Artificial neural network-genetic algorithm based optimization for the adsorption of phenol red (PR) onto gold and titanium dioxide nanoparticles loaded on activated carbon

Ghaedi, Mehrorang; Daneshfar, Ali; Ahmadi, Azita; Momeni, Maryam

doi:10.1016/j.jiec.2014.03.024

Cited by 61 publications

(20 citation statements)

References 52 publications

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“…7 Pseudo-second-order models silica-filled ENR/PVC and granite were, respectively, used as adsorbents. Ghaedi et al (2014) also reported the same feature when gold and titanium dioxide nanoparticles were loaded on activated carbon to adsorb phenol red. But, Vala and Tichagwa found that the Freundlich model was the better fitting model for adsorption of phenol red onto clinoptilolite modified with siloxanes.…”

Section: Isotherms Of Phenol Red Adsorbed Onto Lignocellulose Modifiementioning

confidence: 66%

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Evaluation of N-terminated siloxanes grafted onto lignocellulose as adsorbent for the removal of phenol red from water

Vala

Tichagwa

Dikio

2014

Int. J. Environ. Sci. Technol.

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This study reports the chemical modification of lignocellulose (from Kikuyu grass) with N-terminated siloxanes and its utilisation as adsorbent for the removal of phenol red from aqueous media. The stability of the bond between lignocellulose and siloxane was assessed after Soxhlet extraction of unreacted siloxanes in tetrahydrofuran for 5 h, as well as soaking the materials in water for 24 h. Adsorption tests showed the prepared materials to be good candidate for the removal of phenol red (dye) from water, with adsorption capacity from 1.454 to 3.312 mg of phenol red adsorbed per gram of adsorbent. The kinetics of adsorption was found to follow a pseudo-second-order equation indicating chemisorption rather than simple forces of association. The linearised form of adsorption isotherms matched with the Langmuir model.

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Section: Isotherms Of Phenol Red Adsorbed Onto Lignocellulose Modifiementioning

confidence: 66%

“…Vala and Tichagwa (2013) investigated the removal of phenol red from water using clinoptilolite modified with N-terminated siloxanes; 0.32 mg of phenol red was removed per g of adsorbents. Ghaedi et al (2014) used gold and titanium dioxide nanoparticles loaded on activated carbon for the removal of phenol red.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of N-terminated siloxanes grafted onto lignocellulose as adsorbent for the removal of phenol red from water

Vala

Tichagwa

Dikio

2014

Int. J. Environ. Sci. Technol.

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“…Genetic algorithm (GA) is one of the techniques of optimization used in some cases during the last decade [20]. The use of a neural network to prune the choice of inputs is well documented in the past [63].…”

Section: Modeling Of the Networkmentioning

confidence: 99%

“…Researchers used different artificial neural network (ANN) and genetic algorithm (GA)-ANN hybrid modeling to solve engineering problems [18][19][20]. The advantage of GA-ANN modeling over the ANN is evident in the less computational time required and gives better predictability.…”

Section: Introductionmentioning

confidence: 99%

Adsorptive removal of Cr(VI) from aqueous solution: kinetic, isotherm, thermodynamics, toxicity, scale-up design, and GA modeling

2019

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Chromium (VI) is a well-known toxic, industrial, water pollutant which has various, adverse effects on environmental health. Utilization of agricultural waste in effluent water treatment would minimize the problem of water pollution. The present study deals with the use of three types of nut shells for Cr(VI) removal. Adsorbents are characterized, using point of zero charges (pH pzc), FTIR, BET surface area analysis, and SEM. The variation of different operating parameters on metal removal was conducted. The best sorption kinetic model was pseudo-second order. The adsorption process is both physical and chemical, and this depends on temperature. The Cr(VI) adsorption is spontaneous and endothermic. According to isotherm studies, Langmuir isotherm model fits fairly well for all adsorbents. Regeneration studies suggest that the adsorbents have proper regeneration criteria and can be used for multiple times. Study on RBC count of Gallus gallus domesticus gives concrete evidence of deadly effects of Cr(VI). It also figures out that the effluent solution treated with bio-adsorbents is less harmful. The scale-up design procedure is reported here. This study proved that groundnut shell, walnut shell, and almond shell have immense potential and can be utilized even after regeneration as replacement of commercial adsorbents for industrial wastewater. GA-ANN modeling has been developed for the best possible wastewater treatment management. Keywords Groundnut shell • Walnut shell • Almond shell • Pseudo-second order • Langmuir model • Genetic algorithm List of symbols A H Harkins-Jura isotherm constant A T Equilibrium binding constant of Temkin isotherm (L/g) a e Adsorption rate (initial) (mg/(g min)) B Heat of adsorption (J/mol) B H Harkins-Jura isotherm constant b Constant of Langmuir model (L/mg) b e Chemisorption activation energy (g/mg) b T Constant of Temkin isotherm C External convective mass transfer (mg/g) C a Cr(VI) ion concentration at the adsorbent at equilibrium (mg/L) C 0 Cr(VI) ion concentration (initial) (mg/L) C e Cr(VI) ion concentration at equilibrium (mg/L) C t Ion concentration of Cr(VI) at time t (mg/L) D e Absorbate's effective diffusion coefficient in the absorbent phase (m 2 /s) E Adsorption free energy (KJ/mol) ΔG 0 Gibbs free energy change (kJ/mol) ΔH 0 Enthalpy change (kJ/mol) K Fractional power model constant (mg/g) k 1 Rate constant of Lagergren model (min −1) k 2 Rate constant of pseudo-second-order model (g/ mg min) K ad Rate constant of Natarajan and Khalaf model (min −1) K f Constant of Freundlich model (mg/g)/(mg/L) 1/n k i Rate constant of intraparticle diffusion model (mg/(g min 0.5)) K 0 c Thermodynamic equilibrium constant K ∕ c Apparent equilibrium constant

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“…neural network [33][34][35] which is a powerful tool for prediction, but it is not usually able to produce practical prediction equations [36]. Adaptive neuro-fuzzy inference system [37][38][39][40] and least square support vector machine [41,42] are some other methods of modeling that were used in this field.…”

Section: Desalination and Water Treatmentmentioning

confidence: 99%

Functionalized copper oxide–zinc oxide nanocomposite: synthesis and genetic programming model of dye adsorption

Mahmoodi

Chamani

Kariminia

2016

Desalination and Water Treatment

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A B S T R A C TThe functionalized copper oxide-zinc oxide nanocomposite (FCZN) was synthesized and characterized using Fourier transform infrared, scanning electron microscopy, X-ray diffraction, X-ray fluorescence, and BET. Dye removal from aqueous solution was done in a batch system using FCZN as an adsorbent. The effects of adsorbent dosage, initial dye concentration, pH, temperature, and additive salts on dye removal were investigated. Isotherms, kinetics, and thermodynamics of dye adsorption were studied. Equilibrium and kinetic data were fitted by Langmuir isotherm and pseudo-second-order kinetic, respectively. The thermodynamic data showed that dye adsorption was spontaneous, endothermic, and physical reaction. In addition, genetic programming (GP) was applied in order to predict dye removal using an explicit formula. The results of proposed GP models were in close agreement with the experimental data.

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Artificial neural network-genetic algorithm based optimization for the adsorption of phenol red (PR) onto gold and titanium dioxide nanoparticles loaded on activated carbon

Cited by 61 publications

References 52 publications

Evaluation of N-terminated siloxanes grafted onto lignocellulose as adsorbent for the removal of phenol red from water

Evaluation of N-terminated siloxanes grafted onto lignocellulose as adsorbent for the removal of phenol red from water

Adsorptive removal of Cr(VI) from aqueous solution: kinetic, isotherm, thermodynamics, toxicity, scale-up design, and GA modeling

Functionalized copper oxide–zinc oxide nanocomposite: synthesis and genetic programming model of dye adsorption

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