Predicting competitive adsorption behavior of major toxic anionic elements onto activated alumina: A speciation-based approach

Su, Tingzhi; Guan, Xiaohong; Tang, Yongchun; Gu, Guowei; Wang, Jianmin

doi:10.1016/j.jhazmat.2009.11.052

Cited by 22 publications

(11 citation statements)

References 20 publications

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“…As (V) have different species in aqueous medium depending on pH of the solution. H 3 AsO 4 , H 2 AsO 4 -, HAsO 4 2-, and AsO 4 3-are the dominant As (V) species in pH ranges of \2.26, 2.26-6.76, 6.76-11.29, and [11.29, respectively [25].…”

Section: Effect Of Ph and Adsorption Mechanismmentioning

confidence: 99%

Use of Orange Peel Waste for Arsenic Remediation of Drinking Water

Khaskheli

Memon

Siyal

et al. 2011

Waste Biomass Valor

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Arsenic is a toxic element and is found in natural waters throughout the globe. The purpose of present study is to demonstrate the As (V) uptake by orange peel from real ground water samples through adsorption. Different parameters such as pH, shaking speed, contact time, adsorbent dosage, concentration, volume and temperature etc, were optimized. The maximum uptake capacity was observed at pH-7. The sorption was found to be time dependent and the kinetics followed well by the Morrisweber, Pseudo second order and Elovich equations. The Freundlich, Langmuir, Dubinin Redushkevich and FloryHuggins isotherm were used to model the adsorption behavior of arsenic retention. Thermodynamic parameters calculated from these isotherms show that the adsorption was spontaneous and endothermic in nature. Orange peel was found to be effective (*85%) for arsenic removal from real water systems containing concomitant ions.

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Section: Effect Of Ph and Adsorption Mechanismmentioning

confidence: 99%

Use of Orange Peel Waste for Arsenic Remediation of Drinking Water

Khaskheli

Memon

Siyal

et al. 2011

Waste Biomass Valor

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“…At a higher pH value than pHPZC of the sample, the surface of the biosorbent charged negatively and led to favor the biosorption of cationic species. On the other hand, the lower pHPZC values, the surface protonated and prone to biosorp the anionic species (Das et al, 2007;Su et al, 2010). In the light of these findings, it could be proposed the biosorption of CV onto GCs probably occurred as a result of electrostatic interaction.…”

Section: Initial Phmentioning

confidence: 97%

Yapay Sinir Ağı Yaklaşımı ile Crystal Violet Katyonik Boyarmaddesinin Biyokütle-temelli Grafen Benzeri Gözenekli Karbon Üzerine Biyosorpsiyonunun Tahmin Edilmesi

Karaman¹

2021

European Journal of Science and Technology

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Textile industries are considered to be the main actors in water pollution. Estimation of the textile dye sorption capacities of the biosorbents/adsorbents are crucial as design considerations. Herein, the feasibility of utilizing orange-peel-derived graphene-like porous carbons (GCs) as a low-cost biosorbent for removal of Crystal Violet (CV) cationic dye from aqueous solution have been evaluated both by batch biosorption experimental-setup and by using an artificial neural network (ANN) approach. The physicochemical characterization results have indicated that as-synthesized GCs has a specific surface area of 985 m 2 .g -1 , a pore volume of 1.04 cm 3 .g -1 , and a point of zero charge (pHPZC) of 6.50. The biosorption capacity of the biosorbent has been investigated as a function of initial pH, bisorbent dosage, initial dye concentration, and temperature. The optimal biosorption performance values have been achieved at pH of 7.5, the biosorbent dosage of 3.0 g.L -1 , the temperature of 25 ℃, in which 91.6% of initial CV (initial dye concentration of 100 ppm) has been successfully removed. The experimental results have indicated that the biosorption process significantly depends on the temperature whereas ca.15 min of contact time is sufficient for reaching equilibrium. The ANN approach has been utilized to forecast the biosorption performance of GCsThe proposed ANN model has been trained by the Levengberg-Marquardt backpropagation algorithm, by using the activation function of purelin and tansig functions at hidden and output layers, respectively. Different hidden topologies have been evaluated to optimize the ANN model. An optimal ANN model structured with two hidden layers with 5 and 10 neurons in each layer has been developed to forecast the biosorption of CV with high-performance parameters (linear correlation coefficient, R= 0.9995; mean squared error, MSE=0.0004). This work has shown that the experimental data are in harmony with ANN-based data, so it can be speculated that the proposed ANN approach can be utilized for predicting the cationic dye biosorption

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“…The solution pH can affect the protonation of surface functional groups besides the dissociation of the molecules (Das et al, 2007;Su et al, 2010). The pH dependence of the biosorption capacity of GPC was investigated between the pH range out from 2.0 to 10.0 at 100.0 mg.L -1 initial As(V) concentration, biosorbent dosage of 1.0 g.L -1 over 120 min of contact time.…”

Section: Effect Of Initial Phmentioning

confidence: 99%

Yapay Sinir Ağı Yaklaşımı ile Atık Portakal Kabuğundan Elde Edilen Grafen Benzeri Gözenekli Karbon Üzerinde Arsenik (V) Biyosorpsiyonunun Modellenmesi

Karaman¹

2020

European Journal of Science and Technology

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The main threats from heavy metals specifically arsenic-contaminated drinking water have been emerging as an environmental and social crucial issue. Herein, the arsenic (V) (As(V)) biosorption performance of waste orange peel (OP) driven-graphene-like porous carbon (GPC) was investigated experimentally and an artificial neural network (ANN) approach was used to model the biosorption process. The initial pH (2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, and 10.0), initial As(V) concentration (25.0, 50.0, 100.0, 250.0, 500.0 and 750.0 mg.L -1 ), biosorbent dosage (1.0, 2.0, 3.0, 4.0 and 5.0 g.L -1 ), and contact time (0-120.0 min) were investigated to optimize the biosorption process. The as-synthesized GPC biosorbents with a high specific surface area (985 m 2 .g -1 ) and pore volume (1.04 cm 3 .g -1 ) offered superior removal efficiency as 88.2% (equilibrium uptake capacity of 46.5 mg.g -1 ) at initial pH 6.0, initial As(V) concentration 100 mg.L -1 , and biosorbent dosage 2.0 g.L -1 . A three-layer ANN model was developed to forecast the Ar(V) biosorption performance of GPCs. Several experimental data points were considered as test data to validate the ANN model. The ANN model was performed with the Levengberg-Marquardt algorithm (LMA), linear transfer function (purelin) at the output layer, and a tangent sigmoid transfer function (tansig) in the hidden layer with 12 neurons. The values of coefficient of determination and mean squared error were calculated to be 0.9858 and 0.0014, respectively. The results revealed that the experimental data were in accordance with ANN-driven data as well as reveling the high accuracy of the ANN approach in estimating the target variable. The developed ANN model is useful for the optimization of process conditions for pilot-scale utilization of As(V) biosorption process by GPC.

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Predicting competitive adsorption behavior of major toxic anionic elements onto activated alumina: A speciation-based approach

Cited by 22 publications

References 20 publications

Use of Orange Peel Waste for Arsenic Remediation of Drinking Water

Use of Orange Peel Waste for Arsenic Remediation of Drinking Water

Yapay Sinir Ağı Yaklaşımı ile Crystal Violet Katyonik Boyarmaddesinin Biyokütle-temelli Grafen Benzeri Gözenekli Karbon Üzerine Biyosorpsiyonunun Tahmin Edilmesi

Yapay Sinir Ağı Yaklaşımı ile Atık Portakal Kabuğundan Elde Edilen Grafen Benzeri Gözenekli Karbon Üzerinde Arsenik (V) Biyosorpsiyonunun Modellenmesi

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