Effect of TDSs on nitrate removal in ROC treatment process
Bibliographic record
Abstract
Reverse osmosis concentrate (ROC) is associated with a high concentration of total dissolved solids (TDSs) which is known to affect a biological wastewater treatment. The aim of this study is to assess the effect of TDSs on the biological denitrification in ROC for biological treatment. This study focused on evaluating the changes in TDSs and substrate adsorption rates in association with polysaccharides (PSs) on the surface of microorganisms. Experiments performed with TDSs injection mode showed that NO3 −-N (nitrate) concentration in the effluent increased from 4·7 ± 1·5 to 9·5 ± 1·9 mg/L. The specific oxygen uptake rates (SOUR) and PSs were found to decrease from 66·05 ± 10·01 and 19·29 ± 5·98 to 42·56 ± 4·19 and 9·73 ± 0·80 mg O2/g MLVSS-h, respectively. Of the specific TDSs components, cations, more specifically calcium, were adsorbed to the surface of microorganisms, and subsequently the substrate adsorption rates were decreased by approximately three times. It is believed that the transfer of substrate typically necessary for denitrification was inhibited by the adsorption of the TDSs. Therefore, physicochemical treatment prior to biological treatment must be performed to control TDSs for the treatment of ROC. It is expected that the effective treatment of ROC will contribute to improving the efficiency of biological wastewater treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".