AI Article Synopsis
- HAOPs are a new type of adsorbent that effectively removes natural organic matter (NOM) from water, outperforming traditional methods like PAC and coagulation with aluminum or ferric chloride.
- The study shows that HAOPs selectively adsorb certain NOM molecules better than other techniques and that their non-adsorbable NOM can be effectively captured by PAC.
- Adding these adsorbents to a microfiltration membrane system dramatically increases its efficiency, allowing it to operate longer and remove significantly more NOM compared to systems without adsorbents.
Article Abstract
Heated aluminum oxide particles (HAOPs) are a newly synthesized adsorbent with attractive properties for use in hybrid adsorption/membrane filtration systems. This study compared removal of natural organic matter (NOM) from water by adsorption onto HAOPs with that by adsorption onto powdered activated carbon (PAC) or coagulation with alum or ferric chloride (FeCl3); explored the overlap between the NOM molecules that preferentially adsorb to HAOPs and those that are removed by the more conventional approaches; and evaluated NOM removal and fouling in hybrid adsorbent/membrane systems. For equivalent molar doses of the trivalent metals, HAOPs remove more NOM, and NOM with higher SUVA254, than alum or FeCl3. Most of the HAOPs-nonadsorbable fraction of the NOM can be adsorbed by PAC; in fact, that fraction appears to be preferentially adsorbed compared to the average NOM in untreated water. Predeposition of the adsorbents on a microfiltration membrane improves system performance. For the water tested, at a flux of 100 L/m2-hr, predeposition of 11 mg/L PAC and 5 mg/L HAOPs (as Al3+) allowed the system to operate 5 times as long before the transmembrane pressure increased by 1 psi and to remove 10-20 times as much NOM as when no adsorbents were added.
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| http://dx.doi.org/10.1021/es7021285 | DOI Listing |
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Article Synopsis
- Nano-self-assembly of natural organic matter (NOM) plays a critical role in affecting both NOM and pollutant dynamics in complex environments, highlighting the need for advanced analysis methods.
- Machine learning (ML) is proposed as a valuable tool for interpreting NOM self-assembly processes by utilizing big data to explore structure-property relationships and environmental impacts.
- The review emphasizes the importance of developing new ML algorithms and frameworks to address challenges in data interpretation, while also proposing an integrated research approach that combines ML, experiments, and theoretical models for better understanding NOM-related environmental issues.
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