Abstract
On the basis of a mathematical model, a linear relation is established between the coke characteristics determined in drum tests. Specifically, the relation between the abradability M10 and crushability M25 is as follows: M10 = 100 – KM25. Analytical methods show that K = 1.00–1.11. As an example, applications of the model are presented, and K is calculated for Russian and Spanish data. The basic model may also be applied to other measures of coke’s mechanical strength, as illustrated for the example of the Irsid indices I10 and I20. Ordinary regression in the analysis of statistical data for M25 and M10 proves less informative than the use of the relationship M10 = 100 – KM25. An approach is suggested for finding the physical basis of the linear relation between M10 and M25.
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REFERENCES
Nyathi, M.S., Kruse, R., Mastalerz, M., and Bish, D.L., Nature and origin of coke quality variation in heat-recovery coke making technology, Fuel, 2016, vol. 176, pp. 11–19. https://doi.org/10.1016/j.fuel.2016.02.050
Nyathi, M.S., Kruse, R., Mastalerz, M., and Bish, D.L., Investigation of coke quality variation between heat-recovery and byproduct cokemaking technology, Energy Fuels, 2017, vol. 27, pp. 7876–7884. https://doi.org/10.1021/acs.energyfuels.6b02817
Gupta, S., Ye, Z., Kanniala, R., Kerkkonen, O., and Sahajwalla, V., Coke graphitization and degradation across the tuyere regions in a blast furnace, Fuel, 2013, vol. 113, pp. 77–85. https://doi.org/10.1016/j.fuel.2013.05.074
Pusz, S. and Buszko, R., Reflectance parameters of cokes in relation to their reactivity index (CRI) and the strength after reaction (CSR), from coals of the Upper Silesian Coal Basin, Poland, Int. J. Coal Geol., 2012, vols. 90–91, pp. 43–49. https://doi.org/10.1016/j.coal.2011.10.008
Sakurovs, R., Koval, L., Grigore, M., et al., Nanostructure of cokes, Int. J. Coal Geol., 2018, vol. 188, pp. 112–120. https://doi.org/10.1016/j.coal.2018.02.006
Flores, B.D., Borrego, A.G., Díez, M.A., et al., How coke optical texture became a relevant tool for understanding coal blending and coke quality, Fuel Process. Technol., 2017, vol. 164, pp. 13–23. https://doi.org/10.1016/j.fuproc.2017.04.015
Numazawa, Y., Saito, Y., Matsushita, Y., and Aoki, H., Large-scale simulation of gasification reaction with mass transfer for metallurgical coke: model development, Fuel, 2020, vol. 266, p. 117080. https://doi.org/10.1016/j.fuel.2020.117080
Huang, J., Guo, R., Wang, Q., et al., Coke solution-loss degradation model with non-equimolar diffusion and changing local pore structure, Fuel, 2020, vol. 263, p. 116694. https://doi.org/10.1016/j.fuel.2019.116694
Chen, Y., Lee, S., Tahmasebi, A., et al., A review of the state-of-the-art research on carbon structure evolution during the coking process: from plastic layer chemistry to 3D carbon structure establishment, Fuel, 2020, vol. 271, p. 117657. https://doi.org/10.1016/j.fuel.2020.117657
Díez, M.A., Alvarez, R., and Barriocanal, C., Coal for metallurgical coke production: predictions of coke quality and future requirements for cokemaking, Int. J. Coal Geol., 2002, vol. 50, nos. 1–4, pp. 389–412. https://doi.org/10.1016/S0166-5162(02)00123-4
North, L., Blackmore, K., Nesbitt, K., and Mahoney, M.R., Methods of coke quality prediction: a review, Fuel, 2018, vol. 219, pp. 426–445. https://doi.org/10.1016/j.fuel.2018.01.090
North, L., Blackmore, K., Nesbitt, K., and Mahoney, M.R., Models of coke quality prediction and the relationships to input variables: a review, Fuel, 2018, vol. 219, pp. 446–466. https://doi.org/10.1016/j.fuel.2018.01.062
Smędowski, L., Krzesińska, M., Kwasny, W., and Kozanecki, M., Development of ordered structures in the high temperature (HT) cokes from binary and ternary coal blends studied by means of X-ray diffraction and Raman spectroscopy, Energy Fuels, 2011, vol. 25, no. 7, pp. 3142–3149. https://doi.org/10.1021/ef200609t
Koval, L. and Sakurovs, R., Variability of metallurgical coke reactivity under the NSC test conditions, Fuel, 2019, vol. 241, pp. 519–521. https://doi.org/10.1016/j.fuel.2018.12.053
Wang, Q., Guo, R., Zhao, X., et al., A new testing and evaluating method of cokes with greatly varied CRI and CSR, Fuel, 2016, vol. 182, pp. 879–885. https://doi.org/10.1016/j.fuel.2016.05.101
Smirnov, A.N., Petukhov, V.N., and Alekseev, D.I., Classification of models for predicting coke quality (M25 and M10), Coke Chem., 2015, vol. 58, no. 5, pp. 170–174. https://doi.org/10.3103/S1068364X15050087
Lipatnikov, A.V., Shmeleva, A.E., Stepanov, E.N., and Shnaider, D.A., Mathematical modeling and optimization of raw coal consumption in PJSC MMK, Vestn. Magnitogorsk. Gos. Tekh. Univ. im. G.I. Nosova, 2018, vol. 16, no. 4, pp. 30–38. https://doi.org/10.18503/1995-2732-2018-16-3-30-38
Maksimenko, I.I., Nagornyi, Yu.S., Glushchenko, I.M., and Ivanchenko, V.A., Influence of technological factors of coking on coke strength parameters, Koks Khim., 1978, no. 8, pp. 12–14.
Alvarez, R.A, Díez, M.A., Barriocanal, C., et al., An approach to blast furnace coke quality prediction, Fuel, 2007, vol. 86, pp. 2159–2166. https://doi.org/10.1016/j.fuel.2006.11.026
Stankevich, A.S., Smelyanskii, A.Z., Berkutov, N.A., et al., Rational distribution of coals and optimization of charge materials for coking, Koks Khim., 2003, no. 9, pp. 8–16.
Stankevich, A.S., Gilyazetdinov, R.R., Popova, N.K., and Koshkarov, D.A., Predicting CSR and CRI of coke on the basis of the chemical and petrographic parameters of the coal batch and the coking conditions, Coke Chem., 2008, vol. 51, no. 9, pp. 357–363.
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Alekseev, D.I., Smirnov, A.N. & Stepanov, E.N. Establishing a Linear Relation between the Strength Characteristics M25 and M10 of Coke. Coke Chem. 64, 542–551 (2021). https://doi.org/10.3103/S1068364X21120024
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DOI: https://doi.org/10.3103/S1068364X21120024