Research needs targeting direct air capture of carbon dioxide: Material & process performance characteristics under realistic environmental conditions

KOREAN JOURNAL OF CHEMICAL ENGINEERING(2022)

引用 33|浏览0
暂无评分
摘要
The extraction of CO 2 from ambient air, or direct air capture (DAC), is a crucial negative CO 2 emissions technology with great potential for contributing to the mitigation of global warming and climate change. However, nearly all published research on DAC has been conducted under indoor temperature conditions: 20 to 30 °C. In contrast, the future global implementation requires DAC to be operational across a wide expanse of geographical areas, in which the local temperatures can vary between −30 to 50 °C. Similarly, the absolute humidity can vary from ∼0 to 84 g/m 3 in various locations. Due to the massive amount of air that would be processed, it may be impractical to preheat or dehumidify the air before the CO 2 separation. Therefore, it is important to develop DAC materials with good performance at realistic outdoor conditions, especially at sub-ambient conditions: −30 to 20 °C. In addition to material development, system-level studies at sub-ambient conditions are also needed for the DAC processes to reach optimal designs, which may be very different from those at ambient conditions. In this perspective article, we first assess the literature to identify the technical gaps that need to be filled for DAC to be applicable at realistic outdoor conditions. We then suggest additional research directions needed for DAC to be viable under varied conditions from the perspective of materials and system designs. For materials, we discuss the expected physical and chemical property changes for the sorbents when the temperature or humidity reaches extremes within their range, and how that will impact performance. Similarly, for system design, we indicate how varied conditions will impact performance and how these changes will impact process optimization.
更多
查看译文
关键词
Direct Air Capture, CO2 Capture, Sub-ambient, Humidity
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要