Optimization of coal size for beneficiation efficiency promotion in gas–solid fluidized bed

With China already committed to peak carbon dioxide emissions before 2030 and aiming at achieving carbon neutrality before 2060, coal separation has recently become a hot research topic, in particular, gas–solid fluidized separation technology has attracted significant research attention. Coal particles with size of +10 mm can be separated efficiently via currently used technologies; nonetheless, specific scientific problems of fine coal separation still need to be systematically explored. In this study, combined with the properties of coal particles, a density model of fine coal in fluidized bed separation process was proposed. The separation effect was found to be better with the increase of feeding coal particle size, in particular, the coal particles with size larger than 5 mm underwent effective separation. The results reveal that the drag force of the fine coal separation process is an important factor restricting the separation efficiency. Fluidization theories, such as pressure drop, gas–solid two-phase model, and particle sedimentation theory could explain that with the decrease in the feeding coal particle size from 10 mm, the probable error E value increases from 0.105 to 0.355 g/cm3, the misplaced material content M increases from 5.48% to 16.13%, and the lower limit of the separation is controlled at 5 mm. This helps to enhance the processing capacity and increase the separation granularity range of the fluidized bed.中国已承诺于2030年前实现二氧化碳排放峰值，并力争2060年前达成碳中和目标，由此煤炭分选技术近期成为研究热点，其中气固流态化分选技术（gas–solid fluidized separation technology）备受学界广泛关注。当前主流分选技术可高效分选+10mm级煤炭颗粒，但细粒级煤炭分选仍存在诸多科学问题亟待系统探究。本研究结合煤炭颗粒自身特性，提出了流态化床分选过程中细粒煤的密度模型。研究结果表明，给料煤颗粒粒径越大，分选效果越佳，其中粒径大于5mm的煤炭颗粒可实现有效分选。进一步分析显示，细粒煤分选过程中的曳力（drag force）是制约分选效率的关键因素。流态化相关理论——包括压降（pressure drop）、气固两相模型（gas–solid two-phase model）、颗粒沉降理论（particle sedimentation theory）——可解释如下规律：当给料煤颗粒粒径从10mm逐步减小时，分选可能误差E（probable error E）值从0.105 g/cm³升至0.355 g/cm³，错配物含量M（misplaced material content M）从5.48%提升至16.13%，且分选下限被控制在5mm。该研究成果有助于提升流态化分选系统的处理能力并拓宽其分选粒度范围。