Analysis of the factors affecting the iron ore pellet FEO index using data mining technique

Mahdi Nakhaeinejad; Farzaneh  Zarei

doi:10.30656/jsmi.v4i2.2572

Authors

Mahdi Nakhaeinejad Department of Industrial Engineering, Yazd University, Iran
Farzaneh Zarei Department of Industrial Engineering, Science and Arts University, Iran

DOI:

https://doi.org/10.30656/jsmi.v4i2.2572

Keywords:

Data mining, Decision tree, CRISP-DM, Iron pellet, Regression

Abstract

One of the most critical factors affecting iron pellet quality is the reduction in FeO (Iron Oxide) index in the final product. This study aims to predict factors affecting the FeO (Iron Oxide) of iron pellets and find out the contribution of each factor to reduce the pellets FeO (the ideal amount is between 0.4 to 0.6) using data mining techÂniques. When the FeO index's value is in the optimal range, the quality and price of pellets are higher. The data used in this study was collected from the pelletizing plant of Gol-E-Gohar in Sirjan, Iran, and the decision tree and regression algorithms are used in this analysis. Forty-five factors that can affect the FeO (Iron Oxide) index of the final product were studied, showing that the Magnesium Oxide and Airflow of the inlet fan of the indurating machine had the greatest impact on the FeO (Iron Oxide) of iron pellets.

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References

T. Umadevi, N. F. Lobo, S. Desai, P. C. Mahapatra, R. Sah, and M. Prabhu, "Optimization of Firing Temperature for Hematite Pellets," ISIJ Int., vol. 53, no. 9, pp. 1673â€“1682, 2013, doi: 10.2355/isijinternational.53.1673.

M. Ghazanfari, S. Alizadeh, and B. Teymourpour, Data mining and knowledge discovery, 2rd edition. Tehran: Publish center of Iran University of Science & Technology, 2011.

H. Baghchehband and A. A. Niknafs, "Predicting the amount of molybdenum in mines using data mining tools," 1th Iranian student Conference on Information Technology. Iran, 2010. Available: https://civilica.com/doc/88107.

J. Lian, X. . Lai, Z. . Lin, and F. . Yao, "Application of data mining and process knowledge discovery in sheet metal assembly dimensional variation diagnosis," J. Mater. Process. Technol., vol. 129, no. 1â€“3, pp. 315â€“320, Oct. 2002, doi: 10.1016/S0924-0136(02)00691-X.

G. Yang, X. Fan, X. Chen, X. Huang, and X. Li, "Optimization of Cooling Process of Iron Ore Pellets Based on Mathematical Model and Data Mining," J. Iron Steel Res. Int., vol. 22, no. 11, pp. 1002â€“1008, Nov. 2015, doi: 10.1016/S1006-706X(15)30103-5.

J. Ronowicz, M. Thommes, P. Kleinebudde, and J. KrysiÅ„ski, "A data mining approach to optimize pellets manufacturing process based on a decision tree algorithm," Eur. J. Pharm. Sci., vol. 73, pp. 44â€“48, 2015, doi: 10.1016/j.ejps.2015.03.013.

D. Wilk-Kolodziejczyk, K. Regulski, G. Gumienny, B. Kacprzyk, S. Kluska-Nawarecka, and K. Jaskowiec, "Data mining tools in identifying the components of the microstructure of compacted graphite iron based on the content of alloying elements," Int. J. Adv. Manuf. Technol., vol. 95, no. 9, pp. 3127â€“3139, 2018, doi: 10.1007/s00170-017-1430-7.

A. Agrawal and A. Choudhary, "A Fatigue Strength Predictor for Steels Using Ensemble Data Mining: Steel Fatigue Strength Predictor," in Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016, pp. 2497â€“2500, doi: 10.1145/2983323.2983343.

Ä°. B. Akinci and F. Ersoz, "Determination of Production Defects in Iron and Steel Sector by Data Mining," in 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2019, pp. 1â€“10, doi: 10.1109/ISMSIT.2019.8932741.

Z. Ghasemivinche and A. Z. Hamadani, "Predicting Mechanical Properties of Galvanized Steels: Data Mining Approach," Int. J. Adv. Eng. Manag. Sci., vol. 3, no. 7, pp. 724â€“729, 2017, doi: 10.24001/ijaems.3.7.3.

S. M. Sharafi and H. R. Esmaeily, "Application of data mining in predicting steel surface defect," Artif. Intell. Appl., vol. 20, no. 2, pp. 87â€“92, 2010. Available: http://www.jatit.org/volumes/research-papers/Vol20No2/4Vol20No2.pdf.

F. A. Cuzzola and C. Aurora, "A Data-Mining Technology for Tuning of Rolling Prediction Models: Theory and Application," in International Conference on Computational Science and Its Applications, Springer, 2017, pp. 633â€“647. doi: 10.1007/978-3-319-62392-4_46.

A. Bustillo, L. N. LÃ³pez de Lacalle, A. FernÃ¡ndez-Valdivielso, and P. Santos, â€œData-mining modeling for the prediction of wear on forming-taps in the threading of steel components,â€ J. Comput. Des. Eng., vol. 3, no. 4, pp. 337â€“348, Oct. 2016, doi: 10.1016/j.jcde.2016.06.002.

T. Nkonyana, Y. Sun, B. Twala, and E. Dogo, "Performance Evaluation of Data Mining Techniques in Steel Manufacturing Industry," Procedia Manuf., vol. 35, pp. 623â€“628, 2019, doi: 10.1016/j.promfg.2019.06.004.

D. Wilk-KoÅ‚odziejczyk, B. MrzygÅ‚Ã³d, K. Regulski, I. Olejarczyk-WoÅ¼eÅ„ska, and K. JaÅ›kowiec, â€œInfluence of process parameters on the properties of austempered ductile iron (adi) examined with the use of data mining methods,â€ Metalurgija, vol. 55, no. 4, pp. 849â€“851, 2016. Available: https://hrcak.srce.hr/157481.

K. Regulski, G. Rojek, D. Wilk-KoÅ‚odziejczyk, G. Gumienny, B. Kacprzyk, and B. MrzygÅ‚Ã³d, â€œApproximation of Ausferrite Content in the Compacted Graphite Iron with the Use of Combined Techniques of Data Mining,â€ Arch. Foundry Eng., vol. 17, no. 3, pp. 117â€“122, 2017. Available: http://journals.pan.pl/dlibra/show-content?id=102059&.

H. Zheng et al., "Data mining of iron(II) and iron(III) bond-valence parameters, and their relevance for macromolecular crystallography," Acta Crystallogr. Sect. D Struct. Biol., vol. 73, no. 4, pp. 316â€“325, Apr. 2017, doi: 10.1107/S2059798317000584.

D. Wedge, A. Lewan, M. Paine, E.-J. Holden, and T. Green, "A Data Mining Approach to Validating Drill Hole Logging Data in Pilbara Iron Ore Exploration," Econ. Geol., vol. 113, no. 4, pp. 961â€“972, Jun. 2018, doi: 10.5382/econgeo.2018.4578.

A. Berson, S. Smith, and K. Thearling, Building data mining applications for CRM. McGraw-Hill Professional, 1999. Available: https://dl.acm.org/doi/book/10.5555/555454.

H. Wiemer, L. Drowatzky, and S. Ihlenfeldt, "Data Mining Methodology for Engineering Applications (DMME)â€”A Holistic Extension to the CRISP-DM Model," Appl. Sci., vol. 9, no. 12, pp. 1â€“18, Jun. 2019, doi: 10.3390/app9122407.