Optimising wastewater treatment for sustainable irrigation using sequencing batch reactors
Bibliographic record
Abstract
Morocco is currently experiencing a severe drought, which has led to the implementation of wastewater reuse strategies to support sustainable development. Ibn Tofail University has installed a biological wastewater treatment plant, but the production of treated water is insufficient to irrigate the university’s green spaces. This study proposes the adoption of sequential batch reactor (SBR) technology to address these challenges, with a comparison to activated sludge (AS) treatment. The results show that the integration of SBR into the biological basin has significantly increased the volume and quality of treated water, while reducing energy consumption, enabling the system to meet irrigation criteria without wasting energy. Compared to AS, SBR demonstrated superior performance in pollutant removal, achieving 114.15 mg/l O2 for chemical oxygen demand, 23.88 mg/l O2 for five-day biochemical oxygen demand, and 74.66 mg/l for total suspended solids. The SBR reduced the pollutant load by 95% instead of 85% for AS, ensuring optimal oxygenation and producing high-quality effluent suitable for sustainable irrigation. This improvement enhances the environmental impact while contributing to the sustainable management of water and energy resources.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".