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In the present paper, the removal of ethidium bromide (EtBr) from aqueous solutions in a batch system using natural (NP) and aluminium-coated pumice (ACP) as alternative low-cost adsorbents was investigated. The maximum adsorption capacity, q m (mg/g) was 58.82 and 76.92 mg/g for NP and ACP, respectively, operating at an initial pH of 8, an adsorbent dose of 8 g/L, a contact time of 210 min and an initial EtBr concentration of 30 mg/L. The equilibrium data of both adsorbents fitted the Freundlich isotherm model, indicating the heterogeneity of the adsorbent surface. In addition, the adsorption rate of both adsorbents was well described by the pseudo-second-order kinetics model. This indicated chemisorption was the rate-controlling step of the adsorption process which occurred by ion exchange. Within the performed study, a three-layer artificial neural network (ANN) model was also developed to predict the efficiency of EtBr removal. Computational results clearly demonstrated that the ANN model was able to predict the combined effect of initial pH, adsorbent dose, contact time and initial EtBr concentration on the adsorption efficiency with a very high determination coefficient ( R 2 = 0.998) and a low relative error (RE = 0.037). [ABSTRACT FROM AUTHOR]

The efficiency of new and nano-scale adsorbents including multi-walled carbon nanotubes (MWCNTs) and γ-alumina in the removal of cobalt(II) from aqueous solutions was experimentally evaluated in a batch-system reactor. To the best of our knowledge, no previous study has specifically attempted to introduce a hybrid strategy based on artificial neural network and genetic algorithm techniques for modelling and optimizing adsorptive removal of cobalt(II) from aqueous solutions via the proposed nanoparticles. The analyses of SEM, TEM, and FTIR were used to characterize both adsorbents. The response surface methodology (RSM) approach suggested a second-order polynomial model with a p -value < 0.0001 and R 2 of 0.9980 for MWCNTs adsorbent and a p -value < 0.0001 and R 2 of 0.9992 for γ-alumina adsorbent. The artificial neural network (ANN) approach suggested a three-layered feed-forward backpropagation model with R 2 of 0.9794 for MWCNTs adsorbent and R 2 of 0.9823 for γ-alumina adsorbent. The results linked to optimization by RSM showed that the maximum cobalt(II) removal efficiency of about 90% was achieved in the case of the MWCNTs adsorbent under the conditions of pH = 10, contact time = 38.6 min, MWCNTs dosage = 1.57 mg/L, and initial cobalt(II) concentration = 56.57 mg/L. About 93% of cobalt(II) removal could be obtained in the case of γ-alumina adsorbent under the conditions of pH = 10, contact time = 35.5 min, γ-alumina dosage = 1.63 g/L, and initial cobalt(II) concentration = 52.15 mg/L. The optimization values using the genetic algorithm (GA) technique were almost the same as those obtained from the RSM method. The kinetic model of Ho and McKay's pseudo-second order (PSO) and the isotherm model of Dubinin–Radushkevich were found to be the best-fitted to the experimental for both MWCNTs and γ-alumina. In addition, the maximum monolayer adsorption capacity of MWCNTs and γ-alumina adsorbents for the adsorption of cobalt(II) was 78.94 mg/g and 75.78 mg/g, respectively. Also, a thermodynamic study exhibited a favorable and spontaneous adsorption process for both materials. The present study clearly concluded that the proposed adsorbents could be effectively used for the removal of cobalt(II) from aqueous solutions at lower adsorbent dose and shorter contact times than various adsorbents reported in literature. Unlabelled Image • Adsorptive removal of cobalt (II) was explored via MWCNTs and nano-alumina. • ANN and genetic algorithm were proposed for this first time in cobalt (II) adsorption. • Isotherms and kinetic data followed Dubinin–Radushkevich and PSO models, respectively. • A cobalt (II) removal efficiency above 90% was possible at pH = 10 and 1 g adsorbent/L. • Favorable and spontaneous removal was corroborated by thermodynamic study. [ABSTRACT FROM AUTHOR]

In the present study, defluoridation study of liquid phase with the help of multi-walled carbon nanotubes (MWCNTs) and single-walled carbon nanotubes (SWCNTs) were well investigated and elucidated. The impact of different experimental conditions such as solution pH, initial fluoride concentration, adsorbent dose, and contact time was well studied and optimized for the maximum fluoride removal from water. The experimental data were fitted by the Freundlich, Langmuir and Dubinin–Radushkevich (D–R) isotherm models and the related equilibrium constants were calculated. The results of the isotherm studies showed that fluoride removal by both adsorbents followed the Freundlich isotherm model. Kinetic studies were conducted and the results demonstrated that the experimental data were fitted well with the pseudo-second order kinetic model. Furthermore, two multiple regression-based equations were also derived to model the removal of fluoride from aqueous solutions by the carbon nanotubes. This study demonstrated that the polynomial equations satisfactorily described the behavior of the present defluoridation process for both MWCNTs ( R 2 = 0.913) and SWCNTs ( R 2 = 0.941). [ABSTRACT FROM AUTHOR]

Azinphos-methyl and chlorpyrifos are the organophosphorous pesticides which pose serious threats to the environment including their detrimental effect on humans and, therefore, their removal from the environment is a must. Hence, in the present paper the ultrasound technique was applied to remove the above-mentioned hazardous compounds. For this, the effect of influential parameters such as pH, initial pesticide concentration, frequency, electric power and treatment time on the ultrasound degradation of azinphos-methyl and chlorpyrifos was well investigated and elucidated. The results obtained showed that azinphos-methyl and chlorpyrifos were effectively and rapidly degraded by the ultrasound technique. Thus, the operating optimal conditions (initial pH 9, initial pesticide concentration 1 mg/L, frequency 130 kHz, electric power 500 W and treatment time 20 min) lead to a degradation of 78.50% for azinphos-methyl and of 98.96% for chlorpyrifos with in 20 min of contact time. Two multiple regression-based equations were derived to describe the degradation process of the pesticides by the ultrasound treatment. The result of this study showed that the polynomial equations satisfactorily described the behavior of the present process for various operating conditions. [ABSTRACT FROM AUTHOR]