Heavy metals are a growing environmental concern as they are unable to be
metabolized in the environment, leading to bioaccumulation in the food chain and
impacting human health. Treating heavy metals is difficult and expensive. Current
methods include precipitation (which generates sludge that is costly to dispose of) or
requires the use of a membrane, which fouls and requires regeneration.
Microbial electrolysis cells (MECs) represent an alternative for treating heavy
metal contaminated wastewater. Reactor components are cheap, and operation requires
only a small amount of electricity. The electrically active biofilm oxidizes organics in the
wastewater while transferring electrons first to the anode, then to the cathode, where
aqueous metals are reduced to a solid deposit, a mechanism called electrodeposition. Few
studies have been conducted to investigate the best operational conditions for heavy
metal removal in MECs. In this study, the effects of hydrodynamics, applied voltage, and
initial metal concentration on heavy metal removal mechanisms are investigated, and the
best operational practices are determined on a high level.
Mixing in the cathode chamber increased electrodeposition by 15%, decreased the
cathode potential by -0.06 V, and increased current generation between 10-30%.
Increasing the applied voltage from 0.6 V to 1.2 V increased electrodeposition by 22%.
With both mixing and higher voltage applied, 93.35% of cadmium was removed from the
catholyte in 24 hours. Although high voltage application maximized electrodeposition for
short-term treatment, long-term treatment indicated lower applied voltage resulted in
healthier MEC reactors, better overall metal recoveries, along with a more stable cathode
potential. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/22830 |
| Date | January 2018 |
| Creators | Fuller, Erin |
| Contributors | Kim, Younggy, Civil Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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