贺琼琼, 李欣源, 苗真勇, 韩超, 王国强, 徐瑗, 张明亮. 低阶煤活性焦的结构调控及基于机器学习的有机污染物吸附特性评价[J]. 煤炭学报, 2021, 46(S2): 1077-1087.
引用本文: 贺琼琼, 李欣源, 苗真勇, 韩超, 王国强, 徐瑗, 张明亮. 低阶煤活性焦的结构调控及基于机器学习的有机污染物吸附特性评价[J]. 煤炭学报, 2021, 46(S2): 1077-1087.
HE Qiongqiong, LI Xinyuan, MIAO Zhenyong, HAN Chao, WANG Guoqiang, XU Yuan, ZHANG Mingliang. Structure control of activated coke from low-rank coal and evaluation of adsorption characteristics of organic pollutants based on machine learning[J]. Journal of China Coal Society, 2021, 46(S2): 1077-1087.
Citation: HE Qiongqiong, LI Xinyuan, MIAO Zhenyong, HAN Chao, WANG Guoqiang, XU Yuan, ZHANG Mingliang. Structure control of activated coke from low-rank coal and evaluation of adsorption characteristics of organic pollutants based on machine learning[J]. Journal of China Coal Society, 2021, 46(S2): 1077-1087.

低阶煤活性焦的结构调控及基于机器学习的有机污染物吸附特性评价

Structure control of activated coke from low-rank coal and evaluation of adsorption characteristics of organic pollutants based on machine learning

  • 摘要: 有机污染物在多级孔吸附材料上的吸附过程中,微孔结构很大程度上决定了吸附剂的比表 面积,为吸附质提供吸附位点,而介孔和大孔可以为吸附质提供传质通道和吸附空间,因此微孔- 介孔-大孔的多级孔结构优化配置对提高吸附剂的吸附效果至关重要。 以万利长焰煤(WL)与昭 通褐煤(ZT)为原料,制备一系列低阶煤活性焦,通过扫描电镜(SEM)、N2 吸附、傅里叶红外光 谱(FTIR)对不同活性焦的物理化学结构进行表征。 系统地研究了低阶煤的炭化、活化过程对其孔 隙结构和化学结构的影响,并针对性制备多级孔活性半焦,并与选取的有机污染物进行匹配,达到 最佳吸附效果。 最后通过机器学习方法实现对活性焦吸附效果的预测以及各因素重要程度的分 析。 结果表明:活性焦孔结构得到充分发育,孔结构发育程度不同的活性焦对不同分子量的有机物 表现出吸附性能的差异,万利(昭通)活性焦对吲哚、酸性红 A、刚果红、直接耐晒蓝 B2RL 四种有机 污染物的吸附量可达 117.11(135.80),104.24(138.56),239.44(313.94),214.86(183.74)mg/ g。 结 合基于机器学习的主因素分析可知,活性焦在吸附小分子污染物时,比表面积和孔容对吸附量有主 导作用,在吸附大分子污染物时,孔容和平均孔径影响较大。 基于吸附质分子结构和尺寸特征,达 到吸附剂多级孔与吸附质的结构适配,为廉价吸附剂制备提供了新的思路。

     

    Abstract: In the adsorption process of organic pollutants on hierarchical pore adsorbents,the microporous structure largely determines the specific surface area of the adsorbent and provides adsorption sites for adsorbates. Mesopores and macropores can provide mass transfer channels and adsorption space for adsorbates,so the optimized configuration of the micropore⁃mesopore⁃macropore hierarchical pore structure is very important to improve the adsorption effect of the adsorbent. A series of low⁃rank active cokes were prepared from Wanli long⁃flame coal(WL)and Zhaotong lignite(ZT)as raw materials. The physicochemical structures of different active cokes were characterized by scanning electron microscopy(SEM),N2 adsorption and Fourier transform infrared spectroscopy(FTIR). The effect of carboni⁃ zation and activation of low⁃rank coal on its pore structure and chemical structure was systematically studied, and the multi⁃porous activated semi⁃coke was prepared and matched with the selected organic pollutants to achieve the best adsorption effect. Finally,machine learning methods are used to predict the adsorption effect of activated coke and analyze the importance of each factor. The results show that the pore structure of activated coke is fully developed. Ac⁃ tivated coke with the different levels of pore structure development shows some differences in adsorption performance to the organics with different molecular weights. The adsorption capacity of Wanli ( Zhaotong ) activa⁃ ted coke for four organic pollutants,i.e.,indole,acid red A,Congo red and direct light fast blue B2RL,can reach 117.11(135.80),104.24(138.56),239.44(313.94) and 214.86(183.74) mg/ g respectively. With the analysis of main factors based on machine learning,it can be seen that when the activated coke adsorbs small molecule pollu⁃ tants,the specific surface area and pore volume have a dominant effect on the adsorption capacity. When adsorbing large molecule pollutants,the pore volume and average pore size have a greater influence. Based on the molecular structure and size characteristics of the adsorbate,the structure matching of the adsorbent’s hierarchical pores and the adsorbate provides a new idea for the preparation of cheap adsorbents.

     

/

返回文章
返回