Predictive capability evaluation and optimization of Pb(II) removal by reduced graphene oxide-based inverse spinel nickel ferrite nanocomposite.
Environ Res
; 204(Pt A): 112029, 2022 03.
Article
em En
| MEDLINE
| ID: mdl-34509486
ABSTRACT
Pb(II) is a heavy metal that is a prominent contaminant in water contamination. Among the different pollution removal strategies, adsorption was determined to be the most effective. The adsorbent and its type determine the adsorption process's efficiency. As part of this effort, a magnetic reduced graphene oxide-based inverse spinel nickel ferrite (rGNF) nanocomposite for Pb(II) removal is synthesized, and the optimal values of the independent process variables (like initial concentration, pH, residence time, temperature, and adsorbent dosage) to achieve maximum removal efficiency are investigated using conventional response surface methodology (RSM) and artificial neural networks (ANN). The results indicate that the initial concentration, adsorbent dose, residence time, pH, and process temperature are set to 15 mg/L, 0.55 g/L, 100 min, 5, and 30 °C, respectively, the maximum removal efficiency (99.8%) can be obtained. Using the interactive effects of process variables findings, the adsorption surface mechanism was examined in relation to process factors. A data-driven quadratic equation is derived based on the ANOVA, and its predictions are compared with ANN predictions to evaluate the predictive capabilities of both approaches. The R2 values of RSM and ANN predictions are 0.979 and 0.991 respectively and confirm the superiority of the ANN approach.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Poluentes Químicos da Água
/
Nanocompostos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article