Abstract
The potential to predict the productivity and the specific electric-energy furnace consumption is very important for the economic operation and performance of a Consteel electric-arc furnace. In this work, these two variables were predicted based on specific operating parameters with the use of machine learning. Actually, three different algorithms were tested for this study: the BRF method of support vector machine (SVM), the light gradient boosting method (lightGBM), and the Keras system with TensorFlow as backend. The results appear to be good enough for production scheduling, and are presented and discussed in this work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Koehle, S.: Improvements in EAF operating practices over the last decade. In: Electric Furnace Conference Proceedings, Iron & Steel Society, Pittsburgh, PA, vol. 57, pp. 3–14 (1999)
Memoli, F., Guzzon, M., Giavani, C.: The evolution of preheating and the importance of hot heel in supersized Consteel® systems. In: AISTech 2011 Proceedings, Indianapolis, IN, vol. I, pp. 823–832 (2011)
Bouganosopoulos, B., Papantoniou, V., Sismanis, P.: Start-up experience and results of Consteel® at the SOVEL meltshop. Iron Steel Technol. (2), 38–46 (2009)
Pretorius, E.B., Carlisle, R.C.: Foamy slag fundamentals and their practical application to electric furnace steelmaking. In: Electric Furnace Conference Proceedings, Iron & Steel Society, New Orleans, LA, vol. 56, pp. 275–292 (1998)
Maes, R.: Celox® for on-line process control in modern steelmaking, (brochure), Heraeus Electro-Nite (2012)
Continuum/Anaconda Homepage. https://www.anaconda.com/. Accessed Sept 2017
McKinney, W.: pandas: a foundational Python library for data analysis and statistics. http://pandas.pydata.org/. Accessed 20 Aug 2018
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: 31st Conference on Neural Information Processing Systems (NIPS 2017), Advances in Neural Information Processing Systems 30, Microsoft Research (2017)
Chollet, F.: Deep Learning with Python. Manning Publications, Shelter Island (2018)
Brownlee, J.: Develop Deep Learning Models on Theano and TensorFlow Using Keras. Deep Learning with Python. Jason Brownlee, Melbourne (2018)
TensorFlow. https://www.tensorflow.org/. Accessed Aug 2018
Sismanis, P.: Analysis of rolled-plates’ mechanical properties with a machine-learning software. In: AISTech 2018 Proceedings, Philadelphia, PA, AIST, pp. 2273–2286 (2018)
Zumel, N., Mount, J.: Practical Data Science with R. Manning Publications, Shelter Island (2014)
Avila, J., Hauck, T.: scikit-learn Cookbook, 2nd edn. Packt Publishing, Birmingham (2017)
Garreta, R., Moncecchi, G., Hauck, T., Hackeling, G.: scikit-learn: Machine Learning Simplified. Packt Publishing, Birmingham (2017)
Acknowledgment
The author is grateful to the top management for the continuous support on this type of studies.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sismanis, P. (2019). Prediction of Productivity and Energy Consumption in a Consteel Furnace Using Data-Science Models. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 353. Springer, Cham. https://doi.org/10.1007/978-3-030-20485-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-20485-3_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20484-6
Online ISBN: 978-3-030-20485-3
eBook Packages: Computer ScienceComputer Science (R0)