Successful treatment of high azo dye concentration wastewater using combined anaerobic/aerobic granular activated carbon-sequencing batch biofilm reactor (GAC-SBBR): simultaneous adsorption and biodegradation processes
Bibliographic record
Abstract
The application of a granular activated carbon-sequencing batch biofilm reactor (GAC-SBBR) for treatment of wastewater containing 1,000 mg/L Acid Red 18 (AR18) was investigated in this research. The treatment system consisted of a sequencing batch reactor equipped with moving GAC as biofilm support. Each treatment cycle consisted of two successive anaerobic (14 h) and aerobic (8 h) reaction phases. Removal of more than 91% chemical oxygen demand (COD) and 97% AR18 was achieved in this study. Investigation of dye decolorization kinetics showed that the dye removal was stimulated by the adsorption capacity of the GAC at the beginning of the anaerobic phase and then progressed following a first-order reaction. Based on COD analysis results, at least 77.8% of the dye total metabolites were mineralized during the applied treatment system. High-performance liquid chromatography analysis revealed that more than 97% of 1-naphthyalamine-4-sulfonate as one of the main sulfonated aromatic constituents of AR18 was removed during the aerobic reaction phase. According to the scanning electron microscopic analysis, the microbial biofilms grew in most cavities and pores of the GAC, but not on the external surfaces of the GAC.
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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) | 0.001 | 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 | 0.001 |
| 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".