〖Ca〗^(2+)ion Adsorption and Total Hardness Removal Using Synthesised Iron Oxide – Fused Metakaolin: Batch and Column Studies
Bibliographic record
Abstract
Groundwater hardness, primarily caused by calcium and magnesium ions, poses significant challenges to domestic, industrial and agricultural uses, as well as health risks associated with cardiovascular disease, kidney stones and other ailments. This study aimed to investigate the removal of total hardness from groundwater using a novel iron oxide-fused metakaolin composite. Initially, calcium adsorption isotherm studies were conducted to assess the composite’s adsorption capacity and mechanism. The favourable adsorption of calcium ions justified further investigation, leading to batch and column experiments that evaluated the composite’s performance in reducing total hardness from real groundwater samples.The calcium adsorption isotherm studies revealed that the adsorption of calcium ions is best described by the Langmuir isotherm with a maximum adsorption capacity of 24.33 mg/g and a separation factor (RL value)of 0.398 which shows that adsorption process was favourable (RL<1).A removal efficiency of 89.78% was achieved for groundwater total hardness using batch studies. Column studies using the adsorbent together with sand and activated carbon as support materials achieved a removal efficiency of 97.37%. The study demonstrates the effectiveness of the synthesised adsorbent in removing total hardness from groundwater, highlighting its potential as a viable treatment option for mitigating health risks and improving water quality.
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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".