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VERSION:2.0
METHOD:PUBLISH
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ORGANIZER;CN=ESTAD 2023:mailto:info@metec-estad.com
LOCATION:Room 2
SUMMARY:Improving process model calculation accuracy by statistical supervision and modification of raw-material properties
DESCRIPTION:Process models for the simulation and control of metallurgical processes have evolved rapidly in recent decades. Today’s state-of-the-art models are very advanced and can describe real-time chemical and physical phenomena in a straightforward way. However, uncertainties in raw-material properties (chemical composition, specific energy consumption, etc.) limit the process models’ ability to correctly describe the outcome of a particular heat or production sequence given a raw material mix and an operational procedure.

Furthermore, the absence of measurements and control of parameters influencing the efficiency of the operational procedure (heat status, lining status, heel status, etc.) also contributes to uncertainties regarding the effect of using specific raw materials. This paper presents a method for supervising raw-material properties based on statistical evaluation of process model calculation errors concerning using different raw materials. The method is applied to detect and correct errors in the estimated chemical composition of charge materials in an electric arc furnace at a stainless-steel plant. A web-based tool for presenting alarms and alternative calculated chemical composition has been developed. Results show that during tests of this tool in industrial trials, the model calculation errors are reduced by 11-16 % by following the tool’s recommendations.

CLASS:PUBLIC
DTSTART:20230615T153000
DTEND:20230615T155000
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