Adsorption of Cr(VI) from aqueous solution using mesoporous metal-organic framework-5 functionalized with the amino acids: Characterization, optimization, linear and nonlinear kinetic models

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The need for fresh water is more than before by population growth, and industrial development have affected the quality of water supplies, one of the important reason for water contamination is synthetic dyes and their extensive use in industries. Adsorption has been considered as a common methods for dye removal from waters. In this study, Acid Red18 removal in batch mode by using Granular Ferric Hydroxide (GFH) was investigated. The GFH characterized by XRD, FESEM and FTIR analysis. Experiments were designed using RSM-CCD method. The maximum removal efficiency was obtained 78.59% at pH = 5, GFH dosage = 2 g/l, AR18 concentration = 77.5 mg/l and 85 min of contact time. Optimization with RSM and Genetic Algorithm carried out and is similar together. The non-linear adsorption Isotherm and kinetic fitted with Freundlich (R2 = 0.978) and pseudo-second-order (R2 = 0.989) models, respectively. Thermodynamic studies showed that the AR18 adsorption is endothermic process and GFH nature was found spontaneous.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Acid red 18 removal from aqueous solution by nanocrystalline granular ferric hydroxide (GFH); optimization by response surface methodology & genetic-algorithm

Hamidi

Dehghani

Kasraee

et al. 2022

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“…In addition, modification of MOFs with amine groups increases the number of electron-rich nitrogen sites, which results in more positive charge in the MOF structure 28 . Some MOFs functionalized with amine groups such as UiO-66-NH 2 29 , NH 2 -Cu-MOF 30 , amino-functionalized MOF-5 31 have recently been studied to remove various water contaminants.…”

Section: Introductionmentioning

confidence: 99%

Facile fabrication of amino-functionalized MIL-68(Al) metal–organic framework for effective adsorption of arsenate (As(V))

Rahmani

Shabanloo

Zabihollahi

et al. 2022

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An amino-functionalized MIL-68(Al) metal–organic framework (amino-MIL-68(Al) MOF) was synthesized by solvothermal method and then characterized by FESEM, XRD, FTIR, EDX-mapping, and BET-BJH techniques. In order to predict arsenate (As(V)) removal, a robust quadratic model (R2 > 0.99, F-value = 2389.17 and p value < 0.0001) was developed by the central composite design (CCD) method and then the genetic algorithm (GA) was utilized to optimize the system response and four independent variables. The results showed that As(V) adsorption on MOF was affected by solution pH, adsorbent dose, As(V) concentration and reaction time, respectively. Predicted and experimental As(V) removal efficiencies under optimal conditions were 99.45 and 99.87%, respectively. The fitting of experimental data showed that As(V) adsorption on MOF is well described by the nonlinear form of the Langmuir isotherm and pseudo-second-order kinetic. At optimum pH 3, the maximum As(V) adsorption capacity was 74.29 mg/g. Thermodynamic studies in the temperature range of 25 to 50 °C showed that As(V) adsorption is a spontaneous endothermic process. The reusability of MOF in ten adsorption/regeneration cycles was studied and the results showed high reusability of this adsorbent. The highest interventional effect in inhibiting As(V) adsorption was related to phosphate anion. The results of this study showed that amino-MIL-68(Al) can be used as an effective MOF with a high surface area (> 1000 m2/g) and high reusability for As(V)-contaminated water.

“…Different adsorbents had been used as effective adsorbents for pollutant removal from aqueous media such as MOFs, natural materials, and mesoporous silica 59 – 62 . In the recent years, metal organic framework (MOF) adsorbents have attracted so many attentions for this application due to their fantastic properties 63 , 64 . These models outperformed the traditional empirical correlations in fitting adsorption data such as the well-known Langmuir adsorption model 13 .…”

Section: Introductionmentioning

confidence: 99%

Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment

Parsaei

Roudbari

Piri

et al. 2022

We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions. The simulated adsorbent was a composite of UiO-66-(Zr)-(COOH)2 MOF grown onto the surface of functionalized Ni50-Co50-LDH sheets. This novel adsorbent showed high surface area for adsorption capacity, and was chosen to develop the model for study of ions removal using this adsorbent. A number of measured data was collected and used in the simulations via the artificial intelligence technique. Artificial neural network (ANN) technique was used for simulation of the data in which ion type and initial concentration of the ions in the feed was selected as the input variables to the neural network. The neural network was trained using the input data for simulation of the adsorption capacity. Two hidden layers with activation functions in form of linear and non-linear were designed for the construction of artificial neural network. The model’s training and validation revealed high accuracy with statistical parameters of R2 equal to 0.99 for the fitting data. The trained ANN modeling showed that increasing the initial content of Pb(II) and Cd(II) ions led to a significant increment in the adsorption capacity (Qe) and Cd(II) had higher adsorption due to its strong interaction with the adsorbent surface. The neural model indicated superior predictive capability in simulation of the obtained data for removal of Pb(II) and Cd(II) from an aqueous solution.