Advancing phosphate batch adsorption: Comprehensive modeling and scalability with granulated clay adsorbent in single and binary systems
Bibliographic record
Abstract
Phosphate pollution from industrial and agricultural wastewater threatens aquatic ecosystems, promoting eutrophication and water quality deterioration. This study investigates the potential of granulated clay (GC) as a low-cost, high-capacity adsorbent for phosphate removal. The clay undergoes calcination at 830 °C, significantly enhancing its adsorption capacity and mechanical stability. The impact of clay dosage, initial phosphate concentration, and agitation speed on adsorption efficiency was examined. A kinetic model, the Film-Pore-Concentration-Dependent Surface Diffusion (FPCDSD) model, was developed to predict adsorption behavior in single and binary systems. Results show that calcined GC achieved a maximum adsorption capacity of 54.29 mg/g for phosphate. The FPCDSD model provided a better fit to experimental data compared to the Film-Pore Diffusion (FPD) and Film-Concentration-Dependent Surface Diffusion (FCDSD) models. The adsorption process is governed by external mass transfer, pore diffusion, and surface diffusion. Competitive adsorption in binary systems with phosphate and nitrate revealed GC's selectivity for phosphate due to its higher affinity. These findings highlight the potential of granulated clay as an effective, eco-friendly adsorbent for phosphate removal from wastewater, offering insights into adsorption kinetics, diffusion mechanisms, and competitive adsorption behavior. This study combats eutrophication with clay adsorbents, efficiently removing phosphate, scaling water treatment, and offering insights via modeling and prediction. • Explores affordable local clay adsorbent. • Introduces Film-Pore-Concentration-Dependent Surface Diffusion model. • Studies factors: concentration, agitation speed, dosage, size, porosity • Identifying key controlling mechanisms and mass transfer resistance • Predicts batch adsorption scalability potential.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".