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Spirin, N., Onorin, O., Gurin, I., Lazic, L., & Istomin, A. (2018). Logical-mathematical Evaluation Model of Blast-furnace Melting Operation. KnE Engineering, 3(5), 109–117. https://doi.org/10.18502/keg.v3i5.2659

https://doi.org/10.18502/keg.v3i5.2659

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authors

  • N Spirin
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  • O Onorin
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  • I Gurin
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  • L Lazic
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  • A Istomin
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Published Date

  • Jul 17, 2018

Logical-mathematical Evaluation Model of Blast-furnace Melting Operation

KnE Engineering / VII All-Russian Scientific and Practical Conference of Students, Graduate Students and Young Scientists (TIM'2018) / Pages 109–117

Abstract

The logical-mathematical evaluation model of blast-furnace melting operation is represented. The model provides an opportunity to evaluate the normal operation mode of blast furnace and further deviations from this mode such as overdeveloped gas flows (peripheral and central), violation of thermal melting conditions (hot and cold course of melt), violation of smooth descent of burned materials in the furnace (tight furnace operation, higher and lower suspension of burden). The functional capabilities of developed software are represented.



Keywords: blast-furnace production, information logical system, software development, blast-furnace melting operation diagnostics

References
[1] Storm, P. (1998). Using expert systems in blast furnace operation—A few preliminary impressions. International Conference of the Manufacturing Value (Chain), vol. 2, pp. 623–630.


[2] Frerichs, D. K. (1992). Artificial-intelligence for supervisory control and operator decision support. Tappi Journal, vol. 75, no. 6, pp. 138–141.


[3] Lida, O. Taniyochi, S., and Hetani, T. (1992). Application of management system and digital intelligence in the blast-furnace production. Application of a techniques to blast furnace operation. Kawasaki Steel Techn. Dept, no. 26, C. 30–37.


[4] Yagi, J. (1991). Recent progress in fundamental and applied researches in blastfurnace ironmaking in Japan. ISIJ International, vol. 31, no. 5, pp. 387–394.


[5] Vapaavuori, E. (1997). Application of expert systems and knowledge based systems to support operation of iron blast furnace. Expert Systems with Applications, vol. 12, no. 3, pp. II.


[6] Matsuzaki, S., Nishimura, T., Shinotake, A., et al. ( July 2006). Development of mathematical model of blast furnace. Nippon Steel Technical Report, no. 94, pp. 87– 95.


[7] Iida, M., Ogura, K., and Hakone, T. (2009). Numerical study on metal/slag drainage rate deviation during blast furnace tapping. ISIJ International, vol. 49, no. 8, pp. 1123– 1132.


[8] Hera, P., Birlan, F., Oprescu, I., et al. (2011). Modeling of metallurgical continuous processes in the blast furnace. U.P.B. Scientific Bulletin, Series B, vol. 73, no. 4, pp. 171–182.


[9] Ueda, S., Natsui, S., Ariyama, T., et al. (2010). Recent progress and future perspective on mathematical modelling of blast furnace. ISIJ International, vol. 50, no. 7, pp. 914– 923.


[10] Bi, X., Li, P., Peng, W., et al. (2013). Study on a MES-based large blast furnace expert system. 3rd International Conference on Mechatronics and Intelligent Materials (MIM 2013). Mechatronics and intelligent materials III, PTS 1-3, vol. 706–707, p. 1971.