微生物燃料电池
磷酸铁
废水
磷酸盐
阳极
质子交换膜燃料电池
化学
污水处理
废物管理
材料科学
环境科学
环境工程
电极
膜
物理化学
工程类
生物化学
有机化学
作者
Ru Wang,Sizhuo Wan,Ling-Ling Lai,Rui Zhang,Bibi Saima Zeb,Mahmood Qaisar,Guotao Tan,Lin-Jiang Yuan
标识
DOI:10.1016/j.scitotenv.2022.154034
摘要
Anaerobic sludge digested (ASD) wastewater is widespread in wastewater treatment plants. Recovering phosphate from ASD wastewater not only removes pollutants but also solves the phosphorus deficiency problem. Iron-air fuel cells were chosen to recover phosphate and generate electricity from ASD wastewater. To optimize cell configuration, a two-chamber and a one-chamber iron-air fuel cell were set up. The phosphate removal efficiency, the vivianite yield and the electricity generation efficiency of the two fuel cells were evaluated. It turned out that the volumetric removal rate (VRR) of phosphate of the two-chamber cell was 11.60 mg P·L-1·h-1, which was about five times of that in the one-chamber cell. The phosphate recovery product vivianite was detected on the surface of the iron anodes and the calculated purities of the two-chamber fuel cell and one-chamber fuel cell were 90.6% and 58.7%, respectively. Considering the content and purity, the iron anode surface in the two-chamber fuel cell was the best point to recover phosphate. The proton exchange membrane (PEM) in the two-chamber fuel cell provided low pH conditions suitable for vivianite formation. Moreover, under the low pH condition, metal ions of Fe2+, Ca2+, Al3+ and so on were kept soluble, leading to a high conductivity. The high conductivity caused low internal resistance, which benefited the electricity generation. The total output electric power of the two-chamber fuel cell was 2.4 times that of the one-chamber fuel cell when treating 25 mL ASD wastewater (0.62 vs. 0.26 mW·h). Overall, the two-chamber fuel cell was the better choice for phosphate recovery and electricity generation from ASD wastewater. Further studies on the long-term operation of two-chamber fuel cells should be carried out.
科研通智能强力驱动
Strongly Powered by AbleSci AI