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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]

A novel application of artificial neural networks (ANN) combined with Taguchi orthogonal experimental design methodology (27 runs, 3 levels, 6 factors) was introduced for modeling and optimization of a new alternating pulse current electrocoagulation-flotation (APC-ECF) process for the removal of humic acid (HA) from aqueous media. Two different ANN architectures, such as multilayer perceptron (MLP NN) and generalized feed forward (GFF NN), were proposed and trained to describe the nonlinear behavior of a laboratory-scale batch APC-ECF reactor. Various operating parameters, such as initial HA concentration (C0), initial pH (pH0), electrical conductivity (EC0), current density (CD), and number of pulses (Npls), were used as inputs for the proposed networks, and the HA removal was selected as the output. According to the goodness-of-fit criteria, the computational results showed that the single hidden-layered GFF NN (5:6:1), where a sigmoid axon transfer function was used at its hidden layer and its output layer was trained by the Levenberg–Marquardt algorithm, showed the best performance (R2 = 0.999, MSE = 0.00006). For the optimal conditions of C0 = 42 mg/L, pH0 = 6.63, CD = 24.3 A/m2, EC0 = 856 μS/cm, and Npls = 3, the maximum HA removal was obtained based on the predicted outputs of the best ANN model (GFF NN). The results of the computational analysis clearly corroborated that ANN integrated design of experiments (DOE)-based modeling was rapidly and effectively used for predicting the optimum performance of a complex electrochemical process in removal of HA from water using aluminum electrodes in monopolar arrangement. [ABSTRACT FROM AUTHOR]

• A new alternating pulse current electrocoagulation-flotation process is introduced. • APC-ECF system is first modeled for removal of humic acid from water using ANN/DOE. • About 100% of HA is removed from water with an energy consumption of 1.08 kWh/kg HA. • ANN/DOE-based approach can describe behavior of a complex electrochemical process. A novel application of artificial neural networks (ANN) combined with Taguchi orthogonal experimental design methodology (27 runs, 3 levels, 6 factors) was introduced for modeling and optimization of a new alternating pulse current electrocoagulation-flotation (APC-ECF) process for the removal of humic acid (HA) from aqueous media. Two different ANN architectures, such as multilayer perceptron (MLP NN) and generalized feed forward (GFF NN), were proposed and trained to describe the nonlinear behavior of a laboratory-scale batch APC-ECF reactor. Various operating parameters, such as initial HA concentration (C 0), initial pH (pH 0), electrical conductivity (EC 0), current density (CD), and number of pulses (N pls), were used as inputs for the proposed networks, and the HA removal was selected as the output. According to the goodness-of-fit criteria, the computational results showed that the single hidden-layered GFF NN (5:6:1), where a sigmoid axon transfer function was used at its hidden layer and its output layer was trained by the Levenberg–Marquardt algorithm, showed the best performance (R 2 = 0.999, MSE = 0.00006). For the optimal conditions of C 0 = 42 mg/L, pH 0 = 6.63, CD = 24.3 A/m2, EC 0 = 856 μS/cm, and N pls = 3, the maximum HA removal was obtained based on the predicted outputs of the best ANN model (GFF NN). The results of the computational analysis clearly corroborated that ANN integrated design of experiments (DOE)-based modeling was rapidly and effectively used for predicting the optimum performance of a complex electrochemical process in removal of HA from water using aluminum electrodes in monopolar arrangement. [ABSTRACT FROM AUTHOR]