Reductions in greenhouse gas (GHG) generation and energy consumption in wastewater treatment plants
Bibliographic record
Abstract
Greenhouse gas (GHG) emission and energy consumption by on-site and off-site sources were estimated in two different wastewater treatment plants that used physical-chemical or biological processes for the removal of contaminants, and an anaerobic digester for sludge treatment. Physical-chemical treatment processes were used in the treatment plant of a locomotive repair factory that processed wastewater at 842 kg chemical oxygen demand per day. Approximately 80% of the total GHG emission was related to fossil fuel consumption for energy production. The emission of GHG was reduced by 14.5% with the recovery of biogas that was generated in the anaerobic digester and its further use as an energy source, replacing fossil fuels. The examined biological treatment system used three alternative process designs for the treatment of effluents from pulp and paper mills that processed wastewater at 2,000 kg biochemical oxygen demand per day. The three designs used aerobic, anaerobic, or hybrid aerobic/anaerobic biological processes for the removal of carbonaceous contaminants, and nitrification/denitrification processes for nitrogen removal. Without the recovery and use of biogas, the aerobic, anaerobic, and hybrid treatment systems generated 3,346, 6,554 and 7,056 kg CO(2)-equivalent/day, respectively, while the generated GHG was reduced to 3,152, 6,051, and 6,541 kg CO(2)-equivalent/day with biogas recovery. The recovery and use of biogas was shown to satisfy and exceed the energy needs of the three examined treatment plants. The reduction of operating temperature of the anaerobic digester and anaerobic reactor by 10°C reduced energy demands of the treatment plants by 35.1, 70.6 and 62.9% in the three examined treatment systems, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".