Adsorption of acid orange 7 using green synthesized CaO/CeO2 composite: An insight into kinetics, equilibrium, thermodynamics, mass transfer and statistical models

Thirunavukkarasu, Arunachalam; Nithya, Rajarathinam

doi:10.1016/j.jtice.2020.04.007

Cited by 37 publications

(6 citation statements)

References 66 publications

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“…The possible reason was many unoccupied spots on the surface of F/CNc in the initial stage, but AB210 molecules gradually were adsorbed on the surface of F/CNc as the time increased, and then slowly entered the voids of F/CNc. This process led to a continuous decrease in the adsorption rate.These results were consistent with the results of using CaO/CeO2 composite to adsorb acid orange 7 dye reported by Thirunavukkarasu et al [33].…”

Section: Effect Of Initial Dye Concentrationsupporting

confidence: 93%

Adsorption of Azo Dye on Magnetically Separable Fe3O4/CeO2 Nanocomposite: Kinetics, Isotherm, Mechanism

Zhang

et al. 2022

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In this study, magnetically separable Fe3O4/CeO2 nanocomposite (F/CNc) was first synthesized in hydrothermal and sol-precipitation methods and characterized by techniques including SEM-EDS, XRD, BET, FT-IR and VSM. The adsorption experiments of acid black dye (AB210) onto F/CNc were carried out under the conditions of temperature (30 – 60 ℃), solution pH (1 – 11), Fe-Ce molar ratio (1:2.5 – 4) and initial dye concentrations (20 – 80 mg/L). The adsorption capacity of AB210 increased with increasing initial dye concentration and temperature, and decreased with increasing solution pH. The maximum adsorption capacity of F/CNc for AB210 reached 124.5 mg/g when Fe-Ce molar ratio was 1:3. Pseudo-first-order and pseudo-second-order models were then used to describe the kinetics of adsorption, of which the latter fitted the adsorption process well, and the adsorption process complied also with the intra-particle diffusion model. The isothermal adsorption was described using Langmuir, Freundlich and Temkin models and the Langmuir model matched. FT-IR and Zeta-potential measurement proved the physical adsorption process of AB210 onto F/CNc. The saturation magnetizations of Fe3O4 nanospheres and F/CNc were measured to be 69.1 emu/g and 22.9 emu/g respectively, indicating the coating layer reduced the magnetization of Fe3O4 nanospheres, but F/CNc were still separated easily by applying a magnetic field, keeping easy separability. Being easy to recover and remaining adsorption efficiency 90 % in comparison to the original even after four adsorption cycles, demonstrate the effectiveness and practical application of F/CNc as an adsorbent.

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Section: Effect Of Initial Dye Concentrationsupporting

confidence: 93%

Adsorption of Azo Dye on Magnetically Separable Fe3O4/CeO2 Nanocomposite: Kinetics, Isotherm, Mechanism

Zhang

et al. 2022

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“…This result was better than the results obtained for many other reported adsorbents. [49][50][51][52] Notably, the adsorption equilibrium of DB15 was reached at 10 min, which was shorter than the time to the adsorption equilibrium of AO7 (60 min). In the binary dye solutions, the removal rate of DB15 remained close to 95.6% in the rst 1 min, whereas only 33.8% of AO7 was adsorbed under the same conditions, indicating that Fe 3 O 4 -pDMC exhibited superior adsorption for DB15 in the competition system.…”

Section: Adsorption Kineticsmentioning

confidence: 95%

Novel Fe₃O₄–poly(methacryloxyethyltrimethyl ammonium chloride) adsorbent for the ultrafast and efficient removal of anionic dyes

et al. 2021

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“…Thirunavukkarasu and Nithya [155] reported the removal of acid orange 7 using CaO/CeO 2 and its modeling via RSM and ANN. The input variables for ANN were the adsorption temperature (301-338 K), contact time (0-300 min), initial concentration of acid orange 7 (10-50 mg/ L), adsorbent dose (0.02-0.2 g), and initial solution pH (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12).…”

Section: °Cmentioning

confidence: 99%

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

Reynel-Ávila

Aguayo‐Villarreal

Díaz-Muñoz

et al. 2022

Adsorption Science & Technology

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The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

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Adsorption of acid orange 7 using green synthesized CaO/CeO2 composite: An insight into kinetics, equilibrium, thermodynamics, mass transfer and statistical models

Cited by 37 publications

References 66 publications

Adsorption of Azo Dye on Magnetically Separable Fe3O4/CeO2 Nanocomposite: Kinetics, Isotherm, Mechanism

Adsorption of Azo Dye on Magnetically Separable Fe3O4/CeO2 Nanocomposite: Kinetics, Isotherm, Mechanism

Novel Fe₃O₄–poly(methacryloxyethyltrimethyl ammonium chloride) adsorbent for the ultrafast and efficient removal of anionic dyes

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

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Adsorption of acid orange 7 using green synthesized CaO/CeO2 composite: An insight into kinetics, equilibrium, thermodynamics, mass transfer and statistical models

Cited by 37 publications

References 66 publications

Adsorption of Azo Dye on Magnetically Separable Fe3O4/CeO2 Nanocomposite: Kinetics, Isotherm, Mechanism

Adsorption of Azo Dye on Magnetically Separable Fe3O4/CeO2 Nanocomposite: Kinetics, Isotherm, Mechanism

Novel Fe3O4–poly(methacryloxyethyltrimethyl ammonium chloride) adsorbent for the ultrafast and efficient removal of anionic dyes

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

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Product

Resources

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Novel Fe₃O₄–poly(methacryloxyethyltrimethyl ammonium chloride) adsorbent for the ultrafast and efficient removal of anionic dyes