Arsenic removal from water using marble powder waste: A comprehensive study on adsorption dynamics and machine learning predictions
Bibliographic record
Abstract
Enhanced aqueous arsenite (As(III) removal by adsorption on marble waste powder (MWP) in batch and continuous mode was investigated. A predictive (ML) algorithm was developed to predict arsenic removal by MWP. This study pioneers the use of ML applications on MWP. The batch-scale data revealed that the adsorption of arsenite on MWP can be best described by the Liu Isotherm model, and non-linear pseudo-first-order kinetics were observed. The intraparticle diffusion model revealed adsorption occurred in more than one step, and further analysis indicated that mass transfer was the dominant step. Under favorable conditions, the regeneration potential of MWP was also observed. From laboratory experiments, three comprehensive datasets (Batch adsorption, continuous adsorption, and regeneration) were generated and used for predictive modelling. Different linear and non-linear ML models were first optimized with hyperparameter tuning using GridSearchCV and then trained and evaluated for their performance. Evaluation metrics and learning curves showed that non-linear ML models outperformed linear models. The extra trees model was the most accurate predictive model, with prediction accuracy of 91.1 %, 99.2 % and 66.8 % in datasets, respectively. Theoretical up-scaling suggests fixed-bed pilot-scale system of MWP can treat around 6000 liters of arsenic-contaminated water in 1.37 days before the breakthrough. • Marble waste powder is potential adsorbent for reducing arsenite contamination, ranging from 5 to 1000 μg/L, in water. • Pilot-scale fixed-bed column system of marble waste powder can treat approximately 6000 liters of water in 1.37 days. • The extra trees model can efficiently predict arsenite removal from water using marble waste powder. • Arsenite saturated marble waste powder can be regenerated in alkaline conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".