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The aim of this study is the simultaneous biosorption of Cd +2 and Ni +2 on a dead biomass, Streptomyces rimosus pretreated with NaOH (0,1 M). Kinetic tests were carried out for the binary mixture (cadmium-nickel) during 6 hours contact time to ensure that balance was reached. The amounts adsorbed at equilibrium were 22.8 mg Ni 2+ /g and 9.86 mg Cd 2+ /g biomass, respectively. The biosorption depends mainly by some parameters, such as the pH, the initial concentration of metal and the initial concentration of biomass. The isotherm of adsorption according to two models, Langmuir and Freundlich, was carried out in our study. The results of the kinetics of adsorption show that the experimental values are well represented by the kinetic model of pseudo-second order. This enables us to determine the behavior of these adsorbents with respect to a real industrial effluent.

In this study, iron-rich mining residue (UGSO) was used as a support to prepare a new Ni-based catalyst via a solid-state reaction protocol. Ni-UGSO with different Ni weight percentages wt.% (5, 10, and 13) were tested for C2H4 dry reforming (DR) and catalytic cracking (CC) after activation with H2. The reactions were conducted in a differential fixed-bed reactor at 550–750 °C and standard atmospheric pressure, using 0.5 g of catalyst. Pure gases were fed at a molar ratio of C2H4/CO2 = 3 for the DR reaction and C2H4/Ar = 3 for the CC reaction. The flow rate is defined by a GHSV = 4800 mLSTP/h.gcat. The catalyst performance is evaluated by calculating the C2H4 conversion as well as carbon and H2 yields. All fresh, activated, and spent catalysts, as well as deposited carbon, were characterized by Brunauer–Emmett–Teller (BET), X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectrometry (EDX), transmission electron microscopy (TEM), temperature programmed reduction (TPR), and thermogravimetric analysis (TGA). The results so far show that the highest carbon and H2 yields are obtained with Ni-UGSO 13% at 750 °C for the CC reaction and at 650 °C for the DR reaction. The deposited carbon was found to be filamentous and of various sizes (i.e., diameters and lengths). The analyses of the results show that iron is responsible for the growth of carbon nanofilaments (CNF) and nickel is responsible for the split of C–C bonds. In terms of conversion and yield efficiencies, the performance of the catalytic formulations tested is proven at least equivalent to other Ni-based catalyst performances described by the literature.

A B S T R A C TThis work has the purpose of evaluating the removal of zinc from aqueous solutions using Algerian untreated bentonite clay as an adsorbent. The choice of using natural untreated clay is justified by the fact that it is both a low cost and friendly environmental adsorbent material. This local bentonite is a montmorillonite clay type with a relatively high cationic exchange capacity of 136.2 meq/100 g. Calcium is the main exchangeable cation. A granulometric analysis shows that 49.1% of the clay particles are submicronic. The zinc adsorption capacity of bentonite was studied in batch mode. A kinetic study shows a fast removal capacity and a highly influenced bentonite adsorption capacity by the operational parameters such as contact time, mass of adsorbent, metal concentration, pH, and temperature. Fifty percent of the Zinc was removed during the first 5 min and equilibrium is reached in 1 h. A maximal removal capacity is obtained for a bentonite concentration of 5 g L −1 and a zinc initial concentration of 100 mg L −1 . A pseudo-second-order kinetic model could be fitted to the experimental data. The equilibrium data could be fitted with a Langmuir isotherm equation. Depending on the negative value of ΔG, the adsorption of Zn 2+ on SIG untreated bentonite surfaces was spontaneous and the adsorption was an endothermic process.

This research focuses on the application of an artificial neural network (ANN) to predict the removal efficiency of tartrazine from simulated wastewater using a photocatalytic process under solar illumination. A program is developed in Matlab software to optimize the neural network architecture and select the suitable combination of training algorithm, activation function and hidden neurons number. The experimental results of a batch reactor operated under different conditions of pH, TiO concentration, initial organic pollutant concentration and solar radiation intensity are used to train, validate and test the networks. While negligible mineralization is demonstrated, the experimental results show that under sunlight irradiation, 85% of tartrazine is removed after 300 min using only 0.3 g/L of TiO powder. Therefore, irradiation time is prolonged and almost 66% of total organic carbon is reduced after 15 hours. ANN 5-8-1 with Bayesian regulation back-propagation algorithm and hyperbolic tangent sigmoid transfer function is found to be able to predict the response with high accuracy. In addition, the connection weights approach is used to assess the importance contribution of each input variable on the ANN model response. Among the five experimental parameters, the irradiation time has the greatest effect on the removal efficiency of tartrazine.

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