A case study on the environmental and economic impact of photovoltaic systems in wastewater treatment plants
Bibliographic record
Abstract
Abstract Ibn Tofail University of Kenitra, Morocco, is committed to a national policy of control and mobilization of water resources and the adoption of a planning approach and integrated water management. Within this framework, the university, which contains 40,000 students, produces a quantity of wastewater of 200 m3 per day. After treatment, the water is used for watering the university’s green space. The treatment process chosen is a membrane bioreactor (MBR), which is considered to be energy intensive. Therefore, the production of energy for the station will be made by renewable energy wind and photovoltaic (PV). The dimensioning of the MBR was made by a research department, which estimated that the energy necessary for the station is 1061.76 kW h/day. The aim of this work is to dimension and optimize the platform for the production of energy, using the Matlab program for the wind turbine and the PVsyst program for PV. The results of coupling our plant with an on-grid PV system and wind turbine show that it was able to reach an electrical coverage of about 72% of the wastewater treatment (WWT) plant’s energy needs. Thus, an estimated reduction of electricity of 0.53 euro on each m3 of water produced by the WWT plant and thus 106.76 euro on the 200 m3 produced daily by the station.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".