Abstract
In the cold rolling of flat steel strips, electric energy consumption is one of the highest expenses. Predicting the power requirements according to the line and product conditions can significantly impact the energy cost and, thus, on the business’s profitability. This paper proposes predicting the power requirements of a tandem cold rolling mill of steel strips on a coil-to-coil base applying Artificial Neural Networks (ANN) as a uni-variate regression problem. The tests yielded an MSE of 300.39 kW or 2.4% and are better than the acceptable 5% error margin for this project, indicating that the ANN presented satisfactory results. The application of six full-month worth of data in the trained ANN model showed the excellent correlation of the ANN predictions with the measured data, leading to the conclusion that the system is ready for the deployment for daily use for line engineers. Overall, the results obtained show that the steel industry can highly benefit from Industry 4.0 and Artificial Intelligence technologies.
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Acknowledgements
The authors are thankful to ArcelorMittal Vega (Sao Francisco do Sul, SC, Brazil) and the FAPESC agency for providing financial support. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research.
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de Oliveira, D.G., da Silva, E.M., Miranda, F.J.F., Filho, J.F.S., Parpinelli, R.S. (2020). Artificial Neural Network Model for Steel Strip Tandem Cold Mill Power Prediction. In: Florez, H., Misra, S. (eds) Applied Informatics. ICAI 2020. Communications in Computer and Information Science, vol 1277. Springer, Cham. https://doi.org/10.1007/978-3-030-61702-8_3
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DOI: https://doi.org/10.1007/978-3-030-61702-8_3
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