1. Study on the thin layer drying and diffusion mechanism of low rank coal in Inner Mongolia and Yunnan
- Author
-
Cheng Wang, Dan Wang, Zengqiang Chen, Chenlong Duan, and Chenyang Zhou
- Subjects
low-quality coal ,thin-layer drying ,drying characteristics ,kinetic analysis ,fitting simulation ,Mining engineering. Metallurgy ,TN1-997 - Abstract
Coal is one of the world's most important energy substances. China is rich in coal resources, accounting for more than 90 % of all ascertained fossil energy reserves. The consumption share of coal energy reaches 56.5 % in 2021. Due to the high moisture content of low-rank coal, it is easy to cause equipment blockage in the dry sorting process. This paper considers low-rank coal coming from Inner Mongolia (NM samples) and Yunnan (YN samples). The weight loss performance of the samples was analyzed using thermogravimetric experiments to determine the appropriate temperature for drying experiments. Thin-layer drying experiments were carried out at different temperature conditions. The drying characteristics of low-rank coal were that the higher the drying temperature, the shorter the drying completion time; the smaller the particle size, the shorter the drying completion time. The effective moisture diffusion coefficient was fitted using the Arrhenius equation. The effective water diffusion coefficient of NM samples was 5.07·10–11 - 9.58·10–11 m2/s. The effective water diffusion coefficients of the three different particle sizes of YN samples were 1.89·10–11 - 4.92·10–11 (–1 mm), 1.38·10–10 - 4.13·10–10 (1-3 mm), 5.26·10–10 - 1.49·10–9 (3-6 mm). The activation energy of Inner Mongolia lignite was 10.97 kJ/mol (–1 mm). The activation energies of Yunnan lignite with different particle sizes were 17.97 kJ/mol (–1 mm), 33.52 kJ/mol (1-3 mm), and 38.64 kJ/mol (3-6 mm). The drying process was simulated using empirical and semi-empirical formulas. The optimal model for Inner Mongolia samples was the Two-term diffusion model, and Yunnan samples were the Hii equation was used.
- Published
- 2024