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Abstract

Substantial boosts in the low-energy nano-oxygenation of incoming process water were achieved at a municipal wastewater treatment plant (WWTP) upstream of activated sludge (AS) aeration lanes on a single-pass basis by means of an electric field nanobubble (NB) generation method (with unit residence times of the order of just 10–15 s). Both ambient air and O2 cylinders were used as gas sources. In both cases, it was found that the levels of dissolved oxygen (DO) were maintained far higher for much longer than those of conventionally aerated water in the AS lane—and at DO levels in the optimal operational WWTP oxygenation zone of about 2.5–3.5 mg/L. In the AS lanes themselves, there were also excellent conversions to nitrate from nitrite, owing to reactive oxygen species (ROS) and some improvements in BOD and E. coli profiles. Nanobubble-enhanced Dissolved Air Flotation (DAF) was found to be enhanced at shorter times for batch processes: settlement dynamics were slowed slightly initially upon contact with virgin NBs, although the overall time was not particularly affected, owing to faster settlement once the recruitment of micro-particulates took place around the NBs—actually making density-filtering ultimately more facile. The development of machine learning (ML) models predictive of NB populations was carried out in laboratory work with deionised water, in addition to WWTP influent water for a second class of field-oriented ML models based on a more narrow set of more easily and quickly measured data variables in the field, and correlations were found for a more facile prediction of important parameters, such as the NB generation rate and the particular dependent variable that is required to be correlated with the efficient and effective functioning of the nanobubble generator (NBG) for the task at hand—e.g., boosting dissolved oxygen (DO) or shifting Oxidative Reductive Potential (ORP).

Details

1009240
Business indexing term
Identifier / keyword
Title
Industrial-Scale Wastewater Nano-Aeration and -Oxygenation and Dissolved Air Flotation: Electric Field Nanobubble and Machine Learning Approaches to Enhanced Nano-Aeration and Flotation
Author
Publication title
Volume
12
Issue
7
First page
228
Number of pages
20
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
Publication subject
e-ISSN
20763298
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-07-05
Milestone dates
2025-02-10 (Received); 2025-07-03 (Accepted)
Publication history
 
 
   First posting date
05 Jul 2025
ProQuest document ID
3233183651
Document URL
https://www.proquest.com/scholarly-journals/industrial-scale-wastewater-nano-aeration/docview/3233183651/se-2?accountid=208611
Copyright
© 2025 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-07-25
Database
ProQuest One Academic