Impact of Long-Term Exposure to High Chlorine and to Low pH Solutions during Chlorine Regeneration of Ammonia-Loaded Zeolite
Bibliographic record
Abstract
An earlier study has shown that chlorine solutions were capable of effectively regenerating an ammonium-loaded zeolite column; however, the chlorine concentrations were high (1000 mg Cl2/L), and for two hours of the regeneration cycle, the pH was approximately 3. This led to concerns regarding the long-term durability of the zeolite. The objective of this study is to investigate the durability of a zeolite by conducting long-term batch exposure tests using (a) high concentration chlorine solutions and (b) low pH solutions. Particle size analysis, SEM images, N2 gas adsorption tests, FTIR characterization and batch loading tests showed that 35-day exposure to 1000 mg Cl2/L solutions did not significantly impact the zeolite studied. This chlorine exposure is equivalent to 840,000 ppm-h, which is three orders of magnitude higher than the values recommended by the supplier. The 90-day-long low pH exposure tests showed that pH = 4 solutions only slightly impacted the zeolite’s characteristics and ammonium uptake; however, the pH = 3 exposure led to discernable changes, and the pH = 2 exposure led to an even greater impact. At pH = 2, there was a breakdown of some external part of the zeolite particles, leading to a 7.1-fold increase in the fines and a 56% reduction in the ammonium uptake. The decrease in the ammonium uptake was proportional to the percent of fines.
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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 | 0.001 | 0.000 |
| 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".