空气分离
膜
渗透
工艺工程
能源消耗
燃烧
变压吸附
膜技术
氧气
蒸馏
化学工程
吸附
化学
材料科学
环境科学
废物管理
色谱法
工程类
有机化学
渗透
生物化学
电气工程
作者
M. Micari,Kumar Varoon Agrawal
标识
DOI:10.1016/j.memsci.2021.119883
摘要
The use of oxygen-enriched air (OEA) is an important strategy for reducing process cost and CO2 emissions in several industrial processes. Membrane-based processes, attributing to their high energy efficiency, are promising alternatives to cryogenic distillation and pressure-swing adsorption (PSA) at small to medium scales. However, so far, the techno-economic assessment of membrane processes has been focused on the production of low-purity O2. This study assesses O2 enrichment by membrane-based processes in a large purity range (30–95%), and reports significant energy and cost savings when high-performance membranes, marked by O2 permeance of 500–1000 GPU and O2/N2 selectivity in the range of 2–20, are developed. The analysis identifies that O2/N2 selectivities of 2–4, 6–15, and 20 are optimal for target purity of 30–40%, 50–65%, and >70%, respectively, leading to specific costs in the range of 20–50 $/tonEPO2 when blending with air is not considered. Purity target below 75% can be achieved by the single-stage process while double-stage processes are needed for higher purity targets. If highly-selective membranes are available, the cost of lower purity OEA can be further reduced by blending higher purity OEA with air. We demonstrate the benefits of the membrane process for four important applications. For natural gas combustion, optimal energy and cost savings of 41% and 22%, respectively, are predicted using membranes with selectivity of 4. An optimized hybrid system combining membrane enricher and post-combustion capture reduces energy consumption (22%) and cost (6%) with respect to the standalone capture. The production of medical-grade oxygen (90% purity) from membranes is economical (0.4 $/kL) compared to that from PSA (0.6–0.8 $/kL). Finally, 95% O2 can also be produced for oxycombustion with a specific cost of 32 $/tonEPO2.
科研通智能强力驱动
Strongly Powered by AbleSci AI