Kinetic modelling of the removal of multiple heavy metallic ions from mine waste by natural zeolite sorption
Bibliographic record
Abstract
This study investigates the sorption of heavy metallic ions (HMIs), specifically lead (Pb2+), copper (Cu2+), iron (Fe3+), nickel (Ni2+) and zinc (Zn2+), by natural zeolite (clinoptilolite). These HMIs are combined in single-, dual-, triple-, and multi-component systems. The batch mode experiments consist of a total initial concentration of 10 meq/L normality for all systems, acidified to a pH of 2 by concentrated nitric (HNO3) acid. A zeolite dosage of 4 g per 100 mL of synthetic nitrate salt aqueous solution is applied, for a contact period of 5 to 180 min. Existing kinetic models on HMIs sorption are limited for multi-component system combinations. Therefore, this study conducts kinetic analysis by both reaction and diffusion models, to quantify the sorption process. The study concludes that the process correlates best with the pseudo-second-order (PSO) kinetic model. In the multi- component system combining all five HMIs, the initial sorption rate and theoretical equilibrium capacity are determined as 0.0033 meq/g.min and 0.1159 meq/g, respectively. This provides significant insight into the mechanisms associated with the sorption process, as well as contributing to the assessment of natural zeolite as a sorbent material in its application in industrial wastewater treatment. Keywords: sorption; kinetics; modelling; natural zeolite; heavy metallic ions; ICP-AES
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".