Diagnostic of electricity consumption, its cost and greenhouse gas emission in the wastewater treatment sector of Algeria
Bibliographic record
Abstract
Wastewater treatment sector, in Algeria, uses exclusively two processes: the activated sludge applied in the north and the lagooning in the highlands and the south. In the operating balance of the National Sanitation Office (ONA), the activated sludge wastewater treatment plants are characterized by a high electricity consumption which induced high cost and greenhouse gas (GHG) emission. In 2010, about 104.32 million m3 of wastewater was treated. This operation consumed 30,900 MWh of electricity which cost 1.04 million Euros (€) and emitted 18,761 tons of CO2-equivalent. In 2013, the treated wastewater increased by 35.2% and the electricity consumption by 45.8%. To establish an exhaustive diagnostic, this study evaluated the electricity consumed during 2009/2010 in an activated sludge wastewater treatment plant of 70,000 population equivalents (PE) (i.e. Unit of a pollution load produced daily per person, fixed at 60 grams of DBO5, which is used for the sizing of the wastewater treatment plants). Three areas were investigated: (1) the treatment process which consumed 89.63% of electricity; (2) the management department and the laboratory with 4.60%; and (3) the outdoor lighting with 5.77%. The biological treatment was the intensive-energy part of the treatment which consumed 70.05% of electricity. The aim of this diagnostic was to evaluate the performance level of the activated sludge wastewater treatment process relatively to the energy, financial and environmental factors in order to optimize the process and, then, to evaluate the benefit that could be provided by the integration of renewable energy in a sustainable wastewater treatment context.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".