烟气
氮氧化物
污染物
环境科学
废物管理
颗粒
燃烧
材料科学
工程类
化学
复合材料
有机化学
作者
Tingyu Zhu,Xindong Wang,Yong Yu,Chao Li,Qun Yao,Yuran Li
标识
DOI:10.1016/j.jes.2022.01.044
摘要
The iron and steel industry is not only an important foundation of the national economy, but also the largest source of industrial air pollution. Due to the current status of emissions in the iron and steel industry, ultra-low pollutant emission control technology has been researched and developed. Liquid-phase proportion control technology has been developed for magnesian fluxed pellets, and a blast furnace smelting demonstration project has been established to use a high proportion of fluxed pellets (80%) for the first time in China to realize source emission reduction of SO2 and NOx. Based on the characteristics of high NOx concentrations and the coexistence of multiple pollutants in coke oven flue gas, low-NOx combustion coupled with multi-pollutant cooperative control technology with activated carbon was developed to achieve efficient removal of multiple pollutants and resource utilization of sulfur. Based on the characteristics of co-existing multiple pollutants in pellet flue gas, selective non-catalytic reduction (SNCR) coupled with ozone oxidation and spray drying adsorption (SDA) was developed, which significantly reduces the operating cost of the system. In the light of the high humidity and high alkalinity in flue gas, filter materials with high humidity resistance and corrosion resistance were manufactured, and an integrated pre-charged bag dust collector device was developed, which realized ultra-low emission of fine particles and reduced filtration resistance and energy consumption in the system. Through source emission reduction, process control and end-treatment technologies, five demonstration projects were built, providing a full set of technical solutions for ultra-low emissions of dust, SO2, NOx, SO3, mercury and other pollutants, and offering technical support for the green development of the iron and steel industry.
科研通智能强力驱动
Strongly Powered by AbleSci AI