Machine Learning Weather Soft-Sensor for Advanced Control of Wastewater Treatment Plants
Problem
Weather conditions strongly affect inflow rates and substance concentrations in wastewater treatment plants, but this weather signal cannot be directly measured with traditional physical sensors, making plant control difficult.
Why it matters
Weather-driven perturbations can destabilize treatment plant control, affecting water quality and plant efficiency.
What the AI did
A machine-learning-based soft-sensor predicts the current weather signal, as an operator perceives it when controlling the plant, from available data.
Results
not stated in source
Impact
The abstract claims the soft-sensor can predict the operator-relevant weather signal, but reports no measured performance.
Record
| Venue | Sensors |
|---|---|
| Published | 2019 |
| Work type | article |
| Authors | Félix Hernández‐del‐Olmo, Elena Gaudioso, Natividad Duro, Raquel Dormido |
| Citations | 51 (OpenAlex, at import time) |
| References | 35 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/s19143139 |
| Open access | gold |
| Keywords | machine learning, soft-sensor, wastewater treatment, weather prediction, process control |
Importance rating
real but narrower advance
Weather perturbations complicate wastewater plant control and water quality, but abstract states no scale; a niche soft-sensor for one control-relevant signal.
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