Machine learning enhanced aeration systems for optimizing oxygen transfer efficiency for sustainable and safe wastewater management
Bishnu Kant Shukla, Arun Goel, Pushpendra Kumar Sharma, Parveen Sihag, Anoop Kumar Shukla · 2025
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Description
Bishnu Kant Shukla and four co-authors teach machine learning to predict how well wastewater aerators push oxygen into water. Their study appeared in twenty twenty-five. Aeration eats much of a treatment plant's energy, so forecasting its efficiency guides cheaper, safer operation.
A lab rig of circular solid-jet aerators produced three hundred twenty controlled observations. Jet count, opening size, jet length, and discharge all varied. Five regression methods trained on seventy percent of the data and faced the rest cold. Random forest won outright, with the tightest errors and the strongest match to measurements. Sensitivity tests crowned discharge as the dominant driver. The authors offer the model as a design tool for aeration systems.
Catalog record
| CREATOR | Bishnu Kant Shukla, Arun Goel, Pushpendra Kumar Sharma, Parveen Sihag, Anoop Kumar Shukla |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 70 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |