基于Bi2WO6/UIO-66异质结催化剂可见光催化氧化干法同时脱硫脱硝的基础研究

Sulfur dioxide (SO2) and nitrogen oxides (NOx) emitted from coal combustion not only damage ecological environment, but also negatively affect agriculture, foresting, and public health. Existing post-combustion flue gas treatment is a complex system featured by high operating cost. Therefore, this project proposes to simultaneously remove SO2 and NOx using dry method based on visible-light driven photocatalytic oxidation. It not only resolves air pollution problem in a cost-effective manner, but also produces value-added fertilizer by-products. It also avoids the treatment of wastewater. The Bi2WO6/UIO-66 heterojunction catalyst will be prepared using hydrothermal approach, and coated onto aluminium oxide support by impregnation method. Such catalyst will be characterized comprehensively by multiple techniques including XRD, SEM, TEM, XPS and UV-Vis DRS. A system for simultaneous desulfurization and denitrification containing a fixed-bed photoreactor and dry cleaner using ammonia will be established. The effects of multiple factors on simultaneous desulfurization and denitrification will be studied systematically to optimize reaction conditions. The quality of fertilizer by-products will be evaluated. In addition, the stability of the prepared catalyst will be examined through reusing the catalyst for several cycles. The mechanism of visible-light driven photocatalytic oxidation of NO and SO2 will be revealed through trapping experiments of free radicals, characterization of surface adsorption, and quantitative analysis of gas-phase products. The kinetics of reactions between ammonia and NO2 as well as between ammonia and SO2 will be investigated. Lastly, this proposed technology will be used to remove NOx and SO2 from real flue gas of a coal-fired domestic boiler to assess its potential for industrial applications.

煤炭燃烧所排放的SO2和NOx不但破坏生态环境，而且危害农业、森林、建筑以及民众健康。现有燃煤烟气处理系统操作复杂，运行费用高。鉴于此，本课题提出基于可见光催化氧化的干法同时脱硫脱硝技术，不仅经济、高效地解决空气污染问题，而且生成化肥副产品，避免废液处理。本项目通过水热法制备Bi2WO6/UIO-66异质结型可见光催化剂，采用浸渍法将其加载至氧化铝载体，运用XRD、SEM、TEM、XPS、UV-Vis DRS等先进手段进行全面的物相表征。建立基于固定床光反应器和氨气干法吸收的同时脱硫脱硝平台，系统地进行试验研究，优化反应条件，评价化肥副产品质量，并通过重复使用试验测试催化剂稳定性。结合自由基捕获试验、催化剂表面吸附表征和气相产物分析，揭示可见光催化氧化NO和SO2的机理。研究氨气吸收NO2和SO2的反应动力学。最后，将本项目技术应用于脱除真实燃煤烟气中的NOx和SO2，评估其工业应用前景。

“减污降碳、协同增效”不仅有助“双碳”目标实现，且保障公众健康安全。作为臭氧和微细颗粒物的主要前驱体，氮氧化物的有效治理已成为空气污染控制的重难点。空气质量管理手段与污染控制技术相辅相成，两者有机结合才能最终解决大气污染顽疾。围绕高效可见光催化降解高浓度氮氧化物和城市环境大气污染物浓度预测机制两个科学问题，在本项目资助下，一方面，利用密度泛函理论（DFT）计算指导开发了一系列可高效降解NO的可见光催化剂，包括GQDs/Bi2WO6 Z型异质结、GQDs/g-C3N4 II型异质结、CNPd单原子催化剂，功能化的MOF光催化剂（UiO-66-NH2）；另一方面，自主开发了时间序列城市大气污染物浓度的人工智能预测方法。..首先，所合成的系列高效可见光催化剂，可以实现ppm级浓度的NO高效降解，与文献中仅能降解ppb级别NO的催化剂相比，转化率提高明显。此外，可针对目标产物进行精准选择性调控；其中，石墨烯量子点基的光催化剂可以高选择性地生成无害的硝酸根，环境友好度更高；CNPd单原子催化剂可以选择性生成可被快速有效吸附的NO2，持续高效降解氮氧化物而不失活，创纪录地让NO转化率在118 h内维持在83%以上；功能化的UiO-66-NH2可将NO快速转化为NO2，并利用自身丰富的空隙进行高效吸附，不排放有毒污染物。该研究方向成果以通讯作者在Chemical Engineering Journal (影响因子16.744)、Carbon （2篇，影响因子 11.307）、JMCA等期刊发表高水平SCI论文6篇。..其次，研发基于深度学习和注意力机制的新型融合模型，用于时间序列数据的预测和图像处理。一方面，可快速、准确预测区域城市群空气质量，为区域联防联控提供重要决策依据。另一方面，可以通过分析浮选泡沫图片，快速测定浮选精煤灰分，推进选煤生产的自动化和智能化管理。最后，将人工智能技术应用于光催化剂禁带宽度的预测，为新型光催化剂的设计与优化提供了新思路和技术手段。该研究方向成果以通讯作者在Environment International、Energy、Computational Materials Science等期刊发表高水平SCI论文3篇。所开发的CAPNet算法和技术，荣获2022年第一届全国煤炭行业矿山AI模型大赛三等奖。