Arsenic removal from water using marble powder waste: A comprehensive study on adsorption dynamics and machine learning predictions

Arsenic removal from water using marble powder waste: A comprehensive study on adsorption dynamics and machine learning predictions

Blog Article

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 read more 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 knowall.blog 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.