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Analysis on Coking Quality of Coal for COREX and Prediction Model

  • XU Da-an WU Keng WANG Ning ZHU LI MEN Zheng-chao ZHANG Li CHAO Chang-yao
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  • State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing State Key Laboratory for Advanced Metallurgy, University of Science and Technology Beijin State Key Laboratory for Advanced Metallurgy, University of Science and Technology Beijin State Key Laboratory for Advanced Metallurgy, University of Science and Technology Beijin State Key Laboratory for Advanced Metallurgy, University of Science and Technology Beijin State Key Laboratory for Advanced Metallurgy, University of Science and Technology Beijin State Key Laboratory for Advanced Metallurgy, University of Science and Technology Beijin

Received date: 2016-03-24

  Revised date: 1900-01-01

  Online published: 2016-12-22

Abstract

The two coals used in COREX-C3000 process were studied. Pulverization, cold strength, hot performance, optical textures and microstructure of coal chars A and B produced by roasting were measured and analyzed. The results showed that the pulverization rate of coal char A declined from 32% to 11% while that of B rose from 12% to 22% with the increase of roasting time. The cold strength of coal chars A and B changed suddenly in about 6 h, indicating that the coking process was finished in 6 h. The mosaic texture content of coal char A was higher than that of B with over 4%, so its function as skeleton in COREX instead of coke would be better than coal char B. The fusain content of coal char B was much higher than that of A with over 25%, which high lighted its function as heat source and reductant. The relationship between optical texture and hot performance of coal char could be obtained by multiple linear regression for establishment of a reliable prediction model of coal char quality.

Cite this article

XU Da-an WU Keng WANG Ning ZHU LI MEN Zheng-chao ZHANG Li CHAO Chang-yao . Analysis on Coking Quality of Coal for COREX and Prediction Model[J]. The Chinese Journal of Process Engineering, 2016 , 16(2) : 252 -258 . DOI: 10.12034/j.issn.1009-606X.216023

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