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VERSION:2.0
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ORGANIZER;CN=ESTAD 2023:mailto:info@metec-estad.com
LOCATION:Room 7
SUMMARY:The Economic and Environmental Benefits of Using Soft Sensors in Iron Pelletizing Plants
DESCRIPTION:Advances in data science, machine learning, and artificial intelligence are transforming mining and metals and making them more algorithm intensive. The paradigm is shifting from one of detection and control to one of prediction and optimization where advanced soft sensors take an essential position. While conventional soft sensors were mainly based on linear and physical models, modern machine learning techniques bring the opportunity to improving the commonly used soft sensors and provide new types of soft sensors that couldn’t be developed through conventional methods.
In this paper, two industrial case studies on advanced soft sensor applications in a pelletizing plant will be discussed. The first one is a soft sensor estimating the cold crushing strength (CCS) index of pellets which helps operators to make better decisions in real-time and improve pellet quality. The value propositions of the application of CCS soft sensor from economical and environmental perspectives have been studied. It was demonstrated that a reduction of pellet rejects and consequently CO2 emission can be achieved in the Direct Reduction Iron (DRI) plant by improving pellet CCS in the pelletizing plant. The second soft sensor predicts the amount of fired pellet FeO% which associates penalty if it goes beyond the target. An economical analysis demonstrates the potential for revenue improvement if FeO% soft sensor is implemented in a 5 MTPA pellet plant.

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DTSTART:20230615T135000
DTEND:20230615T141000
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