Citation: QIN Zhi-hong, BU Liang-hui, LI Xiang. Prediction model for coke quality and mechanism based on coking coal composition and structure parameters[J]. Journal of Fuel Chemistry and Technology, ;2018, 46(12): 1409-1422. shu

Prediction model for coke quality and mechanism based on coking coal composition and structure parameters

China University of Mining and Technology, Xuzhou 221116, China

Corresponding author: QIN Zhi-hong, qinzh1210@163.com
Received Date: 12 September 2018
Revised Date: 16 October 2018

Fund Project: the National Natural Science Foundation of China 51674260the Coal Joint Fund from National Natural Science Foundation of China and Shenhua Group Corporation Limited U1361116the National Basic Research Program of China (973 program) 2012CB214900The project was supported by the Coal Joint Fund from National Natural Science Foundation of China and Shenhua Group Corporation Limited (U1361116), the National Natural Science Foundation of China (51674260) and the National Basic Research Program of China (973 program, 2012CB214900)

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Five coking coals and 44 groups of blended coals were studied, and the coking experiments with coal cup were completed using a 40 kg small coke oven. According to the yields of heavy component, dense medium component and loose medium component (Y_HC, Y_DMC and Y_LMC) obtained by all-component separation as well as the FT-IR parameters of I₃ and I₄ which reflect hydrogen bond association, aliphatic chain length and branched degree, the prediction model for coke quality was established with the BP neural network. Then, the characteristics of the model were discussed and the coking mechanism by the new model was analyzed. The results show that using new defined coal structure parameters to predict coke quality has some advantages. The predicted and measured values of coke formation rate (CR), micro-strength (MSI), reactivity of particulate coke (PRI) and post-reaction strength (PSR) are in good agreement, and the fitting correlation coefficient of y versus x reaches 0.986, 0.982, 0.956 and 0.926, respectively. The prediction results of CR, MSI and PRI by the model are good with the mean variation of nine samples being 0.53%, 1.58% and 1.28%, respectively. However, the prediction result of (PSR) is poor with the mean variation being 12.22%. The results can provide a good foundation for the establishment of a new method for coal blending.
- coal blending for coking,
- coke quality,
- prediction model,
- BP neural network,
- coking mechanism
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