Removal of total petroleum hydrocarbons from oil refinery waste using granulated NaA zeolite nanoparticles modified with hexadecyltrimethylammonium bromide
Bibliographic record
Abstract
Petroleum pollution is a common problem in industrial areas to such an extent that it poses a global threat. The wastes of oil refineries contain a wide variety of hydrocarbons. The aim of this study was to investigate the possibility to removal of total petroleum hydrocarbons (TPH) from the Behregan oil refinery, Bushehr, Iran, by granulated nanozeolite NaA particles (NaA-ZNPs) modified with cationic surfactants. Synthesized NaA-ZNPs with a silica source extracted from Hordeum vulgare were granulated by an alginate granulation method and were modified by the cationic surfactant hexadecyltrimethylammonium bromide (CTAB). The CTAB-modified granulated NaA-ZNPs were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, and the Brunauer–Emmett–Teller theory. Adsorption of TPH was studied using both a batch process and a continuous-flow process in a fixed-bed column. The effect of various parameters, including time, pH, adsorbent dosage, flow rate, and column height, were investigated and the results were analyzed by gas chromatography – mass spectroscopy. The efficiency of the CTAB-modified granulated NaA-ZNPs was evaluated with one-way analysis of variance software SPSS 21. The highest TPH removal efficiency for batch processing (92.3%) was achieved at an adsorbent dosage 0.5 mg, pH = 7, and 20 min of contact time; the highest TPH removal efficiency for continuous processing (87.4%) was achieved at 15 cm column height and a 17.26 mL/min flow rate. The results indicated the potential of CTAB-modified granulated NaA-ZNPs for absorbing TPH in treating oil refinery waste.
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) | 0.000 | 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".