Removal of mercury from natural gas by a new activated adsorbent from olive stones
Bibliographic record
Abstract
Abstract This study was devoted to the valorization of a plant waste (olive stones): that is widely available in Mediterranean countries in order to remove mercury from natural gas. The raw material from olive stones was prepared by pyrolysis, chemical activation with phosphoric acid, and physical activation under steam. Two olive stone‐based granular activated carbons were prepared: one with the virgin stones, while the other was impregnated with sulphur. After treatment, the adsorbents obtained were characterized by determining the iodine number, the methylene blue index, and by estimating the porous properties by N2 adsorption at 77 K. Thermogravimetric analysis and infrared spectroscopy analysis were carried out to determine the functional groups before and after mercury adsorption. An experimental study of vapour‐phase mercury adsorption by the activated carbons (virgin and sulphur‐impregnated) and a comparison with a commercial material (HGR) were performed. The comparison, made by analyzing the adsorption in a continuous mode, showed that the proportion of sulphur and the porosity were important for the removal of mercury. In the conditions used, the mercury adsorption on the ACs studied follows a physisorption mechanism. The results showed that granular activated carbon‐based olive stones (sulphur‐impregnated) are very efficient to remove mercury (with 2864 μg/g) and also less expensive than commercial activated carbon due to their local availability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".