Continuous treatments of estrogens through polymerization and regeneration of electrolytic cells
[Display omitted] •Trace natural and synthetic estrogens were removed effectively by electrochemical oxidation process.•Removal efficiency of 93–98% was achieved through electrochemical polymerization.•Passivated electrodes were completely recovered to their initial conditions in continuous operatio...
Gespeichert in:
Veröffentlicht in: | Journal of hazardous materials 2015-03, Vol.285, p.304-310 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | [Display omitted]
•Trace natural and synthetic estrogens were removed effectively by electrochemical oxidation process.•Removal efficiency of 93–98% was achieved through electrochemical polymerization.•Passivated electrodes were completely recovered to their initial conditions in continuous operation.•Removal performance could be controlled and enhanced by a mathematical model.
This study proposes a novel electrolytic method for simultaneous removal of trace estrogens and regeneration of electrolytic cells for long-term wastewater treatment. Continuous treatments of estrogens estrone (E1), 17β-estradiol (E2) and 17α-ethinyl estradiol (EE2) were theoretically and experimentally studied using an electrolytic reactor equipped with a multi-packed granular glassy carbon electrode reactor. Experimental results demonstrated that E1, E2 and EE2 were effectively removed through electro-polymerization on the granular glassy carbon (and Pt/Ti) anode counter. Polymer formed during continuous treatment was quickly decomposed and electrodes were regenerated completely by OH radicals produced through the reduction of ozone. Calculated overall energy consumptions were less than 10Wh/m3, demonstrating extremely low energy consumptions. In addition, a mathematical model developed based on the limiting mass transfer rate and post-regeneration could represent general trends in time series data observed in experiments. |
---|---|
ISSN: | 0304-3894 1873-3336 |
DOI: | 10.1016/j.jhazmat.2014.12.010 |