变压吸附
烟气
生物量(生态学)
燃烧
环境科学
废物管理
碳捕获和储存(时间表)
空气分离
碳纤维
工艺工程
吸附
化学
工程类
材料科学
气候变化
地质学
复合材料
海洋学
有机化学
复合数
氧气
生物
生态学
作者
Mauro Luberti,Gabriel Oreggioni,Hyungwoong Ahn
标识
DOI:10.1016/j.jece.2017.07.029
摘要
It was aimed to design a novel RVPSA (Rapid Vacuum Pressure Swing Adsorption) unit for CO2 concentration and recovery in order to achieve the aggressive CO2 capture target, i.e. 95+% CO2 purity and 90+% CO2 recovery at the same time, applied to an existing 10 MWth biomass-fuelled CHP plant. Biomass-fuelled CHP plants are deemed carbon-neutral on the grounds of the net CO2 addition to the atmosphere as a result of its operation being practically zero, ignoring the CO2 emissions involved in the ancillary processes, such as soil enhancement, biomass transport and processing, etc. Furthermore, integrating the biomass-fuelled CHP plant with carbon capture, transport and storage enables carbon-negative energy generation, as its net effect is to recover some CO2 in the air and then store it underground through this plant operation. By the way, a RVPSA process features more efficient utilisation of the adsorbents in the column, leading to much higher bed productivity than a conventional adsorption process. Such a high bed productivity makes it easier to scale up this adsorption process for its application to industrial post-combustion capture. A two-stage, two-bed RVPSA unit was designed and simulated to capture CO2 from the biomass-fuelled CHP plant flue gas containing 13.3% CO2 mole fraction. Effects of operating conditions such as the Purge-to-Feed ratio (P/F) and desorption pressure on the specific power consumption were investigated in detail. It was found that the integrated two-stage RVPSA unit was capable of achieving the following overall performances: CO2 recovery of 90.9%, CO2 purity of 95.0%, bed productivity of 21.2 molCO2/kg/h and power consumption of 822.9 kJ/kgCO2. The productivity of the RVPSA unit designed in this study was 20-30 times higher than those of the conventional CO2 capture VPSA processes.
科研通智能强力驱动
Strongly Powered by AbleSci AI