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In this study, the novel low-cost Pinus eldarica leaves-based biosorbent (Pe) was introduced to remove heavy metal ions from aqueous media. The Pe ability of Pb(II), Cu(II), and Ni(II) ions biosorption was investigated in aqueous solutions. The maximum sorption capacity was 40, 26, and 33 mg g −1 for Pb(II), Cu(II), and Ni(II), respectively, that was so promising. Experiments were carried out in lab-scale batch reactors as function of initial pH (pH 0 ), initial concentration of heavy metal ions (C 0 ), and contact time (t). The statistical analysis was applied to determine the significant parameters, rank of parameter influences, and type of influences. The adsorption kinetics data were modeled using the pseudo-first, pseudosecond-order, and intraparticle diffusion kinetics equations. The pseudo-first-order and pseudo-second-order kinetics equations described the adsorption kinetics very well. Finally, the equilibrium data were described by 15 isotherm models with different number of parameters from two to five. The results revealed that the Langmuir and Fritz-Schlunder models provided the best correlation of the experimental data.

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In the present study, ultrasonic-assisted Fenton process called sono-Fenton (SF) with low concentration of Fenton reagents was studied via degradation of Direct Blue 71. Influences of seven operational parameters including initial pH (pH 0 ), initial concentration of pollutant (C 0 ), dose of Fenton reagents (C Fe and C H2O2 ), ultrasound irradiation frequency (Fr S ), ultrasound irradiation power (P S ), and treatment time (t SF ) were investigated on the dye removal efficiency (DR). A combined design of experiments consists of full factorial for t SF , and Taguchi for other six parameters was designed, and experiments were conducted in accordance with the design. The experimental data were collected using a batch reactor equipped with controllable ultrasonic bath. The DR of 0-33.5 mg/l was achieved under experimental conditions. These results approved that the SF process can be a promising approach in terms of colored wastewater treatment. The data were used for model building by Taguchi and artificial neural network. Further statistical tests were applied to exhibit models goodness and to compare models. Finally, optimization process was carried out using Taguchi and genetic algorithm. The optimization procedure causes optimal point which gives an insight of optimal operating condition.