基于炼焦煤族组成和结构参数的焦炭质量预测模型及其成焦机理

以五种炼焦煤和44组配合煤为研究对象,在40 kg小焦炉环境下完成煤杯炼焦实验,以煤全组分分离所获得的煤重质组、密中质组和疏中质组收率Y_(HC)、Y_(DMC)、Y_(LMC)及反映煤中氢键缔合情况和脂肪链长短或支链化程度的红外光谱参数I3、I4为主要指标,通过BP神经网络分析方法建立了焦炭质量预测模型,并讨论了模型的特点,分析了新模型下的成焦机理。结果表明,使用新的煤组成结构参数预测焦炭质量具有一定优势,成焦率(CR)、显微强度(MSI)、粒焦反应性(PRI)和反应后强度(PSR)的预测值和实测值有较好一致性,对y=x的拟合相关系数分别达到0.986、0.982、0.956和0.926。模型对CR、MSI和PRI的预测效果较好,九个预测样本的平均偏差分别为0.53%、1.58%和1.28%;但对反应后强度PSR预测效果较差,平均偏差在12.22%。研究结果为建立炼焦配煤新方法提供了良好基础。

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

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.