Exploring efficiency and regeneration of magnetic zeolite synthesized from coal fly ash for water treatment applications
Bibliographic record
Abstract
This study investigates novel synthetic magnetic zeolites from coal fly ash (CFA) and laboratory-grade LTA zeolite enhanced with nano magnetite particles for the remediation of heavy metal ions (Pb 2+ , Cu 2+ , Zn 2+ , Ni 2+ ) from wastewater. Utilizing a continuous flow system with a laboratory scale Wet High Intensity Magnetic Separator (WHIMS), heavy metal removal and magnetic particles’ recovery from the treated solution was investigated in a process that could be appropriate to real-world systems. The adsorption performance was investigated under various operational conditions, maintaining a consistent selectivity order of Pb > Cu > Zn > Ni, repeating the selectivity order found in previous study of magnetic CFA zeolite in batch systems. Moreover, magnetic CFA zeolite was shown to be a more effective adsorbent when compared to magnetic LTA zeolite. Thus, when tested in continuous flow system under selected conditions, magnetic CFA zeolite removed 63 % Pb, 37 % Cu, 13 % Zn, and 7 % Ni while magnetic LTA zeolite removed 25 % Pb, 16 % Cu, 6 % Zn, and 3 % Ni. Furthermore, treated solution that passed through WHIMS did not contain any zeolite particles, as they were successfully captured in the metal grid. Additionally, regeneration of metal-laden magnetic zeolites through desorption experiments was investigated by enhancing the ion-exchange process using a saturated NaCl solution. The results indicated that Pb, Zn, and Ni ions were fully desorbed from magnetic zeolite, while approximately 70 % of the Cu remained to the sample. The Cu remained in the sample may be attributed to its partial adsorption onto the carbonized binder, a highly oxygenated graphenic structure, which does not readily release the adsorbed Cu ions. As these findings highlight the difference between adsorption and desorption selectivity order, further investigation into the topic would be beneficial. This research underscores the operational advantages of using magnetic LTA and CFA zeolites in industrial water treatment applications, illustrating their high adsorption capacity and straightforward desorption processes.
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".