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Evaluation on a combined model for low-rank coal pyrolysis

机译:低阶煤热解联合模型的评价

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Pyrolysis is an initial step of the upgrading lignite that exhibits a structurally complex connection between physicochemical changes and unknown pyrolyzed compounds, which complicates process simulation for downstream processing. Combined the functional group-depolymerization vaporization cross-linking (FG-DVC) model with non-linear programming (NIP) theory would link between coal pyrolysis and process simulation. First, we adjust the range of the van Krevelen diagram and predict the char and volatiles yields from coal pyrolysis using the FG-DVC model. The tar ultimate analysis is then estimated based on mass/element conservation, and the tar group composition is calculated using the NLP model on the basis of the total tar yield and ultimate analysis. Upon completion of these steps, the process simulation and energy consumption distribution of coal pyrolysis is carried out using Aspen Plus. Results show that the FG-DVC model with the adjusted van Krevelen diagram can accurately predict coal pyrolysis products with better performance than that obtained using empirical correlations. Results show that the energy consumption of drying coal was the largest with 653.2 MJ when drying 1000 kg of coal, followed by pyrolysis with 482.2 MJ. The combined coal pyrolysis model, being independent on experiments, can be used for process design. (C) 2018 Elsevier Ltd. All rights reserved.
机译:热解是提质褐煤的第一步,褐煤在物理化学变化和未知的热解化合物之间表现出结构上复杂的联系,这使下游加工的过程模拟变得复杂。将官能团解聚汽化交联(FG-DVC)模型与非线性编程(NIP)理论相结合,可以在煤热解和过程模拟之间建立联系。首先,我们调整van Krevelen图的范围,并使用FG-DVC模型预测煤热解过程中的焦炭和挥发物产率。然后基于质量/元素守恒估计焦油最终分析,并基于总焦油产量和最终分析,使用NLP模型计算焦油基团组成。完成这些步骤后,使用Aspen Plus进行了煤热解过程的模拟和能耗分配。结果表明,带有调整后的van Krevelen图的FG-DVC模型可以准确预测煤热解产物,其性能要优于使用经验相关性得到的煤热解产物。结果表明,干燥1000 kg煤时,干燥煤的能耗最大,为653.2 MJ,其次是482.2 MJ的热解。独立于实验的组合煤热解模型可用于工艺设计。 (C)2018 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Energy》 |2019年第15期|1012-1021|共10页
  • 作者

    Yi Lan; Feng Jie; Li Wen-Ying;

  • 作者单位

    Zhejiang Univ, State Key Lab Clean Energy Utilizat, Hangzhou 310027, Zhejiang, Peoples R China|Taiyuan Univ Technol, Training Base State Key Lab Coal Sci & Technol Jo, Taiyuan 030024, Shanxi, Peoples R China|Taiyuan Univ Technol, Minist Sci & Technol, Taiyuan 030024, Shanxi, Peoples R China;

    Taiyuan Univ Technol, Training Base State Key Lab Coal Sci & Technol Jo, Taiyuan 030024, Shanxi, Peoples R China|Taiyuan Univ Technol, Minist Sci & Technol, Taiyuan 030024, Shanxi, Peoples R China;

    Zhejiang Univ, State Key Lab Clean Energy Utilizat, Hangzhou 310027, Zhejiang, Peoples R China|Taiyuan Univ Technol, Training Base State Key Lab Coal Sci & Technol Jo, Taiyuan 030024, Shanxi, Peoples R China|Taiyuan Univ Technol, Minist Sci & Technol, Taiyuan 030024, Shanxi, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Low rank coal pyrolysis; Product prediction; Tar group composition; Evaluation; Process design;

    机译:低阶煤热解;产品预测;塔尔族组成;评估;工艺设计;

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