From prediction to sustainability: AI for smart energy management in wastewater treatment plants
Saeed Hamood Alsamhi, Ammar Hawbani, Mohammed A A Al-qaness, Niall O’Brolchain, Liang Zhao, Ahmed Al-Dubai, Mamoona Asghar, Redhwan Algabri, Mohsen Guizani · 2025
Read From prediction to sustainability: AI for smart energy management in wastewater treatment plants by Saeed Hamood Alsamhi, Ammar Hawbani, Mohammed A A Al-qaness, Niall O’Brolchain, Liang Zhao, Ahmed Al-Dubai, Mamoona Asghar, Redhwan Algabri, Mohsen Guizani (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12738775.txt.
Description
Alsamhi and eight co-authors compare smart models for energy use in sewage plants, in work from twenty twenty-five. Treatment plants use and make a lot of energy. Good forecasts help them run well, rely more on their own power, and cut waste.
The team tested five machine learning methods on daily plant records. They forecast both energy made and energy used, and scored each model with standard error measures. One method, based on gated recurrent units, scored best in their tests. Others based on long memory networks also did well. The authors argue that strong forecasts are a core step toward greener, cheaper plant operation.
Catalog record
| CREATOR | Saeed Hamood Alsamhi, Ammar Hawbani, Mohammed A A Al-qaness, Niall O’Brolchain, Liang Zhao, Ahmed Al-Dubai, Mamoona Asghar, Redhwan Algabri, Mohsen Guizani |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 46 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |