Published July 27, 2021
| Version v1
Journal article
Open
A slag prediction model in an electric arc furnace process for special steel production
- 1. TECNALIA, Basque Research and Technology Alliance (BRTA), Mikeletegi 7, 20009 Donostia-San Sebastián (Spain)
- 2. TECNALIA, Basque Research and Technology Alliance (BRTA), Mikeletegi 2, 20009 Donostia-San Sebastián (Spain)
- 3. TECNALIA, Basque Research and Technology Alliance (BRTA), Astondo Bidea, Edificio 700, 48160 Derio (Spain)
- 4. Sidenor I+D, Barrio Ugarte s/n, 48970 Basauri (Spain)
Description
In the steel industry, there are some parameters that are difficult to measure online due to technical difficulties. In these scenarios, soft-sensors, which are online tools that aim forecasting of certain variables, play an indispensable role for quality control. In this investigation, different soft sensors are developed to address the problem of predicting the slag quantity and composition in an electric arc furnace process. The results provide evidence that the models perform better for simulated data than for real data. They also reveal higher accuracy in predicting the composition of the slag than the measured quantity of the slag.
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