Industrial-Scale Wastewater Nano-Aeration and -Oxygenation and Dissolved Air Flotation: Electric Field Nanobubble and Machine Learning Approaches to Enhanced Nano-Aeration and Flotation
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| Pubblicato in: | Environments vol. 12, no. 7 (2025), p. 228-247 |
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| Autore principale: | |
| Pubblicazione: |
MDPI AG
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| Soggetti: | |
| Accesso online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 001 | 3233183651 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2076-3298 | ||
| 024 | 7 | |a 10.3390/environments12070228 |2 doi | |
| 035 | |a 3233183651 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a English, Niall J | |
| 245 | 1 | |a Industrial-Scale Wastewater Nano-Aeration and -Oxygenation and Dissolved Air Flotation: Electric Field Nanobubble and Machine Learning Approaches to Enhanced Nano-Aeration and Flotation | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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). | |
| 653 | |a Organic chemicals | ||
| 653 | |a Aeration | ||
| 653 | |a Wastewater treatment plants | ||
| 653 | |a Flotation | ||
| 653 | |a Bubbles | ||
| 653 | |a Water treatment | ||
| 653 | |a Dissolved oxygen | ||
| 653 | |a Electric contacts | ||
| 653 | |a Reactive oxygen species | ||
| 653 | |a Municipal wastewater | ||
| 653 | |a Separation techniques | ||
| 653 | |a Machine learning | ||
| 653 | |a Activated sludge | ||
| 653 | |a Electric fields | ||
| 653 | |a Dependent variables | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Efficiency | ||
| 653 | |a Batch processing | ||
| 653 | |a Biochemical oxygen demand | ||
| 653 | |a Batch flotation | ||
| 653 | |a Sludge | ||
| 653 | |a Gases | ||
| 653 | |a Oxygen | ||
| 653 | |a Influent water | ||
| 653 | |a Particulates | ||
| 653 | |a Oxygenation | ||
| 653 | |a Process water | ||
| 653 | |a E coli | ||
| 653 | |a Wastewater treatment | ||
| 773 | 0 | |t Environments |g vol. 12, no. 7 (2025), p. 228-247 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233183651/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233183651/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233183651/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |