Evaluating the Efficiency of Activated Sludge Processes in Treating Industrial Wastewater from Nata De Coco Production
Bibliographic record
Abstract
This study examines the effects of Chemical Oxygen Demand (COD) loading on the performance of the activated sludge process at the nata de coco Wastewater Treatment Plant (WWTP) in Gunung Putri, Bogor Regency, which has a daily capacity of 100 m³.From February 2019 to June 2023, bi-weekly assessments were carried out to measure pH in the aeration tank and COD concentrations at both the inlet and outlet of the treatment facility.COD measurements in the equalization tank varied from 933 to 5,080 mg/L, averaging 2,550 mg/L.The Biochemical Oxygen Demand (BOD)/COD ratio fluctuated between 0.34 and 0.40, suggesting a degree of biodegradability resistance.Initially, in February and March 2019, the treatment process achieved an average COD removal efficiency of 95.7%, with a COD load of 0.56 kg COD/m³.day.However, from April 2019 through June 2023, despite an increase in the average COD load to 0.64 kg COD/m³.day, the COD removal efficiency improved to 96.6%.The findings underscore the capability of the activated sludge process to consistently manage varying organic loads in wastewater from nata de coco production, maintaining a relatively stable COD removal efficiency and presenting a viable technical and economic solution.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".