电渗析
海水
碳酸氢盐
电解质
阳极
体积流量
二氧化碳
环境科学
堆栈(抽象数据类型)
化学工程
环境工程
化学
膜
工程类
海洋学
地质学
电极
热力学
生物化学
物理
有机化学
物理化学
计算机科学
程序设计语言
作者
Mehran Aliaskari,Jochen Wezstein,Florencia Saravia,Harald Horn
标识
DOI:10.1016/j.seppur.2024.126679
摘要
With the escalating environmental impact of carbon dioxide (CO2) emissions, effective CO2 capture is of paramount importance. Fully electrochemical processes can play a key role in this endeavor. In particular, bipolar membranes can induce the necessary ΔpH to convert the bicarbonate present in water into dissolved CO2, which can later be extracted. The primary goal of this study is to better understand the working mechanisms of the BPMED pH swing carbon capture process. Factors such as flow rate, voltage, current density, feed water salinity and alternative electrolytes are investigated to optimize carbon capture from water. Here, a pilot scale BPMED membrane stack and a membrane contactor were used with model water similar to seawater. The extent of bicarbonate removal, mass flow of CO2 gas and energy intensity were measured. By using a new electrolyte solution (0.1 M K3/K4[Fe(CN)6]), a 20 % reduction in energy consumption was observed (by avoiding the water dissociation reaction in cathode and anode). While at low salinities (about 2 mS/cm) CO2 production was limited and resulted in high energy consumption, at salinities > 9 mS/cm an increasing energy demand was observed due to increased ohmic losses and limited bicarbonate concentration. Increasing the flow rate in the membrane stack allowed more bicarbonate and consequently more CO2 gas to be extracted. Changing the velocity from 1 to 3 cm/s resulted in a reduction in energy consumption from 3.7 to 2.5 kWh/kgCO2. The study concludes that further research is needed to increase the efficiency of the BPMED process, particularly in long-term operation and to mitigate scaling/fouling effects on the bipolar membrane. Despite its current limitations, BPMED provides a fully electrified alternative for CO2 capture from water.
科研通智能强力驱动
Strongly Powered by AbleSci AI