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Process intensification of non-uniform additive pattern for coal slime drying by microwave heating

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  • Wang, Guangyu
  • Meng, Jinghui
  • Zhang, Kai

Abstract

Microwave drying is an advanced technology that can improve the efficiency of coal slime drying. Additives can effectively increase the microwave drying rate, but with the risk of over-heating. For Blank pattern (no additives) and Uniform pattern (additives added uniformly), this work firstly systematically discusses the additive addition strategies based on a detailed analysis of the additive strengthening mechanism through an established multiphase porous media model. The additive strengthening mechanism is revealed through a detailed study of temperature, vapor, and pressure distribution for Uniform pattern, finding that the evaporation rate of liquid water, migration of liquid water and vapor play important roles in the uniform addition scheme. Considering Uniform pattern would deteriorate the temperature uniformity of coal slime sample, three non-uniform additives addition designs (Mixed pattern, Gradient pattern and Edge pattern) are proposed based on the above analysis. The simulation results show that, due to the strengthening absorption efficiency of microwave for the edge region of coal slime, design with additives accumulating nearby the edge of coal slime (Edge pattern) can increase the drying rate, and improve temperature uniformity effectively, which is the recommended optimal design of additives. The findings of this paper can contribute towards enhancing the efficiency of microwave drying processes for coal slime and facilitating its industrial application.

Suggested Citation

  • Wang, Guangyu & Meng, Jinghui & Zhang, Kai, 2024. "Process intensification of non-uniform additive pattern for coal slime drying by microwave heating," Energy, Elsevier, vol. 302(C).
    • Handle: RePEc:eee:energy:v:302:y:2024:i:c:s0360544224016323
    DOI: 10.1016/j.energy.2024.131859
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    References listed on IDEAS

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    1. Ni, Zhanshi & Bi, Haobo & Jiang, Chunlong & Sun, Hao & Zhou, Wenliang & Qiu, Zhicong & He, Liqun & Lin, Qizhao, 2022. "Research on the co-pyrolysis of coal slime and lignin based on the combination of TG-FTIR, artificial neural network, and principal component analysis," Energy, Elsevier, vol. 261(PA).
    2. Jiang, Chunlong & Zhou, Wenliang & Bi, Haobo & Ni, Zhanshi & Sun, Hao & Lin, Qizhao, 2022. "Co-pyrolysis of coal slime and cattle manure by TG–FTIR–MS and artificial neural network modeling: Pyrolysis behavior, kinetics, gas emission characteristics," Energy, Elsevier, vol. 247(C).
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