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Use Cases 01 Chemical Correction
- Corrections occur when the chemical composition after melting is non optimal.
- Corrections are expensive due to energy loss, cost of ferro-alloys to be added, loss of capacity.
- A strong Data Science approach found the main cause of wrong chemical composition in the furnace loading phase.
- Our system is now able to reduce the need of a correction by dynamically recalculate the cast iron recipe.
- The algorithm evaluates the weights of the material loaded and the history of past chemical analysis and acts accordingly
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Use Cases 02 Air
- After a deep data analysis, wrong melting temperature reported by a defective sensor was found to be the main issue related with high scrap rate.
- Since human operators where able to “see” the potential failure of the gauge by observing the temperature history, an A.I. algorithm was trained with their help.
- A.I. tells the operator to replace the thermal gauge of the furnace one shift ahead the failure will occur.
- Scrap rate reduced by 10%.
- Avoided to have operators watching the temperature gauge every hour.
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