Performance evaluation of the BioCAST technology: a new multi-zone wastewater treatment system
Bibliographic record
Abstract
A new wastewater treatment technology, called BioCAST, has been designed and developed for high rate and simultaneous removal of organic carbonaceous compounds as well as nitrogen and phosphorus, along with reduced sludge generation. The treatment system has two interlinked reactors containing four independent zones with different environmental conditions of aerobic, microaerophilic, anoxic and anaerobic for the biological treatment of wastewater, as well as two clarification zones and a filtration unit for solid-liquid separation. The treatment system contains suspended as well as fixed-film microorganisms. The performance evaluation of the BioCAST system was carried out at organic loading rates of 0.95 to 1.86 kg/m(3) d, and nitrogen and phosphorus loading rates of 0.02 to 0.08 kg/m(3) d and 0.014 to 0.02 kg/m(3) d, respectively. The results demonstrated high removal efficiencies of carbon and nitrogen throughout the operation period, reaching 98.9 and 98.3%, respectively. Phosphorus removal efficiency was lower than 50% during the first 160 days of operation but it increased with the increase of nitrogen loading rate above 0.05 kg/m(3) day and concomitant reduction of C/N ratio below 15. Phosphorus removal efficiency reached 94.1%, producing an effluent concentration of 1.4 mg/L after 225 days of operation. The overall biomass yield based on the consumed COD was 3.7%.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".