Superheated water pretreatment combined with CO2 activation/regeneration of the exhausted activated carbon used in the treatment of industrial wastewater
Bibliographic record
Abstract
This paper examines a novel method of regenerating saturated activated carbon after adsorption of complex phenolic, polycyclic aromatic hydrocarbons with low energy consumption by using superheated water pretreatment combined with CO2 activation. The effects of the temperature of the superheated water, liquid–solid ratio, soaking time, activation temperature, activation time, and CO2 flow rate of regeneration and adsorption of coal-powdered activated carbon (CPAC) were studied. The results show that the adsorption capacity of iodine values on CPAC recovers to 102.25% of the fresh activated carbon, and the recovery rate is 79.8% under optimal experimental conditions. The adsorption model and adsorption kinetics of methylene blue on regenerated activated carbon (RAC) showed that the adsorption process was in accordance with the Langmuir model and the pseudo-second-order kinetics model. Furthermore, the internal diffusion process was the main controlling step. The surface properties, Brunauer–Emmett–Teller (BET) surface area, and pore size distribution were characterized by Fourier transform infrared spectroscopy (FT-IR) and BET, which show that the RAC possesses more oxygen-containing functional groups with a specific surface area of 763.39 m2 g−1 and a total pore volume of 0.3039 cm3 g−1. Micropores account for 79.8% and mesopores account for 20.2%.
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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 | 0.000 | 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) | 0.001 | 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".