Abstract
Now a day’s an effective process management system is essential for the sustainability of integrated steel plant. Their effective process enhances quality of product and increases the cost efficiency. The nature of the raw material, its mix proportion, size, chemical composition and process parameter plays a very vital role in sinter mineralogy. The main objective of this study is to optimize the sinter plant process parameters to get the best productivity of Sinter Plant. Sinter has a very vital role for the production of hot metal in blast furnace. A huge number of industrial parameters as large as 106 numbers control the productivity of sinter plant in a very complex manner. As such there is not much study on the prediction of sinter yield as function of those parameters combined. Perhaps it is for the first time an attempt has been made to predict the sinter yield by using Artificial Neural Networking (ANN), with large number of industrial data available at Vizag Steel over a fairly long period of time. One of the most important achievement of this paper is that the reduction in the number of parameters using metallurgical knowledge and experience (without using any sophisticated optimization technique). The prediction of sinter yield with this reduced number of parameters is almost as good as that predicted by using the exhaustive number of 106 parameters within the framework of ANN.
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
References
Biswas, A. K. (1981). Principles of blast furnace iron making: Theory and practice (Ch. VI). Australia: Cootha Publ. House.
Umadevi, T., Brahmacharyulu, A., Roy, A. K., Mahapatra, P. C., Prabhu, M., & Ranjan, M. (2011). Influence of fines feed size on microstructure, productivity and quality of iron ore sinter. ISIJ International, 51(6), 922–929. https://doi.org/10.2355/isijinternational.51.922.
Loo, C. E., & Leung, W. (2003). Factors influencing the bonding phase structures of iron ore sinters. ISIJ International, 41(2), 128–135. https://doi.org/10.2355/isijinternational.43.1393.
Panigraphy, S. C., Jallouli, M., & Rigaud, M. (1984). Porosity of sinters and pellets and its relationship with some of their properties. In Ironmaking Proceedings (Vol. 43, pp. 233–240), AIME, Chicago, Illinois, USA.
Ishikawa, Y., Shimomura, Y., Sasaki, M., Hida, Y., & Hideo, T. (1983). Improvement of sinter quality based on the mineralogical properties of ores. In Ironmaking Proceedings (Vol. 42, pp. 17–29), Atlanta, GA, USA.
Hida, Y., Sasaki, M., Shimomura, Y., Haruna, S., & Soma, H. (1983). Basic factors for ensuring high quality of sintered ore (pp. 60–74). Australia: BHP Central Research Laboratories.
Loo, C. E. (2005). A Perspective of geothitic ore sintering fundamentals. ISIJ International, 45, 436–448. https://doi.org/10.2355/isijinternational.45.436.
Tang, W.-D., Xue, X.-X., Yang, S.-T., Zhang, L.-H., & Huang, Z. (2018). Influence of basicity and temperature on bonding phase strength, microstructure, and mineralogy of high-chromium vanadium–titanium magnetite. International Journal of Minerals, Metallurgy, and Materials, 25, 871–880. https://doi.org/10.1007/s12613-018-1636-1.
Bhagat, R. P. (1999). Factors affecting return sinter fines regimed and strand productivity in iron ore sintering. ISIJ International, 39, 889–895. https://doi.org/10.2355/isijinternational.39.889.
Brimacombe, J. K. (1985). Role of mathematical modelling in metallurgical engineering. In International Conference on Progress in Metallurgical Research, Fundamental and Applied Aspect, Feb 11–15, IIT Kanpur.
Higuchi, K., Kawaguchi, T., Kobayashi, M., Toda, Y., Tsubone, Y., Ito, Y., & Furusho, S. (2006). High-productivity operation of commercial sintering machine by stand-support sintering. Nippon Steel Technical Report No. 94.
Shalmabeebi, A., Saranya, D., & Sathya, T. (2015). A study on neural networks. International Journal of Innovative Research in Computer and Communication Engineering, 3(12), 12890–12895. https://doi.org/10.15680/IJIRCCE.2015.0312055.
Fan, X.-H., Lib, Y., & Chen, X.-L. (2012). Prediction of iron ore sintering characters on the basis of regression analysis and artificial neural network. Energy Procedia, 16, 769–776. https://doi.org/10.1016/j.egypro.2012.01.124.
Bhadeshia, H. K. D. H. (1999). Neural networks in materials science. ISIJ International, 39(10), 966–979. https://doi.org/10.2355/isijinternational.39.966.
Mehta, A. J., Mehta, H. A., Manjunath, T. C., & Ardi, C. (2007). A multi-layer artificial neural network architecture design for load forecasting in power systems. International Journal of Applied Mathematics and Computer Science, 10(4), 227–240.
Stevenson, R. L. (1981). Power system analysis. Singapore: Mc. Graw Hill.
Acknowledgements
The authors would like acknowledge the support of RINL Management for completing this paper successfully which otherwise would not have been possible. The authors also acknowledge the support of Kazi Nazrul University, Asansol and Jadavpur University, Kolkata.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Majumder, A., Biswas, C., Dhar, S., Dey, R., Das, G.C. (2021). Use of Artificial Neural Network to Predict the Yield of Sinter Plant as a Function of Production Parameters. In: Favorskaya, M.N., Peng, SL., Simic, M., Alhadidi, B., Pal, S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2020. Learning and Analytics in Intelligent Systems, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-030-65407-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-65407-8_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65406-1
Online ISBN: 978-3-030-65407-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)