tapblock. The PCA model, when implemented into the control system, enables operators to monitor and diagnose the performance and integrity of the tapblock.
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