New insights on the adsorption of phenol red dyes from synthetic wastewater using activated carbon/Fe2(MoO4)3.

Water shortage is considered as one of the main challenges of human life. A practical solution to this problem is the wastewater treatment. The removal of dyes from wastewaters has received considerable critical attention by researchers due to their high volume and toxicity. In the current research, the adsorption of phenol red dyes from synthetic wastewater using the activated carbon produced from Mespilus germanica modified with Fe2(MoO4)3 was studied. The proposed adsorbent was characterized by Fourier transform infrared (FTIR), X-ray diffraction (XRD), scanning electron microscope (SEM), energy dispersive X-ray analysis (EDX)/Map, Brunauer-Emmett-Teller (BET), and Raman techniques. The optimal adsorption operating parameters including pH, stirring rate, temperature, dosage of adsorbent, dye initial concentration, and contact time were 3, 500 rpm, 25 °C, 1 g/L, 10 mg/L, and 60 min, respectively. Furthermore, the successful regeneration of the adsorbent for 3 times, using methanol solution as a regeneration medium, denoted its capability in performing adsorption and desorption processes. Equilibrium studies showed that the adsorption of phenol red dyes by activated carbon (AC)/Fe2(MoO4)3 was desirable and physical and the experimental data were fitted well by the Freundlich model. In addition, the kinetic behavior of the current adsorption process was well described by the pseudo-second-order kinetic model, while thermodynamic calculations showed that the process was exothermic and spontaneous.

Modelling and optimization of fenton process for decolorization of azo dye (DR16) at microreactor using artificial neural network and genetic algorithm.

The Fenton process is widely employed for decolorizing industrial wastewater. Therefore, it is imperative to construct a model for optimizing the operational parameters and estimating the efficiency of decolorization within this process. In this study, an artificial neural network (ANN) model was created based on experimental data provided by a previous researcher who examined the decolorization of Direct Red 16 dye (DR16) using a heterogeneous Fenton process within a microchannel reactor. This model was utilized to optimize and forecast the efficiency of decolorization in the Fenton process. The accuracy of the model was validated by comparing its outcomes with actual experimental data. To further improve the efficiency of decolorization, optimal operational parameters were ascertained utilizing the genetic algorithm method. The study revealed that as dye concentrations increased from 10 to 40 mg/l, decolorization efficiencies improved proportionately, peaking at 89.78 %. Optimal operational parameters for maximizing efficiency were identified as a feed flow rate of 1 ml/min, H2O2 concentration at 500 mg/l, Fe2+ concentration of 4 mg/l, and maintaining pH between 2.6 and 2.8. Insights derived from both experimental and model-generated data were used to analyze the impact of operational parameters on decolorization efficiency.

Adsorption of tetracycline antibiotic onto modified zeolite: Experimental investigation and modeling.

Artificial Neural Networks (ANNs) model and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to estimate and predict the removal efficiency of tetracycline (TC) using the adsorption process from aqueous solutions. The obtained results demonstrated that the optimum condition for removal efficiency of TC were 1.5 g L-1 modified zeolite (MZ), pH of 8.0, initial TC concentration of 10.0 mg L-1, and reaction time of 60 min. Among the different back-propagation algorithms, the Marquardt-Levenberg learning algorithm was selected for ANN Model. The log sigmoid transfer function (log sig) at the hidden layer with ten neurons in the first layer and a linear transfer function were used for prediction of the removal efficiency. Accordingly, a correlation coefficient, mean square error, and absolute error percentage of 0.9331, 0.0017, and 0.56% were obtained for the total dataset, respectively. The results revealed that the ANN has great performance in predicting the removal efficiency of TC.•ANNs used to estimate and predict tetracycline antibiotic removal using the adsorption process from aqueous solutions.•The model's predictive performance evaluated by MSE, MAPE, and R2.

Fuzzy modeling and simulation for lead removal using micellar-enhanced ultrafiltration (MEUF).

In the present paper, a three factor, three-level response surface design based on Box-Behnken design (BBD) was developed for maximizing lead removal from aqueous solution using micellar-enhanced ultrafiltration (MEUF). Due to extremely complexity and nonlinearity of membrane separation processes, fuzzy logic (FL) models have been driven to simulate MEUF process under a wide range of initial and hydrodynamic conditions. Instead of using mathematical model, fuzzy logic approach provides a simpler and easier approach to describe the relationships between the processing variables and the metal rejection and permeation flux. Statistical values, which quantify the degree of agreement between experimental observations and numerically calculated values, were found greater than 91% for all cases. The results show that predicted values obtained from the fuzzy model were in very good agreement with the reported experimental data.