Removal of naphthenic acids using adsorption process and the effect of the addition of salt
Bibliographic record
Abstract
In this study, various types of adsorbents were examined for the removal of Naphthenic acids (NAs) in the preliminary stage of this study. Among them, activated carbon and nickel (Ni) based alumina (Ni-Al2O3) possess relatively high adsorption capacity of NAs. The removal of NAs was evaluated comparing the total organic carbon (TOC) of the solution before and after the adsorption process. The effect of Ni loading was investigated using Ni-Al2O4 with various nickel loadings. The highest adsorption capacity (20 mg of TOC/1 mg of adsorbent) was belong to Ni-Al2O4 with 10.7% Ni loading. By the addition of salt (1500 ppm NaCl) to NAs solutions having concentrations from of 15 to 38 ppm, it was observed that the adsorption decreased dramatically (up to 80%) depending on the concentration of TOC. The kinetics of the adsorption of TOC on Ni-based alumina was also investigated. The decrease of TOC was more that 40% in the first half hour, indicating that adsorption was very rapid in the beginning. The adsorption increased slightly for up to 5 h and then leveled off when the TOC reached to 50% of initial TOC concentration. However, when sodium chloride (NaCl) was added to the solution, the adsorption decreased to almost 9% within the first half hour, reaching to almost 5% after 3 h. These phenomena suggest that the effectiveness of adsorbents may be improved by decreasing the total dissolved salts in tailings pond wastewater.
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 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".