The objective of this work is to determine the reflection elements, allowing the understanding of the phosphorus fixation mechanisms. The samples were taken from Oued Boufekrane in the Meknes region (Northwest of Morocco). In fact, the sediment characterization was examined by the Brunauer–Emmett–Teller (BET) specific surface area and Fourier-transform infrared (FTIR) spectroscopy measurements. A series of experiments were then carried out to study the impact of some parameters on the adsorption capacity. Indeed, the effect of contact time, sediment mass, pH, initial concentration of potassium dihydrogen phosphate KH2PO4, and the temperature has been studied. The characterization of sediment by FTIR spectroscopy shows the existence of carbonates, iron hydroxides, and organic matter. The results obtained showed that the retention of phosphorus on the sediments studied is maximal at pH = 12 and increases with the temperature and the mass of sediments. Phosphorus adsorption kinetics of phosphorus on sediments studied follows the pseudo-second-order model, and the activation energy value (48.51 kJ/mol) indicates the predominance of chemical nature of adsorption (>40 kJ/mol). The experimental data of the adsorption isotherms are well interpreted by the Freundlich model. The values of the thermodynamic parameters ΔG°, ΔH°, and ΔS° indicate that the adsorption reaction is endothermic and occurs spontaneously on the surface of the sediments studied.
Facing the increase of surface water samples contaminated by ETMs, usually from the geochemical background, the emergence of new human diseases is worrying. To solve this problem, we have developed several models based on different learning algorithms qualified by high performance, using different transfer functions. We have shown that all the Neural Models presented more or less important performance compared to the one based on multiple linear regressions. The best revealed model ANN in the current work is a MLP type that uses the LevenbergMarquardt algorithm as a learning algorithm, with Tansig and Purelin as transfer functions, respectively in the hidden layer and the output layer. This successful model can be considered as an important tool of great effectiveness in the context of environmental prediction and especially in anticipation of the iron contents of the Oubeira Lake water.
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