THE USE OF ARTIFICIAL NEURAL NETWORKS IN THE CONTROL OF ELECTRIC ARC FURNACES

Year 2022, , 578 - 591, 31.12.2022

Edip Yıldız , Ersin Özdemir

https://doi.org/10.46519/ij3dptdi.1206747

Abstract

Today, control systems have become an important branch of science in parallel with the increase of production and quality needs. There are purpose-specific automatic control systems and algorithms controlling them for production in industrial facilities.
In this study, modeling electric arc furnace scrap melting plant, which has an essential place in the iron-steel industry has been made using artificial neural networks. The facility where the study is carried out is in active production and controlled by classical algorithms. Artificial neural networks were trained using the data taken over the current control system and pressure sensors attached to the electrodes and the modeling and control of the arc furnace with the trained network was carried out.
The software developed with an artificial neural network to control the electrodes used in electric arc furnaces provided 98% success in monitoring the system including the operator’s intervention out of the algorithm. All input and output data of an active production facility were copied to the network with the developed software. Since this software does not require various calculations, calibrations and parameter changes, it responds faster than the classical control algorithm used in the factory.

Keywords

Electric Arc Furnace, Artificial Neural Networks, Steel Production

References

• 1. Çamdalı, Ü. & Tunç, M., “Elektrik Ark Fırınında Fiziksel Ekserji Potansiyelinin ve Veriminin Elde Edilmesi”, Trakya Üniversitesi Fen Bilimleri Dergisi, Vol. 5, Issue 1, Pages 53-61, 2016.
• 2. Öztemel, E., “Yapay Sinir Ağları”, (İkinci Baskı). İstanbul: Papatya Yayıncılık, 2006.
• 3. Mat Daut, M. A., Hassan, M. Y., Abdullah, H., Rahman, H. A., Abdullah, M. P., and Hussin, F., “Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods”, Renewable and Sustainable Energy Reviews, 2017.
• 4. Turgut, A. , Temir, A. , Aksoy, B. & Özsoy, K., “Yapay Zekâ Yöntemleri ile Hava Sıcaklığı Tahmini için Sistem Tasarimi ve Uygulaması”, International Journal of 3D Printing Technologies and Digital Industry, Vol. 3, Issue 3, Pages 244-253, 2019.
• 5. Hussain, M. A., Hassan, C. R. C., Loh, K. S., Mah, K. W., “Application of Artificial Intelligence Techniques in Process Fault Diagnosis”, Journal of Engineering Science and Technology, Vol. 2, Issue 3, Pages 260–270, 2007.
• 6. Öztürk, E., Ulu, A. & Çavdar, T., “Creating an Optimal Ad Hoc Network in Internet of Vehicles with Artificial Neural Networks”, International Journal of 3D Printing Technologies and Digital Industry, Vol. 3, Issue 3, Pages 261-268, 2019.
• 7. Mahajan, V., Agarwal, P., and Om Gupta, H., “dSPACE implementation of cascaded H-bridge inverter for harmonics minimization using artificial-intelligence”, COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 33, Issue 6, Pages 2053-2081, 2014.
• 8. Nandi, S., Toliyat, H. A., and Li, X., “Condition monitoring and fault diagnosis of electrical motors”, A review. IEEE transactions on energy conversion, Vol. 20, Issue 4, Pages 719-729, 2005.
• 9. Hauksdóttir, A. S., Soderstrom, T., Thorfinnsson, Y. P., and Gestsson, A., “System identification of a three-phase submerged-arc ferrosilicon furnace”, IEEE Transactions on Control Systems Technology, Vol. 3, Issue 4, Pages 377-387, 1995.
• 10. Duan J., Li F., “Transient heat transfer analysis of phase change material melting in metal foam by experimental study and artificial neural network”, Journal of Energy Storage, Vol. 33, Pages 102-160, 2021.
• 11. Manojlović V., Kamberović Ž., Korać M., Dotlić M., “Machine learning analysis of electric arc furnace process for the evaluation of energy efficiency parameters”, Applied Energy, Vol. 307, Pages 118-209, 2022.
• 12. Kim S. -W., Cho B., Shin S., Oh J. -H., Hwangbo J. and Park H. -W., “Force Control of a Hydraulic Actuator With a Neural Network Inverse Model”, in IEEE Robotics and Automation Letters, Vol. 6, Issue 2, Pages 2814-2821, April 2021
• 13. Vinayaka, K.U., Puttaswamy, P.S. “Prediction of Arc Voltage of Electric Arc Furnace Based on Improved Back Propagation Neural Network”, Vol. 2, Page 167, 2021.
• 14. Andersen, K., Cook, G. E., Karsai, G., and Ramaswamy, K., “Artificial neural networks applied to arc welding process modeling and control”, IEEE Transactions on Industry Applications, Vol. 26, Issue 5, Pages 824–830, 1990.
• 15. Hong, Z., Sheng, Y., Li, J., Kasuga, M., and Zhao, L. “Development of AC electric arc-furnace control system based on fuzzy neural network”, International Conference on Mechatronics and Automation, Pages. 2459-2464. IEEE, June 2006.
• 16. Sadeghian, A. R., and Lavers, J. D., ”Application of feedforward neuro-fuzzy networks for current prediction in electric arc furnaces”, In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, Vol. 4, Pages 420-425. IEEE, 2000.
• 17. Wang, F., Jin, Z., and Zhu, Z., “Modeling and prediction of electric arc furnace based on neural network and chaos theory”, In International Symposium on Neural Networks, Pages 819-826. Springer, Berlin, Heidelberg, May 2005.
• 18. Sheppard C. P., Gent C. R. and Ward R. M., “A Neural Network based Furnace Control System”, American Control Conference, Pages 500-504, 1992.
• 19. Hui, Z., Wang X. and Wang, X., “Prediction Model of Arc Furnace Based on Improved BP Neural Network”, International Conference on Environmental Science and Information Application Technology, Pages 664-669, 2009.
• 20. Paranchuk, Y. S., and Paranchuk, R. Y., “Neural network system for continuous voltage monitoring in electric arc furnace”, Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, Vol. 2, Pages 74–80, 2016.
• 21. Staib, W. E., and Staib, R. B., “The intelligent arc furnace controller: a neural network electrode position optimization system for the electric arc furnace”. In [Proceedings 1992] IJCNN International Joint Conference on Neural Networks, IEEE, Vol. 3, Pages 1-9, June 1992.
• 22. King, P. E., and Nyman, M. D. “Modeling and control of an electric arc furnace using a feedforward artificial neural network”, Journal of Applied Physics, Vol. 80, Issue 3, Pages 1872–1877, 1996.
• 23. Garcia-Segura, R., Castillo, J. V., Martell-Chavez, F., Longoria-Gandara, O., and Aguilar, J. O., “Electric Arc furnace modeling with artificial neural networks and Arc length with variable voltage gradient”. Energies, Vol. 10, Issue 9, 2017.