Adsorption of BTEX on Surfactant Modified Granulated Natural Zeolite Nanoparticles: Parameters Optimizing by Applying Taguchi Experimental Design Method
Bibliographic record
Abstract
Abstract In this paper, a novel adsorbent developed by means of granulating of natural zeolite nanoparticles (i.e., clinoptilolite) was evaluated for possible removal of the petroleum monoaromatics (i.e., benzene, toluene, ethylbenzene, and xylene, BTEX). To do this, the natural zeolite was ground to produce nanosized particulate, then modified by two cationic surfactants and granulated. The effect of various parameters including temperature, initial pH of the solution, total dissolved solids (TDS), and concentration of a competitive substance (i.e., methyl tert‐butyl ether, MTBE) were studied and optimized using a Taguchi statistical approach. The results ascertained that initial pH of the solution was the most effective parameter. However, the low pH (acidic) was favorable for BTEX adsorption onto the developed adsorbents. In this study, the experimental parameters were optimized and the best adsorption condition by determination of effective factors was chosen. Based on the S/N ratio, the optimized conditions for BTEX removal were temperature of 40°C, initial pH of 3, TDS of 0 mg/L, and MTBE concentration of 100 µg/L. At the optimized conditions, the uptake of each BTEX compounds reached to more than 1.5 mg/g of adsorbents.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".